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Jurnal Dinamika Informatika (JDI)
Published by Universitas PGRI Yogyakarta
ISSN : 19781660     EISSN : 25498517     DOI : 10.31316
Core Subject : Science,
Computer Science & IT
Enterprise Systems (ES) Enterprise Resource Planning Business Process Management Customer Relationship Management Marketing Analytics System Dynamics E-business and e-Commerce Marketing Analytics Supply Chain Management and Logistics Business Analytics and Knowledge Discovery Production Management Task Analysis Process Mining Discrete Event Simulation Service Science and Innovation Innovation in the Digital Economy Information Systems Management (ISM) Software Engineering Software Design Pattern System Analysis and Design Software Quality Assurance Green Technology Strategies Strategic Information Systems IT Governance and Audits E-Government IT Service Management IT Project Management Information System Development Research Methods of Information Systems Adoption and Diffusion of Information Technology Health Information Systems and Technology Accounting Information Systems Human Behavior in Information System Social Technical Issues and Social Inclusion Domestication of Information Technology ICTs and Sustainable Development Information System in developing countries Software metric and cost estimation IT/IS audit IT Risk and Management Data Acquisition and Information Dissemination (DAID) Open Data Social Media Knowledge Management Social Networks Big Data Web Services Database Management Systems Semantics Web and Linked Data Visualization Information Social Information Systems Social Informatics Spatial Informatics Systems Geographical Information Systems Data Engineering and Business Intelligence (DEBI) Business Intelligence Data Mining Intelligent Systems Artificial Intelligence Autonomous Agents Intelligent Agents Multi-Agent Systems Expert Systems Pattern Recognition Machine Learning Soft Computing Optimization Forecasting Meta-Heuristics Computational Intelligence Decision Support Systems IT Infrastructure and Security (ITIS) Information Security and Privacy Digital Forensics Network Security Cryptography Cloud and Virtualization Emerging Technologies Computer Vision and Image Ethics in Information Systems Human Computer Interaction Wireless Sensor Networks Medical Image Analysis Internet of Things Mobile and Pervasive Computing Real-time Systems and Embedded Systems Parallel and Distributed Systems
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 124 Documents
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Perbandingan Kinerja Naive Bayes dan KNN Dalam Klasifikasi Sentimen Ulasan Film Horor Simbolon, Cantriya; Maria Angelina Lubis; Sardo Pardingotan Sipayung
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.464

Abstract

Lonjakan ulasan film horor di platform digital memerlukan sistem klasifikasi otomatis untuk memahami sentimen penonton secara efisien. Penelitian ini bertujuan membandingkan kinerja algoritma Naive Bayes dan K-Nearest Neighbors (KNN) dalam mengklasifikasikan sentimen ulasan film horor berbahasa Inggris. Metodologi penelitian melibatkan pengolahan 3.000 data dari Kaggle menggunakan perangkat lunak RapidMiner, dengan tahapan pra-pemrosesan meliputi pembobotan TF-IDF, tokenization, filtering, dan stemming. Pengujian dilakukan melalui skema 10-fold cross validation untuk menjamin stabilitas hasil. Temuan penelitian menunjukkan perbedaan performa yang signifikan, di mana Naive Bayes meraih akurasi sebesar 88,53%, jauh mengungguli KNN yang hanya mencapai 40,47%. Rendahnya akurasi KNN disebabkan oleh kompleksitas perhitungan jarak pada data teks berdimensi tinggi. Disimpulkan bahwa Naive Bayes merupakan model yang lebih reliabel dan efektif untuk klasifikasi sentimen ulasan film horor. Hasil ini memberikan kontribusi berupa rekomendasi algoritma optimal bagi pengembangan sistem analisis opini otomatis.
Analisis Visualisasi Data Pengangguran Di Indonesia Tahun 2018-2024 Dengan Menggunakan Dashboard Tableau ELISKA, Nur
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.475

Abstract

The unemployment is still a problem in Indonesia. Unemployment has an impact on society and the country. The unemployment rate has fluctuated from year to year, indicating that there is still no stability in this situation. Data from the Central Statistics Agency (BPS) shows that Indonesia still has an unresolved unemployment problem. The available data is so vast and complex that it poses a challenge in receiving this information. The method used is qualitative. Researchers need to visualize the data to see the distribution of unemployment rates in Indonesia. This data visualization uses a Tableau dashboard. Tableau is an interactive platform that aims to facilitate the delivery of information related to unemployment rates in a more practical, fast, and accurate manner. The highest unemployment rate occurred in 2020, then decreased in 2023 and increased again in 2024. The highest unemployment rate was in the West Java region. Thus, the use of a Tableau dashboard for such a large amount of data makes it easier to receive information and understand the unemployment rate in Indonesia, which aims to address this condition Keywords: Data Visualization, Unemployment of Indonesia, Tableau
Sistem Informasi Pengelolaan Asrama Putra Universitas Katolik Santo Thomas Berbasis Web Dengan Metode Waterfall Peranginangin, Sephia Sari; Harianja, Andy Paul
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.517

Abstract

This study examines the management system of the Santo Thomas Catholic University Men's Dormitory, which is still manual and conventional. This conventional method often causes problems, such as the risk of data errors, loss of archives, and administrative inefficiency. The main objective of this study is to design and implement a web-based dormitory management information system by applying the Waterfall Method. This development method was chosen because of its structured and systematic nature, from needs analysis to testing. The results of the implementation show that the resulting system is capable of automating the registration process, room management, payment recording, and report generation. System testing proves that this system runs according to user needs and significantly improves the speed and accuracy of dormitory management administration. These findings have important implications for the transformation of campus facility management to be more structured, efficient, and accessible.
Analisis Sentimen Publik Terhadap RUU KUHAP di Platform X Menggunakan Metode TF-IDF dan Naïve Bayes Junaidy; Muhammad Fauzan; Roberto Kaban
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.518

Abstract

The rapid development of social media has established Platform X as one of the primary channels for the public to express opinions on public policy issues, including the Draft Criminal Procedure Code (RUU KUHAP). This study aims to analyze public sentiment toward the RUU KUHAP based on tweet data collected from Platform X. A total of 2,273 valid data points were obtained and utilized in this research. The selected data underwent several preprocessing stages, including case folding, cleansing, tokenizing, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, while the sentiment classification process employed the Multinomial Naive Bayes algorithm, with the dataset split into training and testing sets. Model performance was evaluated using a confusion matrix alongside precision, recall, and F1-score metrics. The results indicate that public sentiment toward the RUU KUHAP is dominated by negative sentiment at 45.5%, followed by neutral sentiment at 32.0%, and positive sentiment at 22.5%. Performance evaluation shows that for the negative class, the model achieved a precision of 0.71, recall of 0.93, and F1-score of 0.80. For the neutral class, the precision was 0.74, recall 0.44, and F1-score 0.55, while the positive class reached a precision of 0.85, recall 0.80, and F1-score 0.82. Overall, the model achieved an accuracy of 74.07%, demonstrating that the application of TF-IDF and Naïve Bayes is effective in classifying public sentiment, despite persistent limitations in identifying neutral sentiment.

Page 13 of 13 | Total Record : 124

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