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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 Jurnal E-Komtek Pancaran Pendidikan Enrichment: Journal of Multidisciplinary Research and Development Journal of Information Technology, Software Engineering and Computer Science Journal of Decision Support Systems and Multi-Criteria Decision Making (JODESMA)
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.
Klasifikasi Citra Magnetic Resonance Imaging (MRI) Penyakit Tumor Otak Menggunakan Metode Convolutional Neural Network (CNN) Dengan Arsitektur VGG-16 Dan Xception Melania Erika Andolo; Herman Bedi Agtriadi; Meilia Nur Indah Susanti
Jurnal E-Komtek Vol 10 No 1 (2026)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v10i1.2974

Abstract

rain tumors are critical medical conditions that require early diagnosis to improve treatmentoutcomes. Magnetic Resonance Imaging (MRI) is widely utilized for brain tumor detection due to itsability to produce high-resolution images of soft tissues. Nevertheless, manual interpretation of MRIimages presents several challenges, including time inefficiency and variability among observers. Toaddress these issues, this study applies the Convolutional Neural Network (CNN) approach usingVGG-16 and Xception architectures to classify brain tumor MRI images and to evaluate theirperformance comparatively. The dataset comprises 2,877 MRI images categorized into four classes:glioma tumor, meningioma tumor, pituitary tumor, and no tumor. Preprocessing stages includeresizing images to 224×224 pixels and dividing the dataset into training, validation, and testing setswith a ratio of 80:10:10. Model performance is assessed using accuracy, precision, recall, and F1-scoremetrics. Experimental results indicate that the VGG-16 architecture achieves an accuracy of 92%, whilethe Xception architecture records an accuracy of 91%.
Literature Review: Taksonomi Artificial Intelligence pada Mekanisme Kriptografi Abdurrasyid Abdurrasyid; Meilia Nur Indah Susanti; Indrianto Indrianto; Rima Rizqi Wijayanti
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v4i3.374

Abstract

Meningkatnya ancaman siber dan komputasi kuantum menimbulkan ancaman signifikan terhadap integritas, kerahasiaan, dan ketersediaan data sensitif. Meskipun berbagai skema keamanan terus diajukan, mekanisme kriptografi konvensional mulai menghadapi titik jenuh dalam hal efisiensi, di samping itu pengamanan sirkuit dari kebocoran data di lingkungan terdistribusi masih menjadi tantangan besar. Tujuan utama dari penelitian ini adalah untuk mengetahui perkembangan implementasi kecerdasan artifisial (AI) pada mekanisme kriptografi agar parameter keamanan data dapat dioptimalkan secara dinamis. Systematic Literature Review dilakukan untuk mengklasifikasikan studi-studi penting guna mencapai tujuan tersebut, dari 86 publikasi penelitian yang diambil dari sumber IEEE Xplore, Google Scholar, ScienceDirect, dan lainnya, berdasarkan kriteria inklusi, eksklusi, dan penilaian kualitas, sebanyak 40 penelitian final dipilih dan dianalisis secara mendalam. Penelitian ini mensintesis taksonomi yang memetakan berbagai teknik AI seperti Machine Learning, Deep Learning, dan Adversarial Networks, serta mengevaluasi 22 risiko data spesifik dan strategi mitigasinya di setiap domain kriptografi modern seperti Homomorphic Encryption dan Post-Quantum Cryptography, serta peluang riset di masa depan dalam domain implementasi AI pada mekanisme kriptografi. Hasilnya, 38,46% menyatakan bahwa penerapan AI dalam teknik kriptografi diimplementasikan dalam lingkungan cloud. Selain itu, 37,5% menyatakan tantangan keamanan dalam integrasi AI adalah beban komputasi, penumpukan noise FHE dan ambiguitas black-box AI itu sendiri. Temuan dari penelitian ini membantu para praktisi dan pengembang sistem keamanan siber dalam meningkatkan ketahanan kerangka keamanan data, serta mengoptimalkan efisiensi komputasi enkripsi secara adaptif
Application of Multiple Linear Regression (MLR) Method in Certification Activities at ITCC ITPLN Hendra Jatnika; Luqman Luqman; Meilia Nur Indah Susanti; Petra Andriyani Mulyo Wibisono; Mulya Jefri
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.
Text-to-speech on health monitoring bracelet for the visually impaired Indrianto Indrianto; Abdurrasyid Abdurrasyid; Meilia Nur Indah Susanti; Arief Ramadhan
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.v12i6.5369

Abstract

Text-to-speech (TTS) is a technology that converts text into sound using a phonetization system and is especially useful to be applied to blind aids who need information in the form of sound because of their limitations. To help the visually impaired know their health conditions where the visually impaired feel limited to going out during the pandemic. For this reason, it is necessary to make an aid that can read text-based data such as body temperature, heart rate per minute, and oxygen levels into a voice that can be heard by the blind, the method used in this study is finite state automata (FSA) which is used to split Indonesian words into words according to its syllable patterns and facilitate the pronunciation process which is included in the blind aids so that it is expected to help the visually impaired to be able to find out their health condition. In this study, the test was carried out using the confusion matrix method and the results obtained were 100% accurate, 99.71% accuracy of temperature sensor, 98% accuracy of heart rate, and 95% accuracy of oxygen saturation.
Integrating LODECI Weighting and ALPAS Methods for Raw Material Supplier Selection in the Textile Industry Pritasari Palupiningsih; Meilia Nur Indah Susanti; Herman Bedi Agtriadi
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 2 (2027): March 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i2.8

Abstract

The selection of raw material suppliers in the textile industry is a complex challenge because it involves various criteria, such as price, delivery timeliness, material quality, color consistency, and production capacity. Subjective decisions or those based on unstructured data often lead to inaccuracies, unfairness, and supply chain disruption risks. This study aims to present an objective and systematic approach by integrating the LODECI method for criteria weighting and ALPAS for alternative ranking. The LODECI method extracts criteria weights rationally from variations in supplier performance, minimizing the influence of subjectivity, while ALPAS combines the criteria weights with the performance data of alternatives to generate final scores and rank suppliers comprehensively. This study involved eleven actual suppliers as alternatives, with performance data reflecting real operational conditions. The ranking results show that alternative A8 has superior and stable performance, followed by A10 and A4, while the sensitivity analysis indicates that changes in criteria weights of ±0.05 do not cause significant shifts in rankings, confirming the model's robustness. These findings demonstrate that the integration of LODECI and ALPAS can enhance the objectivity, consistency, and accountability of supplier decision-making, supporting supply chain efficiency, product quality, and the overall competitiveness of the textile industry.
Co-Authors Abdurrasyid, Abdurrasyid Arief Ramadhan Asri, Yessy Aziza, Rosida Nur Hendra Jatnika Herman Bedi Agtriadi, Herman Bedi Indrianto Indrianto Luqman Luqman Melania Erika Andolo Mulya Jefri Nurul Dyah Budiana Palupiningsih, Pritasari Petra Andriyani Mulyo Wibisono Purwanto, Yudhi Purwati Putri J. Rakhmat Arianto Riki Ruli A. Siregar, Riki Ruli A. Rima Rizqi Wijayanti Siswipraptini, Puji Catur