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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Edulib JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Technomedia Journal Astonjadro Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal Of World Science International Journal Of Science Education and Technology Management (IJSETM) SmartComp TECHNOVATAR The Indonesian Journal of Computer Science
Christina Juliane, Christina
STEI-Institut Teknologi Bandung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences

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Journal : technovatar

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Opinion Mining Pengguna X (Twitter) terhadap Putusan MK Tentang Pemilu 2024 dengan model Naive Bayes Classifier dan Gradient Boosted Machines Permana, Indra Surya; Juliane, Christina
TECHNOVATAR Jurnal Teknologi, Industri, dan Informasi Vol 2 No 4 (2024): OKTOBER
Publisher : Awatara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61434/technovatar.v2i4.385

Abstract

This study explores sentiment analysis of Twitter reactions to the Constitutional Court's verdict on Indonesia's 2024 election, using Naive Bayes and Gradient Boosted Machines (GBM). Aimed at assessing the efficacy of these models in categorizing sentiments as positive, negative, or neutral, the data were processed using TF-IDF vectorization. Results reveal that Naive Bayes excels in identifying positive sentiments with high precision and recall, yet struggles with negative and neutral sentiments. Conversely, GBM demonstrates a more balanced performance across all categories, particularly in detecting neutral sentiments. This highlights the strengths and weaknesses of each model, suggesting Naive Bayes for scenarios where positive sentiment detection is critical, and GBM for situations requiring nuanced sensitivity across diverse sentiments. This research offers insights into the strategic selection of models for sentiment analysis based on specific needs.
Co-Authors AA Sudharmawan, AA Aji, Tri Wahyu ALAM Alana, Divi Adiffia Freza Alfian, Iqbal Amirulloh, Moch Mumin Azhar, Rizaldi Bani Riyan, Ade Damayunita, Aina Fathah, Adittia Fauzia, Fathia Alisha Fikri Rifai, Mochamad Hermawan, Sari Hikmawati, Nina Kurnia Indra Gunawan, Gun Gun Indra Surya Permana Irawan Irawan Kannisa Adjani Karsa, Koko Lesmana, Agrian Dwi Luffi Septian Marlia, Sari Mutia, Acep Sandi Nugraha, Anwar Nurwijayanti Pascalina, Donna Prasakti, Lukito Angga Pratama, Denni Purwandi Putra, Rendri Ridian Gusdiana Rifqi Syamsul Fuadi Saepulloh, Yusep Setiawan, Kiki SRI RAHAYU Surahman, Adi Syakur, Achmad Teguh Aljauza Widhiastono, Raymondhus Yayan Sopyan Yosep Bustomi