An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage.

Electricity has become the basic need for everyone, from industry to domestic. Today electricity source still depends heavily on fossil fuels that soon will be diminished from the earth in around 50 years. This condition demands us to find the renewable energy to support our everyday life. One of the famous renewable energy sources is from solar, harnessed by energy conversion device named solar cells. Countries like Indonesia are gifted with an abundance of sunlight all the yearlong. The application of solar cells with its photovoltaic (PV) technology harnesses the sunlight and converts it into electricity. Although this technology is emerging very fast, it still has some limitation due to the current PV technology, economic feasibility, and its environmental impacts. Life cycle assessment is the method to analyze and evaluate the sustainability of PV system and its environmental impact. This paper presents literature study of PV system from the cradle to grave, it begins with the material choices (from the first generation and the possibility of the fourth generation), manufacturing process, implementation, and ends it with the after-life effect of PV modules. The result of this study will be the insights look of the PV system application in Indonesia, from the best option of material choice, the best method of application, the energy payback time, and finally the possible after life recycle of PV materials.

An unmanned vehicle can be deployed in a dull area such as a repetitive task of a transport vehicle in a factory setting which can be considered as automated transportation. The current problem faced by the transportation sectors are the reduction of fossil fuel availability, therefore, solar-powered automated transport vehicle is a great alternative. This paper discussed the experimental analysis of solar-powered mobile robot as the prototype for environmentally friendly automated transportation. The solar cell output is used to charge the capacitor banks functioning as the substitute for a battery system. The robot moves from one station to another imitating the stations in a factory scenario. The solar-powered robot is equipped with a voltage sensor to ensure efficient charging time. The charging time was conducted in 5 days to show the charging fluctuation affected by external factors such as weather condition. The highest average charging in a day is 4.96 V during 100.687 Lux of irradiance received by the solar cell. The highest power required during the loaded task is 0.0121 W, and during unloaded is 0.0106 W. The experiment results show that the proposed method is effective for environmentally friendly automated transportation.