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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Simantec Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Knowledge Engineering and Data Science Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri Network Engineering Research Operation [NERO] Jurnal Pertahanan : Media Informasi tentang Kajian dan Strategi Pertahanan yang Mengedepankan Identity, Nasionalism dan Integrity Applied Information System and Management Jurnal Teknoinfo Jurnal ICT : Information Communication & Technology Community Development Journal Jurnal Informasi dan Teknologi JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Jurnal Mnemonic Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat Journal of Electrical Engineering and Computer (JEECOM) Archipelago Engineering Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) J-Intech (Journal of Information and Technology) Joutica : Journal of Informatic Unisla Jurnal Informatika Teknologi dan Sains (Jinteks) NEAR: Jurnal Pengabdian kepada Masyarakat Jurnal Rekayasa Lampung (JRL) Biner : Jurnal Ilmiah Informatika dan Komputer Seminar Nasional Ilmu Terapan JEECS (Journal of Electrical Engineering and Computer Sciences) Nusantara Journal of Computers and its Applications NERO (Networking Engineering Research Operation) Jurnal Informatika: Jurnal Pengembangan IT semanTIK Journal of Computer Science Advancements Journal of Information Technology
Hozairi
Universitas Islam Madura
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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Analisis Sentimen Publik Debat Pilkada Pamekasan menggunakan BERT Imamah Mailah; Moh. Aminollah Hamzah; Hozairi
SemanTIK : Teknik Informasi Vol. 11 No. 2 (2025): SemanTIK : Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i2.152

Abstract

Pemilihan kepala daerah merupakan momen penting dalam demokrasi yang memunculkan beragam opini publik di media sosial. Debat calon bupati dan wakil bupati Pamekasan tahun 2024 menjadi perhatian masyarakat dan menghasilkan banyak komentar daring. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap debat tersebut menggunakan pendekatan deep learning berbasis transformer. Data penelitian berupa 818 komentar dari YouTube dan TikTok yang diperoleh melalui web scraping. Tahapan penelitian meliputi pembersihan data, case folding, tokenisasi, serta terjemahan. Proses pelabelan sentimen dilakukan dengan TextBlob, sedangkan klasifikasi menggunakan model DistilBERT yang telah di-fine-tune. Hasil penelitian menunjukkan model mampu mengklasifikasikan komentar menjadi tiga kategori, yaitu positif, netral, dan negatif, dengan akurasi 80% serta F1-score tertinggi 0,91 pada kelas positif. Sebagian besar komentar tergolong netral (44,03%), diikuti positif (37,03%) dan negatif (18,96%). Temuan ini menunjukkan bahwa respon publik cenderung biasa tanpa ekspresi emosional yang kuat. Penelitian ini menyimpulkan bahwa model berbasis transformer efektif untuk menganalisis opini publik dalam konteks politik lokal, sehingga dapat membantu pengambil kebijakan, pengamat politik, maupun tim kampanye memahami persepsi masyarakat secara lebih cepat dan akurat. Regional elections are a crucial moment in democracy that generate diverse public opinions on social media. The 2024 Pamekasan regent and deputy regent candidate debate attracted public attention and sparked many online comments. This study aims to analyze public sentiment toward the debate using a transformer-based deep learning approach. The dataset consists of 818 comments collected from YouTube and TikTok through web scraping. The research process included data cleaning, case folding, tokenization, and translation. Sentiment labeling was carried out using TextBlob, while classification employed a fine-tuned DistilBERT model. The results show that the model successfully categorized comments into three sentiment classes—positive, neutral, and negative—with an accuracy of 80% and the highest F1-score of 0.91 in the positive class. Most comments were classified as neutral (44.03%), followed by positive (37.03%) and negative (18.96%). These findings indicate that the majority of the public responded in a neutral manner without strong emotional bias. This study concludes that transformer-based models are effective in analyzing public opinion in local political contexts, providing valuable insights for policymakers, political observers, and campaign teams to better understand community perceptions quickly and accurately.
Forecasting Number of Legal Violations in Indonesian Sea Using the Fuzzy Double Exponential Smoothing Method Hozairi; Syariful Alim; Marcus Tukan
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 5 No. 2 (2020): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v5i2.97

Abstract

Maritime security in Indonesia is an indicator of the success of the Government in managing the sovereignty of the State because two-thirds of Indonesia is sea, so Indonesia is called a maritime country. This study aims to predict the number of law violations in Indonesian seas. Predicting events is a strategic step to set the next security operation strategy. The method used to predict violations of law at sea in Indonesia is Fuzzy Double Exponential Smoothing, the Fuzzy method is used to normalize violation data and the Double Exponential Smoothing method is used to predict future events, a combination of fuzzy and double exponential smoothing methods was developed to improve some previous research which only use exponential smoothing only in making predictions. The data processed is data on violations of law at sea in Indonesia from 1996 to 2019 from the Indonesian Maritime Security Agency. The results obtained from this study are the data smoothing constant value (α = 0.81), the trend smoothing value (γ = 0.08), the mean absolute percentage error value (MAPE = 21.78%) and the root mean value average error (RMSE = 60.72). The results of this study predict that the number of violations of law at sea in Indonesia in 2020 will decrease to 98 cases, this is due to several factors, including the focus of the Government on carrying out security operations in Indonesian seas in an integrated manner involving many institutions. The research contribution can be considered by Indonesian Maritime Security Agency to improve Indonesia's maritime security by involving institutions that have legal authority in Indonesian seas
Co-Authors AA. Masroeri Aang Kisnu Darmawan Abd. Wafi Ach. Nurul Qomar Achmad Baihaqi Akhmad Arif Kurdianto Alim, Syariful Alim, Syarigul Anwari Anwari Anwari, Anwari Arfianto, Afif Zuhri Artanti, M. D. Arya Yudhi Wijaya Asmara, I Putu Sindhu Bakir Bakir Bakir Bakir Bakir, Bakir Baskoro, Fajar Bernardo, Januario Freitas Araujo Billy Jhones Camerling Budhi Hascaryo Iskandar Buhari Buhari Buhari Camerling, Billy Jhones Chafid, Nurul Efenie, Yuri Finanatun Halimiyah Fitra, Anis Hakiem, Luqmanul Heru Lumaksono Heru Lumaksono Heru Lumaksono Heru Lumaksono Hoiriyah, . Hoiriyah, Hoiriyah Husnul Khatimah Husnul Khatimah Imamah Mailah John Haluan Juhairiyah Juhairiyah Juhairiyah Ketut Buda Artana Kuddus, Abd Kuzairi Lizami, Makinul Lumaksono, Heru Lumaksono, Heru M. Ali Fikri M. Furqon Wahyudi Madukil Makruf Maisyaroh, Rohimatul Marcus Tukan Marcus Tukan Masdukil Makruf, Masdukil Maulidanitamyizi, Moh Fahmi mochamad zainul Asrori Moh Fahmi Maulidanitamyizi Moh. Aminollah Hamzah Moh. Badri Tamam Moh. Badri Tamam Mohammad Isa Irawan Mohammad Thezar Afifudin Mohammad Thezar Afifudin Muhammad Agus Muljanto Muhsi, Muhsi Munadi Munadi Muyammina, Ittrotul Nur Azizah Nurul Badriyah Nurul Hidayat Putri, Dewi Amiliana Putri, Nadira Hijriani Rofiudin Roland Koswara Sa'diyah, Aminatus Safira, Aulia Salman Alfarisi Santosa, A. F. Santoso, Teguh Budi Syamsiar, Syamsiar Syariful Alim Syariful Alim Syariful Alim Syariful Alim Taufiqurrohman Taufiqurrohman Tukan, Markus Wahyudi, Moh. Rafiqi Walid, Miftahul Wawan Kurniawan Yaser Krisnafi Yaser Krisnafi, Yaser Yuri Efenie Zaifuddin, Zaifuddin