by I Ketut Agung Enriko, Ryan Anugrah Putra, Estananto

Green Intell. Syst. Appl. 2021, 1(1), pp 37-43; https://doi.org/10.53623/gisa.v1i1.40

1880 views
Abstract Chicken farmers in Indonesia are facing a problem as a result of the country's harsh weather conditions. Poultry species are very susceptible to temperature and humidity fluctuations. As a result, an intelligent poultry farm is necessary to intelligently adjust the temperature in the chicken coop. A smart poultry farm is a concept in which farmers may automatically manage the temperature in the chicken coop, thereby improving the livestock's quality of life. The purpose of this research is to develop a chicken coop prototype that focuses on temperature control systems on smart poultry farms via the PID control approach. The PID control method is expected to allow the temperature control system to adapt to the temperature within the cage, thereby assisting chicken farmers in their task. The sensor utilized is a DHT22 sensor with a calibration accuracy of 96.88 percent. The PID response was found to be satisfactory for the system with Kp = 10, Ki = 0, and KD = 0.1, and the time necessary for the system to reach the specified temperature was 121 seconds with a 1.03 % inaccuracy.[...] Read more. Full text

by Yew Fai Cheah

Green Intell. Syst. Appl. 2021, 1(1), pp 12-18; https://doi.org/10.53623/gisa.v1i1.32

396 views
Abstract Chest X-ray images can be used to detect lung diseases such as COVID-19, viral pneumonia, and tuberculosis (TB). These diseases have similar patterns and diagnoses, making it difficult for clinicians and radiologists to differentiate between them. This paper uses convolutional neural networks (CNNs) to diagnose lung disease using chest X-ray images obtained from online sources. The classification task is separated into three and four classes, with COVID-19, normal, TB, and viral pneumonia, while the three-class problem excludes the normal lung. During testing, AlexNet and ResNet-18 gave promising results, scoring more than 95% accuracy.[...] Read more. Full text

by Savitri Amalia, Ibrahim Amyas Aksar Tarigan, Anita Rizkiyani, Catur Apriono

Green Intell. Syst. Appl. 2021, 1(1), pp 1-11; https://doi.org/10.53623/gisa.v1i1.29

554 views
Abstract In Indonesia, E-waste continues to grow rapidly, along with the increasing use of electronic goods such as telecommunications devices, households, offices, etc. Although it can be recycled, only a small portion can be done, and the recycling process is still under minimal control. Most E-waste is categorized as hazardous and toxic material waste. E-waste has a very high hazard impact if it is not recycled properly and correctly, such as polluting, damaging, and endangering the environment. This article uses forecasting of e-waste growth and canalization e-waste in Indonesia. The first data was obtained from EWasteRJ, a social community engaged in e-waste collection. The second data is obtained from questionnaires distributed to 110 respondents, focusing on knowledge and ways of handling E-waste. Using statistical analysis on both data shows that the amount of E-waste in Indonesia continues to increase every year, and public awareness of the dangers of E-waste is increasing.[...] Read more. Full text

by Regina Reine, Filbert H. Juwono, W. K. Wong

Green Intell. Syst. Appl. 2021, 1(1), pp 26-36; https://doi.org/10.53623/gisa.v1i1.42

396 views
Abstract The pandemic of Coronavirus Disease 2019 (COVID-19) has forced the teaching and learning activities to be conducted remotely. Before the pandemic, many academic institutions had offered online distance learning for selected courses. However, in practice, most of these programs were delivered as blended learning programs instead of full-fledged distance learning programs. Distance learning programs faced challenges and limitations in terms of communication, integrity, and interactions compared to the traditional face-to-face teaching and learning method. Despite the challenges and limitations in distance teaching and learning, academic staff is expected to accomplish the same (or better) outcomes than traditional face-to-face teaching and learning. Hence, the distance learning method was not popular with many academic staff and students before the pandemic time. In order to improve the quality of the full distance learning delivery, emerging technologies and more interactive platforms are being developed rapidly. This article discusses the emerging technologies and strategies to make full distance learning or remote education competitive compared to the traditional teaching and learning method. The future potential teaching and learning technology, i.e., digital twins, is also briefly presented.[...] Read more. Full text

by Filbert H. Juwono, Regina Reine

Green Intell. Syst. Appl. 2021, 1(1), pp 19-25; https://doi.org/10.53623/gisa.v1i1.34

1460 views
Abstract The vision towards 6G communication networks demands higher transmission rates, massive amounts of data processing, and low-latency communication. Orthogonal Frequency Division Modulation (OFDM) has been adopted in the current 5G networks and has become one of the potential candidates for the future 6G and beyond communication systems. Although OFDM offers many benefits including high spectrum efficiency and high robustness against the multipath fading channels, it has major challenges such as frequency offset and high Peak-to-Average Power Ratio (PAPR). In order to deal with the increasingly complex communication network, Machine Learning (ML) has been increasingly utilised to create intelligent and more efficient communication network. The role of ML in dealing with frequency offset and high PAPR is discussed in this paper. In addition, ML techniques may be utilized for channel estimation, M2M networks, and biomedical applications. Finally, this paper discusses the challenges and benefits of incorporating ML into OFDM-based communication systems.[...] Read more. Full text