Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Data Insights
Fatah, Zaihol
Unknown Affiliation
Computer Science & IT Mathematics

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

Found 2 Documents
Search

 1 
Sentiment Analysis of YouTube User Comments on Government Policies Using the Naïve Bayes Method: Analisis Sentimen Komentar Pengguna Youtube Terhadap Kebijakan Pemerintah Menggunakan Metode Naïve Bayes Ismardani, Trisnawadi; Fatah, Zaihol
Journal of Data Insights Vol 3 No 2 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i2.876

Abstract

This research endeavors to analyze public sentiment expressed in YouTube user comments regarding the government's policy pertaining to the confiscation of undeveloped land after a two-year period of non-utilization. The methodology employed leverages the Naïve Bayes algorithm for classification, implemented within the Google Colaboratory environment. Data were systematically collected from specific YouTube videos discussing the aforementioned land confiscation policy. The research workflow encompassed comprehensive stages: data acquisition, rigorous text preprocessing, feature weighting utilizing the Term Frequency-Inverse Document Frequency (TF-IDF) technique, and final classification using the Naïve Bayes algorithm. Evaluation results demonstrate that the proposed model achieved a high accuracy level of 90%, with the highest F1-score recorded within the neutral sentiment class. However, an imbalance in the dataset's class distribution led to comparatively lower precision and recall values for both the positive and negative classes. Overall, this study confirms the high efficacy of the Naïve Bayes algorithm in analyzing Indonesian-language text data from social media platforms, specifically YouTube comments, and provides a crucial foundation for the future development of more balanced sentiment models.
Analysis of Data Mining in Predicting Poverty Levels in Indonesia Using the Decision Tree Method : Analisa Data Mining Dalam Memprediksi Tingkat Kemiskinan Masyarakat Indonesia Dengan Metode Decision Tree Ilallah, Ahsin; Fatah, Zaihol
Journal of Data Insights Vol 3 No 2 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i2.878

Abstract

This study aims to examine the application of the Decision Tree method in predicting poverty levels in Indonesia using the RapidMiner software. Poverty is a complex issue influenced by social, economic, and educational factors. Through a data mining approach, this research seeks to identify patterns within poverty data to support more accurate decision-making. The research data were obtained from the public platform Kaggle and include key variables such as individual expenditure, the Human Development Index (HDI), average study time, access to proper sanitation and safe drinking water, as well as the open unemployment rate. The results show that the Decision Tree model achieved an accuracy of 94.90%, with a precision of 95.24% and a recall of 93.75%, based on the confusion matrix. The use of RapidMiner also facilitates the analysis, as the results are presented visually and are easy to understand. This model is recommended for implementation in government information
Co-Authors Ilallah, Ahsin Ismardani, Trisnawadi