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Cahyani, Okta Nur
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Computer Science & IT Education

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Performa Logistic Regression dan Naive Bayes dalam Klasifikasi Berita Hoax di Indonesia Cahyani, Okta Nur; Budiman, Fikri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.28987

Abstract

The spread of false information has become a major challenge in Indonesian society, with 2,484 cases recorded in 2022. This highlights the importance of developing a system that can effectively identify and filter out fake news. This research aims to develop a more accurate fake news detection model by applying logistic regression, which is optimized by grid search and oversampling to overcome data imbalance. The main focus of this research is to improve the performance of the model in detecting fake news on unbalanced datasets. The dataset used is the Indonesian Fake News dataset, which consists of 4,231 entries with two categories: valid (3,465 entries) and hoax (766 entries). Preprocessing steps include stemming, stopword removal, and text normalization using TF-IDF. Random oversampling was applied to balance the data between hoax and valid classes, and parameter optimization was performed using grid search to improve model performance. The results show that the optimized logistic regression achieved the highest accuracy of 93%, surpassing naive bayes, which achieved 86% accuracy. These findings suggest that the developed fake news detection model can be used to improve the social media news monitoring system, and increase digital literacy among Indonesians.
Perbandingan Naive Bayes dan Support Vector Machine dalam Klasifikasi Tingkat Kemiskinan di Indonesia Mukharyahya, Zulfa Alviandri; Astuti, Yani Parti; Cahyani, Okta Nur
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29512

Abstract

Poverty in Indonesia is a complex issue influenced by various economic and socio-cultural factors. This study aims to compare the performance of Naïve Bayes and Support Vector Machine (SVM) in classifying poverty levels in Indonesia while also evaluating the effectiveness of random oversampling in addressing data imbalance. The dataset consists of 514 samples from various districts and cities in Indonesia, with 452 samples classified as "not poor" and 62 as "poor." After applying oversampling, the total number of samples increased to 730, with a balanced distribution (365 samples per class). The observed data include socio-economic indicators such as the percentage of the poor population, per capita expenditure, the Human Development Index, and the open unemployment rate. The study splits the data using an 80:20 ratio for training and testing. Experimental results show that SVM achieved a higher accuracy of 81% compared to naïve bayes, which reached 76%. Additionally, SVM demonstrated a more stable balance between precision and recall. On the other hand, the oversampling technique effectively improved the model’s ability to identify the minority class, particularly for Naïve Bayes, which was more responsive to data duplication. These findings highlight the role of machine learning in designing more effective social policies for poverty data management.
Co-Authors Astuti, Yani Parti Fikri Budiman Mukharyahya, Zulfa Alviandri