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ANALISIS SENTIMEN TERHADAP ISU KERACUNAN MBG DI MEDIA SOSIAL INSTAGRAM MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)



ABSTRAK


ANALISIS SENTIMEN TERHADAP ISU KERACUNAN MBG DI MEDIA SOSIAL INSTAGRAM MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

Media sosial Instagram menjadi salah satu sarana bagi masyarakat untuk menyampaikan opini terhadap berbagai isu, termasuk isu keracunan Program Makan Bergizi Gratis (MBG). Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap isu tersebut menggunakan metode Support Vector Machine (SVM). Data penelitian diperoleh dari unggahan Instagram berdasarkan hashtag yang berkaitan dengan Program Makan Bergizi Gratis. Proses penelitian meliputi preprocessing, confusion matrix, akurasi, presisi, recall, dan f1-score pembobotan kata menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), serta klasifikasi sentimen ke dalam kategori positif, netral, dan negatif menggunakan metode Support Vector Machine (SVM). Hasil analisis divisualisasikan menggunakan word cloud untuk mengidentifikasi kata-kata yang dominan pada setiap kategori sentimen. Hasil penelitian menunjukkan bahwa metode Support Vector Machine (SVM) mampu mengklasifikasikan sentimen unggahan Instagram berdasarkan hashtag terhadap isu keracunan Program Makan Bergizi Gratis. Visualisasi word cloud menunjukkan bahwa pembahasan didominasi oleh kata-kata seperti "mbg", "program", "makan", "bergizi", dan "siswa". Penelitian ini diharapkan dapat memberikan gambaran mengenai persepsi masyarakat terhadap isu keracunan Program Makan Bergizi Gratis. Hasil persentase sentimen untuk negatif 64.4%, positif 30.6% dan netral 5.0%.
Kata Kunci: Analisis Sentimen, Makan Bergizi Gratis, word cloud, Support Vector Machine



ABSTRACT
SENTIMENT ANALYSIS OF THE MBG FOOD POISONING ISSUE ON INSTAGRAM USING THE SUPPORT VECTOR MACHINE (SVM) METHOD

Instagram serves as a platform for the public to express opinions on various issues, including the poisoning incidents associated with the Free Nutritious Meal (MBG) Program. This study aims to analyze public sentiment regarding this issue using the Support Vector Machine (SVM) method. Research data were obtained from Instagram posts identified via hashtags related to the Free Nutritious Meal Program. The research process involved preprocessing, word weighting using Term Frequency–Inverse Document Frequency (TF-IDF), and sentiment classification into positive, neutral, and negative categories using SVM. Performance was evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The analysis results were visualized using word clouds to identify dominant words within each sentiment category. The results demonstrate that the Support Vector Machine (SVM) method effectively classifies sentiment in Instagram posts—identified by hashtags—regarding the poisoning incidents linked to the Free Nutritious Meal Program. Word cloud visualizations reveal that discussions were dominated by terms such as "mbg," "program," "makan" (meal/eat), "bergizi" (nutritious), and "siswa" (student). This study is expected to provide insight into public perception concerning the poisoning incidents associated with the Free Nutritious Meal Program. The sentiment percentage results are 64.4% negative, 30.6% positive, and 5.0% neutral.

Keywords: Sentiment Analysis, Free Nutritious Meal Program, Word Cloud, Support Vector Machine.

Ketersediaan
S260908023607.2 PRA aPerpustakaan STMIK AMIKBANDUNGTersedia
Informasi Detil
Judul Seri
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No. Panggil
607.2 PRA a
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: .,
Deskripsi Fisik
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Indonesia
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NONE
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Tipe Media
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SKRIPSI IF
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