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All Journal Jurnal Ilmiah Kursor Buletin Ilmiah Sarjana Teknik Elektro IJEE (International Journal of Engineering Education)
Kohei Arai
Saga University
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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EYE-BASED HUMAN-COMPUTER INTERACTION (HCI): A NEW KEYBOARD FOR IMPROVING ACCURACY AND MINIMIZING FATIGUE EFFECT Ronny Mardiyanto; Kohei Arai
Jurnal Ilmiah Kursor Vol 6 No 3 (2012)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Permasalahan penggunaan keyboard dengan kendali mata adalah tingkat akurasi, kecepatan yang rendah, dan kesulitan dalam menggunakan tombol kombinasi. Penggunaan sistem Interaksi Komputer Manusia (IKM) berbasis mata dalam jangka waktu yang lama dapat menyebabkan kelelahan. Pada penelitian ini diusulkan keyboard baru dengan sifat bergerak. Keyboard yang diusulkan terdiri dari dua bagian yaitu bagian utama (bersifat bergerak, dapat digerakkan oleh pengguna menggunakan mata dalam proses pemilihan hurufnya) dan bagian pengendali gerak (terdiri dari lima tombol besar yang transparan, digunakan untuk mengendalikan gerak keyboard bagian utama). Metode pendeteksi keberadaan pengguna digunakan untuk mengurangi kelelahan. Penambahan tombol shortcut pada layout utama memungkinkan pengguna melakukan fungsi khusus. Keyboard baru ini memiliki kelebihan diantaranya memiliki tingkat akurasi yang tinggi, lebih cepat dalam melakukan pengetikan, memiliki ukuran yang lebih kecil, memungkinkan pengguna menggunakan fungsi tombol kombinasi, dan dapat meminimalkan efek kelelahan saat pengguna menggunakan sistem IKM berbasis mata dalam jangka waktu yang lama. Hasil pengujian yang dilakukan membuktikan bahwa keyboard ini memilki tingkat akurasi yang lebih baik (92.26%) dibandingkan keyboard jenis tetap (78.57%). Juga, dalam melakukan pengetikan 14 huruf keyboard ini lebih cepat (134.69 detik) dibandingkan keyboard jenis tetap (210.28 detik). Pada pengukuran efek kelelahan menggunakan alat Electro Enchephalo Graf (EEG), keyboard ini lebih dapat meminimalkan efek kelelahan dibandingkan keyboard jenis tetap. Kata kunci: Keyboard Bergerak, Sistem IKM Berbasis Mata, Akurasi, Kecepatan, Kelelahan. Abstract The current problems of keyboard on eye-based Human Computer Interaction (HCI) are accuracy, typing speed, fatigue, and the use of combination keys. We propose a new keyboard consist of two parts: the moveable layout and the navigator keys (fixed and transparent). The user appearance detection method is used for reducing the fatigue effect. The adding shortcut keys to the main layout allowing user executes a special functions through combination keys. The new keyboard has advantages on high accuracy, fast, allowing combination keys, and could minimize fatigue effect. The experiment results show that the new keyboard could achieve better accuracy (92.26%) compared to the fixed keyboard (78.57%). Also, the new keyboard improved accuracy 134.69% than the fixed keyboard(210.28%) when used for typing fourteen character over eye-based HCI. Moreover, we measured the fatigue effect by using Electro Encephalo Graph (EEG) over both methods and the result shows that the new keyboard could minimize fatigue better than the fixed keyboard. By implementing the new keyboard on real eye-based HCI, user could type characters easily, fastly, and no burdened with fatigue effect.
Interactive M-Learning Media Technology to Enhance the Learning Process of Basic Logic Gate Topics in Vocational School and Engineering Education Aulia Akhrian Syahidi; Herman Tolle; Ahmad Afif Supianto; Tsukasa Hirashima; Kohei Arai
International Journal of Engineering Education Vol 2, No 2 (2020)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (31.847 KB) | DOI: 10.14710/ijee.0.0.%p

Abstract

The process of learning to use smartphones is now highly promoted. Almost everyone has a smartphone. The latest trend in learning is known as Mobile Learning (M-Learning). M-Learning can be used anywhere and anytime. Thus, we propose the use of the M-Learning application for computer system subjects in the basic logic gate topics so that students can be motivated to learn. We call this application BLG-LeMed. The focus of this research is on the process of using BLG-LeMed applications on classroom learning that is used directly by vocational high school students, then testing with alpha testing, User Acceptance Tests (UAT), usability evaluations, and knowing the effect of motivating students to use five dimensions of motivation and student learning outcomes. The development model used is Extreme Programming (XP). The design used in this study, by conducting trials in one class and observing students using the BLG-LeMed application as learning media, 38 students consisted of 26 men and 12 women involved in this study, with a duration of 135 minutes at one time of the meeting. We conclude that using the BLG-LeMed application based on M-Learning in the learning process of this basic logic gate, strongly supported by the testing team, can be accepted by users, has a usefulness as a interactive learning media, can have an effect in motivating students to learn, and provide results very satisfying learning.
Adaptive Feature Selection using Fisher-Based Supervised Hill Climbing for Dysgraphia Handwriting Classification Kartika Candra Kirana; Anik Nur Handayani; Nur Eva; Aji Prasetya Wibawa; Wahyu Nur Hidayat; Kohei Arai
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.14983

Abstract

Dysgraphia features selection remains a challenge. Fisher’s criterion excels at highlighting the discriminative features of dysgraphia but lacks guidance for choosing the optimal number of features. Whereas Hill Climbing shows robust feature selection but often gets trapped in local optima. This study aims to avoid the Hill Climbing trap in local optima when selecting the best dysgraphia feature. Thus, the Fisher-Based Supervised Hill Climbing (FSHC) method is introduced. The contribution of this study is an optimized machine-learning-guided hill-climbing method that uses a classifier on a validation set as the objective function. A plateau mechanism also guided Hill Climbing exploration, not by a single Fisher point but by the neighboring subsets. The dataset used contains the graphomotor slant line task from 119 children aged 8-15 years (47.5% diagnosed with dysgraphia), with 10000 to 50000 data points per user. It is organized into kinematic, spatial, dynamic, and temporal features, yielding 117 sub-features. A stratified 5-fold cross-validation is set for training and testing, reaching 21 features. Comparative test—Linear SVM, SVM RBF, Sigmoid SVM, Polynomial SVM, Random Forest, AdaBoost, KNN, Decision Tree, Gradient Boosting, Gaussian Naive Bayes, and Gaussian Classifier—showed that linear SVM achieves the best performance with a weighted average precision, recall, and F1 score of 0.93. Linear SVM also outperformed the three approaches: no feature selection, the traditional Fisher, and machine-learning-based feature selection (weighted KNN and SVM). It can be concluded that the proposed method is more robust than the state of the art by highlighting key points for avoiding overfitting.
Co-Authors Ahmad Afif Supianto Aji Prasetya Wibawa Anik Nur Handayani Aulia Akhrian Syahidi Herman Tolle Heru Wahyu Herwanto Kartika Candra Kirana Katya Lindi Chandrika Muhammad Ainur Ilmy Noprianto, Noprianto Nur Eva Ronny Mardiyanto, Ronny Rosa Andrie Asmara Tsukasa Hirashima Wahyu Nur Hidayat