{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T21:19:01Z","timestamp":1784236741454,"version":"3.55.0"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3088152","type":"journal-article","created":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T19:43:15Z","timestamp":1623440595000},"page":"87370-87377","source":"Crossref","is-referenced-by-count":33,"title":["Aggregating Time Series and Tabular Data in Deep Learning Model for University Students\u2019 GPA Prediction"],"prefix":"10.1109","volume":"9","author":[{"given":"Harjanto","family":"Prabowo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1537-8570","authenticated-orcid":false,"given":"Alam Ahmad","family":"Hidayat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9872-9646","authenticated-orcid":false,"given":"Tjeng Wawan","family":"Cenggoro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Rahutomo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kartika","family":"Purwandari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7404-9005","authenticated-orcid":false,"given":"Bens","family":"Pardamean","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"ALBERT: A lite BERT for self-supervised learning of language representations","author":"lan","year":"2019","journal-title":"arXiv 1909 11942"},{"key":"ref38","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"raffel","year":"2020","journal-title":"J Mach Learn Res"},{"key":"ref33","first-page":"1757","article-title":"Recurrent dropout without memory loss","author":"semeniuta","year":"2016","journal-title":"Proc 26th Int Conf Comput Linguistics Tech Papers (COLING)"},{"key":"ref32","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref31","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Statist"},{"key":"ref30","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref37","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref36","article-title":"High-dimensional probability estimation with deep density models","author":"rippel","year":"2013","journal-title":"arXiv 1302 5125"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2009.5179010"},{"key":"ref34","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2016.070659"},{"key":"ref40","article-title":"StructBERT: Incorporating language structures into pre-training for deep language understanding","author":"wang","year":"2019","journal-title":"arXiv 1908 04577"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3991\/ijet.v14i14.10310"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.7763\/IJIET.2016.V6.745"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.11591\/ijeecs.v9.i2.pp447-459"},{"key":"ref14","first-page":"212","article-title":"Educational data mining & students’ performance prediction","volume":"7","author":"saa","year":"2016","journal-title":"Int J Adv Comput Sci Appl"},{"key":"ref15","first-page":"2491","article-title":"Predicting students’ performance using modified ID3 algorithm","volume":"5","author":"ramanathan","year":"2013","journal-title":"Int J Eng Technol"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICELET.2012.6333365"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.23956\/ijarcsse\/V7I2\/01219"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s10639-018-9839-7"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.12988\/ams.2015.53289"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICSITech.2016.7852655"},{"key":"ref4","article-title":"Using machine learning to predict student performance","author":"pojon","year":"2017"},{"key":"ref27","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume":"27","author":"sutskever","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2021.104190"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s10734-020-00520-7"},{"key":"ref29","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","volume":"24","author":"bergstra","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICIS.2018.8466475"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCIC.2017.8524317"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1177\/2378023118817702"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1030037959"},{"key":"ref9","article-title":"Machine learning based student grade prediction: A case study","author":"iqbal","year":"2017","journal-title":"arXiv 1708 08744"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2740980"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICSIMA.2013.6717966"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICIEV.2016.7760058"},{"key":"ref24","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Proc of USENIX Symp on Operating Systems Design and Implementation (OSDI)"},{"key":"ref23","author":"chollet","year":"2015","journal-title":"Keras"},{"key":"ref26","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume":"28","author":"shi","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015015"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09452125.pdf?arnumber=9452125","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:32Z","timestamp":1639770992000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9452125\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3088152","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}