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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik ITS IPTEK Journal of Proceedings Series Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Journal of Animation & Games Studies Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknologi dan Sistem Komputer Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CYCLOTRON Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JAVA Journal of Electrical and Electronics Engineering Indonesian Journal of Electrical Engineering and Computer Science JAREE (Journal on Advanced Research in Electrical Engineering) JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Sewagati
Supeno Mardi Susiki Nugroho, Supeno Mardi
Departemen Teknik Komputer Institut Teknologi Sepuluh Nopember
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Multimodel Prediction Score Based on Academic Procrastination Behavior in E-Learning Sartana, Bruri Trya; Nugroho, Supeno Mardi Susiki; Yuhana, Umi Laili; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.85880

Abstract

This research investigates the impact of academic procrastination on student performance in online learning environments and explores a multimodel approach for grade prediction. Academic procrastination is a well-documented issue that negatively affects learning outcomes, often leading to lower academic performance and increased dropout rates in self-paced learning platforms. This study analyzes behavioral data from 377 students, extracted from Moodle activity logs, which record real-time student interactions with learning materials. To address the gap in understanding procrastination patterns through activity logs, key procrastination-related features were derived from timestamps of task access, submission, and engagement duration. Using K-Means clustering with the Elbow method, students were categorized into three procrastination clusters: low procrastination with high academic performance, high procrastination with low performance, and moderate procrastination with average performance. Seven machine learning models were evaluated for predicting student grades, with Random Forest (RF) achieving the highest accuracy (R² = 0.812, MAE = 6.248, RMSE = 8.456). These findings highlight the potential of using activity logs to analyze procrastination patterns and predict student performance, allowing educators to develop early intervention strategies that support at-risk students and improve learning outcomes.
Peramalan Pencemaran Udara Di Kota Surabaya Menggunakan Metode DSARIMA dengan Pendekatan Percentile Error Bootstrap (PEB) Koesoemaningroem, Novi; Endroyono, Endroyono; Nugroho, Supeno Mardi Susiki
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021855216

Abstract

Peramalan pencemaran udara yang  akurat  diperlukan untuk mengurangi dampak pencemaran udara. Peramalan yang belum akurat akan berdampak kurang efektifnya tindakan yang dilakukan untuk mengantisipasi dampak pencemaran udara. Sehingga diperlukan sebuah pendekatan yang dapat mengetahui keakuratan plot data hasil peramalan. Penelitian ini dilakukan dengan tujuan melakukan peramalan pencemaran udara berdasarkan parameter PM10, NO2, CO, SO2, dan O3dengan metode DSARIMA. Data dalam penelitian ini sebanyak 8.760 data yang berasal dari Dinas Lingkungan Hidup Kota Surabaya. Berdasarkan hasil peramalan selama 168 jam kadar parameter PM10, NO2, SO2 dan O3 cenderung  menurun. Hasil peramalan selama 168 jam dengan menggunakan DSARIMA memberikan hasil peramalan yang nilainya mendekati data aktual terbukti dari polanya yang sesuai atau mirip dengan grafik plot data aktual dengan hasil ramalan. Dengan pendekatan PEB, selisih antara data aktual dan data ramalan kecil dan plot grafik PEB mengikuti plot grafik di data aktual, sehingga dapat dikatakan bahwa model sudah sesuai. Hasil akurasi terbaik yang dihasilkan adalah model DSARIMA dengan RMSE terkecil 0,59 didapatkan dari parameter CO yaitu ARIMA(0,1,[1,2,3])(0,1,1)24(0,1,1)168. AbstractAccurate air pollution forecasting is needed to reduce the impact of air pollution. Inaccurate forecasting will result in less effective actions taken to anticipate the impact of air pollution. So we need an approach that can determine the accuracy of the forecast data plot. This research was conducted with the aim of forecasting air pollution based on the PM10, NO2, CO, SO2, and O3 parameters using the DSARIMA method. The data in this study were 8.760 data from the Surabaya City Environmental Service. Based on the results of forecasting for 168 hours, the levels of PM10, NO2, SO2, and O3 parameters tend to decrease. Forecasting results for 168 hours using DSARIMA provide forecasting results whose values are close to the actual data as evidenced by the pattern that matches or is similar to the actual data plot graph with the forecast results. With the PEB approach, the difference between the actual data and the forecast data is small and the PEB graph plot follows the graph plot in the actual data, so it can be said that the model is appropriate. The best accuracy result is DSARIMA with the smallest RMSE 0,59 obtained from the CO parameter, namely ARIMA(0,1,[1,2,3])(0,1,1)24(0,1,1)168.  
Co-Authors Abd Kadir Mahamad Achmad Affandi Adhi Dharma Wibawa Aditiya, Fajar Adlan Hakim Ahmad Ahmad Dwi Arianto Ahmad Dwi Arianto Ahmad Zaini Aji Prasetya Wibawa Alan Luthfi Alfonsus Haryo Sangaji Anang Kukuh Adisusilo Arianto, Ahmad Dwi Binti Umayah Cahyo Darujati Cahyo Darujati Citra Ratih Prameswari Damastuti, Fardani Annisa Deny Kurniawan Eko Mulyanto Yuniarno Endroyono Endroyono Endroyono, E Fachri, Moch Fachrul Kurniawan Fachrul Kurniawan Feby Artwodini Muqtadiroh Fresy Nugroho Fresy Nugroho Handayeni, Ketut Dewi Martha Erli Hervit Ananta Vidada I Ketut Eddy Purnama I Ketut Eddy Purnama Jihad Hammad Khothibul Umam Khrisna Aprianto Koesoemaningroem, Novi Kurniawan, Fachrul Lukman Hakim Mauridhi H Purnomo Mauridhi Hery Purnomo Maydlinne Liudyvia Moch Fachri Mochamad Hariadi Mochammad Hariadi Mochammad Hariadi Muhammad Reza Pahlawan Muhammad Rifqi Mukhtar Muharman Lubis Munir Munir Nugrahardi Ramadhani Prasetyo, Didit Prioko, Kentani Langgalih Putu Gde Ariastita Putu Gde Ariastita Ariastita Rachmadi, Reza Fuad Rahman, Muhammad Daffa Abiyyu Rahmat Fauzi Rahmawati, Vira Nur Reza Fuad Rachmadi Riris Diana Rachmayanti Sartana, Bruri Trya Sharifah Saon Sri Wiwoho Mudjanarko, Sri Wiwoho Surya Sumpeno Susi Juniastuti Syahri Mu'min Tsuyoshi Usagawa, Tsuyoshi Wicaksono, Alif Aditya Yuhana, Umi Laili Yuniarto, Eko Mulyanto Yunifa Miftachul Arif Yuri Pamungkas Zaini, Ahmad