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All Journal Journal of Technology and System Information
Dhanny Karewur
STMIK AMIKBANDUNG
Computer Science & IT

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SAW-ROC Feeder Route Prioritization with Thermal Criteria: Cimahi City Rasoki Daulay; Dhanny Karewur; Okyza Prabowo
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.6335

Abstract

This study addresses the prioritization of angkutan kota (angkot) routes to be integrated as feeder services for the Bus Rapid Transit (BRT) system in Cimahi City, West Java, extending a previous GeoAI-based spatial model of urban surface temperature developed for the same city. Four registered feeder-corridor routes were evaluated using seven operational and environmental criteria: load factor, operation rate, service evenness, route overlap, schedule deviation, headway, and mean land surface temperature (LST) extracted through zonal statistics within a 150 m buffer of each route, based on a random-forest-predicted LST raster. Criteria weights were derived objectively using the Rank Order Centroid (ROC) method based on an importance ranking, then aggregated with Simple Additive Weighting (SAW) to produce a composite priority score. Results show that route 01.02.05 (Ps. Antri-Cibeber via Contong) ranked highest (SAW score 0.827), driven mainly by its superior load factor, while the route previously rated highest in the official performance report ranked second once the thermal criterion was included. The findings indicate that incorporating a spatial thermal criterion shifts prioritization outcomes relative to conventional operational-only assessment, offering transport authorities a more environmentally responsive basis for feeder network restructuring decisions.
Comparative Evaluation of ChatGPT, Gemini, and Claude Response Quality on Programming Questions Based on Bloom's Taxonomy Kahfi Bintang; Median Zikri; Rivaldo Nugraha; Dhanny Karewur
Journal of Technology and System Information Vol. 3 No. 3 (2026): July
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i3.6354

Abstract

The rapid advancement of Large Language Models (LLMs) has significantly influenced programming education and software development by providing automated code generation and problem-solving assistance. However, differences in the quality of responses generated by ChatGPT, Gemini, and Claude require a comprehensive evaluation using multidimensional assessment criteria. This study aims to compare the quality of responses produced by these three LLMs on programming questions based on Bloom's Revised Taxonomy and the ACM/IEEE Computing Curricula assessment rubric. A quantitative comparative approach was employed using 40 programming questions distributed across six Bloom cognitive levels (C1–C6). Each response was evaluated using five indicators: accuracy, completeness, clarity, logical reasoning, and efficiency. The collected data were analyzed using descriptive statistical methods and mean comparison. The results indicate that ChatGPT achieved the highest overall mean score of 21, followed by Claude (20) and Gemini (19). ChatGPT also obtained the highest scores in accuracy (4.3), clarity (4.1), logical reasoning (4.4), and efficiency (4.5), while ChatGPT and Claude shared the highest completeness score (4.1). Furthermore, performance across all models declined as the cognitive level increased from Remember (C1) to Create (C6), although ChatGPT demonstrated the most consistent performance across all Bloom levels. These findings indicate that ChatGPT provides superior and more consistent programming solutions and that integrating Bloom's Taxonomy with multidimensional evaluation criteria offers a comprehensive framework for assessing the quality of LLM-generated programming responses.
Co-Authors Kahfi Bintang Median Zikri Okyza Prabowo Rasoki Daulay Rivaldo Nugraha