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All Journal Mechatronics, Electrical Power, and Vehicular Technology Jurnal Teknologi Informasi dan Ilmu Komputer
Yuwana, Raden Sandra
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder Yuliani, Asri Rizki; Pardede, Hilman Ferdinandus; Ramdan, Ade; Zilvan, Vicky; Yuwana, Raden Sandra; Amri, M Faizal; Kusumo, R. Budiarianto Suryo; Pramanik, Subrata
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.905

Abstract

Using lithium-ion (Li-ion) batteries exceeding their useful lifetime may be dangerous for users, and hence, developing an accurate prediction system for batteries that remain useful for life is necessary. Many deep learning models, such as gated recurrent units and long short-term memory (LSTM), have been proposed for that purpose and have shown good results. However, their performance when dealing with noisy data degrades significantly. This may hamper their implementations for the real world since battery data are prone to noise. In this paper, we develop a robust prediction model in a noisy environment for predicting the remaining useful life (RUL) of Li-ion batteries. We propose a denoising autoencoder (DAE) utilized to remove noise from the data. The DAE is built with convolutional layers instead of traditional feed-forward networks here. We combine DAE with LSTM as the predictor. The proposed framework is evaluated using artificially corrupted battery data provided by National Aeronautics and Space Administration (NASA). The results reveal that our proposed method improves robustness when data contain various types of noise. A comparative study using the traditional approach has also been conducted. Our evaluation shows that convolutional layers are more effective than the traditional approach and that the original composition of the DAE was built using traditional feed-forward networks. DAE with convolutional layers has the best average performance with MSE of 0.61 and is the most consistent model.
Distracted driver behavior recognition using modified capsule networks Kadar, Jimmy Abdel; Dewi, Margareta Aprilia Kusuma; Suryawati, Endang; Heryana, Ana; Zilfan, Vicky; Kusumo, Budiarianto Suryo; Yuwana, Raden Sandra; Supianto, Ahmad Afif; Pratiwi, Hasih; Pardede, Hilman Ferdinandus
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.177-185

Abstract

Human activity recognition (HAR) is an increasingly active study field within the computer vision community. In HAR, driver behavior can be detected to ensure safe travel. Detect driver behaviors using a capsule network with leave-one-subject-out validation. The study was done using CapsNet with leave-one-subject-out validation to identify driving habits. The proposed method in this study consists of two parts, namely encoder and decoder. The encoder used in this study modifies Sabour’s capsule network architecture by adding a convolution layer before going to the primary capsule layer. The proposed method is evaluated using a primary dataset with 10 classes and 300 images for each class. The dataset is split based on hold-out validation and leave-one-subject-out validation. The resulting models were then compared to conventional CNN architecture. The objective of the research is to identify driving behavior. In this study, the proposed method results an accuracy rate of 97.83 % in the split dataset using hold-out validation. However, the accuracy decreased by 53.11 % when the proposed method was used on a split dataset using leave-one-subject-out validation. This is because the proposed method extracts all features including the attributes of each participant contained in the input image (user-independent). Thus, the resulting model in this study tends to overfit.
Rekomendasi Pengembangan Fasilitas Wisata Tugu Pahlawan Surabaya Melalui Visualisasi Dashboard Hasil Klasifikasi Analisis Sentimen Ulasan Pengunjung Mahardika, Fawwaz Roja; Supianto, Ahmad Afif; Setiawan, Nanang Yudi; Yuwana, Raden Sandra; Suryawati, Endang
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 2: April 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022925655

Abstract

Tugu Pahlawan Surabaya merupakan salah satu pariwisata andalan Kota Surabaya yang selalu berupaya memperhatikan ulasan pengunjung sebagai acuan evaluasi. Namun, pengelola tidak memiliki teknologi yang mampu mengumpulkan, mengolah, dan menganalisis seluruh data ulasan yang dapat menghasilkan informasi secara ringkas. Salah satu solusi dapat dilakukan melalui analisis sentimen pada level aspek terhadap aspek edukasi, fasilitas, kebersihan, pelayanan, dan umum dengan penyajian informasi dalam bentuk dashboard. Analisis sentimen dilakukan menggunakan Support Vector Machine terhadap 2180 data ulasan selama 2 tahun terakhir yang diambil dari Google Review. Ulasan terbanyak terdapat pada aspek fasilitas sebanyak 538 ulasan dengan sebaran sentimen 285 ulasan positif, 95 ulasan negatif, dan 158 ulasan netral. Rekomendasi berdasarkan kekuatan dan kelemahan saat ini adalah penyediaan lahan atau objek foto bernuansa sejarah pahlawan secara lebih nyata serta penyediaan ventilasi terbuka atau standing cooler di beberapa area. Berdasarkan confusion matrix, nilai F1-Score menjadi penentu seberapa baik model mengklasifikan data daripada nilai Accuracy dikarenakan dataset yang dimiliki bersifat imbalance sehingga kesalahan prediksi pada precision atau recall sangat memungkinkan terjadi. Kesalahan prediksi banyak ditemukan pada kelas sentimen netral. Keseluruhan hasil klasifikasi disajikan dalam bentuk dashboard dengan nilai SUS Score 77,5, menandakan bahwa dashboard dapat diterima dengan baik oleh responden sebagai pengguna. Abstract Tugu Pahlawan Surabaya is one of the mainstays of tourism in Surabaya city which always tries to pay attention to visitor reviews as a reference for evaluation. However, the managers do not have technology capable of collecting, processing and analyzing all review datas that can produce information in a concise manner. One solution can be done through sentiment analysis at the aspect level of education, facilities, cleanliness, service, and general aspects by presenting information in the form of a dashboard. Sentiment analysis was carried out using the Support Vector Machine on 2180 review datas for the last 2 years taken from Google Reviews. The most reviews were on the facility aspect in total of 538 reviews with a sentiment distribution of 285 positive reviews, 95 negative reviews and 158 neutral reviews. Recommendations based on current strengths and weaknesses are providing more real area or photo objects with historical nuances of heroes and providing open ventilation or standing coolers in several areas. Based on the confusion matrix, the F1-Score value determines how well the model classifies data rather than the Accuracy value because the dataset is imbalance so that prediction errors in precision or recall are very possible. Prediction errors are more likely to be found in the neutral sentiment class. The overall classification results are presented in the form of a dashboard with a SUS Score of 77.5, indicating that the dashboard is well received by respondents
Co-Authors Ade Ramdan Ahmad Afif Supianto Amri, M Faizal Dewi, Margareta Aprilia Kusuma Heryana, Ana Kadar, Jimmy Abdel Kusumo, Budiarianto Suryo Kusumo, R. Budiarianto Suryo Mahardika, Fawwaz Roja Nanang Yudi Setiawan Pardede, Hilman Ferdinandus Pramanik, Subrata pratiwi, hasih Suryawati, Endang Vicky Zilvan, Vicky Yuliani, Asri Rizki Zilfan, Vicky