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All Journal Sistemasi: Jurnal Sistem Informasi JSAI (Journal Scientific and Applied Informatics)
Ali Asgar Zainal Abidin
Institut Teknologi Sains dan Bisnis Muhammadiyah Selayar
Computer Science & IT 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: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

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.
Co-Authors Abdul Ma’arief Al Imran Aqilah Zahra Muh Salim Muhammad Ihsan Maro Mursyid Ardiansyah Sulistiawati Rahayu Ahmad