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All Journal Cakrawala Pendidikan Harmonia: Journal of Research and Education Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Letters in Information Technology Education (LITE) Journal of Applied Data Sciences Science in Information Technology Letters International journal of education and learning Bulletin of Social Informatics Theory and Application
Dwiyanto, Felix Andika
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Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : knowledge engineering and data science

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Face Images Classification using VGG-CNN Astawa, I Nyoman Gede Arya; Radhitya, Made Leo; Ardana, I Wayan Raka; Dwiyanto, Felix Andika
Knowledge Engineering and Data Science
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Abstract

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image. The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%).
Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction Pranolo, Andri; Mao, Yingchi; Prasetya Wibawa, Aji; Utama, Agung Bella Putra; Dwiyanto, Felix Andika
Knowledge Engineering and Data Science
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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.
Can Multinomial Logistic Regression Predicts Research Group using Text Input? Rosyid, Harits Ar; Putra, Aulia Yahya Harindra; Akbar, Muhammad Iqbal; Dwiyanto, Felix Andika
Knowledge Engineering and Data Science
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While submitting proposals in SISINTA, students often confuse or falsely submit their proposals to the less relevant or incorrect research group. There are 13 research groups for the students to choose from. We proposed a text classification method to help students find the best research group based on the title and/or abstract. The stages in this study include data collection, preprocessing data, classification using Logistic Regression, and evaluation of the results. Three scenarios in research group classification are based on 1) title only, 2) abstract only, and 3) title and abstract. Based on the experiments, research group classification using title-only input is the best overall. This scenario gets the most optimal results with accuracy, precision, recall, and f1-score successively at 63.68%, 64.91%, 63.68%, and 63.46%. This result is sufficient to help students find the best research group based on the text titles. In addition, lecturers can comment more elaborately since the proposals are relevant to the research group’s scope.
Co-Authors Aji Prasetya Wibawa Andri Pranolo Atmaja, Nimas Hadi Citra, Hana Rachma Drezewski, Rafal Eddy Sutadji Hakkun Elmunsyah Harits Ar Rosyid Hariyono Hariyono Herlina Jayadianti Hidhayat, Ahmad Rizkyka Agung Humairoh, Nanda Lailatul I Nyoman Gede Arya Astawa I Wayan Raka Ardana Irawan, Novta Danyel Lauretta, Giovanny Cyntia Mao, Yingchi Muhammad Aris Ichwanto Muhammad Iqbal Akbar Nabila, Khurin Nafalski, Andrew Ningtyas, Yana Nirwana, Nirwana Noviani, Erina Fika Purnama, Ida Bagus Irawan Purnawansyah Purnawansyah Purnomo Purnomo Putra, Aulia Yahya Harindra Rabbimov, Ilyos Radhitya, Made Leo Ramadhan, Sany Putra Saifullah, Shoffan Sutoko, Sutoko Tri Kuncoro Tuti Purwaningsih, Tuti Utama , Agung Bella Putra Utama, Agung Bella Putra Wilis Kaswidjanti Yoto Yoto, Yoto Yudistira, Setya Yusriel Ardian Zaeni, Ilham Ari Elbaith