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All Journal Jurnal Masyarakat Informatika Rekursif: Jurnal Informatika Jurnal Pseudocode Jurnal PGSD: Jurnal Ilmiah Pendidikan Guru Sekolah Dasar Jurnal Teknik Informatika (JUTIF) These proceedings represent the work of researchers participating in The International Conference on Elementary Education (ICEE) which is being hosted by the Elementary Education Study Programme School of Postgraduate Studies, Universitas Pendidikan Dikdimas : Jurnal Pengabdian Kepada Masyarakat The Indonesian Journal of Computer Science Jurnal Abdimas Bencoolen IJIS Edu : Indonesian Journal of Integrated Science Education
Endina Putri Purwandari
Program Studi Teknik Infomatika, Fakultas Teknik, Universitas Bengkulu
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Journal : Rekursif: Jurnal Informatika

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Implementasi Standar ISO 15489 Dalam Perancangan SOP Kearsipan di Dinas Dukcapil Kabupaten Seluma Nabila, Nisreina; Purwandari, Endina Putri; Ramadani, Niska
Rekursif: Jurnal Informatika Vol 13 No 2 (2025): Volume 13 Nomor 2 November 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i2.42418

Abstract

The digital era encourages the transformation of archive management, demanding a standardized system. Based on Indonesian Law No. 43 of 2009, archives are classified into active and inactive. At the Dukcapil Office of Seluma Regency, the management of inactive archives is not yet supported by written guidelines, which poses a risk to information security and access. This research designs Standard Operating Procedures (SOP) based on ISO 15489 and integrates them with a digital archiving system. Qualitative methods were used to explore the process and its impact. The results show that the security score (62) and accessibility score (63) are still relatively low. The designed SOP serves as an operational reference and the foundation for the development of the digital archive information system, which has proven to enhance efficiency and compliance with regulations. This research makes an important contribution to archival policy and suggests evaluating the implementation of SOPs as well as developing a digital system prototype in future studies.
Pengembangan Sistem Deteksi Dini Mahasiswa Berisiko Menggunakan Machine Learning Berbasis Data Learning Management System: Studi Kasus: rumahilmu.org Syahputra, Wahyu; Purwandari, Endina Putri; Oktoeberza, Widhia KZ
Rekursif: Jurnal Informatika Vol 13 No 2 (2025): Volume 13 Nomor 2 November 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i2.43948

Abstract

Abstract: This research aims to develop an early detection system for at-risk students using machine learning based on data from the Learning Management System (LMS) rumahilmu.org. The system was designed for the Information Systems Study Programs at the University of Bengkulu, analyzing data from 459 student enrollments across five courses. A total of 37–76 features were extracted from LMS activities to predict students likely to score below the 30th percentile at three strategic time points (25%, 50%, and 75% of the semester). This study implemented a per-class optimization approach, testing 11 algorithms to find the best model for each course. The results showed that no single algorithm was universally superior; the most effective models varied for each course, with Gaussian Process, Logistic Regression, and Voting Classifier being the most frequently chosen. However, evaluation on the test data revealed significant challenges: despite high cross-validation scores (F1-score > 0.80), overfitting and performance degradation occurred. The most critical finding was the model's low capability in detecting the 'At-Risk' minority class, with the Recall (At-Risk) metric reaching 0.00 in 8 out of 15 scenarios. The best detection performance was achieved in the Statistics & Probability course with a Recall of 0.50. The implemented system, featuring a 3-tier architecture (FastAPI and React), provides an interactive dashboard, but its predictive effectiveness for early detection is limited by small and imbalanced datasets.
Co-Authors Abdul Muktadir, Abdul Agusdianita, Neza Ahmad, Mubarik Azmi, Yoan Leo Daimun, Daimun Dalifa, Dalifa Desi Andreswari Dianita, Meisy Endang Widi Winarni Ernawati Ernawati Harmika, Lita Harry Budi Santoso Heryanto, Debi Kasiyah Junus Lakshita, Stefanie Laksmi Manurung, Wahyu Ozorah Mochammad Yusa, Mochammad Muh Syaiful Romadhon Nabila, Nisreina Natalio, Jandri Niska Ramadani, Niska Noperman, Feri Nurul Renaningtias, Nurul Oktoeberza, Widhia KZ Rusdi Efendi Sucipto, Sigit Syahputra, Wahyu Wijanarko, Andang Yudi Setiawan Yusnia, Yusnia Zuliuskandar, Valleryan Virgil