Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer
Aditiya, Natasya
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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

Found 2 Documents
Search

 1 
ANALISIS GAME 2D “MONKEY BANANA SURVIVAL” SERTA PENGUJIAN MENGGUNAKAN METODE BLACKBOX Aditiya, Natasya; Diana Laily Fithri
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 11 No. 2 (2024): Prosisko Vol. 11 No. 2 September 2024
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v11i2.8507

Abstract

Industri Game di Indonesia mengalami perkembangan secara bertahap dari tahun ke-tahun ke arah yang baik, sehingga mendorong pertumbuhan industri game untuk terus tumbuh dan konsisten. Optimisme terhadap kemajuan industri game di Indonesia pada tahun 2023 tetap berjalan walaupun sempat terdapat isu ekonomi yang beredar di masyarakat seperti pelemahan ekonomi dan resesi, sebab game dinilai sebagai bentuk hiburan yang paling murah. Salah satunya adalah game Monkey Banana Survival. Game ini membawa cerita dari permalasahan yang ada pada lingkungan masyarakat terutama kebakaran hutan di daerah Kalimantan. Metode analisa yang nantinya akan digunakan mencakup tinjauan menyeluruh terhadap aneka aspek, seperti gameplay, grafik, dan elemen desain lainnya. Tinjauan gameplay akan mencakup pengalaman pemain dalam menjelajahi level dan menghadapi rintangan, sementara analisis grafik akan mengevaluasi kualitas visual dan estetika yang ditawarkan. Dengan harapan nantinya analisa ini dapat menjadi salah satu bentuk saran untuk pengembangan game yang akan datang kepada pengembang game Monkey Banana Survival.
Sentiment Analysis of Public Satisfaction with the 'INFO BMKG' Application using Naive Bayes, SVM, and KNN Aditiya, Natasya; Setiaji, Pratomo; Supriyono, Supriyono
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5223

Abstract

This study aims to analyze public sentiment regarding the Info BMKG application on the Google Play Store. With the increasing number of users of information-based applications, understanding how users perceive and evaluate such applications has become essential. This research employs three classification algorithms—Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—to categorize user reviews into positive, neutral, or negative sentiments. The dataset was obtained through web scraping from the Google Play Store, consisting of usernames, dates, star ratings, and user comments. Data preprocessing was conducted to clean and prepare the data for analysis. Additionally, a web-based data mining application was developed to facilitate data processing and result visualization. The study aims to present the distribution of sentiment (positive, neutral, and negative) toward the Info BMKG app and help developers understand the factors that influence user satisfaction. Moreover, it contributes to the field of sentiment analysis and information technology, particularly in disaster-related information applications. Based on model evaluation, the Naive Bayes algorithm demonstrated the best performance with an accuracy of 79.84%, precision of 60%, recall of 58%, and the fastest runtime at 0.19 seconds. KNN achieved an accuracy of 74.35% with the lowest recall at 44%, while SVM had an accuracy of 72.26% but required the longest runtime at 611 seconds. AUC validation further confirmed the superiority of Naive Bayes, with the highest scores across all sentiment categories. Thus, Naive Bayes is shown to be the most optimal for sentiment analysis in this study, whereas KNN and SVM showed certain limitations, particularly in efficiency and classification accuracy.
Co-Authors Diana Laily Fithri Pratomo Setiaji Supriyono Supriyono