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All Journal Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of Advances in Intelligent Informatics Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah WIDYA LAKSANA SABDAMAS Science in Information Technology Letters International Journal of Robotics and Control Systems Applied Engineering and Technology Bulletin of Social Informatics Theory and Application Journal of Artificial Intelligence in Architecture
Andri Pranolo
Universitas Ahmad Dahlan
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Public Health Social Sciences Other

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Journal : Knowledge Engineering and Data Science

 1 
Parallel Approach of Adaptive Image Thresholding Algorithm on GPU Adhi Prahara; Andri Pranolo; Nuril Anwar; Yingchi Mao
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/um018v4i22021p69-84

Abstract

Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational times to deal with high-resolution images. The approach involves parallel reduction and parallel prefix sum (scan) techniques to optimize the calculation. The proposed approach was tested on various sizes of grayscale images. The result shows that the parallel implementation of three adaptive image thresholding methods on GPU achieves 4-6 speeds up compared to the CPU implementation, reducing the computational time significantly and effectively dealing with high-resolution images. 
Neural Network Classification of Brainwave Alpha Signals in Cognitive Activities Ahmad Azhari; Adhi Susanto; Andri Pranolo; Yingchi Mao
Knowledge Engineering and Data Science Vol 2, No 2 (2019)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (666.504 KB) | DOI: 10.17977/um018v2i22019p47-57

Abstract

The signal produced by human brain waves is one unique feature. Signals carry information and are represented in electrical signals generated from the brain in a typical waveform. Human brain wave activity will always be active even when sleeping. Brain waves will produce different characteristics in different individuals. Physical and behavioral characteristics can be identified from patterns of brain wave activity. This study aims to distinguish signals from each individual based on the characteristics of alpha signals from brain waves produced. Brain wave signals are generated by giving several mental perception tasks measured using an Electroencephalogram (EEG). To get different features, EEG signals are extracted using first-order extraction and are classified using the Neural Network method. The results of this study are typical of the five first-order features used, namely average, standard deviation, skewness, kurtosis, and entropy. The results of pattern recognition training show that 171 successful iterations are carried out with a period of execution of 6 seconds. Performance tests are performed using the Mean Squared Error (MSE) function. The results of the performance tests that were successfully obtained in the pattern test are in the number 0.000994.
Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction Andri Pranolo; Yingchi Mao; Aji Prasetya Wibawa; Agung Bella Putra Utama; Felix Andika Dwiyanto
Knowledge Engineering and Data Science Vol 5, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i12022p53-66

Abstract

Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, temperature, cumulated wind speed, hours of snow, and hours of rain. The deep learning network inputs consist of three different scenarios: daily, weekly, and monthly. The results show that the proposed M-1 with three hidden layers produces the best results of RMSE and MAPE compared to the proposed M-2, M-3, and all the baselines. A recommendation for air pollution management could be generated by using these optimized models.
Exploring LSTM-based Attention Mechanisms with PSO and Grid Search under Different Normalization Techniques for Energy demands Time Series Forecasting Pranolo, Andri; Zhou, Xiaofeng; Mao, Yingchi; Pratolo, Bambang Widi; Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Ba, Abdoul Fatakhou; Muhammad, Abdullahi Uwaisu
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p1-12

Abstract

Advanced analytical approaches are required to accurately forecast the energy sector's rising complexity and volume of time series data.  This research aims to forecast the energy demand utilizing sophisticated Long Short-Term Memory (LSTM) configurations with Attention mechanisms (Att), Grid search, and Particle Swarm Optimization (PSO). In addition, the study also examines the influence of Min-Max and Z-Score normalization approaches in the preprocessing stage on the accuracy performances of the baselines and the proposed models. PSO and Grid Search techniques are used to select the best hyperparameters for LSTM models, while the attention mechanism selects the important input for the LSTM. The research compares the performance of baselines (LSTM, Grid-search-LSTM, and PSO-LSTM) and proposes models (Att-LSTM, Att-Grid-search-LSTM, and Att-PSO-LSTM) based on MAPE, RMSE, and R2 metrics into two scenarios normalization: Min-Max, and Z-Score. The results show that all models with Min-Max normalization have better MAPE, RMSE, and R2 than those with Z-Score. The best model performance is shown in Att-PSO-LSTM MAPE 3.1135, RMSE 0.0551, and R2 0.9233, followed by Att-Grid-search-LSTM, Att-LSTM, PSO-LSTM, Grid-search-LSTM, and LSTM. These findings emphasize the effectiveness of attention mechanisms in improving model predictions and the influence of normalization methods on model performance. This study's novel approach provides valuable insights into time series forecasting in energy demands.
Co-Authors ., Suparman AA Sudharmawan, AA Abdalla, Modawy Adam Ali Achmad Fanany Onnilita Gaffar Adhi Prahara Adhi Prahara Adhi Susanto Afief Akmal Afiqa, Nurul Agung Bella Putra Utama Agus Dianto Agus Salim Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Albas, Juan Alin Khaliduzzaman Andiko Putro Suryotomo Anton Satria Prabuwono Anton Yudhana Azhari, Ahmad Azlan, Faris Farhan Ba, Abdoul Fatakhou Bambang Widi Pratolo Camargo, Jair Dani Fadillah Elhindi, Mohamed Fachrul Kurniawan Fadhilla, Akhmad Fanny Felix Andika Dwiyanto Firdaus, Nalendra Firdaus, Nalendra Putra Ghazali, Ahmad Badaruddin Hanafi Hanafi Hariyanti, Nunik Heni Pujiastuti Heri Pramono Hoz, César De La Ismail, Amelia Ritahani Khadir, Mohammed Tarek Leonel Hernandez Leonel Hernandez, Leonel Mao, Yingchi Mirghani, Abdelhameed Mokhtar, Nur Azizah Mohammad Muhammad, Abdullahi Uwaisu Nanang Fitriana Kurniawan Nathalie Japkowicz Nisa, Syed Qamrun Noormaizan, Khairul Akmal Nor Amalina Abdul Rahim Nuril Anwar Nuryana, Zalik Omar, Abdalwahab Omer, Abduelrahman Adam Onie Yudho Sundoro Paramarta, Andien Khansa’a Iffat Prayitno Prayitno Rafal Drezewski Rafał Dreżewski Roman Voliansky Saifullah, Shoffan Sarina Sulaiman Sarina Sulaiman Seno Aji Putra Setyaputri, Faradini Usha Snani, Aissa Sri Winiarti Sularso Sularso, Sularso Suparman Supriadi Supriadi Taqwa Hariguna Tedy Setyadi Triono, Alfiansyah Putra Pertama Uriu, Wako Utama, Agung Bella Putra Wilis Kaswijanti Yingchi Mao Yingchi Mao Yingchi Mao Yingchi Mao Zhou, Xiaofeng