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All Journal Teknik dan Ilmu Komputer International Journal of Supply Chain Management ComTech: Computer, Mathematics and Engineering Applications Infotech: Journal of Technology Information
Nindito, Hendro
Bina Nusantara University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Transportation

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Journal : Infotech: Journal of Technology Information

 1 
MENGOPTIMALKAN ORACLE SPASIAL UNTUK ANALISIS KEDEKATAN GEOGRAFIS Nindito, Hendro
Infotech: Journal of Technology Information Vol 10, No 2 (2024): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v10i2.318

Abstract

The study explores the utilization of Oracle Spatial in the determination of the shortest path between locations. This is very important for the analysis and management of geographic information. Its ability to handle multiple attributes, rasters, and vectors, is greatly enhanced by its support for data management. The objective of the study is to find the closest locations of Binus BB S and Binus Square to investigate the use of Oracle's Geospatial technology. The research entails populating a table with geometry clusters, performing spatial queries, and capturing data. The study utilized SDO_GEOMETERY and SDO_NN to find the Binus Square and BBS campuses' closest locations. The findings reinforced Oracle Spatial's practicality and accuracy for proximity analysis, emphasizing the significance of maintaining and managing such databases. The study also identified an issue which suggests that the product could be improved in the future. The integration of its features with other Oracle applications can provide more effective management and visualization of spatial data. The study highlighted Oracle Spatial's potential to support complex spatial analyses and improve the operations of spatial databases. ABSTRAKPenelitian ini mengeksplorasi pemanfaatan Oracle Spatial dalam penentuan jalur terpendek antar lokasi. Hal ini sangat penting untuk analisis dan pengelolaan informasi geografis. Kemampuannya untuk menangani banyak atribut, raster, dan vektor, sangat ditingkatkan dengan dukungannya terhadap manajemen data. Tujuan penelitian adalah mencari lokasi terdekat Binus BBS dan Binus Square untuk mengetahui penggunaan teknologi Geospasial Oracle. Penelitian ini memerlukan pengisian tabel dengan cluster geometri, melakukan kueri spasial, dan menangkap data. Penelitian ini memanfaatkan SDO_GEOMETERY dan SDO_NN untuk mencari lokasi terdekat kampus Binus Square dan BBS. Temuan ini memperkuat kepraktisan dan akurasi Oracle Spatial untuk analisis kedekatan, menekankan pentingnya memelihara dan mengelola database tersebut. Studi ini juga mengidentifikasi masalah yang menunjukkan bahwa produk tersebut dapat ditingkatkan di masa depan. Integrasi fitur-fiturnya dengan aplikasi Oracle lainnya dapat memberikan pengelolaan dan visualisasi data spasial yang lebih efektif. Studi ini menyoroti potensi Oracle Spatial untuk mendukung analisis spasial yang kompleks dan meningkatkan pengoperasian database spasial
STUDI PERBANDINGAN KEAKURATAN MODEL GLM DAN SVM DALAM MEMPREDIKSI TINGKAT PENGANGGURAN DI INDONESIA Nindito, Hendro; Imanuel, Marchelle; Calvin, Calvin; Lukman, Michelle Pandojo
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.374

Abstract

Unemployment is one of the major issues faced by Indonesia. As of February 2024, the open unemployment rate in Indonesia reached 4.82% of the total labor force. The decline in labor force participation rates and the Human Development Index (HDI) in provinces with high open unemployment rates indicates a correlation as a key contributing factor to unemployment.  This study aims to predict the open unemployment rate in regions of Indonesia using the Generalized Linear Model and Support Vector Machine algorithms through Oracle Machine Learning, and to compare the accuracy of both models in predicting regional unemployment levels in Indonesia. The CRISP-DM framework was applied to support a structured analytical process.  The results of the study show that the Generalized Linear Model developed to predict the open unemployment rate in Indonesia achieved a Mean Absolute Error (MAE) of 0.156 and a Root Mean Square Error (RMSE) of 0.246. In comparison, the Support Vector Machine model yielded a lower MAE of 0.014 and an RMSE of 0.097.
Co-Authors Albert V. Dian Sano Albert Verasius Dian Sano Calvin Calvin Condrobimo, A. Raharto Debri Pristinella Didik Madyatmadja, Evaristus Evaristus Didik Madyatmadja Hendy Tannady Imanuel, Marchelle Lukman, Michelle Pandojo Madyatmadja, Evaristus Didik Maryani Maryani Sano, Albert V. Dian Sano, Albert V. Dian Sano, Albert Verasius Dian Sari, Azani Cempaka