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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Jurnal Ilmiah Kursor 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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Journal : Knowledge Engineering and Data Science

 1 
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. 
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.
Co-Authors Ahmad Afif Supianto Ali Ridho Barakbah Anik Nur Handayani Aulia Akhrian Syahidi Herman Tolle Heru Wahyu Herwanto Katya Lindi Chandrika Muhammad Ainur Ilmy Noprianto, Noprianto Ronny Mardiyanto, Ronny Rosa Andrie Asmara Tsukasa Hirashima