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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.
Co-Authors Dwicahayaniawan, Agnes Septi