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All Journal JSAI (Journal Scientific and Applied Informatics) Bulletin of Information Technology (BIT)
Muh Salim
Institut Teknologi Sains dan Bisnis Muhammadiyah Selayar
Computer Science & IT

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Evaluasi Pengembangan Sistem Informasi Penerimaan Mahasiswa Baru Berbasis Workflow Otomatis Menggunakan Pendekatan Research and Development Abdul Ma'arief Al Imran; Muhammad Ichsan M; Muh Salim; Sulistiawati Rahayu Ahmad; Ali Asgar Zainal Abidin
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9730

Abstract

The New Student Admissions (PMB) process is a strategic endeavor that influences the quality of university entrants. Nonetheless, in several colleges, particularly in remote regions, the execution of PMB remains manual, which may lead to issues regarding efficiency, accuracy, and openness in data management. This study seeks to create and assess an automated workflow-oriented PMB information system to enhance PMB management at the Muhammadiyah Selayar Institute of Technology, Science, and Business. The system development employs the Waterfall methodology, encompassing the phases of requirements analysis, design, implementation, and system testing. Evaluation is conducted by assessing the validity, practicality, and efficacy of the system with the participation of internal users as evaluators. The evaluation results demonstrated a validity rate of 100%, practicality of 95.83%, and effectiveness of 89.56%, demonstrating the system's feasibility for supporting the PMB process. This system can systematically combine the registration process, document verification, selection, and results announcement. This research contributes an automated workflow implementation model within the PMB information system, enhancing process management efficiency and facilitating the oversight of PMB activities. While the test remains confined to the institution's internal setting, the findings of this study are anticipated to serve as a benchmark for the establishment of a comparable PMB system in universities with analogous attributes.
Analisis Komparatif Algoritma Klasifikasi untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Hariati Husain; sulistiawati Rahayu Ahmad; Muh Salim
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2619

Abstract

- Timely student graduation is an important indicator in assessing the quality of higher education management. However, not all students are able to complete their studies within the prescribed study period, making it necessary to implement data-driven predictive approaches to identify students at risk of delayed graduation. This study aims to compare the performance of the Decision Tree and Naïve Bayes algorithms in classifying timely student graduation based on academic data. The dataset consists of alumni records from the Informatics Engineering Study Program for the 2015–2016 cohorts, totaling 610 valid records after data cleaning and attribute selection. Predictor variables include gender, class type, and Semester Grade Point Index (IPS) from semester 1 to semester 5, while the target variable is graduation status. Model evaluation was conducted using an 80% training and 20% testing split, and performance was measured through a confusion matrix to obtain accuracy, precision, and recall values. The results show that the Decision Tree achieved an accuracy of 69.54%, while Naïve Bayes achieved 68.38%. The 1.16% difference indicates that the Decision Tree performs slightly better for this dataset. These findings suggest that early semester academic performance significantly contributes to predicting timely graduation and can support data-driven academic decision-making.
Co-Authors Abdul Ma’arief Al Imran Ali Asgar Zainal Abidin Hariati Husain Muhammad Ihsan Maro Sulistiawati Rahayu Ahmad