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p-Index From 2021 - 2026
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All Journal Bulletin of Electrical Engineering and Informatics Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science
Farooq, Umar
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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A Wirelessly Controlled Robot-based Smart Irrigation System by Exploiting Arduino Hassan, Ahmed; Abdullah, Hafiz Muhammad; Farooq, Umar; Shahzad, Adil; Asif, Rao Muhammad; Haider, Faisal; Rehman, Ateeq Ur
Journal of Robotics and Control (JRC) Vol 2, No 1 (2021): January
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.2148

Abstract

In recent years, because of the limitations of fossil fuels and emissions resulting from the use of photovoltaic cells increase. Due to the changing state of the sun, solar cells must follow the sun's radiation to receive more energy. But, in this research, the modeling and analysis of the solar tracking system were carried out to obtain the optimal angle in photovoltaic systems for generating maximum power using genetic algorithm (GA). In this paper, the control system is proposed by the GA genetic algorithm that optimizes the output energy of the PV system by adjusting the spatial angles of the solar panel in both vertical and horizontal axes. In this method, without the need for additional hardware, the optimal panel position angles are calculated by using the Matlab software to capture the most sun and maximize output energy. The main advantage is that the system operates discretely during operation and losses are reduced, as well as in the clouds, solar radiation is received and the output energy rises. The important results of this study can be the system is optimized, the output power of the photovoltaic system in a fixed array mode increases by 15.85%.
Application of the outlier detection method for web-based blood glucose level monitoring system Nurhaliza, Rachma Aurya; Octava, Muhammad Qois Huzyan; Hilmy, Farhan Mufti; Farooq, Umar; Alfian, Ganjar
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7717

Abstract

Recent advancements in biosensors have empowered individuals with diabetes to autonomously monitor their blood glucose levels through continuous glucose monitoring (CGM) sensors. Nevertheless, the data collected from these sensors may occasionally include outliers due to the inherent imperfections of the sensor devices. Consequently, the identification of these outliers is critical to determine whether blood glucose levels deviate significantly from the norm, necessitating further action. This study employs an outlier detection approach based on the 3-sigma method and the interquartile range (IQR), along with the application of the Winsorizing technique to correct the identified outliers. Additionally, a web-based system for visualizing blood glucose levels is developed, utilizing both outlier detection methods. In order to assess the system's performance, two types of testing are conducted: black box testing and load testing. The results of black box testing indicate that all test scenarios operate as anticipated. As for the load testing response times, it is observed that the 3-sigma visualization page loads an average of 606.75 milliseconds faster compared to the IQR visualization page. This study's outcomes are expected to enhance data quality, enhance the precision of analyses, and facilitate more informed decision-making by identifying and addressing extreme data points.
Utilizing association rule mining for enhancing sales performance in web-based dashboard application Teja Nursasongka, Raden Mas; Fahrurrozi, Imam; Oktiawati, Unan Yusmaniar; Taufiq, Umar; Farooq, Umar; Alfian, Ganjar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1105-1113

Abstract

Data is increasingly recognized as a valuable asset for generating new insights and information. Given the importance of data, businesses must always look for ways to get more value from data generated from sales transactions. In data mining, association rule mining is a good standard technique and is widely used to find interesting relationships in databases. Association rule is closely related to market basket analysis to find items that often appear together in one transaction. This study proposes the frequent pattern growth (FP-Growth) algorithm in finding association rules on sales transaction data. Our methodology includes dataset preparation for modeling, evaluation of model performance, and subsequent integration into a web-based platform. We conducted a comparative analysis of the FP-Growth algorithm against the Apriori algorithm, finding that FP-Growth outperformed Apriori in efficiency. Using the same dataset and constraint level, both algorithms produce the same number of frequent itemsets. However, in terms of computation time, FP-Growth excels by taking 2.89 seconds while Apriori takes 5.29 seconds. We integrated trained FP-Growth algorithm into a web-based dashboard application using the streamlit framework. This system is anticipated to simplify the process for businesses to identify customer purchasing patterns and improve sales.
Co-Authors Abdullah, Hafiz Muhammad Alfian, Ganjar Asif, Rao Muhammad Fahrurrozi, Imam Haider, Faisal Hassan, Ahmed Hilmy, Farhan Mufti Nurhaliza, Rachma Aurya Octava, Muhammad Qois Huzyan Oktiawati, Unan Yusmaniar Rehman, Ateeq Ur Shahzad, Adil Taufiq, Umar Teja Nursasongka, Raden Mas