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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Jurnal Telematics and Information Technology (TELEFORTECH)
Parjito, Parjito
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Computer Science & IT Education

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Journal : Building of Informatics, Technology and Science

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Analisis Sentimen Opini Publik Program Makan Siang Gratis dengan Random Forest Pada Media Azhari, Muhamad; Parjito, Parjito
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6423

Abstract

The "Free Lunch Program," introduced as part of the 2024 Indonesian election campaign, became a hot topic on social media, especially on the platform X. This program aims to improve children's health and nutrition while reducing stunting rates by providing free lunches and milk to children and pregnant women. A study was conducted to analyze public sentiment regarding the program using the Random Forest algorithm. The data consisted of 9,347 tweets collected through web crawling. The analysis revealed that the majority of sentiments were negative (8,021 entries), while positive sentiments accounted for only 430 entries. The preprocessing steps included data cleaning, case folding, tokenization, stopword removal, and stemming. The imbalance between positive and negative sentiment data was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), resulting in a more balanced dataset. After applying SMOTE, the model achieved 100% accuracy, with significant improvements in precision, recall, and F1-Score. The analysis showed that positive sentiments focused on the program's health and educational benefits, while negative sentiments highlighted criticism of implementation and budget allocation. This study demonstrates the value of sentiment analysis in evaluating social programs and understanding public perceptions.
Analisis Sentimen Publik terhadap Virus HMPV Berdasarkan Media Sosial X dengan Algoritma Logistic Regression Wijaya, Feri Aldi; Parjito, Parjito
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7053

Abstract

Human Metapneumovirus (HMPV) is a virus that affects the respiratory tract, causing flu-like symptoms such as cough, fever, and nasal congestion. This virus was first discovered in 2001 and generally causes mild infections. However, certain groups, such as children, the elderly, and individuals with weakened immune systems, are at higher risk of developing severe conditions like bronchitis or pneumonia. Based on this issue, a sentiment analysis of public responses to Human Metapneumovirus (HMPV) cases was conducted using data collected from the X platform, consisting of 10,199 tweets. The data was gathered between December 1, 2024, and January 30, 2025, using Tweet Harvest in Google Colab with the Twitter API. This study applied the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance, with an 80% to 20% split between training and testing data. The results showed that before applying SMOTE, the logistic regression algorithm had an accuracy of 83%, with precision for positive sentiment at 90%, neutral at 80%, negative at 85%, while recall for positive sentiment was 89%, neutral 89%, negative 92%. After applying SMOTE, accuracy increased to 90%, with the most significant improvement observed in positive sentiment. The precision for positive sentiment reached 90%, neutral 87%, and negative 95%, while recall for positive sentiment was 96%, neutral 90%, negative 84%. This research provides insights into the use of logistic regression algorithms in sentiment analysis related to HMPV and serves as a reference for governments and health organizations in designing more effective communication strategies and interventions.
Analisis Sentimen Komentar YouTube terhadap Kenaikan Tunjangan DPR RI menggunakan Naïve Bayes, SVM, dan Random Forest Dani, Jemmi Rama; Parjito, Parjito
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8513

Abstract

The rise of digital technology encourages the public to actively voice their opinions through social media, including in response to political issues such as the policy on increasing the remuneration of the Indonesian House of Representatives (DPR RI). This research aims to analyze public sentiment towards this issue on the YouTube platform using a comparative approach with three Machine Learning algorithms: Naïve Bayes, Support Vector Machine, and Random Forest. The data was acquired from viewer comments via the YouTube Data Application Programming Interface (API), totaling 78,866 lines of comments collected from seven videos discussing the DPR RI controversy. The data collection process utilized the googleapiclient.discovery.build module with API version V3, where the API_Key served as the authentication key to access data from YouTube. The research stages included preprocessing for data cleaning, sentiment labeling based on the InSet Lexicon Based method, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in the data. The results show that before SMOTE application, the Support Vector Machine (SVM) model achieved the highest accuracy of 89%, followed by Random Forest at 81%, and Naïve Bayes at 62%. After applying SMOTE, the performance of all three models increased significantly, with SVM obtaining the highest accuracy of 93%, followed by Random Forest at 86%, and Naïve Bayes at 75%. For the positive class, SVM also demonstrated the best performance with a Precision value of 96%, Recall of 95%, and an F1-Score of 95%. Overall, the findings of this study confirm that SVM is superior in maintaining class balance in classification, both before and after SMOTE. The Machine Learning-based sentiment analysis approach is proven capable of providing a comprehensive overview of public opinion on political issues, while also offering important input for policymakers in formulating more transparent and responsive communication strategies.
Co-Authors Arisula, Juan Pala Dani, Jemmi Rama Erwanto Megawaty, Dyah Ayu Muhamad Azhari Pratama, Edvan Agus Putra, M. Pajar Kharisma Romdoni, Muhammad S. Samsugi Saputra, M Rafli Tono, Jimmy Julian Wijaya, Feri Aldi