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All Journal Jurnal Informatika dan Teknik Elektro Terapan Journal of Informatics, Electrical and Electronics Engineering
Tuasamu, Abdulrahman
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis Sentimen Program Makan Bergizi Gratis pada Podcast Bocor Alus Politik dengan Algoritma Naive Bayes Tuasamu, Abdulrahman; Gumilang, Rapanca Cahya; Fachrian, Mohammad Akmal; Krisnandi, Daiva Rakha; Indryani, Azizah Wardah; Hasan, Fuad Nur
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 2 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i2.2874

Abstract

The Free Nutritious Meal (MBG) program initiated by the elected administration has evolved into a strategic public policy, yet it has garnered diverse responses from various strata of society. These opinion dynamics are clearly evident through the high volume of interaction on social media, particularly within the comment section of the "Bocor Alus Politik" podcast on YouTube. This phenomenon reflects a significant polarization of public sentiment, which is crucial to map as a basis for evaluating government policy. This study is conducted with the primary objective of developing a sentiment analysis model capable of classifying public opinion regarding the MBG Program into two major categories, namely positive and negative sentiments, utilizing the Naive Bayes algorithm known for its effectiveness in text processing. The research methodology utilizes primary data in the form of collected public comments which undergo a systematic series of text preprocessing stages, including cleaning, tokenization, filtering, and vectorization, to prepare the data for processing. Model performance evaluation is subsequently conducted using the k-fold cross-validation method to ensure the reliability of the classification results. Experimental results indicate that the model successfully achieved an overall accuracy rate of 77.1%. In-depth analysis of per-class performance demonstrates that the model possesses a stronger capability in detecting negative opinions, evidenced by a precision of 0.795, recall of 0.884, and f1-score of 0.837, compared to positive sentiments (precision 0.701, recall 0.545, and f1-score 0.613). Based on these evaluation metrics, it can be concluded that the Naive Bayes algorithm proves to be sufficiently effective in classifying the direction of public sentiment, particularly in identifying public aspirations that are critical of the MBG program.
ANALISIS SENTIMEN PUBLIK PERNYATAAN 'LALAI' TRANS 7 TAYANGAN PESANTREN DENGAN SUPPORT VECTOR MACHINE Tuasamu, Abdulrahman; Rapanca Cahya Gumilang; Mohammad Akmal Fachrian; Daiva Rakha Krisnandi; , Azizah Wardah Indryani
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8693

Abstract

Abstrak. Perkembangan pesat teknologi informasi dan komunikasi telah memperluas serta meningkatkan intensitas interaksi masyarakat melalui berbagai platform media sosial. Salah satu platform yang paling populer dan banyak dimanfaatkan adalah YouTube, yang tidak hanya berperan sebagai media hiburan, tetapi juga menjadi ruang publik digital bagi masyarakat untuk menyampaikan opini, gagasan, serta beragam respons kritis terhadap isu-isu sosial, fenomena yang berkembang, dan berbagai tayangan media.Komentar yang ditinggalkan oleh pengguna YouTube dapat mencerminkan persepsi, sikap, dan sentimen publik terhadap suatu konten tertentu. Namun, analisis komentar secara manual dalam jumlah besar membutuhkan waktu dan tenaga yang tidak sedikit, sehingga dinilai kurang efisien dan kurang objektif. Dengan demikian, penelitian ini diarahkan pada pengkajian sentimen komentar pengguna YouTube terhadap tayangan “Pesantren” yang ditayangkan di Trans7 melalui penerapan algoritma Support Vector Machine (SVM). Metode penelitian yang digunakan meliputi tahap pengumpulan data komentar dari YouTube, proses pra-pengolahan data seperti pembersihan teks dan normalisasi, serta tahap klasifikasi sentimen menjadi kategori positif dan negatif menggunakan algoritma SVM. Untuk mengukur kinerja model, dilakukan pengujian menggunakan metrik evaluasi berupa akurasi, presisi, dan recall melalui metode validasi silang k-fold.Hasil penelitian menunjukkan bahwa algoritma Support Vector Machine mampu melakukan klasifikasi sentimen komentar secara optimal dengan tingkat akurasi mencapai 88,3%. Capaian ini menegaskan bahwa algoritma SVM memiliki kinerja yang efektif dan andal dalam mengolah data teks serta menganalisis sentimen pada komentar YouTube berbahasa Indonesia secara akurat dan konsisten.
Co-Authors , Azizah Wardah Indryani Daiva Rakha Krisnandi Fachrian, Mohammad Akmal Gumilang, Rapanca Cahya Hasan, Fuad Nur Indryani, Azizah Wardah Krisnandi, Daiva Rakha Mohammad Akmal Fachrian Rapanca Cahya Gumilang