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All Journal 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.
Co-Authors Aboadla, Ali Aboadla, Ezzidin Hassan Alghifari, Muhammad Fahreza Habaebi, Mohamed H. Hamida, Belal A. Muhammad Sadikin Nordin, Mimi Aminah binti Wan Rika Rosnelly, Rika Roslina Roslina, Roslina Sheroz Khan Vedenyapin, Dmitry Wanayumini Yaacob, Mashkuri Bin