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All Journal Journal of Applied Geospatial Information Jurnal Mantik Penusa JOURNAL OF SCIENCE AND SOCIAL RESEARCH CSRID (Computer Science Research and Its Development Journal) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer International Journal of Economics Development Research (IJEDR) Journal of Intelligent Decision Support System (IDSS) Jurnal Teknik Informatika (JUTIF) International Journal of Basic and Applied Science Jurnal Mandiri IT Jurnal Minfo Polgan (JMP) Jurnal Pengabdian Kepada Masyarakat Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Venus: Jurnal Publikasi Rumpun Ilmu Teknik Indonesia Bergerak: Jurnal Hasil Kegiatan Pengabdian Masyarakat Jurnal Bisnis Digital Global Science: Journal of Information Technology and Computer Science Jurnal Teknik Informatika dan Teknologi Informasi
Mutiara S. Simanjuntak
Politeknik Negeri Lhokseumawe
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Implementasi Algoritma Klasifikasi untuk Analisis Sentimen Media Sosial Tiktok Tahun 2025 Pius Deski Manalu; Mutiara Simanjuntak; Chairil Umri
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 1 (2025): April: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5644

Abstract

TikTok has emerged as one of the fastest-growing social media platforms in 2025, especially among the younger generation. Beyond being a space for creative content sharing, TikTok has also become a vital platform for the exchange of public opinion, primarily through user comments. As user engagement intensifies, sentiment analysis on TikTok comments becomes increasingly essential to understanding public perception of various issues, trends, public figures, and brands. This study aims to analyze sentiment in TikTok user comments using machine learning classification algorithms. The research compares the performance of three widely used algorithms in text classification: Naive Bayes, Support Vector Machine (SVM), and Random Forest. A dataset of 5,000 public TikTok comments was collected through web scraping of trending videos from January to March 2025. The comments, written in Indonesian, underwent several text pre-processing steps, including tokenization, stopword removal, and stemming, to normalize the data. The TF-IDF method was then applied to extract numerical features from the textual data. A stratified data split was used to divide the dataset into training (80%) and testing (20%) subsets, ensuring balanced sentiment class distribution. Performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. Among the tested models, SVM achieved the highest accuracy of 89.7%, outperforming Naive Bayes and Random Forest across all metrics. These results indicate that SVM is particularly well-suited for classifying short, informal text such as TikTok comments. The findings contribute to the advancement of sentiment analysis in social media environments, specifically for Indonesian language data on TikTok. Moreover, the study provides valuable insights for industry stakeholders, marketers, and academic researchers seeking to implement data-driven public opinion analysis using machine learning techniques on emerging social media platforms.
Analisis Ketepatan Model CNN dalam Deteksi Asap Berbasis Citra Simangunsong, Juanto; Simanjuntak, Mutiara S.; Simanjuntak, Nurmala Dewi
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15908

Abstract

Deteksi asap merupakan tahap kritis dalam sistem peringatan dini kebakaran, karena keberadaan asap biasanya muncul lebih dahulu sebelum api terlihat. Penelitian ini bertujuan untuk menganalisis ketepatan model Convolutional Neural Network (CNN) dalam mengklasifikasikan citra asap dan non-asap menggunakan Smoke Detection Dataset. Proses penelitian meliputi praproses citra, pelatihan model CNN, serta evaluasi performa menggunakan metrik akurasi, precision, recall, F1-score, dan Area Under Curve (AUC). Hasil penelitian menunjukkan bahwa model CNN mencapai akurasi sebesar 0.994, precision 0.69, recall 0.78, dan F1-score 0.73, sementara nilai AUC sebesar 0.992 menegaskan kemampuan diskriminatif model yang sangat tinggi. Confusion matrix mengungkapkan bahwa model efektif dalam mengidentifikasi citra non-asap maupun asap, meski masih terdapat kesalahan pada citra dengan intensitas asap rendah dan kondisi visual menyerupai asap. Secara keseluruhan, CNN terbukti menjadi metode yang andal dan efisien untuk deteksi asap berbasis citra, serta berpotensi dikembangkan lebih lanjut untuk aplikasi sistem deteksi kebakaran berbasis visi komputer.
Co-Authors Aji Priyambodo Allwine Arnold Pakpahan Baehaqi Chairil Umri Elshad Yusifov Ginting, Syahril Hongjie Dai Iskandar Iskandar Joel Panjaitan Juanto Simangunsong Juanto Simangunsong Junifa, Dira LUBIS, IRNA TRIANNUR Manalu, Pius Deski Manullang, Evi Yanti Nissa, Fachrun Nurafni Damanik Pieter Leuvanggi Hutagalung Pius Deski Manalu Pulungan, Wahyu Aji Regina Sirait - Rico Wijaya Riduan, Suwardy Rika Rosnelly Sairun, Aryani Selvi Aristantya Simangunsong, Juanto Simanjuntak, Andrean Simanjuntak, Nurmala Dewi Siti Aisyah Syofyan Anwar Syahputra Syofyan Anwar Syahputra Teddy Surya Gunawan Wanayumini