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All Journal DiJITAC : Digital Journal of Information Technology and Communication Journal of Vocational, Informatics and Computer Education
Karkhi Mirza Kavari
Universitas Siber Asia
Religion Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences

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ERP TERINTEGRASI PADA KOPERASI SYARIAH UNTUK OPTIMALISASI PENILAIAN KELAYAKAN KREDIT, KEPATUHAN SYARIAH, DAN MANAJEMEN RISIKO BERBASIS SLIK OJK Sufyaldy; Suherman; Muhammad Shaleh; Karkhi Mirza Kavari; Hasnawati; Mawardi Kudin
DiJITAC : Digital Journal of Information Technology and Communication DiJITAC, Vol 6 No.2, April 2026
Publisher : Universitas Islam Negeri Sultan Aji Muhammad Idris Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21093/fhz0az18

Abstract

Lembaga Keuangan Mikro Syariah (LKMS), khususnya Koperasi Simpan Pinjam dan Pembiayaan Syariah (KSPPS) atau Baitul Maal wat Tamwil (BMT), memiliki peran strategis dalam mendorong inklusi keuangan bagi sektor Usaha Mikro, Kecil, dan Menengah (UMKM) di Indonesia. Namun, eksistensi dan operasional entitas ini kerap dihadapkan pada kendala fragmentasi sistem informasi, tingginya angka pembiayaan bermasalah (Non-Performing Financing/NPF), serta ketidakpatuhan terhadap standar pelaporan akuntansi dan regulasi pengawasan. Penelitian ini bertujuan untuk merancang, mengembangkan, dan mengevaluasi sebuah platform sistem informasi terintegrasi berbasis Enterprise Resource Planning (ERP) khusus untuk KSPPS. Pengembangan sistem ini mengadopsi kerangka kerja Design Science Research Methodology (DSRM) yang disinergikan dengan pendekatan User-Centered Design (UCD). Platform ERP yang dibangun menggunakan lingkungan kerja ERP (modul tipd_koperasi) dengan arsitektur Model-View-Controller (MVC) yang menjembatani dualisme fungsi BMT, yakni fungsi komersial (Tamwil) dan fungsi sosial (Maal). Hasil implementasi menunjukkan bahwa sistem mampu mengotomatisasi siklus akuntansi syariah berdasarkan standar PSAK 106, menghitung rasio tingkat kesehatan koperasi sesuai regulasi Kementerian Koperasi dan UKM, serta menyediakan instrumen mitigasi risiko pembiayaan melalui algoritma credit scoring internal yang terintegrasi secara konseptual dengan Sistem Layanan Informasi Keuangan (SLIK) Otoritas Jasa Keuangan (OJK). Intervensi teknologi ini memberikan implikasi manajerial berupa efisiensi operasional, eliminasi bias subjektif dalam persetujuan kredit, dan peningkatan transparansi tata kelola lembaga keuangan mikro syariah.
Temporal Representation Learning of LMS Teaching Activity Sequences for Predicting Lecturer Performance in Higher Education Sulfadly; Muhammad Ahsan; Mawardi Kudin; Maemunah; Karkhi Mirza Kavari
Journal of Vocational, Informatics and Computer Education Vol 4, No 3 (2026): September 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i3.1365

Abstract

Purpose – This study evaluates a Temporal Representation Learning framework that addresses limitations of Learning Management System (LMS) evaluation based on aggregate activity counts by mapping chronological instructional interactions into latent representations to predict institutional Lecturer Performance Scores (LPS). Methods – Long Short-Term Memory (LSTM) and Transformer encoders modeled 144,178 activity logs from 262 instructors across 1,585 classes as event sequences. Performance was evaluated using five-fold cross-validation against aggregate-frequency baselines, including Decision Tree, Random Forest, Support Vector Regression, and XGBoost. An order-shuffling ablation assessed the contribution of chronological event order. Findings – Sequential models achieved lower prediction errors than conventional baselines. LSTM achieved a root mean square error (RMSE) of 4.35 ± 0.22 and R² of 0.89 ± 0.02, while the Transformer performed best, achieving RMSE of 4.10 ± 0.19, mean absolute error of 2.95 ± 0.15, and R² of 0.92 ± 0.01. Shuffling event order reduced Transformer performance to R² of 0.63 ± 0.04 and RMSE of 9.75 ± 0.35, supporting the predictive contribution of chronology. Exploratory K-means clustering identified three teaching activity archetypes, with a silhouette coefficient of 0.41. Research implications – Chronological interaction patterns capture predictive information overlooked by aggregate metrics, supporting the development of sequence-aware institutional analytics for lecturer performance evaluation. Originality – This study applies deep sequential representation learning to extract latent instructional activity patterns directly from uncurated LMS event streams and examines their predictive value through chronological order ablation.
Co-Authors Hasnawati Maemunah Mawardi Kudin Muhammad Ahsan Muhammad Shaleh Sufyaldy Suherman Sulfadly