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Devi
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Automotive Engineering Computer Science & IT Control & Systems Engineering

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Chinese Script Handwriting Pattern Introduction Application Design with Algorithm CNN-SVM Jacqueline Kwanori; Huliman; Devi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2290

Abstract

The Chinese script has a high level of visual complexity because each character consists of thousands of intricate strokes. This is a big challenge for second-language learners, especially in recognizing the various variations of human handwriting. This study aims to design an accurate and efficient application for the recognition of Chinese handwriting patterns based on Android using a hybrid model of Convolutional Neural Network (CNN) and Support Vector Machine (SVM). In this system, the CNN works like a human eye that distinguishes the details of the shape of an image, while the SVM serves as the brain that decides what characters are being written. The data used in the training process included 7,330 Chinese characters pulled from the Kaggle platform. The results of the study show that the application was successfully designed and able to display character shapes, how to read (pinyin), and the meaning of words offline without the need for an internet connection. Based on testing the Black Box method, all of the app's features are proven to work validly. The study concluded that the use of the CNN-SVM hybrid model was highly effective in recognizing diverse handwriting variations, although the degree of accuracy remained dependent on the clarity of the quality of the images taken by the user.
Student Mental Health Monitoring System Based on Daily Activities with the SVM Method Stella Crystal; Robby Huang; Devi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2299

Abstract

Student mental health is a crucial issue that requires effective and responsive self-monitoring systems. This study aims to develop "LacakJiwa," an Android-based mobile application designed to monitor student mental health through the analysis of daily activity patterns. The method employed is the Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel to classify mental health risks into low and high categories. Input data includes sleep duration, daily step count, gadget usage, and social interaction duration collected from 146 student data entries. The SVM model is integrated into the application using TensorFlow Lite to enable on-device classification, ensuring user privacy through SQLite local database storage. Testing results on 44 test samples showed an accuracy rate of 52.27%, precision of 36.36%, and recall of 22.22%. While the system was successfully implemented technically, the low recall value indicates significant challenges in detecting complex non-linear behavioral patterns in students. This research provides a foundation for developing digital self-control instruments that are adaptive to Indonesian local culture.
Co-Authors Huliman Jacqueline Kwanori Robby Huang Stella Crystal