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All Journal IJISTECH (International Journal Of Information System & Technology) Jurnal Ilmiah Dikdaya JOURNAL OF SCIENCE AND SOCIAL RESEARCH Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) Management Studies and Entrepreneurship Journal (MSEJ) Jurnal Pengabdian Masyarakat Multidisiplin Brahmana : Jurnal Penerapan Kecerdasan Buatan INVEST : Jurnal Inovasi Bisnis dan Akuntansi IJISTECH Comsep : Jurnal Pengabdian Kepada Masyarakat Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence J-COSCIS : Journal of Computer Science Community Service Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Didik Siswanto
Teknik Informatika Fakultas Ilmu Komputer Universitas Lancang Kuning
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DIGITALISASI PENDIDIKAN: ANALISIS DAN PREDIKSI POLA PEMBELAJARAN DIGITAL PADA LMS EDLINK MENGGUNAKAN ALGORITMA NAIVE BAYES Zamzami Zamzami; Didik Siswanto; Lasri Nijal
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 5 (2026): October (2026)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i5.7341

Abstract

Abstract: Learning Management Systems (LMS) produce large volumes of learning activity data, yet at Universitas Lancang Kuning these data have so far been analysed only descriptively. This study builds a Naive Bayes model to classify and predict digital learning patterns on the Edlink LMS and designs an Early Warning System (EWS). Following the Knowledge Discovery in Databases framework, Edlink monitoring and evaluation reports for the odd and even semesters of 2025/2026 were extracted, cleaned, discretised, and labelled into three classes: Proactive, Standard, and Passive (At Risk). The dataset comprised 1,014 lecturer–study programme records (353 unique lecturers; 6,380 course classes). Categorical Naive Bayes with Laplace smoothing was evaluated using stratified 10-fold cross validation. Model 1 (within-semester classification) achieved a mean accuracy of 86.78%, with a recall of 95.11% and an F1-score of 95.47% for the Passive class. Model 2 (predicting the next semester, n = 333) achieved 73.27% accuracy and a Passive-class recall of 90.61%, comparable to a simple persistence rule (73.87%). Learning Assessment and Lesson Planning were the most influential variables. In the high-risk EWS group, 92.81% of lecturers were indeed Passive in the following semester. Edlink use increased significantly between semesters (Wilcoxon p < 0.001), although 63.98% of even-semester records remained Passive. Keyword: digitalisation of education; digital learning patterns; Naive Bayes; educational data mining; early warning system.   Abstrak: Learning Management System (LMS) menghasilkan jejak data aktivitas pembelajaran dalam jumlah besar, tetapi di Universitas Lancang Kuning data tersebut selama ini hanya dianalisis secara deskriptif. Penelitian ini membangun model Naive Bayes untuk mengklasifikasikan dan memprediksi pola pembelajaran digital pada LMS Edlink serta merancang Early Warning System (EWS). Mengikuti kerangka Knowledge Discovery in Databases, laporan Monitoring dan Evaluasi Edlink Semester Ganjil dan Genap 2025/2026 diekstraksi, dibersihkan, didiskretisasi, dan dilabeli ke dalam tiga kelas: Proaktif, Standar, dan Pasif (Berisiko). Dataset terdiri atas 1.014 rekaman dosen–program studi (353 dosen unik; 6.380 kelas mata kuliah). Categorical Naive Bayes dengan Laplace smoothing dievaluasi menggunakan stratified 10-fold cross validation. Model 1 (klasifikasi dalam semester) mencapai akurasi rata-rata 86,78%, dengan recall 95,11% dan F1-score 95,47% pada kelas Pasif. Model 2 (prediksi semester berikutnya, n = 333) mencapai akurasi 73,27% dan recall kelas Pasif 90,61%, setara dengan aturan sederhana “kelas tetap” (73,87%). Learning Assessment dan Lesson Planning merupakan variabel paling berpengaruh. Pada kelompok risiko Tinggi EWS, 92,81% dosen benar-benar berada pada kelas Pasif di semester berikutnya. Penggunaan Edlink meningkat signifikan antarsemester (uji Wilcoxon p < 0,001), tetapi 63,98% rekaman Semester Genap masih berada pada kelas Pasif. Kata kunci: digitalisasi pendidikan; pola pembelajaran digital; Naive Bayes; educational data mining; early warning system.
Pengembangan Lanjut Sistem Informasi Pelayanan Dan Pendataan UMKM Provinsi Riau (Kasus Dinas Perindustrian, Perdagangan, Koperasi Usaha Kecil Dan Menengah Provinsi Riau) Didik Siswanto; Yuvi Darmayunata; Z Zamzami; Bayu Febriadi
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 1 (2023): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.276

Abstract

Based on the previous authors, it was concluded that the Department of Industry, Trade, Small and Medium Enterprises Cooperatives of Riau Province had limitations in collecting data on the status of MSMEs that had attended training or had not attended training, making the government's training incomplete and not on target. Efforts have been made to develop an information system that can verify MSME data using optical character recognition to record MSMEs that are spread across Riau Province has a lack of features so that the government can manage all products uploaded by MSME actors and MSME actors can register with a registered NIK. Application of Services and Data Collection for UMKM in Riau Province This further research includes developing features according to the analysis to the Disperindagkop UKM. The conclusion from the results of the development of this information system has successfully developed a feature where MSME actors cannot register with the same NIK, there is a feature so that the admin can manage all products that have been uploaded by MSME actors, product data collection where currently there is a product category that is produced by MSMEs and the categories of each type of service prepared so that it is hoped that it will make it easier for users to operate the information system.
Co-Authors Acep Solihin Adolf Bastian Agusviyanda Agusviyanda Bayu Febriadi Bayu Febriadi Bayu Febriadi, Bayu Febrizal Alfarasy Syam Febrizal Alfarasy Syam Imran Al Ucok Imran Alucok Lasri Nijal Lasri Nijal Maherizal Marihot Manullang Muhammad Nur Muhammad Sadar Muhammad Sadar Nurliana Nasution, Nurliana Oemar, Fahmi Pandu Pratama Putra Pandu Pratama Putra, Pandu Pratama rahmat, adi Richa Afriana Munthe Ridho Alfanandi Rio Pramono Jati Ririn Handayani Rrin Handayani Rully Safrul Rajab Senti Ridar Wilis Rambe Sri Rahayu Prastyaningsih Surya Dharma Susi Handayani Yogi Yunefri, Yogi Yuvi Darmayunata Zamzami, Zamzami