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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Data and Science SKANIKA: Sistem Komputer dan Teknik Informatika
Jullev Atmadji, Ery Setiyawan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Mathematics

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PENERAPAN HORIZONTAL POD AUTOSCALER DAN REDIS CLUSTER BERBASIS KUBERNETES UNTUK MENINGKATKAN PERFORMA WEBSITE ELEARNING Gurohman, Diki Taufik; Susanto, Bekti Maryuni; Hariyanto, Agus; Jullev Atmadji, Ery Setiyawan; Gumilang, Mukhamad Angga; Antika, Elly; Mukhlisoh, Nanik Anita
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 7 No 2 (2024): Jurnal SKANIKA Juli 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v7i2.3211

Abstract

Elearning is a very vital tool in learning today. To provide optimal service, elearning servers require quite large computing resources when a large number of users access them simultaneously. However, expensive computing resources such as CPU, memory and disk storage make it difficult for organizations to meet the needs of large users. Previous research compared the performance of two public clouds on a moodle-based learning management system. The results showed that backup and restore times increased by about 10 seconds for every additional 500 MB of data size. This research aims to apply Kubernetes-based horizontal pod autoscaler and Redis cluster on the Moodle elearning server. Moodle is used to run elearning and Redis as database memory which can improve website performance. Horizontal implementation of pod autoscaler and Redis cluster was able to increase the performance of the Moodle e-learning website by 4.3% compared to a monolithic approach. Research shows that implementing Kubernetes and Redis clusters can improve the performance of Moodle e-learning websites. This research also shows that the microservice approach has better performance compared to the monolithic approach..
Evaluating the Performance of Voting Classifier in Multiclass Classification of Dry Bean Varieties Adi Pratama, I Putu; Jullev Atmadji, Ery Setiyawan; Purnamasar, Dwi Amalia; Faizal, Edi
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.124

Abstract

This study explores the application of a voting classifier, integrating Decision Tree, Logistic Regression, and Gaussian Naive Bayes models, for the multiclass classification of dry bean varieties. Utilizing a dataset comprising 13,611 images of dry bean grains, captured through a high-resolution computer vision system, we extracted 16 features to train and test the classifier. Through a rigorous 5-fold cross-validation process, we assessed the model's performance, focusing on accuracy, precision, recall, and F1-score metrics. The results demonstrated significant variability in the classifier's performance across different data subsets, with accuracy rates fluctuating between 31.23% and 96.73%. This variability highlights the classifier's potential under specific conditions while also indicating areas for improvement. The research contributes to the agricultural informatics field by showcasing the effectiveness and challenges of using ensemble learning methods for crop variety classification, a crucial task for enhancing agricultural productivity and food security. Recommendations for future research include exploring additional features to improve model generalization, extending the dataset for broader applicability, and comparing the voting classifier's performance with other ensemble methods or advanced machine learning models. This study underscores the importance of machine learning in advancing agricultural classification tasks, paving the way for more efficient and accurate crop sorting and grading processes.
Face recognition using haar cascade classifier and FaceNet (A case study: Student attendance system) Maryuni Susanto, Bekti; Surateno, Surateno; Jullev Atmadji, Ery Setiyawan; Pramulintang, Ardian Hilmi; Apriliano, Galuh; Wulansari, Tanti; Angga Gumilang, Mukhamad
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp272-284

Abstract

Face recognition is increasingly widely utilised, and there are numerous face recognition systems. Face recognition is typically utilised for attendance on e-learning platforms in the field of education. The haar cascade classifier is one method for face identification; it is used to identify facial areas. Faces are classified using an alternative model, FaceNet. In this research, we purposefully designed an e-learning platform that authenticates students based on face recognition. Based on the findings of this investigation, the system can accurately recognise faces. Ten students were evaluated based on their participation in two attendance trials. Successful presence has an achievement success value of 19, and 1 failed out of a total of 20 attempts. Several variables, such as illumination, and the use of marks on hats, that could have influenced attendance caused the experiment to fail.
Co-Authors Adi Pratama, I Putu Agus Hariyanto Angga Gumilang, Mukhamad Apriliano, Galuh Bekti Maryuni Susanto Edi Faizal Elly Antika, Elly Gumilang, Mukhamad Angga Gurohman, Diki Taufik Maryuni Susanto, Bekti Mukhlisoh, Nanik Anita Pramulintang, Ardian Hilmi Purnamasar, Dwi Amalia Surateno, Surateno Wulansari, Tanti