Article Per Year (5 Year)
p-Index From 2021 - 2026
4.177
P-Index
This Author published in this journals
All Journal International Journal of Evaluation and Research in Education (IJERE) Jurnal Pendidikan Karakter Cakrawala Pendidikan AL KAUNIYAH Biosfer: Jurnal Tadris Biologi Mozaik Humaniora Al-Jabar : Jurnal Pendidikan Matematika Biosfer: Jurnal Pendidikan Biologi Jurnal SOLMA Voices of English Language Education Society JPBIO (Jurnal Pendidikan Biologi) Jurnal Abdi: Media Pengabdian Kepada Masyarakat Bubungan Tinggi: Jurnal Pengabdian Masyarakat Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi dan Sains Teknologi Journal of Applied Data Sciences Bioscientist : Jurnal Ilmiah Biologi Scriptura: Jurnal Ilmiah Komunikasi Innovative: Journal Of Social Science Research MDP Student Conference Jurnal Edukasi Biologi Prosiding Temu Ilmiah Nasional Guru Journal of Environment and Sustainability Education Biodik: Jurnal Ilmiah Pendidikan Biologi AL QUWWAH : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi, Manajemen dan Teknologi Informasi
Meilinda Meilinda
English Department, Faculty of Letters, Petra Christian University, Surabaya, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Applied Data Sciences

 1 
Sentiment and Emotion Classification Model Using Hybrid Textual and Numerical Features: A Case Study of Mental Health Counseling Ramayanti, Indri; Hermawan, Latius; Syakurah, Rizma Adlia; Stiawan, Deris; Meilinda, Meilinda; Negara, Edi Surya; Fahmi, Muhammad; Ghiffari, Ahmad; Rizqie, Muhammad Qurhanul
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.632

Abstract

Mental health issues among individuals, particularly in counseling contexts, require practical tools to understand and address emotional states. This study explores the application of machine learning models for emotion detection in mental health counseling conversations, focusing on four algorithms: Bernoulli Naive Bayes, Decision Tree, Logistic Regression, and Random Forest. The dataset, derived from transcribed counseling sessions, underwent preprocessing, including stemming, stopword removal, and TF-IDF vectorization to create structured inputs for classification. Emotional categories such as "Depresi" (Depression), "Kecewa" (Dissapointed), "Senang" (Happy), "Bingung" (Confused) and "Stres" (Stress) were analyzed to evaluate model performance. Results indicated that Logistic Regression achieved the highest accuracy at 82%, showcasing its reliability and scalability, followed closely by Random Forest with 81%, demonstrating robustness in handling complex data structures. Bernoulli Naive Bayes performed competitively at 80%, excelling in computational efficiency, while Decision Tree recorded the lowest accuracy at 70%, reflecting its limitations in managing overlapping features and high-dimensional data. These findings highlight the potential of machine learning in addressing the increasing demand for scalable mental health support tools. The study underscores the importance of model selection, balanced datasets, and feature engineering to improve classification accuracy. Future work includes developing AI-driven chatbots for real-time emotion detection and integrating multimodal data to enhance interpretability. This research contributes to advancing automated solutions for mental health care, offering new pathways for timely and personalized interventions.
Co-Authors Adeng Slamet Afifa, Mardiah Agustarina, Mitta Agustianda, Selpiana Agustio Dwitama Ahmad Ghiffari Amelia, Novela Amizera, Suzy Andri Wijaya Anggraini, May Liza Anggraini, Nike Ani, Neng Ari Syahidul Shidiq Aswandi, Megi Budiharto Budiharto Cecil Hiltrimartin Cynthia Sari Sunjaya Dariyani, Nuriz Dede Trie Kurniawan Deris Stiawan Dwitama, Agustio Edi Surya Negara Efendi, Doki Elvira Destiansari FRANKY, FRANKY Hapizah Haryanto, Muhammad Ravianda Aditya Ida Sriyanti Ishak, Nor Asniza Ismail, Gunawan Ismet Ismet, Ismet Iswari, Rosada Dwi Jermi A. Pello, Jermi A. Julian Masidin, Nevin Juliana, Fitri Kamiliani, Kamiliani Kemimaro, Fathannah Ketang Wiyono Khalifah, Nur Khoiron Nazip, Khoiron Khoiron, Nazip Kodri Madang, Kodri Latius Hermawan Maisa, Kamilah Nada Mardhiyah, Sayang Ajeng Mardiani, Tri Marisya Pratiwi Marlina Ummas Genisa Masidin, Nevin Julian Mirna Fitrani MUHAMMAD FAHMI Muhammad Qurhanul Rizqie Muhti, Rahmadillah Nainggolan, Septri Monalisa Natami, Rehan Tri Nazip, Khiron Nur Fadhilah Nyimas Aisyah Pratama, Ilham Puspita Sari Puteri, Anisya Sefina Rachmania, Setyaningsih Rahmadhani, Sakilah Rahmi Susanti Ramayanti, Indri Ratih Puspa Ratu Ilma Indra Putri Retno Cahya Mukti Rita Inderawati Riyanto Riyanto Rizma Adlia Syakurah Rohmawati, Shofi Sari, Indah Karunia Sary Silvhiany Somakim, Somakim Sukardi, Rendi Restiana Sumah, Astrid SW Syamsiar, Syamsiar Tibrani, Masagus Muhammad Trilestari, Kuntum Wahyu Indra Bayu Wardhan, Utari Christya Wibagso, Stefanus Setyo Wibawa, Satrya Winti Ananthia Wulandari, Ratu Mutiara Yenita . Yenny Anwar Yoane M. Trianida, Yoane M. Zammardi, Syakhain Zulkardi Zulkardi