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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) J-Icon : Jurnal Komputer dan Informatika
Setyan, Ardath Prahara
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Computer Science & IT Education

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VISUALISASI DASHBOARD POWER BUSINESS INTELLIGENCE JUMLAH KERUGIAN KENDARAAN BERMOTOR DI JAWA TIMUR INDONESIA Setyan, Ardath Prahara; Pratama, I Putu Agus Eka
J-Icon : Jurnal Komputer dan Informatika Vol 11 No 1 (2023): Maret 2023
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v11i1.9920

Abstract

East Java Province has a high number of motorized vehicle losses with various modes of crime. There needs to be a system that can process the data to assist the decision-making process for law enforcement officers in investigating the crime of motor vehicle theft and reducing the amount of loss. For this reason, in this research, data processing of motor vehicle theft was carried out using Business Intelligence (BI) and displayed on the Power BI dashboard. The research methodology used is literature study, interviews, data collection, implementation, evaluation, and documentation. The test results show the highest number of vehicle theft cases occurred in 2016 (1,635 cases) with the most modes using fake keys, and with the highest vehicle brand being Honda. The final result of the study shows that the presentation of data and information visually and interactively based on Power BI can help law enforcers to understand data, obtain information, and make decisions and policies to handle motorcycle theft cases.
K-MEANS CLUSTERING FOR DATA-DRIVEN TRAFFIC ACCIDENT IN EAST JAVA Nafiiyah, Nur; Wardhani, Retno; Huda, Muhammad Nurul; Setyan, Ardath Prahara; Prakasa, Esa
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9236

Abstract

Traffic accident analysis plays a crucial role in improving road safety and reducing accident rates. Besides enforcing traffic regulations and promoting driver vigilance, analyzing accident data can provide valuable insights into patterns and risk factors that contribute to accidents. This research aims to apply k-Means clustering to accident data in East Java from 2016 to 2020 in order to identify hidden patterns based on victim age, victim type, vehicle type, gender, and accident causes. The clustering process categorizes most variables into two groups (low and high), while victim age is divided into three groups (young, middle, and older). The results reveal distinct accident patterns across age groups and victim types, with high accident clusters dominated by young drivers and motorcyclists. These findings provide insights into the characteristics of high-risk groups and can serve as a reference for designing more targeted road safety policies and preventive strategies.
Co-Authors Esa Prakasa, Esa I Putu Agus Eka Pratama Muhammad Nurul Huda Nafiiyah, Nur Retno Wardhani