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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) SMATIKA Briliant: Jurnal Riset dan Konseptual Journal of Development Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab KOMPUTIKA - Jurnal Sistem Komputer JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Teknologi Komputer dan Sistem Informasi Jurnal ICT : Information Communication & Technology JSAI (Journal Scientific and Applied Informatics) Jurnal Teknologi Informasi dan Multimedia ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Bumigora Information Technology (BITe) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Journal of Computer Networks, Architecture and High Performance Computing Journal of System and Computer Engineering Bima Abdi: Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) GANESHA: Jurnal Pengabdian Masyarakat Journal of Science Nusantara Journal Automation Computer Information System (JACIS) Informatics, Electrical and Electronics Engineering Biner : Jurnal Ilmiah Informatika dan Komputer JAMU : Jurnal Abdi Masyarakat UMUS Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Sains Sistem Informasi
Abd. Charis Fauzan
Program Studi Ilmu Komputer, Fakultas Ilmu Eksakta, Universitas Nahdlatul Ulama Blitar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Journal : Informatics, Electrical and Electronics Engineering

 1 
Comparative Analysis of Machine Learning Models for Identifying Cybercrimes in Social Media Comments Fauzan, Abd. Charis; Arifin, Mochammad; Mafula, Veradella Yuelisa
Jurnal Teknik Elektro dan Informatika Vol 4 No 2 (2024): INFOTRON
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/infotron.v4i2.23069

Abstract

The rapid growth of social media has created opportunities for digital interaction but has also introduced challenges, particularly in addressing cybercrimes such as defamation, threats, and SARA-related content. Cybercrime detection on social media is critical as it helps mitigate the spread of harmful behavior, safeguard users, and support law enforcement in addressing violations like Indonesia's Information and Electronic Transactions Law (UU ITE). This study conducts a comparative analysis of machine learning algorithms—Naive Bayes, Support Vector Machines (SVM), and Random Forests—to identify cybercrimes in social media comments. Using a sentiment-labeled dataset obtained from Kaggle, consisting of Indonesian social media comments from Twitter (X), the comments are categorized into seven specific classes: Neutral Sentiment, Positive Sentiment, Negative Sentiment, Insulting Government, Insulting or Defaming Others, Threatening Others, and SARA-Based Content. The results show that Random Forest achieved the highest overall accuracy (91%) and performed best in detecting moderately represented classes such as Insulting Government. SVM demonstrated robust performance with 88% accuracy, particularly excelling in identifying dominant classes like Negative Sentiment, while Naive Bayes, though computationally efficient, struggled with minority classes, achieving an accuracy of 73%. However, the dataset's imbalance posed challenges for all algorithms, particularly with underrepresented categories. This limitation underscores the need for more diverse and representative datasets to improve model performance and ensure broader applicability of the findings.
Leveraging BiLSTM for Deep Learning-Based Mental Health Chatbots Agustina, Nur Afnis; Fauzan, Abd. Charis; Harliana, Harliana
Jurnal Teknik Elektro dan Informatika Vol 5 No 1 (2025): INFOTRON
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/infotron.v5i1.23242

Abstract

The high prevalence of mental health issues and limited access to professional information and support have driven the search for innovative solutions. One promising approach is the development of chatbot systems that provide quick and accessible mental health information. This study evaluates the performance of the Bidirectional Long Short-Term Memory (BiLSTM) algorithm in identifying and classifying user inputs within a mental health chatbot system. BiLSTM is chosen for its ability to process sequential data in both directions, allowing it to capture context more effectively than unidirectional models and better understand user intent. Deep learning methods like BiLSTM have also demonstrated higher accuracy compared to traditional machine learning models. This study focuses solely on BiLSTM to evaluate its performance in this context. The mental health dataset used in this study was sourced from previous research published on the GitHub platform and contains 100 classes of mental health-related questions and statements. This dataset was used to train the BiLSTM model to recognize user intent and generate relevant responses. The model achieved 98% accuracy on the training data. For evaluation on the test set, a confusion matrix was used, yielding an accuracy of 82%. The chatbot is implemented as a web-based application using a Python framework and is designed to provide users with insights and knowledge through text-based interactions. These results highlight the potential of the BiLSTM-based chatbot system to deliver effective and efficient mental health information services
Co-Authors A. Zainul Muttaqin Sph Achmad Nuuril Faizin Adhitya Prayoga Permana Afivatu Pratama Agustin Afrida Danar Pratama Agus Yulianto Agustin Ely Rahayu Agustin Ely Rahayu Agustina, Nur Afnis Ahmad Agung Saputra Ahmad Fayyadh Qaimul Haq Ahmad Gufron Ahmad Makhi Ahmad Makhi Ahmad Yunus Ainafatul Nur Muslikah Alvi Durunnafis Annisa Heparyanti Safitri Ardisca Evanandy Asfilia N. Anggraini Asna Andi Auladi Ayu Eviana Baqi, Utrodus Said Al Bharin Rizqi Waridhon Binti Aulatul Mufida Bunga Cahyaning Untari Caska - Chandra Gunawan Choirunnufatul Chusna Citra Mirna Wati Defy Lukbatul Qolbiah Deny Restyo Nugroho Dewana Firman Abdul Mu'izz Dewi Lestari Dimas Fahmi Rizaldi Dwi Danu Handoyono Dwi Zulva Uliunuha Dyah Ayu Wiranti Edoardo Jofan Rifano Edy Prabowo Egy Nadya Etvin Trio Sagita Fadhila Nur Hanifah Faizin Choirul Umam Fatmawati, Fariza Uma FATRA NONGGALA PUTRA Fiqih Ainul Qhabib Gunawan Gunawan Hardiana Riski Riswanto Harliana Harliana Harliana Harliana Harliana Harliana Hinayu Diniatul Fahma Huda Maariful Muhamat Ibrohim Aqimuddin Ida Ayu Putu Sri Widnyani Ike Wahyu Septiani Ilham Kurniawan Imam Machfud Indana Nuril Hidayah Indera Cahyo Wibowo Iqbal Fikri Al hadi Iza Arfiana Fauziah Jayanti Galuh Condrokirono Junaedi Abdillah Kafi, Elfiya Khanatul Kasanah, Siti Uswatun Khamaida Safinah Kharisma Sabbihatul Mustaghfaroh Khasanah, Dhea Lutfiatul Khoiril Hikmah Khoiril Hikmah Khoiril Hikmah Kurnia Siwi Kinasih Kurnia Z. Matondang Kurniyatul Ainiyah Lestariningsih Lestariningsih Lestariningsih Linda Salma Angreani M Maulana Ikhsan M. Subhan Ansori Machmud Naufal Mafula, Veradella Yuelisa Marshella Dwi Putri Yustiana Maskuri, Muhammad Naja Mochamad Buqori Muslim Mochammad Arifin Mochammad Rizqy Pratama Moh Afif Rofiqi Moh Bagus Sholikul Adib Muhamat Maariful Huda Muhamat Maariful Huda Muhammad Ammarullah Ridho Muhammad Asfa Ilhami Muhammad Holili Muhammad Nasyithul Ibad Muhammad Rizky Ramadhan Muhammad Yusuf Ashari Muhammat Maariful Huda Munziah Ahmad Nadila Oktavia Ningtias Naela Nur Choiriyah Nahdiyah, Umi Nanang Zamroji Nanik Yustia Neni Febiani Nurul Aziz Tri Wahyuni Panky Yoga Pratama Prabowo, Tito Puan Maharani Kurniawan Putri Handayani Raden Mohamad Herdian Bhakti Rahmat Yanu Sutrisno Redhitya Wempi Ansori Riska Fitri Nur Alifah Rouf, M. Abd. Safrinadi Ilham Salma Fatia Salnan Sabdo Wibowo Salsabella Elizzah Sari, Hetty Elvina Seta Murdha Pamungkas Setyawan, Dimas Ari Shinta Rizki Firdina Sugiono Shofiyatun Najah Shofiyatun Najah Subana Subana Sugeng Wahyudi Suraji Suraji Syarif Alqoroni Tamaulina Br Sembiring Tito Prabowo Toto Ricky Fernando Tricahyo, Vion Age Utrodus Said Al Baqi Veradella Yuelisa Mafula Veradella Yuelisa Mafula Wasimin Wasimin Winda Puji Larasati Yayang Galuh Nur Khamidatullailiyah Yeni Ratih Pratiwi Zainun Nasikin