Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
Gede Dikka Widya Prana
Unknown Affiliation
Humanities Computer Science & IT Engineering Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PERANCANGAN SISTEM KENDALI PENERIMAAN DOKUMEN SAMPEL SAKERNAS DI BPS KABUPATEN GIANYAR Gede Dikka Widya Prana; Luh Gede Astuti; Anak Agung Istri Ngurah Eka Karyawati
Jurnal Pengabdian Informatika Vol. 3 No. 1 (2024): JUPITA Volume 3 Nomor 1, November 2024
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengembangan platform berbasis website untuk pelaporan Survei Angkatan Kerja Nasional (Sakernas) di BPS Kabupaten Gianyar merupakan langkah inovatif dalam meningkatkan efisiensi, akurasi, dan responsivitas dalam proses pengumpulan data statistik. Melalui implementasi teknologi ini, harapannya terjadi perubahan positif dalam pencatatan, pelaporan, dan komunikasi internal, mengatasi permasalahan konvensional yang melambatkan proses. Penekanan pada efisiensi operasional, responsivitas yang meningkat, kolaborasi yang ditingkatkan, serta dukungan keputusan yang lebih baik diharapkan sebagai dampak dari langkah inovatif ini. Platform ini diharapkan membantu BPS Kabupaten Gianyar menyediakan data yang lebih akurat, relevan, serta mendukung perencanaan dan kebijakan di tingkat lokal.
Analisis Performa Algoritma K-Nearest Neighbor dalam Klasifikasi Tingkat Kerontokan Rambut Gede Dikka Widya Prana; Luh Gede Astuti
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 1 No. 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
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.2023.v01.i03.p21

Abstract

Hair loss can lead to baldness and affect one's self-confidence. Normally, hair falls out in 80-120 strands per day, and the average number of hair follicles on the head is around 100,000. If the amount is reduced by 50%, it can be considered a disorder. Therefore, a classification of hair loss levels is necessary to determine appropriate actions. Previous study has shown that the KNearest Neighbor algorithm is capable of classifying various diseases. In this study, the Luke Hair Loss Dataset from the website kaggle.com, consisting of 400 data points, was used. To evaluate the method's feasibility, a confusion matrix was employed. The objective of this research is to analyze the performance of the K-Nearest Neighbor algorithm. Several scenarios were utilized, including testing the model before and after SMOTE oversampling, testing before and after data normalization, testing based on different K values, and testing with varying ratios of training and testing data. The results of this study indicate that the K-Nearest Neighbor algorithm achieved the highest accuracy value of 0,9853, precision of 0,9886, recall of 0,9833, and f1-score of 0,9856. 
Co-Authors Anak Agung Istri Ngurah Eka Karyawati Luh Gede Astuti