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All Journal JURNAL DINAMIKA EKONOMI PEMBANGUNAN Prosiding Seminar Nasional Official Statistics
Saadi, Terry Devara Tri
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Manfaat Algoritma Djikstra dalam Pemetaan Granular Jarak Sekolah dari Pemukiman Penduduk Saadi, Terry Devara Tri
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2178

Abstract

The Indonesia Emas 2024 Vision emphasizes the importance of educational development in improving future human resources, particularly for the productive-age population in 2045. Enhancing access and participation through equitable education distribution and removing geographic barriers has become a primary focus in achieving educational targets. This study aims to explore the potential of utilizing Dijkstra’s algorithm for granular mapping of school distances from residential areas. The analysis involved calculating school distances on a 1 km populated grid in West Nusa Tenggara, using 2019 infrastructure data, residential tagging, and road graphs from OpenStreetMap. The results demonstrate the potential of the Dijkstra algorithm in granular mapping of school distances and reveal that 21.84% of the population in the area must travel more than 5 km to access senior high school education, and 8.11% for junior high school. This study also discusses the limitations and drawbacks of this approach and explores potential improvements for identifying areas that face challenges in accessing educational facilities.
Potensi Pemanfaatan Machine Learning dan Transfer Learning untuk Klasifikasi Baku Pekerjaan Dwicahayaniawan, Agnes Septi; Saadi, Terry Devara Tri
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2180

Abstract

Monitoring the state of labor data in Indonesia involves standardized classification to ensure uniformity. The coding process relies on the knowledge of personnel, which often leads to issues such as potential differences in understanding and interpretation among individuals, resulting in inconsistencies in the standardized classification coding outcomes. This study aims to explore the potential of Machine Learning in classifying the Indonesia Business Field Classification (KBLI) and the Indonesian Standard Classification of Occupations (KBJI). Models were developed and evaluated to classify KBLI and KBJI based on open-ended questions about the job, the output produced, and the field of work from respondents' answers collected through the National Labor Force Survey (Sakernas). The results show that although the performance of the IndoBERT method is slightly superior with accuracy is 0,76 for KBLI and 0,65 for KBJI. This advantage is not significant given the higher computational load and longer training time compared to machine learning.
Enhancing Regional Competitiveness through Intermodal Logistics: A Case Study of Central Java Saadi, Terry Devara Tri; Dwicahayaniawan, Agnes Septi
Jurnal Dinamika Ekonomi Pembangunan Vol 8 (2025): Special Issue: Call for Paper Pusaka Jateng
Publisher : Fakultas Ekonomika dan Bisnis, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jdep.8.0.1-18

Abstract

Focusing on Central Java, this study presents a regional case study of logistics route optimization. Logistics inefficiency remains a critical bottleneck to regional competitiveness in Indonesia, particularly in provinces like Central Java, where dense population and industrial activity demand high-performing transport systems. This study explores the potential of intermodal logistics (integrating rail and road transport) to reduce distribution costs and enhance supply chain sustainability. Using Dijkstra’s algorithm on a custom-built transport network graph derived from OpenStreetMap data, we simulate logistics costs across 16 districts in Semarang City under two scenarios: direct road transport and intermodal routing via Semarang Tawang Station. The analysis incorporates vehicle capacities, fuel consumption rates, and population-based demand estimates to calculate total delivery costs, revealing that intermodal solutions yield substantial savings in most districts. Statistical testing confirms the significance of these cost differences. Unlike previous studies, this research contributes to the literature by providing a network-level, cost-optimization assessment of logistics modal in a regional context. Key policy recommendations include developing intermodal hubs, upgrading rail access, and tailoring vehicle deployment to local infrastructure constraints. This study not only contributes to the academic discourse on transport optimization and sustainable logistics but also provides actionable insights for planners and decision-makers aiming to enhance regional connectivity and reduce logistics burdens on businesses.
Co-Authors Dwicahayaniawan, Agnes Septi