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All Journal Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Liana Nadzirah
Universitas Bina Sarana Informatika
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering

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Analisis Sentimen Publik terhadap Pengadaan Motor Listrik bagi SPPG Program MBG di Platfrom X Menggunakan SVM Tahun 2026 Liana Nadzirah; Endang Wahyudi; Jimmi Jimmi
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 4 No. 5 (2026): September : Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Inf
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v4i5.1639

Abstract

The viral spread of a video in April 2026 featuring electric motorcycles branded with the National Nutrition Agency (Badan Gizi Nasional) logo sparked public debate on X regarding the budget transparency of the Free Nutritious Meal (MBG) Program, specifically concerning the procurement of electric motorcycles for the Nutrition Fulfillment Service Units (SPPG). The sheer volume of data and the rapid dissemination of information on social media render manual analysis inadequate. This study aims to map the distribution of public sentiment (positive, neutral, negative), measure the performance of the classification model, and identify dominant keywords within each sentiment category. Data were collected via web scraping from X using keywords related to the SPPG electric motorcycle procurement issue during the April–May 2026 period. Following preprocessing, automated labeling was performed using the IndoBERT model with a confidence threshold of 0.60, and the results were manually validated against a sample of 500 data points. Feature extraction utilized TF-IDF, while sentiment classification employed a Support Vector Machine (SVM) algorithm based on LinearSVC, evaluated using Stratified 5-Fold Cross-Validation. The results reveal a predominance of neutral sentiment (43.25%), followed by negative sentiment (38.73%) and positive sentiment (18.01%). The SVM model achieved an accuracy of 78.18% with a macro F1-score of 0.77. Data from May 2026 indicate a rise in negative sentiment to 66.01%, signaling widespread public concern regarding the transparency and budget governance of the MBG program.
Co-Authors Endang Wahyudi Jimmi jimmi