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All Journal Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Gusti Ngurah Bagus Lanang Purbhawa
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PERANCANGAN SISTEM INFORMASI DAN ADMINISTRASI DESA DAN KELURAHAN PADA FITUR MASTER PROFIL DESA I Gusti Ngurah Bagus Lanang Purbhawa; I Gede Artha Wibawa; I Made Widiartha
Jurnal Pengabdian Informatika Vol. 4 No. 2 (2026): JUPITA Volume 4 Nomor 2, Februari 2026
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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Abstract

Pengabdian kepada masyarakat ini bertujuan mengembangkan sistem web informasi dan administrasi untukmendukung pelayanan desa/kelurahan. Selama dua bulan, kegiatan difokuskan pada perancangan sistem yangmencakup diagram aktivitas, database, antarmuka pengguna (UI), dan dokumentasi, dengan penekanan pada fiturprofil desa.Hasil kegiatan menunjukkan bahwa rancangan sistem telah selesai dan siap diimplementasikan. Fiturprofil desa memungkinkan penyajian informasi desa secara terstruktur dan mudah diakses, sehingga diharapkandapat meningkatkan efisiensi pelayanan administrasi di tingkat desa/kelurahan.
Klasifikasi Tingkat Keparahan Kecelakaan Lalu Lintas Menggunakan Random Forest Classifier I Gusti Ngurah Bagus Lanang Purbhawa; I Gede Arta Wibawa
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v03.i01.p07

Abstract

Traffic accidents are a common problem that often occurs. Many factors cause and determine the severity of traffic accidents. These factors can include road conditions, weather, light conditions, driver age, and the cause of the accident. In this study, researchers will try to apply the Random Forest method to classify the severity of traffic accidents. The Random Forest method was chosen because of its excellent ability to handle high-dimensional data and tolerance for overfitting. The dataset used in this research was taken from Kaggle, consisting of 12316 records and 32 features covering various attributes related to traffic accidents. Before applying random forest, it is necessary to carry out a preprocessing stage on the dataset to remove irrelevant features, fill in empty values and divide the data into training and testing data. The results of this research show that Random Forest can produce a good level of in classifying the severity of traffic accidents with 92% accuracy. This shows the potential of this method as a useful tool in the analysis and prediction of traffic accidents. Therefore, this research makes a significant contribution to efforts to improve road safety. 
Co-Authors I Gede Arta Wibawa I Gede Artha Wibawa I Made Widiartha