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All Journal BERKALA FISIKA Jurnal Pendidikan Matematika dan IPA Jurnal Teknik Kimia USU Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Jurnal Transformatika Biology, Medicine, & Natural Product Chemistry MPI (Media Pharmaceutica Indonesiana) Jurnal Penelitian Pembelajaran Fisika Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Bubungan Tinggi: Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Ideguru: Jurnal Karya Ilmiah Guru Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pijar MIPA Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Adopsi Teknologi dan Sistem Informasi Depik Jurnal Ilmu-Ilmu Perairan, Pesisir dan Perikanan Depik Jurnal Ilmu-Ilmu Perairan, Pesisir, dan Perikanan Unnes Science Education Journal Scientific Journal of Informatics SWAGATI: Journal of Community Service JIPFRI (Jurnal Inovasi Pendidikan Fisika dan Riset Ilmiah) Berita Biologi
Mega Novita
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Journal : Scientific Journal of Informatics

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Mental Health Chatbot Application on Artificial Intelligence (AI) for Student Stress Detection Using Mobile-Based Naïve Bayes Algorithm Mariyana, Ekanata Desi Sagita; Novita, Mega; Nur Latifah Dwi Mutiara Sari
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24307

Abstract

Purpose: This study aims to design and evaluate a chatbot-based artificial intelligence system to identify stress levels in students using the Naïve Bayes classification method. With increasing mental health concerns among students, early stress detection is considered crucial for timely intervention Methods: This study proposes an AI-based chatbot system to detect student stress levels using a comparative approach between Naïve Bayes and Support Vector Machine (SVM) algorithms. A Kaggle dataset with 15 psychological and academic indicators was preprocessed and balanced using SMOTE. Naïve Bayes showed higher accuracy (90%) than SVM (89%). The trained model was deployed via Flask with Ngrok tunneling and integrated into a Flutter mobile app connected to the Gemini AI API for real-time stress screening. This research offers a practical and scalable solution for early mental health detection in students through intelligent chatbot interaction. Result: The findings show that the Naïve Bayes model achieves a classification accuracy of 90%, slightly surpassing the SVM model, which records an accuracy of 89%. Evaluation through ROC and AUC metrics supports the reliability of Naïve Bayes in detecting stress levels. The integrated chatbot offers a responsive and engaging platform for preliminary mental health assessments. Novelty: This research presents a unique contribution by combining AI-driven stress detection with a real-time chatbot interface, offering an accessible and scalable approach to student mental health support. The integration of machine learning models with conversational AI provides an innovative solution for early intervention. Future developments may involve deep learning and more diverse psychological inputs to further improve accuracy and effectiveness.
Ultra-Low-Cost Hybrid OCR–LLM Architecture for Production Grade E-KTP Extraction Saputro, Anjar Tiyo; Herlambang, Bambang Agus; Novita, Mega
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.38200

Abstract

Purpose: The purpose of this study is to be able to avoid limitations of inexpensive ID card data extraction services and preserve privacy, which can simultaneously achieve reliable operation even under an environment with minimum infrastructure, in particular if no dependency on GPU-based servers are required. Method: The proposed approach is a microservice pipeline with three stages: (1) local lightweight pre-processing on devices, (2) Tesseract CPU-based OCR. js, (3) fast text tokenization through a small premature external LLM. The system is developed as TypeScript backend utilizing the Hono framework with all image processing taking place locally in order to keeping user data private. Result: The result of the experimental evaluations with real ID card samples is that the system can run stably in low-performance VPS (1 vCPU, 1 GB RAM) with operation cost approximately IDR 2.5047 per extraction process and its accuracy level is acceptable for use in a production environment. Moreover, the results indicate that system latency is dominated by LLM inference at the cloud. Novelty: The main contribution and novelty of this study is that we demonstrate, for the first time, a cost-effective (privacy-preserving) OCR-LLM hybrid pipeline without demanding expensive GPU models at large scale which makes our system suitable under limited storage and resource constraints on-premises or edge environments in small organizations including micro-SaaS services.
Co-Authors AA Sudharmawan, AA Amanda, Nathasya Gracya Anggarini, Dola Mareta Anggraeni, Cerly Nurlita Anggraini, Natasya Dila Putri Annajah, Abdillah Fathan Generus Aris Tri Jaka Harjanta, Aris Tri Bambang Agus Herlambang, Bambang Burhanuddin, Aan Desi Purwaningsih Dwi Saputro, Nugroho fafa nurdyansyah, fafa Febrian Murti Dewanto Febrihastatiwi, Dinda Fenny Roshayanti Ferdy Semuel Rondonuwu Ghani Nurfiana Fadma Sari Hayat, M. Syaipul Hermana, Rifki Hukmi Hukmi, Hukmi Iffah Muflihati, Iffah Ingsan, Roies Nur Intan Indiati Jubhar Mangimbulude Karyawan Kusumo, Haryo Latifa, Khoiriya Lukman Anugrah Agung Mardikaningrum, Tri Ambarwati Mariyana, Ekanata Desi Sagita Marlina, Dian Maulana, Novan Mawardi, Dimas Putra Muhammad Syaipul Hayat Muzakki, Tsaqif Nilawati, Anita Nofitasari, Shindi Noora Qotrun Nada, Noora Qotrun Nugroho Dwi Saputro Nugroho Dwi Saputro, Nugroho Dwi Nur Khoiri Nur Latifah Dwi Mutiara Sari Paramita, Diva Julia Pramukantoro, Ganet Eko Priyanto, Widodo Puspitasari, Ismi Putri, Cinthiya Ekwinta Renaldy, Ramadhan Rukmi, Pramastuti Adiar Salwa, Faiza Rulla Saputro, Anjar Tiyo Setianingsih, Siti Nurlaela Setyoningsih Wibowo Siti Patonah Siwi, Andini Prabandaru Sri Wahyuni Sutomo Sutomo Ujianti, Rizky Muliani Dwi Utami, Tri Wahyu Wafa, Alfan Najihil Waliyansyah, Rahmat Robi Wibowo, Aries Setyo Yunitha, Emmellia