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All Journal Jurnal Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science International Journal of Electrical, Energy and Power System Engineering (IJEEPSE)
Dirman Hanafi
Universiti Tun Hussein Onn Malaysia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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NEURAL NETWORK BASED SYSTEM IDENTIFICATION OF AN AXIS OF CAR SUSPENSION SYSTEM Hanafi, Dirman; bin Rahmat, Mohd. Fua?ad
Jurnal Teknik Elektro Vol 8, No 1 (2008): MARET 2008
Publisher : Institute of Research and Community Outreach

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (185.079 KB) | DOI: 10.9744/jte.8.1.1-7

Abstract

Neural networks system identification have been widely used for estimate the nonlinear model of system. In this paper, multilayer perceptron neural network is used for identifying the Nonlinear AutoRegressive with eXogenous input (NARX) model of a quarter car passive suspension system. Input output data are acquired by driving a car on a special road event. The networks structure is developed based on system model. The Networks learning algorithm is derived using Fisher?s scoring method. Then the Fisher information is given as a weighted covariance matrix of inputs and outputs the network hidden layer. Unitwise Fisher?s scoring method reduces to the algorithm in which each unit estimate its own weights by a weighted least square method. The results show that the method uses suitable for modeling a quarter car passive suspension systems.
Health Monitoring System for Transformer by using Internet of Things (IoT) Hanafi, Dirman; Aziz, Zarkhoni
International Journal of Electrical, Energy and Power System Engineering Vol. 5 No. 1 (2022): International Journal of Electrical, Energy and Power System Engineering (IJEEP
Publisher : Electrical Engineering Department, Faculty of Engineering, Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/ijeepse.5.1.19-23

Abstract

Transformer is an important device in electrical field that used to transfer electricity from one circuit to another with changing the voltage level. The transformer will be having a problem such as increasing in temperature and make a lot of noise. Typical transformer measurement system will take time for testing and less accurate. The aim of this project is to design the IoT system for monitoring and evaluate the performance of the transformer current, sound and temperature. The monitoring system using three types of sensors to sense the current, sound and temperature. ESP32 is being used to keep and process the data before sending to the Blynk application to show the value by using internet. Two different conditions are being tested to the transformer which are transformer without load and with load. Ammeter and ACS712 current sensor are used to get the current value. Besides, for temperature value used two equipment which are using thermometer and MLX90614 temperature sensor. The transformer sound obtained from the sound sensor. The data will be display in Blynk application. The result obtained show the health monitoring system for transformer by using Internet of Things is acceptable because has low error and save time to measure the parameters.
Development of Rechargeable Lawn Mower Hanafi, Dirman; Afrianto, Muhamad Wendi; Kwad, Ayad Mahmood; Wahid, Herman; Ghazali, Rozaimi; Gunardi, Yudhi
International Journal of Electrical, Energy and Power System Engineering Vol. 6 No. 2 (2023): The International Journal of Electrical, Energy and Power System Engineering (I
Publisher : Electrical Engineering Department, Faculty of Engineering, Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/ijeepse.6.2.145-150

Abstract

Lawn mower is device to mow grass. The lawn mower will be having a problem such as the lawn mower widely used around by handle manually with hand. Next, the power source for lawn mowers is from petrol energy. That energy is nonrenewable energy and has bad impacts on the environment and people. In this paper, a prototype of lawn mower is designed and fabricated with its operation movement system through the Smartphone via Wi-Fi connection. The lawn mower is powered using the sealed acid battery that it can charging by using the photovoltaic cell or AC voltage source. The development of rechargeable lawn mower using ESP32 to keep and process data before sending to the webpage IP Address 192.168.4.1 to show the control movement of lawn mower via Smartphone. Based on experimental test results, the lawn mower was able to control its movement via a smartphone through a Wi-Fi connection. Next, the results were revealing the ability of solar panels for 2 hours and AC voltage for 1 hour to fully charged the sealed lead-acid batteries. Finally, a grass trim performance test was carried out on a lawn mower in the yard of the house and the result was that the grass could be cut. After that, the battery consumption of lawn mower operation for 1 hour was obtained the result around 50% of the capacity which is fully discharge because occurs the maximum depth of discharge of sealed lead acid batteries.
Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review Wahyuni, Retno Tri; Hanafi, Dirman; Tomari, M. Razali; Sihabudin Sahid, Dadang Syarif
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1317-1327

Abstract

Spatial modeling is commonly used to map research variables, including particulate matter 2.5 (PM2.5) concentrations, in specific areas. The article that surveys publications on the application of machine learning in spatial modeling of PM2.5 using bibliometric methods has not been identified yet. This paper aims to analyze trends in applying machine learning in the spatial modeling of PM2.5 using bibliometric methods. The review was conducted on publications indexed in the Scopus database over the decade (2014–2023) comprising 335 articles. The analysis included co-authorship and co-occurrence using VOSviewer. From the two stages of analysis, it can be concluded that research on this topic has constantly increased over the past 10 years, with the highest productivity coming from researchers in China. This research topic is multidisciplinary, with most publications appearing in environmental science. The research also shows a very high collaboration rate of 0.98. A deeper examination of the keywords reveals the most commonly used machine learning techniques by researchers. The random forest method is the most frequently found in the analyzed documents, followed by deep learning, long short-term memory (LSTM), extreme gradient boosting (XGBoost), and ensemble model.
Co-Authors Afrianto, Muhamad Wendi Aziz, Zarkhoni bin Rahmat, Mohd. Fua?ad Dadang Syarif Sihabudin Sahid Kwad, Ayad Mahmood Rozaimi Ghazali Tomari, M. Razali Wahid, Herman Wahyuni, Retno Tri Yudhi Gunardi