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All Journal Natural Science: Journal of Science and Technology Jurnal Pendidikan Jasmani, Olahraga dan Kesehatan Jurnal Pendidikan Bahasa Jepang Undiksha KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Electronic Journal of Graph Theory and Applications (EJGTA) Journal of the Indonesian Mathematical Society JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vygotsky: Jurnal Pendidikan Matematika dan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics Parameter: Journal of Statistics Fraktal: Jurnal Matematika dan Pendidikan Matematika Arus Jurnal Sosial dan Humaniora Almufi Jurnal Pengabdian kepada Masyarakat Sulawesi Tenggara Educational Journal Journal Of Human And Education (JAHE)
I Wayan Sudarsana
Universitas Tadulako
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : InPrime: Indonesian Journal Of Pure And Applied Mathematics

 1 
A Study on Sentiment Analysis of Public Response to The New Fuel Price Policy In 2022: A Support Vector Machine Approach Putri, Niluh Putu Aprillia Puspitadewi Sudarsana; Angreni, Dwi Shinta; Sudarsana, I Wayan
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 7, No 1 (2025)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v7i1.42717

Abstract

The Indonesian government's decision to raise fuel prices in 2022, following a global surge in crude oil prices, triggered widespread public debate. Understanding public sentiment toward such policy decisions is essential for determining the appropriate timing of implementation while minimizing negative reactions. This study aims to classify public sentiment regarding the fuel price hike using the Support Vector Machine (SVM) algorithm. Data were collected from Twitter through web scraping using the SNScrape library in Python. A total of 3,000 tweets were gathered and underwent preprocessing steps such as case folding, tokenization, stopword removal, and stemming. The classification model was built in Google Colab using the SVM algorithm to categorize tweets as positive (+) or negative (–). Model performance was evaluated using a confusion matrix, achieving an accuracy of 81.0%. The results showed that 63.6% of public responses were negative, while 36.4% were positive. Additionally, it was observed that the accuracy converged to 81.1% as the number of training iterations increased. The findings were presented through word clouds and pie charts to enhance interpretability, and a simple graphical user interface (GUI) was developed for user interaction. The study indicates that the government’s repeated delays in implementing the price adjustment may have reflected sensitivity to public sentiment. This research demonstrates the potential of sentiment classification as a tool for evidence-based policymaking, offering insights into the social dynamics surrounding policy changes. Future research could expand by incorporating multi-class sentiment categories or real-time data for dynamic policy evaluation.Keywords: Fuel price; Public opinion; Sentiment analysis; Social media; SVM. AbstrakKeputusan pemerintah Indonesia untuk menaikkan harga bahan bakar minyak pada tahun 2022 dan disusul oleh lonjakan harga minyak mentah global, memicu perdebatan publik yang meluas. Memahami sentimen publik terhadap keputusan kebijakan tersebut sangat penting untuk menentukan waktu implementasi yang tepat untuk meminimalkan reaksi negatif. Penelitian ini bertujuan untuk mengklasifikasikan sentimen publik terhadap kenaikan harga bahan bakar minyak menggunakan algoritma Support Vector Machine (SVM). Data dikumpulkan dari Twitter melalui web scraping menggunakan pustaka SNScrape dalam bahasa Python. Sebanyak 3.000 tweet dikumpulkan dan dilakukan tahap praproses seperti case folding, tokenization, stopword removal, dan stemming. Model klasifikasi dibangun di Google Colab menggunakan algoritma SVM untuk mengkategorikan tweet sebagai positif (+) atau negatif (–). Kinerja model dievaluasi menggunakan matriks confusion dan mencapai akurasi 81,0%. Hasil penelitian menunjukkan bahwa 63,6% tanggapan publik bersifat negatif, sedangkan 36,4% bersifat positif. Selain itu, akurasi konvergen menjadi 81,1% seiring dengan peningkatan jumlah iterasi pelatihan. Temuan tersebut disajikan melalui word cloud dan diagram pai untuk meningkatkan interpretabilitas, dan graphical user interface (GUI) sederhana dikembangkan untuk interaksi pengguna. Studi ini menunjukkan bahwa penundaan berulang pemerintah dalam menerapkan penyesuaian harga mungkin mencerminkan kepekaan terhadap sentimen publik. Penelitian ini menunjukkan potensi klasifikasi sentimen sebagai alat untuk pembuatan kebijakan berbasis bukti, yang menawarkan wawasan tentang dinamika sosial seputar perubahan kebijakan. Penelitian di masa mendatang dapat diperluas dengan menggabungkan kategori sentimen multikelas atau data waktu nyata untuk evaluasi kebijakan yang dinamis.Kata Kunci: Bahan bakar; Opini public; Analisis sentiment; Mesia social; SVM. 2020MSC: 62H30, 91D30.
A Study on Sentiment Analysis of Public Response to The New Fuel Price Policy In 2022: A Support Vector Machine Approach Putri, Niluh Putu Aprillia Puspitadewi Sudarsana; Angreni, Dwi Shinta; Sudarsana, I Wayan
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 7 No. 1 (2025)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/2twt5d12

Abstract

The Indonesian government's decision to raise fuel prices in 2022, following a global surge in crude oil prices, triggered widespread public debate. Understanding public sentiment toward such policy decisions is essential for determining the appropriate timing of implementation while minimizing negative reactions. This study aims to classify public sentiment regarding the fuel price hike using the Support Vector Machine (SVM) algorithm. Data were collected from Twitter through web scraping using the SNScrape library in Python. A total of 3,000 tweets were gathered and underwent preprocessing steps such as case folding, tokenization, stopword removal, and stemming. The classification model was built in Google Colab using the SVM algorithm to categorize tweets as positive (+) or negative (–). Model performance was evaluated using a confusion matrix, achieving an accuracy of 81.0%. The results showed that 63.6% of public responses were negative, while 36.4% were positive. Additionally, it was observed that the accuracy converged to 81.1% as the number of training iterations increased. The findings were presented through word clouds and pie charts to enhance interpretability, and a simple graphical user interface (GUI) was developed for user interaction. The study indicates that the government’s repeated delays in implementing the price adjustment may have reflected sensitivity to public sentiment. This research demonstrates the potential of sentiment classification as a tool for evidence-based policymaking, offering insights into the social dynamics surrounding policy changes. Future research could expand by incorporating multi-class sentiment categories or real-time data for dynamic policy evaluation. Keywords: Fuel price; Public opinion; Sentiment analysis; Social media; SVM.   Abstrak Keputusan pemerintah Indonesia untuk menaikkan harga bahan bakar minyak pada tahun 2022 dan disusul oleh lonjakan harga minyak mentah global, memicu perdebatan publik yang meluas. Memahami sentimen publik terhadap keputusan kebijakan tersebut sangat penting untuk menentukan waktu implementasi yang tepat untuk meminimalkan reaksi negatif. Penelitian ini bertujuan untuk mengklasifikasikan sentimen publik terhadap kenaikan harga bahan bakar minyak menggunakan algoritma Support Vector Machine (SVM). Data dikumpulkan dari Twitter melalui web scraping menggunakan pustaka SNScrape dalam bahasa Python. Sebanyak 3.000 tweet dikumpulkan dan dilakukan tahap praproses seperti case folding, tokenization, stopword removal, dan stemming. Model klasifikasi dibangun di Google Colab menggunakan algoritma SVM untuk mengkategorikan tweet sebagai positif (+) atau negatif (–). Kinerja model dievaluasi menggunakan matriks confusion dan mencapai akurasi 81,0%. Hasil penelitian menunjukkan bahwa 63,6% tanggapan publik bersifat negatif, sedangkan 36,4% bersifat positif. Selain itu, akurasi konvergen menjadi 81,1% seiring dengan peningkatan jumlah iterasi pelatihan. Temuan tersebut disajikan melalui word cloud dan diagram pai untuk meningkatkan interpretabilitas, dan graphical user interface (GUI) sederhana dikembangkan untuk interaksi pengguna. Studi ini menunjukkan bahwa penundaan berulang pemerintah dalam menerapkan penyesuaian harga mungkin mencerminkan kepekaan terhadap sentimen publik. Penelitian ini menunjukkan potensi klasifikasi sentimen sebagai alat untuk pembuatan kebijakan berbasis bukti, yang menawarkan wawasan tentang dinamika sosial seputar perubahan kebijakan. Penelitian di masa mendatang dapat diperluas dengan menggabungkan kategori sentimen multikelas atau data waktu nyata untuk evaluasi kebijakan yang dinamis. Kata Kunci: Bahan bakar; Opini public; Analisis sentiment; Mesia social; SVM. 2020MSC: 62H30, 91D30.
Co-Authors Abdi Abdul Mahatir Najar Agusman Sahari Al Fajri, Iman Andi Hendra Anita Ahmad Kasim Ayu Lestari D Farhamsa, D Desy Lusiyanti Dwi Shinta Angreni Erwanda, Muhammad Tegar Faisal Susanto Farih, Muhamad Fitria Fitria Habibi Habibi Hayyan Ahmad Ulul Albab Hervisari, Hervisari Huzaimah Huzaimah I Gede Mahendra Darmawiguna I Made Gede Sunarya Ikram Indalestari, Wa Ode Dien Ismail, M S Ismiyanti, Ismiyanti Jaenuddin, Arsyad Junaidi Junaidi Kadek Eva Krishna Adnyani Khasmawati, Julia Kristiana Wijaya Mariani Mariani Marsalena, Nindi Ade Marsalena, Nindy Ade Merdekawati, Dian Ayu Muhammad Rivai Mutmainna, Nurul Naim, Ifin Ni Nengah Suartini, Ni Nengah Noviana Noviana Nur Aisyah Nurainun Nurainun Nurhayati, Listi Nurul Arifin Patulak, Lucia Evianti Putri, Niluh Putu Aprillia Puspitadewi Sudarsana Raiza, Raiza Rajuddin, Wa Ode Nursaadha Rasna Resnawati Ridha Taurisma Lajaria Rina Ratianingsih Rita, Wa Rita, Wa Ade Rizki Amalia S Fatimah, S S Slamin Selvy Musdalifah Suhartien, Inten Sulistiawati Mendi Sultan Andhika Suprianto, Ghalib Talaba, Djeliyana Titop, Herman Wahyuli, Diana Wahyuningsi, Sri Wati, Kristin Lita Yesi, Indah