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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA
Teddy Surya Gunawan
International Islamic University Malaysia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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The Disruptometer: An Artificial Intelligence Algorithm for Market Insights Nordin, Mimi Aminah binti Wan; Vedenyapin, Dmitry; Alghifari, Muhammad Fahreza; Gunawan, Teddy Surya
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v8i2.1494

Abstract

Social media data mining is developing to be a mainstream tool for marketing insights in today’s world, due to the abundance of data and often freely accessed information. In this paper, we propose a framework for market research purposes called the Disruptometer. The algorithm uses keywords to provide different types of market insights from data crawling. The preliminary algorithm data-mines information from Twitter and outputs 2 parameters – Product-to-Market Fit and Disruption Quotient, which is obtained from a brand’s customer value proposition, problem space, and incumbent space. The algorithm has been tested with a venture capitalist portfolio company and market research firm to show high correlated results. Out of 4 brand use cases, 3 obtained identical results with the analysts ‘studies.
A novel optimization harmonic elimination technique for cascaded multilevel inverter Aboadla, Ezzidin Hassan; Khan, Sheroz; Habaebi, Mohamed H.; Gunawan, Teddy Surya; Hamida, Belal A.; Yaacob, Mashkuri Bin; Aboadla, Ali
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v8i2.1500

Abstract

The main goal of utilizing Selective Harmonic Elimination (SHE) techniques in Multilevel Inverters (MLI) is to produce a high-quality output voltage signal with a minimum Total Harmonic Distortion (THD). By calculating N switching angles, SHE technique can eliminate (N-1) low order odd harmonics of the output voltage waveform. To optimized and obtained these switching angles, N of nonlinear equations should be solved using a numerical method. Modulation index (m) and duty cycle play a big role in selective harmonic elimination technique to obtain a minimum harmonic distortion and desired fundamental component voltage. In this paper, a novel Optimization Harmonic Elimination Technique (OHET) based on SHE scheme is proposed to re-mitigate Total Harmonic Distortion. The performance of seven-level H-bridge cascade inverter is evaluated using PSIM and validated experimentally by developing a purposely built microcontroller-based printed circuit board.
Penerapan Data Mining Pada Penerimaan Dosen Tetap Menggunakan Metode Naive Bayes Classifier dan C4.5 Sadikin, Muhammad; Rosnelly, Rika; Roslina, Roslina; Gunawan, Teddy Surya; Wanayumini, Wanayumini
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2434

Abstract

Recruitment is an important step in creating professional HR (Human Resources). The application of classification methods such as the Naïve Bayes method and C4.5 can be used in the classification of potential lecturers and can be accepted by the campus by calculating the equations for each criterion. The difficulty experienced is the ineffective use of the method to generate the required lecturer acceptance so that it is not in accordance with the applicant's expertise. One of the classification methods applied to data mining is the naïve Bayes method and C4.5. The purpose of this study is to determine the level of accuracy of the two methods used by using the Weka 3.8 tool based on the calculation of Correctly Classified Instance and Incorrectly Classified Instance. The accuracy results obtained with the naïve Bayes method are 83.7838% and the C4.5 method is 91.8919% from 37 training data. So the C4.5 method is a more appropriate method to use than naïve Bayes.
Exploring the research trends and development of augmented reality and virtual reality in ASEAN countries: a bibliometric study Hariyanto, Didik; Rafiq, Arif Ainur; Gunawan, Teddy Surya; Quynh, Nguyen Vu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4430-4444

Abstract

This review of Association of Southeast Asian Nations (ASEAN) augmented reality (AR) and virtual reality (VR) studies uses bibliometric analysis and VOSviewer mapping. This study looks at an extensive set of Scopus articles from reliable sources to determine who contributes to ASEAN AR and VR research, the themes, how people work together, and how people cite each other. A study of bibliographies shows that the number of ASEAN AR and VR research articles has grown significantly since 2010. It also talks about important ASEAN study institutions, authors, and countries. The study themes are shown visually on VOSviewer mapping, showing how AR and VR can be used in healthcare, travel, gaming, and business. Co-authorship and reference networks shed light on how people work together on research projects and how ideas move within and outside of ASEAN. This organized review of ASEAN AR and VR research helps researchers, policymakers, and business stakeholders understand the current situation, find research gaps, and work together. The results can change research, resource use, and policy changes to encourage the growth and use of AR and VR technologies in ASEAN. It can lead to more innovation, economic development, and positive social effects.
Machine learning-based pavement crack detection, classification, and characterization: a review Ashraf, Arselan; Sophian, Ali; Shafie, Amir Akramin; Gunawan, Teddy Surya; Ismail, Norfarah Nadia
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5345

Abstract

The detection, classification, and characterization of pavement cracks are critical for maintaining safe road conditions. However, traditional manual inspection methods are slow, costly, and pose risks to inspectors. To address these issues, this article provides a comprehensive overview of state-of-the-art machine vision and machine learning-based techniques for pavement crack detection, classification, and characterization. The paper explores the process flow of these systems, including both machine learning and traditional methodologies. The paper focuses on popular artificial intelligence (AI) techniques like support vector machines (SVM) and neural networks. It underscores the significance of utilizing image processing methods for feature extraction in order to detect cracks. The paper also discusses significant advancements made through deep learning strategies. The main objectives of this research are to improve efficiency and effectiveness in pavement crack detection, reduce inspection costs, and enhance safety. Additionally, the article presents data gathering approaches, various datasets for developing road crack detection models, and compares different models to demonstrate their advantages and limitations. Finally, the paper identifies open challenges in the field and provides valuable insights for future research and development efforts. Overall, this paper highlights the potential of AI-based techniques to revolutionize pavement maintenance practices and significantly improve road safety.
Battery management system employing passive control method Fahmi, Muhamad Aqil Muqri Muhamad; Yusoff, Siti Hajar; Gunawan, Teddy Surya; Zabidi, Suriza Ahmad; Abu Hanifah, Mohd Shahrin
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i1.pp35-44

Abstract

A battery management system (BMS) is essential for maintaining peak efficiency and longevity of rechargeable batteries. Conventional battery management system techniques often struggle to monitor, protect, and particularly have difficulties in balancing batteries. The project proposed has introduced a battery management system that employs passive control techniques to address excess energy and overcome these challenges. In the proposed design, a shunt resistor dissipates surplus energy from lithium-ion battery cells into heat following the proposed BMS design. This passive control technique is economically efficient, uncomplicated, and does not require an external power source. A prototype of the proposed BMS design was tested and was able to accurately monitor the battery, dissipate excess energy, and protect the battery while maintaining the cell charge balance. These findings suggest that the proposed BMS has the potential to improve both the effectiveness and longevity of rechargeable batteries.
Co-Authors Aboadla, Ali Aboadla, Ezzidin Hassan Abu Hanifah, Mohd Shahrin Alghifari, Muhammad Fahreza Ashraf, Arselan Didik Hariyanto Fahmi, Muhamad Aqil Muqri Muhamad Habaebi, Mohamed H. Hamida, Belal A. Ismail, Norfarah Nadia Muhammad Sadikin Nordin, Mimi Aminah binti Wan Quynh, Nguyen Vu Rafiq, Arif Ainur Rika Rosnelly, Rika Roslina Roslina, Roslina Shafie, Amir Akramin Sheroz Khan Sophian, Ali Vedenyapin, Dmitry Wanayumini Yaacob, Mashkuri Bin Yusoff, Siti Hajar Zabidi, Suriza Ahmad