{"status":"ok","message-type":"work-list","message-version":"1.0.0","message":{"facets":{},"total-results":126,"items":[{"indexed":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T22:02:43Z","timestamp":1648764163899},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Sweet Potato is a useful plant as a source carbohydrates, proteins, and is used as an animal feed and ingredient industry. Based on data from the Badan Pusat Statistik (BPS), the production fluctuations of the sweet potato in Central Java from year to year are caused by many factor. The production of sweet potato and the factors that affected it if they are described into a pattern of relationships then they do not have a specific pattern and do not follow a particular distribution, such as harvest area, the allocation of subsidized urea fertilizer, and the allocation of subsidized organic fertilizer. Therefore, the production model of sweet potato could be applied into nonparametric regression model. The approach used for nonparametric regression in this study is smoothing spline regression. The method used in regression smoothing spline is generalized cross validation (GCV). The value of the smoothing parameter (\u03bb) is chosen from the minimum GCV value. The results of the study show that the optimum \u03bb value for the factors of harvest area, urea fertilizer and organic fertilizer are 5.57905e-14, 2.51426e-06, and 3.227217e-13 that they result a minimum GCV i.e 2.29272e-21, 1.38391e-16, and 3.46813e-24.<\/p><p>\u00a0<\/p><p><strong>Keywords<\/strong>: Sweet potato; nonparametric; smoothing spline; generalized cross validation.<\/p><\/jats:p>","DOI":"10.13057\/ijas.v1i2.26250","type":"journal-article","created":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T05:37:01Z","timestamp":1552455421000},"page":"117","source":"Crossref","is-referenced-by-count":0,"title":["Penerapan Generalized Cross Validation dalam Model Regresi Smoothing Spline pada Produksi Ubi Jalar di Jawa Tengah"],"prefix":"10.13057","volume":"1","author":[{"given":"Trionika Dian","family":"Wahyuningsih","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sri Sulistijowati","family":"Handajani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diari","family":"Indriati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2019,3,13]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/26250\/19498","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/26250\/19498","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T05:37:04Z","timestamp":1552455424000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/26250"}},"issued":{"date-parts":[[2019,3,13]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,3,13]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v1i2.26250","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2019,3,13]]}},{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T03:16:46Z","timestamp":1783307806480,"version":"3.54.6"},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Electricity has become an essential part of our daily lives. As technology has rapidly developed, many modern activities and devices have become highly dependent on electricity. The more electricity that is used, the higher the monthly cost. This cost is influenced by usage patterns and various uncertain factors. Fuzzy logic is one approach that can be used in decision support systems in the face of uncertainty like this. This study aims to apply the Mamdani and Sugeno fuzzy methods based on house building area, number of electronic devices, number of family members, and income to determine which method more accurately predicts household electricity consumption costs based on the mean absolute percentage error (MAPE) value. Data for this study were obtained through questionnaires and interviews with residents of Margorejo Village. Data processing yielded a MAPE value of 12.3% for the Mamdani method and a MAPE value of 9.9% for the Sugeno method. Based on these results, the MAPE value for the Sugeno method is smaller than that for the Mamdani method. Therefore, it can be concluded that the Sugeno method is more accurate for predicting household electricity consumption costs in Margorejo Village.<\/p><p><br \/><strong>Keywords<\/strong>: Mamdani Method, Sugeno Method, MAPE.<\/p><\/jats:p>","DOI":"10.13057\/ijas.v8i2.103436","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T03:03:53Z","timestamp":1783307033000},"page":"113","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Analysis of Fuzzy Mamdani Method and Fuzzy Sugeno Method in Predicting Household Electricity Consumption Costs"],"prefix":"10.13057","volume":"8","author":[{"given":"Luthfia","family":"Zahra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mashuri","family":"Mashuri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5236","published-online":{"date-parts":[[2026,2,12]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/103436\/53759","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/103436\/53759","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T03:04:01Z","timestamp":1783307041000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/103436"}},"issued":{"date-parts":[[2026,2,12]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1,14]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v8i2.103436","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2026,2,12]]}},{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T03:16:47Z","timestamp":1783307807400,"version":"3.54.6"},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Indonesia terletak pada pertemuan tiga lempeng tektonik aktif sehingga memiliki tingkat kerawanan yang tinggi terhadap bencana alam seperti gempa bumi, banjir, letusan gunung api, dan tanah longsor. Pulau Sulawesi merupakan salah satu wilayah dengan aktivitas seismik dan hidrometeorologi yang tinggi, sehingga identifikasi daerah rawan bencana menjadi penting dalam upaya pengurangan risiko dan perencanaan mitigasi yang efektif. Penelitian ini bertujuan untuk memetakan daerah rawan bencana di Pulau Sulawesi menggunakan algoritma Density-Based Spatial Clustering of Applications with Noise (DBSCAN). DBSCAN merupakan metode klasterisasi berbasis kepadatan yang mampu mengidentifikasi pola spasial tanpa harus menentukan jumlah klaster di awal serta dapat mendeteksi data pencilan (outlier). Data yang digunakan adalah data sekunder dari Badan Nasional Penanggulangan Bencana (BNPB) tahun 2020\u20132024 yang mencakup kejadian bencana di seluruh kabupaten\/kota di Pulau Sulawesi. Variabel yang dianalisis meliputi frekuensi kejadian banjir, tanah longsor, cuaca ekstrem, kekeringan, gempa bumi, letusan gunung api, dan gelombang pasang. Sebelum proses klasterisasi, data dinormalisasi menggunakan metode Min\u2013Max. Hasil terbaik diperoleh pada parameter \u03b5 = 0,28 dan MinPts = 5, yang menghasilkan dua klaster utama dan satu kelompok noise. Klaster 1 menunjukkan wilayah dengan tingkat kejadian bencana tertinggi, terutama banjir, tanah longsor, dan cuaca ekstrem. Klaster 0 mencakup wilayah dengan intensitas bencana sedang, sedangkan kelompok noise terdiri atas wilayah dengan tingkat kejadian bencana yang rendah atau pola bencana yang tidak jelas. Penerapan algoritma DBSCAN terbukti efektif dalam pemetaan kerawanan bencana karena mampu menangani distribusi spasial yang tidak merata serta mengungkap pola tersembunyi. Hasil penelitian ini diharapkan dapat menjadi dasar dalam pengembangan strategi mitigasi bencana yang lebih terarah. Penelitian selanjutnya disarankan untuk menambahkan indikator kerentanan sosial-ekonomi serta memperluas cakupan data.<\/p><p><strong>Kata kunci<\/strong>: DBSCAN; Sulawesi; Klasterisasi Spasial; Pemetaan Bencana; Mitigasi Risiko<\/p><p>Indonesia is located at the confluence of three active tectonic plates, making it highly vulnerable to natural disasters such as earthquakes, floods, volcanic eruptions, and landslides. Sulawesi Island is one of the regions with the highest seismic and hydro-meteorological activity in Indonesia, so identifying its disaster-prone areas is crucial for effective risk reduction and mitigation planning. This study aims to map disaster-prone areas in Sulawesi Island using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN is a density-based clustering method that is able to identify spatial patterns without determining the number of clusters from the start, as well as detect outlier data. The data used is secondary data from National Disaster Management Authority (BNPB) for 2020\u20132024 covering disaster events in all districts\/cities in Sulawesi. The variables analyzed include the frequency of floods, landslides, extreme weather, droughts, earthquakes, volcanic eruptions, and tidal waves. The data was normalized using the Min-Max method before the clustering process. The best results were obtained at parameters \u03b5 = 0.28 and MinPts = 5, resulting in two main clusters and one noise group. Cluster 1 shows areas with the highest disaster occurrences, especially floods, landslides, and extreme weather. Cluster 0 includes areas with moderate disaster intensity, while the noise group consists of areas with low or unclear disaster patterns. The application of DBSCAN has proven effective for disaster vulnerability because it is able to handle uneven spatial distribution and reveal hidden patterns. These results are expected to be the basis for developing more targeted disaster mitigation strategies. Further research is recommended to add socio-economic vulnerability indicators and expand data coverage.<\/p><p><br \/><strong>Keywords<\/strong>: DBSCAN; Sulawesi; Spatial Clustering; Disaster Mapping; Risk Mitigation<\/p><p>\u00a0<\/p><\/jats:p>","DOI":"10.13057\/ijas.v8i2.106040","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T03:03:53Z","timestamp":1783307033000},"page":"137","source":"Crossref","is-referenced-by-count":0,"title":["Pemetaan Daerah Rawan Bencana di Pulau Sulawesi menggunakan Metode Density-Based Spatial Clustering of Applications with Noise (DBSCAN)"],"prefix":"10.13057","volume":"8","author":[{"given":"Havinka Angel","family":"Salsabilla","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nandini Lova","family":"Diani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abimanyu Arya","family":"Ramadhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Al","family":"Haris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5236","published-online":{"date-parts":[[2026,1,14]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/106040\/53916","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/106040\/53916","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T03:04:04Z","timestamp":1783307044000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/106040"}},"issued":{"date-parts":[[2026,1,14]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1,14]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v8i2.106040","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2026,1,14]]}},{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T19:40:14Z","timestamp":1743104414704},"reference-count":0,"publisher":"UNS Solo","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. Moran\u2019s I test results stated that there are spatial dependencies between dependent and independent variables. The best model produced is the SAR model because it has the smallest AIC value of 49.61<\/jats:p>","DOI":"10.13057\/ijas.v1i1.24026","type":"journal-article","created":{"date-parts":[[2018,9,22]],"date-time":"2018-09-22T06:07:56Z","timestamp":1537596476000},"page":"1","source":"Crossref","is-referenced-by-count":3,"title":["Analisis Faktor-Faktor yang Mempengaruhi Penyebaran Penyakit Demam Berdarah Dengue (Dbd) di Provinsi Jawa Tengah dengan Metode Spatial Autoregressive Model dan Spatial Durbin Model"],"prefix":"10.13057","volume":"1","author":[{"given":"Arkadina Prismatika Noviandini","family":"Taryono","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dwi","family":"Ispriyanti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan","family":"Prahutama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2018,9,16]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/24026\/17170","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/24026\/17170","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,10,15]],"date-time":"2018-10-15T07:52:42Z","timestamp":1539589962000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/24026"}},"issued":{"date-parts":[[2018,9,16]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,9,16]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v1i1.24026","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2018,9,16]]}},{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T12:39:18Z","timestamp":1648989558027},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"DOI":"10.13057\/ijas.v3i2.48002","type":"journal-article","created":{"date-parts":[[2021,1,24]],"date-time":"2021-01-24T07:04:14Z","timestamp":1611471854000},"source":"Crossref","is-referenced-by-count":0,"title":["Back Matter Vol 3 No 2 2020"],"prefix":"10.13057","volume":"3","author":[{"given":"Hasih","family":"Pratiwi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2021,1,23]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/48002\/29850","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/48002\/29850","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,24]],"date-time":"2021-01-24T07:04:21Z","timestamp":1611471861000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/48002"}},"issued":{"date-parts":[[2021,1,23]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1,23]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v3i2.48002","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2021,1,23]]}},{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T13:35:33Z","timestamp":1742391333201},"reference-count":0,"publisher":"UNS Solo","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Epidemic is an outbreak of an infectious disease rapidly in a population at a certain place and time. Epidemic models are used to explains the spread pattern of disease. The continuous time Markov chain susceptible infected recovered-bacterial population in the aquatic reservoir (CTMC SIR-B) model is a stochastic model, which considers the effect of bacterial population. The human population are classified into 3 groups. There are susceptible, infected, and recovered groups. Then, there are bacterial population which can infectious the cholera disease to human. CTMC SIR-B model considers treatment and water sanitation parameters. The spread of cholera disease can be modeled as CTMC SIR-B. Cholera is an acute intestinal infectious disease caused by the bacterium Vibrio cholerae. Cholera can be transmitted through the human digestive system. The symptoms of cholera disease are diarrhea, vomiting, and dehydration. The dehydration if not handled properly, may cause death. The aims of this research are to build and simulate the CTMC SIR-B model for cholera disease. The result of the model simulation shows that there is no significant difference between various values of treatment and water sanitation parameters. The pattern of the cholera disease spread describes that the transmission of cholera can occur from human to human even though there is no population of bacteria in the aquatic reservoir.<\/p><p><em><\/em><strong><em>Keywords<\/em><\/strong><strong><em>: <\/em><\/strong><em>cholera<\/em><em>; ctmc sir-b;<\/em><em> epidemic model<\/em><em>;<\/em><em> stochastic.<\/em><\/p><p>\u00a0<\/p><\/jats:p>","DOI":"10.13057\/ijas.v6i1.71801","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T16:40:30Z","timestamp":1709138430000},"page":"1","source":"Crossref","is-referenced-by-count":1,"title":["Model Simulation of Continuous Time Markov Chain Susceptible Infected Recovered-Bacterial Population for Cholera Disease"],"prefix":"10.13057","volume":"6","author":[{"given":"Aulia Maulani Syifa Nur","family":"Hidayati","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Respatiwulan","family":"Respatiwulan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sri","family":"Subanti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2024,1,18]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/71801\/43849","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/71801\/43849","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T16:40:30Z","timestamp":1709138430000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/71801"}},"issued":{"date-parts":[[2024,1,18]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1,18]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v6i1.71801","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2024,1,18]]}},{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T08:44:38Z","timestamp":1783154678909,"version":"3.54.6"},"reference-count":0,"publisher":"UNS Solo","issue":"2","license":[{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p><strong>A<\/strong><strong>bstract<\/strong><strong>.<\/strong> Employees are one of the company's assets that must be managed properly. Therefore the selection of the best employees is now needed. The problem faced in determining the best and qualified employees is that there are still no standards in assessing only one person subjectively in determining the best employee, which consequently lacks appropriate or objective results. To provide rewards for the best employees, we need a system to support the decisions of the best employees who deserve to receive rewards to be on target. The purpose of this research is to design and build a decision support system application in determining the best employees using the analytic hierarchy process and weighted product methods. Stages of software development of the Software Development Life Cycle (SDLC) uses a waterfall, that is data analysis, system design, construction, coding, testing and implementation. The results of this process are in the form of calculation applications that have been obtained from the analytic hierarchy process and weighted product methods in determining the best employee. The result gives an accuracy rate of 82.3%.<\/p><p><strong>Keywords<\/strong><strong>: <\/strong>analytic hierarchy process, weighted product, decision support system, employees<\/p><\/jats:p>","DOI":"10.13057\/ijas.v4i2.44059","type":"journal-article","created":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T16:44:03Z","timestamp":1638549843000},"page":"103","source":"Crossref","is-referenced-by-count":5,"title":["Application of Analytic Hierarchy Process and Weighted Product Methods in Determining the Best Employees"],"prefix":"10.13057","volume":"4","author":[{"given":"Sri","family":"Harjanto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Setiyowati","family":"Setiyowati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Retno Tri","family":"Vulandari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5236","published-online":{"date-parts":[[2021,11,29]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/44059\/33520","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/44059\/33520","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T16:44:03Z","timestamp":1638549843000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/44059"}},"issued":{"date-parts":[[2021,11,29]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,11,29]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v4i2.44059","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2021,11,29]]}},{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:09:57Z","timestamp":1780589397170,"version":"3.54.1"},"reference-count":0,"publisher":"UNS Solo","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Classic time series data analysis techniques, such as autoregressive, model stationary data in which the values of prior observations influence the current observations through a process known as linear regression. There are several requirements for error assumptions in autoregressive, including independence, normal distribution with a zero mean and constant variance. It is frequently discovered that these assumptions are challenging to verify when modelling real data. Kernel time series regression is an alternative model that does not require error assumptions. Non-stationary time series data can be effectively modelled using the kernel time series method. Time series data that isn't yet stationary is made stationary first, then the data is modified by forming the current stationary time series data as the response variable and the previous period data as the predictor variable. Next, regression kernel modelling is carried out while applying kernel weight function and determining the optimal bandwidth. For development of science, the optimal bandwidth can be achieved by minimizing the MSE, CV, GCV, or UBR values. It is possible to use R<sup>2<\/sup> or MAPE as the kernel time series regression model's goodness metric. A strong model is generated while modelling MDKA stock price data using kernel regression utilizing the Gaussian kernel function and optimal bandwidth selection using GCV since R<sup>2<\/sup> is 0.9828372 more than 0.67 and MAPE is 1.985681% under 10%.<\/p><p><strong>Keywords<\/strong><strong>: <\/strong>3 time series; kernel regression; GCV; MDKA stock price.<\/p><\/jats:p>","DOI":"10.13057\/ijas.v6i1.79385","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T16:52:41Z","timestamp":1709139161000},"page":"22","source":"Crossref","is-referenced-by-count":1,"title":["Analisis Data Time Series Menggunakan Model Kernel: Pemodelan Data Harga Saham MDKA"],"prefix":"10.13057","volume":"6","author":[{"given":"Suparti","family":"Suparti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rukun","family":"Santoso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5236","published-online":{"date-parts":[[2024,1,18]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/79385\/43851","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/79385\/43851","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T16:52:42Z","timestamp":1709139162000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/79385"}},"issued":{"date-parts":[[2024,1,18]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1,18]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v6i1.79385","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2024,1,18]]}},{"indexed":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T04:01:31Z","timestamp":1749096091556,"version":"3.41.0"},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Poverty is a multifaceted problem that poses a challenge for developing countries across the world, including Indonesia. Poverty is one of the global and national obligations stated in the first Sustainable Development Goals (SDGs), namely \"Without Poverty\". This study seeks to examine the factors that determine poverty in Java in 2022 while accounting for regional effects. Geographically Weighted Regression (GWR) is the methodology employed. The findings revealed that geographic\/spatial characteristics had a substantial impact on poverty rates, and the GWR model generated a more accurate assessment measure than the global model. The structure of the parameters varies by location, with the variable coefficients of the health, salary, and credit indicators fluctuating, whilst the coefficients of the Education and Inflation Indicators remain similar throughout districts and cities. The government must implement proper measures to eliminate poverty not just nationally, but also in each district\/city in Indonesia, particularly on Java Island. Policies might include improving human resources in education and health, monetary policies to sustain market pricing and determine minimum salaries, and policies to infuse credit assistance money into people's companies.<em><\/em><\/p><p><strong><em>Keywords<\/em><\/strong><strong><em>: <\/em><\/strong><em>poverty, Geographically Weighted Regression (GWR), spatial analysis<\/em><em><\/em><\/p><\/jats:p>","DOI":"10.13057\/ijas.v7i2.87342","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T05:48:13Z","timestamp":1738561693000},"page":"131","source":"Crossref","is-referenced-by-count":0,"title":["Analisis Spasial Kemiskinan di Pulau Jawa Tahun 2022 dengan Metode Geographically Weighted Regression (GWR)"],"prefix":"10.13057","volume":"7","author":[{"given":"Wisly Ryan","family":"Eliezer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2025,1,25]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/87342\/49062","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/87342\/49062","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T03:56:50Z","timestamp":1749009410000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/87342"}},"issued":{"date-parts":[[2025,1,25]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,12,9]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v7i2.87342","ISSN":["2621-086X"],"issn-type":[{"type":"electronic","value":"2621-086X"}],"published":{"date-parts":[[2025,1,25]]}},{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T23:33:44Z","timestamp":1740180824295,"version":"3.37.3"},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p dir=\"ltr\"><span>The paper investigated the effects of fertilizer (Zinc, Iron and Manganese), location of farm land and their interaction on cassava yield. The secondary data used for the study were collected from the National Cereal Research Institute of Nigeria Outstanding Farm, Nung Udo, Uyo, Akwa Ibom State. The data comprised of cassava yield (Hectares) for 2016 planting season, five separate farms where three types of fertilizer were applied. The two-way analysis of variance (ANOVA) technique with interaction was used in the analysis of the data. Furthermore, the Tukey HSD test was conducted to compare the treatment means. The result of the study showed that there is significant mean difference in the yield of cassava based on the three types of fertilizer applied. On the basis of farm locations, the result shows that there is no significant mean difference in cassava yield while the interaction between fertilizer and farm location affects cassava yield significantly at 5% level of significance.<\/span><\/p><p dir=\"ltr\"><span>Keywords: <\/span><span>two-way analysis of variance with interaction; Tukey test; fertilizer; cassava yield; farm location<\/span><\/p><div><span><br \/><\/span><\/div><\/jats:p>","DOI":"10.13057\/ijas.v6i2.71440","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T15:15:50Z","timestamp":1713280550000},"page":"105","source":"Crossref","is-referenced-by-count":0,"title":["A Study on the Effects of Selected Micronutrients, Locations, and Their Interaction on Cassava Yield Based on the Two-Way ANOVA Model with Interaction"],"prefix":"10.13057","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6578-8324","authenticated-orcid":false,"given":"Emmanuel W.","family":"Okereke","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T M","family":"Pokalas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B C","family":"Ufondu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2024,4,16]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/71440\/44989","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/71440\/44989","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T15:15:51Z","timestamp":1713280551000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/71440"}},"issued":{"date-parts":[[2024,4,16]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,4,16]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v6i2.71440","ISSN":["2621-086X"],"issn-type":[{"type":"electronic","value":"2621-086X"}],"published":{"date-parts":[[2024,4,16]]}},{"indexed":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T23:03:37Z","timestamp":1648854217970},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Market segmentation is a classic topic in marketing which is never loss its attractiveness. In addition to market segmentation, customer satisfaction is important in the field of marketing. Customer satisfaction is a person's feelings after using goods or services produced by a company. High customer satisfaction shows a company's success in producing goods or services. Statistics provides many tools for segmentation research. One of statistical tool for segmentation research which takes the dependency method as an approach is Chi-Squared Automatic Interaction Detection (CHAID) analysis. CHAID analysis would provide decision tree like diagram which provide information about degree of association from dependent variable to the independent variables and the information about segments characteristic. In this case, the CHAID analysis is used to determine the type of service revocation segmentation by Indihome customers. Based on CHAID analysis, 25 segmentations were obtained, which consisted of revocation of the downgrade category of 45314 customers and the number of revocation of the Churn Out category by 11137 customers.<\/p><strong>Keywords : <\/strong>market segmentation, customer satisfaction<strong>, <\/strong>CHAID, Indihome<\/jats:p>","DOI":"10.13057\/ijas.v2i2.34526","type":"journal-article","created":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T14:53:52Z","timestamp":1577717632000},"page":"80","source":"Crossref","is-referenced-by-count":0,"title":["Klasifikasi Jenis Pencabutan Layanan oleh Pelanggan Indihome Menggunakan Metode Chi-Square Automatic Interaction Detection"],"prefix":"10.13057","volume":"2","author":[{"given":"Siti","family":"Khodijatunnuriyah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hasih","family":"Pratiwi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2019,12,27]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/34526\/25431","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/34526\/25431","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T14:53:52Z","timestamp":1577717632000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/34526"}},"issued":{"date-parts":[[2019,12,27]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,12,27]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v2i2.34526","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2019,12,27]]}},{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T10:09:31Z","timestamp":1648980571249},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>On July 1, 2014 Financial Services Authority (OJK) issued a new regulation number 8\/PJOK.13\/2014 concerning the health of general sharia banks that can be valued from several aspects including credit risk, liquidity risk, Return on Asset (ROA), Net of Margin (NOM) and Capital Adequacy Ratio (CAR). The purpose of this study is to compare the models of Cox proportional hazard and Weibull regression for the resistance of sharia bank in 2017-2018 for 24 data. The data were analyzed by describing each variable and modeling in each method. Comparison result shows that Weibull regression model is better than the Cox proportional hazard model because it has smaller AIC and MSE.<\/p><p><strong>Keywords : <\/strong>Sharia Bank, Survival Analysis, AIC, MSE<\/p><\/jats:p>","DOI":"10.13057\/ijas.v2i2.33082","type":"journal-article","created":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T09:52:52Z","timestamp":1577699572000},"page":"127","source":"Crossref","is-referenced-by-count":0,"title":["Perbandingan Model Cox Proportional Hazard dan Regresi Weibull untuk Menganalisis Ketahanan Bank Syariah"],"prefix":"10.13057","volume":"2","author":[{"given":"Yusrillah Ihza Zianita","family":"Afni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamad","family":"Fatekurohman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dian","family":"Anggraeni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2019,12,27]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/33082\/25478","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/33082\/25478","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T09:52:53Z","timestamp":1577699573000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/33082"}},"issued":{"date-parts":[[2019,12,27]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,12,27]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v2i2.33082","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2019,12,27]]}},{"indexed":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T23:10:34Z","timestamp":1648854634613},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Indonesia is one of the most prone countries to natural disasters in the world because of the climate, soil, hydrology, geology, and geomorphology. There are many different natural disasters, but the three most common natural disasters in Indonesia are flood, landslide, and tornado. This research aimed to cluster the provinces in Indonesia based on the flood, landslide, and tornado\u2019s intensity in 2018. The results of clustering by K-Means method in this research divided the provinces in Indonesia into four clusters. The second cluster contained West Java, Central Java, and Bali, the third cluster contained DKI Jakarta, the fourth cluster contained DI Yogyakarta, and the first cluster contained the other 29 provinces. The result of this research hopefully can help the government in order to make decision and improve the natural disaster management system, such as preparedness, disaster response, and disaster recovery based on the most common disaster in each province. Furthermore, the society is expected to be more aware on natural disaster management based on the most common natural disaster in province that they lived.<\/p><p><strong>Keywords : <\/strong>natural disaster, cluster, k-means<\/p><\/jats:p>","DOI":"10.13057\/ijas.v2i2.34911","type":"journal-article","created":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T09:53:30Z","timestamp":1577699610000},"page":"137","source":"Crossref","is-referenced-by-count":0,"title":["Analisis Cluster Intensitas Kebencanaan di Indonesia Menggunakan Metode K-Means"],"prefix":"10.13057","volume":"2","author":[{"given":"Hafiz Yusuf","family":"Heraldi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nabila Churin","family":"Aprilia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hasih","family":"Pratiwi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2019,12,27]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/34911\/25489","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/34911\/25489","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T09:53:31Z","timestamp":1577699611000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/34911"}},"issued":{"date-parts":[[2019,12,27]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,12,27]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v2i2.34911","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2019,12,27]]}},{"indexed":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T04:01:31Z","timestamp":1749096091606,"version":"3.41.0"},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>This study employs dynamic time warping (DTW) to analyze the farmer\u2019s terms of trade (FTT) across 34 provinces in Indonesia, aiming to identify patterns and cluster similarities in time series data. DTW is recognized for its effectiveness in measuring flexible similarities under time distortions, making it particularly suitable for time series classification across various fields. The FTT is utilized to assess farmers' purchasing power by comparing the prices they receive for their products to the prices they pay for goods and services. K-Medoid clustering techniques were applied to group provinces based on their DTW distances, revealing three distinct clusters. The silhouette score indicates that three clusters as the optimum cluster for the FTT data. The findings show that the first and third clusters have low mean of FTT and the second cluster has the highest mean FTT. These indicates disparities in farmers\u2019 income and purchasing power across regions where the government needs to enhance agricultural strategies and improve economic conditions for farmers in the first and third clusters.<\/p><p><strong><em>Keywords<\/em><\/strong><strong><em>: <\/em><\/strong><em>Clustering; Dynamic Time Warping; Farmers Term of Trade; K-Medoid<\/em><em>.<\/em><\/p><\/jats:p>","DOI":"10.13057\/ijas.v7i2.94456","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T05:48:13Z","timestamp":1738561693000},"page":"112","source":"Crossref","is-referenced-by-count":0,"title":["Grouping Indonesian Province Farmers\u2019 Term of Trade Using Dynamic Time Warping"],"prefix":"10.13057","volume":"7","author":[{"given":"Imtikhanah Anis","family":"Mahmudiati","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7132-3937","authenticated-orcid":false,"given":"Rohmatul","family":"Fajriyah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2024,12,24]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/94456\/48653","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/94456\/48653","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T03:56:51Z","timestamp":1749009411000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/94456"}},"issued":{"date-parts":[[2024,12,24]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,12,9]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v7i2.94456","ISSN":["2621-086X"],"issn-type":[{"type":"electronic","value":"2621-086X"}],"published":{"date-parts":[[2024,12,24]]}},{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:36:40Z","timestamp":1780591000649,"version":"3.54.1"},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p class=\"AbstractEnglish\">Investment is an important way to manage finances for profit. One of the most popular investments in Indonesia is buying and selling shares. In addition to getting profits, they also have risks.\u00a0 Therefore, analyzing stock prices before buying and selling is an important key in stock investing. Investors should buy stocks at a low price and sell them at a high price. One of the methods used is parametric regression analysis, but it has assumptions that must be met. A more flexible alternative is local polynomial regression without any particular assumptions. PT Merdeka Copper Gold Tbk with MDKA stock code is a company engaged in the mining and industrialization of gold, silver, and other associated minerals. The study of modeling the lowest daily price of MDKA shares using local polynomial regression showed excellent results. The high coefficient of determination exceeding 67% on the in-sample data indicates strong model performance, and the Mean Absolute Percentage Error (MAPE) value on the out-of-sample data is less than 10%, ensuring excellent model accuracy.<\/p><p class=\"AbstractEnglish\"><strong>Keywords: <\/strong>local polynomial regression; MDKA shares; time series<\/p><\/jats:p>","DOI":"10.13057\/ijas.v6i2.80118","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T15:15:50Z","timestamp":1713280550000},"page":"186","source":"Crossref","is-referenced-by-count":1,"title":["Pemodelan Data Time Series Menggunakan Pendekatan Regresi Polinomial Lokal Pada Data Harga Saham MDKA"],"prefix":"10.13057","volume":"6","author":[{"given":"Febrian Adri Nur","family":"Fauzi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rukun","family":"Santoso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di Asih I","family":"Maruddani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5236","published-online":{"date-parts":[[2024,4,16]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/80118\/44996","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/80118\/44996","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T15:16:12Z","timestamp":1713280572000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/80118"}},"issued":{"date-parts":[[2024,4,16]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,4,16]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v6i2.80118","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2024,4,16]]}},{"indexed":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T05:03:19Z","timestamp":1738645399421,"version":"3.35.0"},"reference-count":0,"publisher":"UNS Solo","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Based on Republic of Indonesia Law No. 22 of 2001, oil and natural gas are vital commodities that play an important role in the country's economy. However, the export of Indonesian oil and gas has been fluctuating, making it necessary to have a strategic plan to prevent minimal exports in the future. This planning can be initiated by first gathering the necessary information. The aim of this research is to forecast oil and gas exports in Indonesia using the best possible model. The data used include the value and volume of Indonesian oil and gas exports. The method begins with determining the ARIMA model, followed by incorporating seasonal elements. ARIMA and SARIMA modeling will tentatively include exogenous variables. Subsequently, parameter estimation, significance tests, diagnostic tests, and the determination of the best model are performed. The research findings indicate that the best model is SARIMAX (1,1,0)(0,1,1)<sup>12<\/sup>. The forecast results show that the value of Indonesia's oil and gas exports will continue to increase until July 2024, followed by a and slow down after that. It is hoped that the government can prepare sufficient supply for export to prevent a deficit during that period.<\/p><\/jats:p>","DOI":"10.13057\/ijas.v7i1.84934","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T05:46:31Z","timestamp":1738561591000},"page":"87","source":"Crossref","is-referenced-by-count":0,"title":["Peramalan Ekspor Migas di Indonesia Menggunakan Pendekatan Seasonal Autoregressive Integrated Moving Average with Exogenous (SARIMAX)"],"prefix":"10.13057","volume":"7","author":[{"given":"Eka","family":"Nurhasanah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuana","family":"Sukmawaty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maisarah","family":"Maisarah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2024,10,31]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/84934\/47661","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/84934\/47661","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T05:46:37Z","timestamp":1738561597000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/84934"}},"issued":{"date-parts":[[2024,10,31]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,10,28]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v7i1.84934","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2024,10,31]]}},{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T03:20:02Z","timestamp":1649128802308},"reference-count":0,"publisher":"UNS Solo","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"DOI":"10.13057\/ijas.v4i1.51602","type":"journal-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T03:48:07Z","timestamp":1622778487000},"source":"Crossref","is-referenced-by-count":0,"title":["Front Matter Vol 4 No 1"],"prefix":"10.13057","volume":"4","author":[{"given":"Hasih","family":"Pratiwi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2021,5,30]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/51602\/31893","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/51602\/31893","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T03:48:25Z","timestamp":1622778505000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/51602"}},"issued":{"date-parts":[[2021,5,30]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,5,30]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v4i1.51602","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2021,5,30]]}},{"indexed":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T16:15:03Z","timestamp":1654013703492},"reference-count":0,"publisher":"UNS Solo","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p align=\"justify\">Poverty is a serious issue that must be addressed immediately by countries in the world, including Indonesia. The Indonesian government has implemented a variety of poverty reduction projects, such as providing education and health insurance. The rising poverty rate is due to the poor quality of education and health care. On Sumatra, there are 5,83 million poor people or 22,06 percent of the total number of poor people in Indonesia. This statistic appears to be quite large, and the government should be concerned about it. Factors causing poverty such as education and health are latent variables that cannot be measured directly. The suitable statistical method used is Structural Equation Model (SEM). In SEM analysis, there are three types of model fit tests: measurement model fit with Confirmatory Factor Analysis (CFA), overall model fit, and structural model fit. The results indicated that the model was fit or suitable for the model's tests. From the SEM model that was formed, it was found that health had a negative and significant effect on poverty and education did not have a significant effect on poverty and 77 percent of the variation in poverty could be explained by the SEM model that was formed.<\/p><p><strong>Keywords<\/strong><strong>:\u00a0<\/strong>poverty; education;\u00a0health; SEM; CFA<\/p><\/jats:p>","DOI":"10.13057\/ijas.v5i1.50493","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T15:32:32Z","timestamp":1654011152000},"page":"48","source":"Crossref","is-referenced-by-count":0,"title":["Structural Equation Model (SEM) dalam Pemodelan Kemiskinan di Pulau Sumatera"],"prefix":"10.13057","volume":"5","author":[{"given":"Hasrat Ifolala","family":"Zebua","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geni Andalria","family":"Harefa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"5236","published-online":{"date-parts":[[2022,5,31]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/50493\/35750","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/50493\/35750","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T15:32:38Z","timestamp":1654011158000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/50493"}},"issued":{"date-parts":[[2022,5,31]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,5,31]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v5i1.50493","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2022,5,31]]}},{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T03:16:47Z","timestamp":1783307807954,"version":"3.54.6"},"reference-count":0,"publisher":"UNS Solo","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Gold is very popular investment instrument due to its annual prices increases. In the long term, gold prices follow a nonlinear pattern, but in the short term, there are fluctuations influenced by various factors, including global market dynamics, monetary policy, and overall economic conditions. Therefore, predicting gold prices is an important step in minimizing risk and maximizing profits for investors. In this study, we analyze the performance of two methods for forecasting global gold prices, namely long short term memory (LSTM) and autoregressive integrated moving average with exogenous variables (ARIMAX). Data used is weekly global gold price data from August 1, 2000, to June 1, 2024. The variables used are Close as the dependent variable and Open as the exogenous variable. The data used is stationary data through the differencing process and algorithmic transformation to overcome non-stationarity issues. The best LSTM model uses the Tanh activation function with 30 LSTM units, 10 timesteps, and a dropout of 0.01, resulting in a MAPE value of 5.323%. The best ARIMAX model obtained was the ARIMAX (0,1,1) model, with a MAPE value of 0.55% for the test data and 0.61% for the training data. The research results, indicate that the higher accuracy of ARIMAX reflects its suitability for linear data such as gold prices, but the accuracy of LSTM which is below 10% still performs well for more complex data patterns.<\/p><p><br \/><strong>Keywords<\/strong>: gold price; forecasting; LSTM; arimax.<\/p><\/jats:p>","DOI":"10.13057\/ijas.v8i2.97739","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T03:03:53Z","timestamp":1783307033000},"page":"102","source":"Crossref","is-referenced-by-count":0,"title":["Gold Price Forecasting with Long Short Term Memory (LSTM) and ARIMAX Method"],"prefix":"10.13057","volume":"8","author":[{"given":"Raisa Naura","family":"Adila","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdurakhman","family":"Abdurakhman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5236","published-online":{"date-parts":[[2026,2,12]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/97739\/53760","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/97739\/53760","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T03:03:59Z","timestamp":1783307039000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/97739"}},"issued":{"date-parts":[[2026,2,12]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1,14]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v8i2.97739","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2026,2,12]]}},{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T23:31:25Z","timestamp":1785713485799,"version":"3.56.0"},"reference-count":0,"publisher":"UNS Solo","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAS"],"abstract":"<p>Population growth can encourage and hinder economic growth. This study aims to analyze the factors that influence gross domestic product (GDP) in Daerah Istimewa Yogyakarta (DIY) using panel data regression. This study uses three independent variables, namely number of population, number of poor population, and investment, while the dependent variable is GDP. We use secondary data obtained from Badan Pusat Statistik (BPS). The results obtained from the regression analysis of the data series time panel are generalized least square (GLS), while for the cross section data panel shows the REM model is more suitable than PLS and FEM. Based on the validity test of the influence or t-test, the variable that shows significant to the economic rate which is categorized as GRDP in the Daerah Istimewa Yogyakarta in 2011-2015 is the variable Total population and Investment which has a positive relationship.<\/p><p><strong>Keywords<\/strong><strong> : <\/strong>economic growth rate, panel data regression, gross regional domestic product<\/p><\/jats:p>","DOI":"10.13057\/ijas.v2i1.28950","type":"journal-article","created":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T09:44:03Z","timestamp":1575971043000},"page":"42","source":"Crossref","is-referenced-by-count":9,"title":["Regresi Data Panel untuk Mengetahui Faktor-Faktor yang Mempengaruhi PDRB di Provinsi DIY Tahun 2011-2015"],"prefix":"10.13057","volume":"2","author":[{"given":"Dea Aulia","family":"Nandita","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lalu Bayu","family":"Alamsyah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enggar Prima","family":"Jati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edy","family":"Widodo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5236","published-online":{"date-parts":[[2019,7,5]]},"container-title":["Indonesian Journal of Applied Statistics"],"link":[{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/28950\/21232","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/viewFile\/28950\/21232","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T09:44:06Z","timestamp":1575971046000},"score":0.0,"resource":{"primary":{"URL":"https:\/\/jurnal.uns.ac.id\/ijas\/article\/view\/28950"}},"issued":{"date-parts":[[2019,7,5]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,7,16]]}},"URL":"https:\/\/doi.org\/10.13057\/ijas.v2i1.28950","ISSN":["2621-086X"],"issn-type":[{"value":"2621-086X","type":"electronic"}],"published":{"date-parts":[[2019,7,5]]}}],"items-per-page":20,"query":{"start-index":0,"search-terms":null}}}