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All Journal J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Muhammad Arib Alwansyah Arib
Universitas Negeri Jakarta
Computer Science & IT

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Handling Missing Data in Bivariate Gamma Generation Data Using the Random Forest Method Muhammad Arib Alwansyah Arib; Ramya Rachmawati Ramya
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.082.02

Abstract

Missing data is a common problem in data analysis that can reduce the quality and accuracy of study results if not handled properly. This study aims to evaluate the performance of the Random Forest (RF) imputation method at various levels of missing value proportions, namely 5%, 10%, 15%, and 20%. The data used are Bivariate Gamma data of 200 observations with two variables, generated using RStudio software. Evaluation of imputation performance is carried out by considering the correlation value between the imputed data and the original data, the p-value as an indicator of the significance of the difference, and the error measures Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).
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
Bahasa Inggris Muhammad Arib Alwansyah Arib; Viola Oktamelisa Viola; Sigit Nugroho Sigit; Etis Sunandi Etis
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.082.04

Abstract

Clustering is a data grouping method applied to identifies groups formed by combining elements that have the same characteristics. One of the clustering methods that can be used is the K-Medoids method known as Partitioning Around Medoids (PAM). This study aims to obtain grouping and determine the characteristics of the results of grouping regencies/cities in the Sumatra Region based on the percentage of poverty using the K-medoids cluster method. The data used are poverty data per district/city totaling 154 in the Sumatra Region with the variables used being the expected length of schooling, average length of schooling, open unemployment rate, and percentage of poor population. The results obtained in this study are that districts/cities in the Sumatra Region have 2 optimum clusters as seen from the silhouette index value and davies-bouldin index value
Co-Authors Etis Sunandi Etis Nurul Hidayati Nurul Ramya Rachmawati Ramya Ridya Destriani Ridya Sigit Nugroho Sigit Viola Oktamelisa Viola