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All Journal Jurnal Ilmiah Kursor Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology Knowledge Engineering and Data Science IJEE (International Journal of Engineering Education)
Kohei Arai
Saga University
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Recognition of Handwritten Javanese Script using Backpropagation with Zoning Feature Extraction Anik Nur Handayani; Heru Wahyu Herwanto; Katya Lindi Chandrika; Kohei Arai
Knowledge Engineering and Data Science Vol 4, No 2 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i22021p117-127

Abstract

Backpropagation is part of supervised learning, in which the training process requires a target. The resulting error is transmitted back to the units below in its training process. Backpropagation can solve complicated problems because it consumes less memory than other algorithms. In addition, it also can produce solutions with a low error rate while executing less time. In image pattern recognition, backpropagation can be utilized for cultural preservation in many places worldwide, including Indonesia. It is used to recognize picture patterns in Javanese script writings. This study concluded that feature extraction approaches, zoning, and backpropagation could be utilized to distinguish handwritten Javanese characters. The best accuracy is attained at 77.00%, with the network architecture comprising 64 input neurons, 40 hidden neurons, a learning rate of 0.003, a momentum of 0.03, and an iteration of 5000. 
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.
Centronit: Initial Centroid Designation Algorithm for K-Means Clustering Ali Ridho Barakbah; Kohei Arai
EMITTER International Journal of Engineering Technology Vol 2 No 1 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v2i1.17

Abstract

Clustering performance of the K-means highly depends on the correctness of initial centroids. Usually initial centroids for the K- means clustering are determined randomly so that the determined initial centers may cause to reach the nearest local minima, not the global optimum. In this paper, we propose an algorithm, called as Centronit, for designation of initial centroidoptimization of K-means clustering. The proposed algorithm is based on the calculation of the average distance of the nearest data inside region of the minimum distance. The initial centroids can be designated by the lowest average distance of each data. The minimum distance is set by calculating the average distance between the data. This method is also robust from outliers of data. The experimental results show effectiveness of the proposed method to improve the clustering results with the K-means clustering.Keywords: K-means clustering, initial centroids, Kmeansoptimization.
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.
Traffic Density Prediction using IoT-based Double Exponential Smoothing Rosa Andrie Asmara; Noprianto Noprianto; Muhammad Ainur Ilmy; Kohei Arai
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p168-178

Abstract

The number of vehicles and currents that tend to increase causes traffic density. A system is proposed to calculate the number of vehicles and predict real-time traffic density. This research uses Haar Cascade to detect the number of cars and motorcycles and the Double Exponential Smoothing (DES) for forecasting the number of vehicles on the road. MAPE describes forecasting accuracy as a base for selecting the best smoothing constant (Alpha). The best test results from June 13 to 20, 2020, are cars on June 14, 2020 (alpha 0.5, MAPE 0%) and Motorcylecycles on June 18, 2020 (alpha 0.5, MAPE 0.1134% ). The most significant MAPE results of the car were on June 15, 2020, with alpha 0.5 and MAPE 2.1073%. The 3 minutes haar cascade detects 72.58% of cars and 81.90% of motorcycles.
Detecting Objects Using Haar Cascade for Human Counting Implemented in OpenMV Mustika Mentari; Rosa Andrie Asmara; Kohei Arai; Haidar Sakti Oktafiansyah
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 2 (2023): July (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i2.3175

Abstract

Sight is a fundamental sense for humans, and individuals with visual impairments often rely on assistance from others or tools that promote independence in performing various tasks. One crucial aspect of aiding visually impaired individuals involves the detection and counting of objects. This paper aims to develop a simulation tool designed to assist visually impaired individuals in detecting and counting human objects. The tool's implementation necessitates a synergy of both hardware and software components, with OpenMV serving as a central hardware device in this study. The research software was developed using the Haar Cascade Classifier algorithm. The research process commences with the acquisition of image data through the OpenMV camera. Subsequently, the image data undergoes several stages of processing, including the utilization of the Haar Cascade classifier method within the OpenMV framework. The resulting output consists of bounding boxes delineating the detection areas and the tally of identified human objects. The results of human object detection and counting using OpenMV exhibit an accuracy rate of 71%. Moreover, when applied to video footage, the OpenMV system yields a correct detection rate of 73% for counting human objects. In summary, this study presents a valuable tool that aids visually impaired individuals in the detection and counting of human objects, achieving commendable accuracy rates through the implementation of OpenMV and the Haar Cascade Classifier algorithm.
Co-Authors Ahmad Afif Supianto Ali Ridho Barakbah Anik Nur Handayani Aulia Akhrian Syahidi Haidar Sakti Oktafiansyah Herman Tolle Heru Wahyu Herwanto Katya Lindi Chandrika Muhammad Ainur Ilmy Mustika Mentari Noprianto, Noprianto Ronny Mardiyanto, Ronny Rosa Andrie Asmara Rosa Andrie Asmara Tsukasa Hirashima