Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Bulletin of Information Technology (BIT) Management of Information System Journal
Siska Atmawan Oktavia
Universitas Teknologi Sumbawa
Computer Science & IT Control & Systems Engineering

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

Found 2 Documents
Search

 1 
Prototype Sistem Deteksi Penyakit Mulut dan Kuku Menggunakan Gambar Citra Digital Sebagai Upaya Menjaga Kesehatan Ternak di Kabupaten Sumbawa Siska Atmawan Oktavia; Wiwin Apri Hartina; Devi Tanggasari; Rabiyatunnisah
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2245

Abstract

The cattle industry worldwide faces a major threat from foot-and-mouth disease (FMD) due to the contagious nature of the virus. In Sumbawa Regency, FMD peaked in 2022 with 12,814 cases. Screening for FMD is very important for early detection and treatment. Currently, detection conducted by animal health officers is still manual, requiring 48 hours per animal to obtain diagnostic results and is prone to errors. The researchers aim to create a Prototype Automatic Detection System that includes an automatic foot-and-mouth disease (FMD) detection system application using App Designer (GUI) system-MATLAB to assist animal health officers in diagnosing animals infected with FMD, saving time and costs and saving animals. The proposed method for automatically extracting distinguishing features of cattle and classifying whether the cattle are sick or healthy utilizes the advantages of the Convolutional Neural Network (CNN) model. Based on the evaluation results of the developed system, the proposed system using the Convolutional Neural Network algorithm has better performance with an accuracy of 100% compared to the WEKA application, namely SMO with an accuracy of 90%, IBk (87%), Trees.J48 (86%), and Naive Bayes (79%). Therefore, highly efficient and accurate digital image processing techniques must be used to produce effective FMD disease screening. The proposed decision support system for clinical screening is expected to make a significant contribution and help reduce the workload of Animal Health Officers in detecting foot-and-mouth disease (FMD).
Analisis Dampak ChatGPT Sebagai Code Assistant Terhadap Kualitas Kode Mahasiswa Informatika UTS Siska Atmawan Oktavia Siska; Siska Atmawan Oktavia; Muhammad Alfan Habib
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.3043

Abstract

The rapid advancement of Artificial Intelligence (AI) has introduced various tools that support software development, one of which is ChatGPT. This study aims to analyze the impact of using ChatGPT as a code assistant on the quality of program code produced by Informatics students at Universitas Teknologi Sumbawa. A quantitative experimental method was employed by comparing programming tasks completed manually and with the assistance of ChatGPT. Code quality was evaluated using SonarQube based on three metrics: Maintainability Index, Cyclomatic Complexity, and Reliability (Bug Count), followed by statistical analysis to examine differences between the two conditions. The results indicate that there were no significant differences across all evaluated metrics between manually written code and code generated with ChatGPT assistance (p > 0.05). These findings suggest that the use of ChatGPT did not affect code quality in this study; however, it still has the potential to improve the efficiency of software development. Furthermore, this study provides empirical evidence regarding the impact of using ChatGPT on code quality, based on SonarQube static analysis metrics. The findings are expected to serve as a reference for educators, students, and researchers in evaluating the use of ChatGPT as a code assistant in both learning and software development contexts.
Co-Authors Devi Tanggasari Muhammad Alfan Habib Rabiyatunnisah Siska Atmawan Oktavia Siska Wiwin Apri Hartina