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All Journal Fidelity : Jurnal Teknik Elektro International Journal Engineering and Applied Technology (IJEAT) Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia
Yufriana Imamulhak
Nusa Putra University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Mechanical Engineering Public Health Social Sciences

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Bitcoin USD Closing Price (BTC-USD) Comparison Using Simple Moving Average And Radial Basis Function Neural Network Methods Muchtar Ali Setyo Yudono; Aryo De Wibowo Muhammad Sidik; Ilman Himawan Kusumah; Anang Suryana; Anggy Pradiftha Junfithrana; Adi Nugraha; Marina Artiyasa; Edwinanto Edwinanto; Yufriana Imamulhak
FIDELITY : Jurnal Teknik Elektro Vol 4 No 2 (2022): Edisi Mei 2022
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/fidelity.v4i2.74

Abstract

Bitcoin is a decentralized electronic money that is not controlled nor insured by a central authority. Because it is still a young system, the price of Bitcoin is extremely unpredictable, making Bitcoin users and investors uneasy. A typical difficulty for investors and traders is predicting the future movement of the value of Bitcoin electronic money based on historical data. Because investors and traders only notice swings in global currency prices and make Bitcoin buy/sell decisions instinctively, they frequently make the erroneous buy/sell decisions. Many investors and traders suffered significant losses as a result of this error. Losses can be reduced by employing an algorithm that predicts the movement of the value of Bitcoin electronic money. Using a comparison of two methodologies, the Simple Moving Average and RBFNN, we will anticipate the closing price of Bitcoin USD (BTC-USD) from January 1, 2021 to January 31, 2021. The results obtained using the simple moving average method MSE = 0.01 percent and MAPE = 36.67 percent, and the results obtained using the RBFNN method MSE = 9.97 x 10-7 and MAPE = 9.97 x 10-5, indicating that the RBFNN method with an accuracy rate of 99.9995 percent is better than the simple moving average method in forecasting the closing price of bitcoin.
EEG-Based Classification of Schizophrenia and Bipolar Disorder with the Fuzzy Method Aryo Sidik; Harurikson Lumbantobing; Anang Suryana; Muchtar Ali Setyo Yudono; Edwinanto; Yudha Putra; Yufriana Imamulhak; Bayu Indrawan
INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT) Vol. 5 No. 2 (2022): November 2022
Publisher : Nusa Putra University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/ijeat.v5i2.68

Abstract

This study demonstrates various fuzzy-based strategies for classifying and diagnosing people with mental illnesses such as schizophrenia and bipolar disorder. The signals collected from 32 unipolar electrodes during non-invasive electroencephalogram analysis were examined to determine their key characteristics. This research uses a sophisticated fuzzy-based radial basis function neural network. Entropy analysis and analysis of variance of other statistical parameters are also used. Three hundred and twelve schizophrenic patients and 105 individuals with bipolar disorder were examined. In contrast to healthy controls, the data indicated that the patients were correctly classified. With close to 96% accuracy, the suggested method outperforms existing machine learning methods, such as support vector machines and k-nearest neighbors. Conclusion: This categorization method will enable the development of highly accurate algorithms to identify and classify various mental illnesses.
Contribution of Disaster Response Team in Assisting Natural Disaster Mitigation in Cianjur Harurikson Lumbantobing; Aryo De Wibowo Sidik; Anggy Pradiftha Junfithrana; Anang Suryana; Handrea Bernando Tambunan; Muchtar Ali Setyo Yudono; Bayu Indrawan; Ilman Himawan Kusumah; Marina Artiyasa; Edwinanto; Yudha Putra; Yufriana Imamulhak
Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Vol. 3 No. 8 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jppmi.v3i8.194

Abstract

This article discusses the contribution of the natural disaster response team in assisting with natural disaster mitigation in Cianjur. The disaster response team consists of several experts in the field of electrical engineering, such as electrical technicians, telecommunications specialists, and electronics experts, who are ready to assist in facing natural disasters in the Cianjur region. The article explains the activities carried out by the disaster response team, ranging from pre-disaster preparedness to post-disaster management. Some of the activities performed by the disaster response team include the development and maintenance of electro-technological infrastructure, training and educating the community about natural disaster mitigation, as well as coordination with relevant agencies in disaster management. The results of these activities indicate that the contribution of the natural disaster response team is crucial in assisting with natural disaster mitigation in Cianjur. With the presence of this team, losses can be reduced, and the recovery of the affected areas can be expedited after a disaster. This article can serve as a reference for relevant parties in enhancing preparedness for facing natural disasters in other regions.
Co-Authors Adi Nugraha Anang Suryana Anggy Pradiftha Junfithrana Aryo De Wibowo Bayu Indrawan Edwinanto Edwinanto Edwinanto Handrea Bernando Tambunan Harurikson Lumbantobing Harurikson Lumbantobing Ilman Himawan Kusumah Marina Artiyasa Muchtar Ali Setyo Yudono Yudha Putra