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All Journal Bulletin of Network Engineer and Informatics (BUFNETS) Jurnal Informatika dan Rekayasa Perangkat Lunak
Kalua, Aditya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Website-Based Running Sports Information System For Communities In North Sulawesi Using Extreme Programming Method Syaban, Inayah; Manurung, Tohap; Kalua, Aditya; Alfonsius, Eric
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 4 (2025): Volume 6 Number 4 Desember 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i4.692

Abstract

The community in North Sulawesi has shown a strong interest in running, as evidenced by the increasing number of running communities and events. However, the absence of an integrated platform that provides information about running routes, communities, and events has become a challenge for runners in accessing information efficiently. This study employs the Extreme Programming method and aims to develop a web-based information system that delivers comprehensive information related to running sports. The website features key components such as community profile pages, a running event calendar, and running route locations, complete with maps, route descriptions, track lengths, difficulty categories, photo galleries, and supporting facilities like toilets and resting spots. The displayed information is sourced from various local communities and is systematically organized to ensure easy access for the public. Additionally, the website includes a contact page that allows users to provide suggestions or feedback to the admin. It also features a function that enables event organizers to directly submit event data and running route locations into the system through the contact page. This system is expected to help the community access information more easily and increase participation in running activities throughout North Sulawesi
Comparative Analysis of Seven Machine Learning Algorithms for Morphology-Based Classification of Cammeo and Osmancik Rice Varieties Kalua, Aditya; Agung Wibowo, Mochamad; Alexander Latumakulita, Luther; Widsli Kalengkongan, Wisard; Ijon Turnip, Rama
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April - September 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/scp7n107

Abstract

Accurate varietal identification of rice grains is crucial for quality assessment and data-driven decision-making in agricultural informatics. This study aims to comparatively eval-uate seven machine learning algorithms for morphology-based classification of Cammeo and Osmancik rice varieties and to identify the most suitable model for structured numerical grain-feature data. Using a dataset of 3,810 instances with seven image-derived morpho-logical features, a systematic comparison was conducted across Logistic Regression, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree, Random Forest, Naive Bayes, and k-Nearest Neighbors. The models were evaluated based on classification quality and computational efficiency. Results show that MLP achieved the highest overall predictive performance with an accuracy of 93.03% and an F1-score of 94.17%. However, when balancing accuracy against computational overhead, SVM emerged as the optimal” sweet spot” for industrial implementation, offering a competitive 92.50% accuracy with a 93-fold reduction in execution time compared to MLP. Naive Bayes demonstrated the fastest computational runtime (0.0022 seconds total). The study identifies a distinct trade-off between predictive quality and runtime efficiency, recommending MLP for high-fidelity research and SVM for real-time agricultural informatics applications.
Co-Authors Agung Wibowo, Mochamad Alexander Latumakulita, Luther Alfonsius, Eric Ijon Turnip, Rama Syaban, Inayah Tohap Manurung Wisard Widsli Kalengkongan