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All Journal International Journal of Advances in Applied Sciences Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor JUITA : Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan SMATIKA RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JURNAL REKAYASA TEKNOLOGI INFORMASI SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Nasional Komputasi dan Teknologi Informasi Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Mnemonic Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Teknika Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal ABDIMAS Budi Darma Joutica : Journal of Informatic Unisla Journal of Artificial Intelligence and Engineering Applications (JAIEA) Seminar Nasional Ilmu Terapan Jurnal Abdimas Terapan Dedication : Jurnal Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Jurnal ilmiah teknologi informasi Asia Smatika Jurnal : STIKI Informatika Jurnal Jurnal Inovasi Informatika dan Bisnis Digital (JIIBD)
Siti Mujilahwati
Teknik Informatika, Fakultas Teknik, Universitas Islam Lamongan
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ANALISIS SENTIMEN KOMENTAR YOUTUBE TENTANG BOIKOT TRANS 7 DENGAN METODE K-Nearest Neighbor (KNN) AH KHAFID NASRUL UMAM umam; Siti Mujilahwati; Danang Bagus Reknandi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10803

Abstract

Abstrak. Perkembangan media sosial menempatkan platform seperti YouTube sebagai barometer opini publik, salah satunya pada fenomena seruan boikot Trans 7 terkait polemik konten Guru Gembul. Penelitian ini bertujuan menganalisis polaritas opini masyarakat serta mengevaluasi kinerja algoritma K-Nearest Neighbor (K-NN) berbasis pembobotan fitur IndoRoBERTa untuk mengklasifikasikan komentar ke dalam kategori negatif, netral, dan positif. Dari 3.700 komentar yang dikumpulkan via YouTube API, serangkaian text preprocessing menghasilkan 1.856 data valid. Pengujian menunjukkan konfigurasi K=5 lebih optimal dibanding K=3. Distribusi data didominasi sentimen negatif sebesar 68,53% (1.272 komentar), netral 24,62% (457 komentar), dan positif 6,84% (127 komentar). Evaluasi K-NN (K=5) menghasilkan akurasi 64,5%, namun mengalami bias akibat class imbalance ekstrem. Model sangat andal mengenali sentimen negatif (presisi 71,9%, recall 82,7%), tetapi berkinerja rendah pada netral (presisi 36,8%, recall 30,4%) dan positif (recall 7,7%). Penelitian ini menyimpulkan bahwa K-NN berbasis IndoRoBERTa mampu memetakan opini publik, tetapi memerlukan teknik penanganan data seperti SMOTE pada riset mendatang. Abstract. The development of social media has positioned platforms like YouTube as a barometer of public opinion, as seen in the boycott call against Trans 7 concerning the controversy surrounding Guru Gembul's content. This study aims to analyze the polarity of public opinion and evaluate the performance of the K-Nearest Neighbor (K-NN) algorithm with IndoRoBERTa feature weighting in classifying comments into negative, neutral, and positive categories. From 3,700 comments collected via the YouTube API, a series of text preprocessing steps yielded 1,856 valid entries. Parameter testing revealed that a configuration of was more optimal than . The data distribution was dominated by negative sentiment at 68.53% (1,272 comments), followed by neutral at 24.62% (457 comments), and positive at 6.84% (127 comments). The evaluation of K-NN () achieved an accuracy of 64.5%, but exhibited strong classification bias due to extreme class imbalance. The model proved highly reliable in identifying negative sentiment (precision 71.9%, recall 82.7%), but performed poorly on neutral sentiment (precision 36.8%, recall 30.4%) and struggled significantly with positive sentiment (recall 7.7%). This study concludes that IndoRoBERTa-based K-NN can map public opinion, but requires data handling techniques such as SMOTE to optimize future research. .
Co-Authors Achmad Nurasel Farizki Agung Wahyudi Agustian, M. Afan AH KHAFID NASRUL UMAM umam Ahmad Dwi Raharjo Septiawan Aliyudin, Muhammad Amri, Sholihul Awaliyah, Bintan Udiyarini Azarki Ubaydillah, Muhammad Azza Abidatin Bettaliyah Azza Abidatin Bettaliyah AZZA ABIDATIN BETTALIYAH Bettaliyah, Azza Abidatin Danang Bagus Reknadi Danang Bagus Reknandi Dimas Lugiantoro Suko Dinar Mahdalena Leksana 1 Endang Setyati Erry Anggraini ERRY ANGGRAINI Febrian Abie Prsatama Fitri Rohmawati Hamid, Rahayu A Indah Ati Kevin Aringgi Salim KHOIRIYATUL MAGHFIROH M Ari Zidan Syafrizal M Chabib Nurroziqin M. Ali Na'im M. Ghofar Rohman M. Rosidi Zamroni M. ZAKI QOMARUDDIN M.Rosidi Zamroni Masruroh Masruroh Miftahus Sholihin Miftahus Sholihin Moh Adam Mufrody Muhammad Alvin Firdaus Muhammad Nur Fikri Alfarisi Muhammad Rayendra AlMuhibbi Nafiiyah, Nur Nawafilah, Nur Qomariyah Noor Zuraidin bin Mohd Safar Nur Nafi'iyah Nur nafi'iyah Nur Nawafilah Putra Samudra Qonit Zirby Rakhmawati, Eka Retno Wardhani Rizki Ardhian Ahmad Rizki Iwan Laksana Robiatul Adawiyah Salim, Kevin Aringgi Seroja, Aulia Nadia Bunga Suci Nur Fauziah Sugeng Dwi Hartantyo Surya Agung Agan Saputra Syamsul Arifin Tri Setyo Utomo