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All Journal J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Nurul Hidayati Nurul
Universitas Bengkulu
Computer Science & IT

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Forest Fire Clustering in Indonesia Using the Clustering Large Applications (CLARA) Method Muhammad Arib Alwansyah Arib; Ridya Destriani Ridya; Sigit Nugroho Sigit; Nurul Hidayati Nurul
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JKOMA.082.03

Abstract

Clustering is a process of grouping, observing or grouping classes that have similar objects. One clustering method that handles large amounts of data is clustering large applications (CLARA). This research aims to identify groups of forest fires in Indonesia using the CLARA method and to determine the characteristics of forest fires and the locations of forest fire occurrence points in Indonesia. The data used is hot spot data totaling 3,265 events, which can be obtained from the NASA LANCE–FIRM MODIS Active Fire website. The variables used to group forest fire events are latitude, longitude, brightness, frp and confidence. So by grouping 3,265 hot spot data by determining the optimum cluster using the Shilhoutte index and Dunn index values, the optimum cluster results were obtained, namely 2 clusters
Analysis of the Relationship Between Taxes and Indonesia's Economic Growth Using the Vector Error Correction Model (VECM) Muhammad Arib Alwansyah Arib; Vitaria Ronauli Silalahi Vitaria; Winalia Agwil Winalia; Nurul Hidayati Nurul
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 9 No 01 (2026): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JKOMA.091.01

Abstract

The Vector Error Correction Model (VECM) is a simultaneous modeling approach designed for variables that are stationary at the same order and exhibit cointegration relationships. This study aims to model the variables of Gross Domestic Product (GDP), Value Added Tax (VAT), Land and Building Tax (LBT), Income Tax (IT), and Import Duties using VECM, as well as to analyze the factors influencing the relationship between taxation and economic growth. The data used are annual data from 1990 to 2023, sourced from the Central Statistics Agency (BPS) and the World Bank. The analysis results indicate that the constructed model is a VECM with an optimal lag of 3 and a cointegration rank of 3. The long-term relationships in the VECM show that Income Tax and Import Duties affect Land and Building Tax and Value Added Tax. Meanwhile, short-term relationships reveal that GDP from the previous period influences Land and Building Tax, Income Tax, and current-period GDP. VAT from the previous period affects Land and Building Tax, Import Duties, and current-period VAT. Land and Building Tax from the previous period influences Income Tax, Import Duties, and current-period Land and Building Tax. Additionally, Import Duties from the previous period affect current-period VAT and Income Tax.
Co-Authors Muhammad Arib Alwansyah Arib Ridya Destriani Ridya Sigit Nugroho Sigit Vitaria Ronauli Silalahi Vitaria Winalia Agwil Winalia