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All Journal JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Achmad Baijuri
Universitas Ibrahimy Situbondo
Computer Science & IT Control & Systems Engineering Engineering

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Web-Based E-Learning Information System with Multi-Role Monitoring Feature at SMP Ibrahimy 2 Sukorejo Rosita Natania Maulani; Achmad Baijuri; Firman Santoso
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5566

Abstract

Learning at SMP Ibrahimy 2 Sukorejo is still conducted conventionally, resulting in limited material access outside school hours and minimal learning monitoring. This study designs and implements a web-based e-learning information system using the Waterfall method. The system accommodates five actors (Admin, Teacher, Student, Class Teacher, Curriculum Vice Principal), with novelty in multi-role monitoring features and integration of five roles in a single platform. Main features include learning material management, online assignment submission, UTS/UAS evaluation with automatic grade calculation (Final Grade = (Assignment Score + Exam Score) / 2), real-time notifications, and per-role monitoring dashboards. Functional testing using Black Box Testing on 20 scenarios across 8 modules yielded a 100% success rate (20/20 scenarios matched expected outcomes). Usability testing using System Usability Scale (SUS) with 15 respondents produced a mean score of 81.3, categorized as Excellent. The system was successfully implemented as an integrated digital platform supporting the teaching and learning process at SMP Ibrahimy 2 Sukorejo.
Student Graduation Prediction at Ibrahimy University Using K-Nearest Neighbor (KNN) Algorithm Herlinatus Safira Muasolli; Achmad Baijuri; Fajriyanto
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5567

Abstract

Student graduation is an important indicator of a university's success in delivering quality education. Ibrahimy University faces challenges in objectively and proactively predicting student graduation, as academic evaluation processes remain conventional and reactive. This study aims to build a student graduation prediction system using the K-Nearest Neighbor (KNN) algorithm based on academic data including GPA, credits, attendance, and number of failed courses. The dataset consists of 150 student records from Ibrahimy University, developed using the Knowledge Discovery in Database (KDD) framework. Data was split into 80% training and 20% testing with StandardScaler normalization. The optimal k value was searched from k=1 to k=15. Results show that k=1 achieved the highest accuracy of 96.67%. The system is deployed as an interactive web application using Streamlit, enabling non-technical users such as lecturers and academic administrators to monitor student graduation potential more effectively and data-driven.
Co-Authors Fajriyanto Firman Santoso Herlinatus Safira Muasolli Rosita Natania Maulani