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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah FIFO Petir Kilat TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri Pancaran Pendidikan Enrichment: Journal of Multidisciplinary Research and Development
Meilia Nur Indah Susanti, Meilia Nur
Informatics Engineering, STT-PLN
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering Physics Social Sciences

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Implementation of Eigenface Method in Improving Security in a Smart Home Systems Abdurrasyid Abdurrasyid; Riki Ruli Afandi Siregar; Indrianto Indrianto; Meilia Nur Indah Susanti
Pancaran Pendidikan Vol 7, No 2 (2018)
Publisher : The Faculty of Teacher Training and Education The University of Jember Jember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (671.647 KB) | DOI: 10.25037/pancaran.v7i2.182

Abstract

Based on data that was extracted from Indonesia Central Bureau of Statistics there were has been theft cases as much as 125.869 times during 2015, consists of Crime against Property / Goods with Violence 11.856 cases, and Crimes against Property / Goods Non-Violent 114.013 cases, theft often occurs in empty homes that no occupants, theft is also common in homes that have security cameras, cameras that were installed cannot provide prevention or warning to homeowners. It can be anticipated if the homeowner gets information about the condition of the house in real time wherever he is. This technology is designed to created smart home system that was integrated by the security method especially in face recognition, Eigenface method as the image processing method used to detect home occupants to avoid thieves, the core of this method is to compare the eigenface value of the captured image with the eigenface value present in the database, the smaller difference between the eigenface training image in the database with the eigenface test face it can be concluded that the image has a higher similarity, greater differences,  will make the system detect that an unknown person is entering the house, and will send a warning message to the homeowner via cell phone about danger that is occurs.
Application of Multiple Linear Regression (MLR) Method in Certification Activities at ITCC ITPLN Jatnika, Hendra; Luqman, Luqman; Indah Susanti, Meilia Nur; Mulyo Wibisono, Petra Andriyani; Jefri, Mulya
Enrichment: Journal of Multidisciplinary Research and Development Vol. 2 No. 12 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v2i12.314

Abstract

Multiple Linear Regression (MLR) is one of the algorithms in Machine Learning. Machine Learning that estimates the linear coefficient equations involved in one or more independent variables that can predict the value of the variable of interest. Algorithms used to predict the value of a variable based on the value of other variables. Based on 2021 data at the Information Technology Certification Center (TCC), it can be seen that the quality and quantity of Microsoft International Certification graduates is decreasing. In the pre-pandemic MOS certification, the percentage of passes was seventy-two percent (72%), while in the MOS certification during the pandemic the percentage of passes dropped to fifty-two percent (52%). Based on the results of the MLR trial test on the dataset of MOS-Word and MCF-AI certification test participants, a calculation formula is obtained as a benchmark in assessing the MOS-Word and MCF AI certification scores. The study provides specific recommendations to optimize certification training programs by tailoring materials to address critical competencies and participant needs. Additionally, a predictive formula developed in this research can serve as a self-assessment tool for participants to evaluate their readiness for certification tests. These findings underscore the potential of MLR as a robust analytical tool for improving certification processes, enhancing training effectiveness, and ensuring better outcomes for participants. This research contributes to advancements in machine learning applications within education and professional development contexts.
Co-Authors Abdurrasyid, Abdurrasyid Asri, Yessy Aziza, Rosida Nur Hendra Jatnika Indrianto Indrianto Jefri, Mulya Luqman, Luqman Mulyo Wibisono, Petra Andriyani Nurul Dyah Budiana Purwanto, Yudhi Purwati Putri J. Rakhmat Arianto Riki Ruli A. Siregar, Riki Ruli A. Siswipraptini, Puji Catur