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      "title": "OPTIMASI KLASIFIKASI MENGGUNAKAN GRID SEARCH\r\nPADA ANALISIS SENTIMEN TERHADAP MAKAN BERGIZI\r\nGRATIS",
      "keywords": "Analisis Sentimen, Grid Search, Support Vector Machine, K-Nearest \r\nNeighbor, Random Forest,  Sentiment Analysis.",
      "subjects": [
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      "institution": "Universitas AMIKOM Yogyakarta",
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      "abstract": "Program Makan Bergizi Gratis merupakan salah satu kebijakan pemerintah\r\nIndonesia yang menimbulkan beragam tanggapan dari masyarakat. Media sosial X\r\n(Twitter) menjadi wadah utama bagi masyarakat untuk menyampaikan opini, baik\r\nberupa dukungan maupun kritik terhadap program tersebut. Banyaknya data teks\r\nyang bersifat tidak terstruktur menjadikan analisis secara manual tidak efektif,\r\nsehingga diperlukan pendekatan otomatis berbasis machine learning untuk\r\nmengklasifikasikan sentimen masyarakat.\r\nPenelitian ini bertujuan untuk menganalisis serta membandingkan kinerja\r\nalgoritma Support Vector Machine (SVM), K-Nearest Neighbor (KNN), dan\r\nRandom Forest dalam mengklasifikasikan sentimen masyarakat terhadap Program\r\nMakan Bergizi Gratis. Data penelitian diperoleh melalui proses scraping dari\r\nTwitter dan kemudian melalui tahapan preprocessing yang meliputi case folding,\r\ntext cleaning, normalisasi kata, tokenisasi, stopword removal, dan stemming. Data\r\nselanjutnya diberi label sentimen positif dan negatif, serta dilakukan undersampling\r\nuntuk mengatasi ketidakseimbangan kelas. Ekstraksi fitur dilakukan menggunakan\r\nmetode Term Frequency–Inverse Document Frequency (TF-IDF), sedangkan\r\noptimasi parameter model dilakukan dengan Grid Search.\r\nEvaluasi kinerja model dilakukan menggunakan confusion matrix dengan\r\nmetrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa\r\nalgoritma Support Vector Machine (SVM) yang dioptimasi menggunakan Grid\r\nSearch menghasilkan performa terbaik dengan akurasi sebesar 89,47%, diikuti oleh \r\nRandom Forest sebesar 83,98% dan K-Nearest Neighbor sebesar 77,43%. Hasil ini\r\nmenunjukkan bahwa penerapan Grid Search mampu meningkatkan kinerja model\r\nklasifikasi, khususnya pada algoritma SVM, dalam analisis sentimen terhadap\r\nProgram Makan Bergizi Gratis.",
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