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All Journal TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi JPNM : Jurnal Pustaka Nusantara Multidisiplin
Asep Saeppani
Universitas Sebelas April Sumedang
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

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Analisis Sentimen Komentar Publik Terhadap Program Makan Bergizi Gratis (MBG) di Platfrom TikTok Menggunakan Algoritma Support Vector Machine (SVM) Fauzi Faturohman; Asep Saeppani
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 6 No 1 (2026): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol6No1.pp57-63

Abstract

This study aims to analyze public sentiment towards the Free Nutritious Food Program (MBG) based on user comments on the TikTok platform using the Support Vector Machine (SVM) algorithm. Data were collected through a scraping process, resulting in 673 comments related to the MBG program. The research stages included text pre-processing consisting of data cleaning, case folding, normalization, tokenization, stopword removal, and stemming. The data were then labeled into positive, negative, and neutral sentiment categories using a lexicon-based approach and converted into numerical features using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was performed using the SVM algorithm to identify public perceptions of the program. The labeling results showed that neutral sentiment dominated with 539 comments, followed by 90 positive comments and 44 negative comments. The model evaluation showed good performance, achieving an accuracy of 82.96%, a precision of 82.95%, a recall of 82.96%, and an F1-score of 76.47%. These findings indicate that SVM is effective for analyzing public sentiment on social media and can assist the government in understanding public perceptions of policy programs.
Analisis Sentimen Publik Program Makan Bergizi Gratis Menggunakan Support Vector Machine M.Arif Firmansyah; Asep Saeppani; Irfan Fadil
JPNM Jurnal Pustaka Nusantara Multidisiplin Vol. 3 No. 4 (2025): December : Jurnal Pustaka Nusantara Multidisiplin (ACCEPTED)
Publisher : SM Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59945/jpnm.v3i4.805

Abstract

Ketersediaan gizi merupakan faktor utama yang menunjang pertumbuhan anak dan kualitas sumber daya manusia. Pemerintah Indonesia memperkenalkan Program Makan Bergizi Gratis (MBG) sebagai langkah strategis untuk mengatasi masalah gizi buruk dan stunting pada anak sekolah. Penelitian ini berfokus pada analisis sentimen masyarakat terhadap program MBG dengan memanfaatkan algoritma Support Vector Machine (SVM) berbasis pengolahan Bahasa Alami (NLP). Data berasal dari 1.574 komentar di media sosial X (Twitter) yang diproses melalui tahapan pembersihan data, tokenisasi, penghapusan stopword, stemming, dan pembobotan TF-IDF. Hasil pengujian menunjukkan bahwa model SVM meraih akurasi 78%, sedangkan Logistic Regression mencapai 82%, dengan sebagian besar sentimen publik cenderung positif. Temuan ini mengindikasikan bahwa masyarakat menerima MBG dengan baik. Penelitian ini turut memperlihatkan manfaat penerapan machine learning untuk menganalisis kebijakan publik dan dapat digunakan pemerintah sebagai acuan menyusun strategi komunikasi dan meningkatkan efektivitas program gizi nasional
Co-Authors Fauzi Faturohman Irfan Fadil M.Arif Firmansyah