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All Journal Sistemasi: Jurnal Sistem Informasi EDUMATIC: Jurnal Pendidikan Informatika JSAI (Journal Scientific and Applied Informatics)
Ali Asgar Zainal Abidin
Institut Teknologi Sains dan Bisnis Muhammadiyah Selayar
Computer Science & IT Education Electrical & Electronics Engineering

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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.
Implementation of a Deep Learning Model for Real-Time Detection and Classification of Toraja Traditional Motifs (Pa’ssura’) for Digital Cultural Preservation Ali Asgar Zainal Abidin; Mursyid Ardiansyah; Aqilah Zahra
SISTEMASI Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6132

Abstract

Toraja traditional motifs, known as Pa’ssura’, represent an intangible cultural heritage rich in philosophical values and deep cultural identity. However, the authenticity and understanding of the meanings behind these motifs are at risk of erosion among younger generations due to the lack of interactive and technologically relevant learning media. This study aims to bridge this gap through an innovative digital cultural preservation strategy by implementing deep learning technology. Specifically, the research focuses on developing a real-time object detection and classification system using a Convolutional Neural Network (CNN) architecture, particularly the YOLO11s model. The main research stages include constructing an annotated image dataset for seven primary Pa’ssura’ motifs: Pa’ Barre Allo, Pa’ Kapu Baka, Pa’ Tangke Lumu, Pa’ Tedong, Pa’ Ulu Karua, Pa’ Kadang Pao, and Pa’ Papan Kandaure. These data were collected from both planar media (such as textiles) and non-planar media, including wood carvings and stone engravings. The results show that the developed model achieved a precision of 0.7109, a recall of 0.6708, and an mAP@50 of 0.6910 after 100 training epochs. The implementation of data augmentation techniques proved effective in increasing the dataset size—from 1,050 images before augmentation to 2,520 images after augmentation—thereby significantly enhancing the model’s robustness in detecting and classifying motifs across both planar and non-planar media. This study produces an accurate and practical model that can be applied as an educational tool in mobile applications. Furthermore, the model plays an important role in preserving Toraja cultural heritage through digitalization.
Revisiting Resampling Strategies under Extreme Class Imbalance: Evidence from Large-Scale Online Payment Fraud Detection Mursyid Ardiansyah; Ali Asgar Zainal Abidin
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33272

Abstract

Extreme class imbalance in online payment fraud detection creates an accuracy paradox and an operational risk in which improving fraud capture can generate costly false alarms. This study uses a quantitative, experiment-based design to evaluate the operational impact of common resampling strategies under extreme skew using interpretable linear decision rules. The Online Payments Fraud dataset (6.36 million transactions) from Kaggle is analysed using six monetary balance/amount variables (amount, oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest) plus the rule-based isFlaggedFraud indicator to predict the isFraud label. Five training variants (no resampling, ROS, RUS, SMOTE, ADASYN) are compared with two linear decision rules: an ordinary least squares linear scoring model (thresholded at 0.5) and a linear SVM, using a leakage-free protocol in which resampling is applied only to the 80% training split and performance is assessed on an untouched, highly imbalanced 20% test set. The findings indicate that LinReg–RUS achieves the most balanced operating point (Precision 65.938%, Recall 47.718%, F1 55.367%, ROC-AUC 98.720%), whereas ADASYN increases recall but collapses precision (~2.1%), yielding F1 ≈4.17%. These results contribute controlled, large-scale evidence that under extreme imbalance, simpler resampling–model combinations can provide more deployable precision–recall trade-offs than aggressive synthetic sampling, supporting interpretable baselines for capacity-constrained payment screening.
Co-Authors Abdul Ma'arief Al Imran Aqilah Zahra Muh Salim Muhammad Ihsan Maro Mursyid Ardiansyah Mursyid Ardiansyah Sulistiawati Rahayu Ahmad