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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Briliant: Jurnal Riset dan Konseptual JOURNAL OF APPLIED INFORMATICS AND COMPUTING JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Vokasi Informatika (JAVIT) J-Intech (Journal of Information and Technology) Jurnal Teknologi Informasi Cyberku Journal of Computing Theories and Applications Jurnal Informatika Polinema (JIP) Journal of Information System and Application Development Journal of Future Artificial Intelligence and Technologies
Muslikh, Ahmad Rofiqul
Faculty Of Information Technology, Universitas Merdeka, Malang, East Java, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
COMPARATIVE ANALYSIS OF PERFORMANCE AND EFFICIENCY OF LOAD BALANCING ALGORITHMS ON INGRESS CONTROLLER Khamdani, Ahmad Rizal; Muslikh, Ahmad Rofiqul; Affandi, Arif Saivul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4040

Abstract

Kubernetes has become the dominant container orchestration platform in production environments, with the ingress controller playing a critical role in managing external traffic to services within the cluster. This study aims to provide recommendations for optimal load balancing algorithms for Kubernetes production environments by analyzing and comparing the performance of four algorithms namely round robin, static-rr, least connection, and random on the HAProxy ingress controller. The research method is conducted through observation using k6 and Grafana performance test tools, as well as literature studies, with measurements including total requests, throughput, latency, CPU usage, and memory at various levels of user load. The data was analyzed using descriptive statistical techniques, normality test, homogeneity test, and tests for group differences using one-way ANOVA or Kruskal-Wallis H. The results show that static-rr excels in throughput, total requests, and CPU and memory efficiency at high load, while least connection is more effective for latency at low load. Round robin and random showed stable performance at low load but less optimal at high load. The conclusion of this study is that choosing the right load balancing algorithm depends on the load characteristics and desired performance metrics, to ensure optimal Kubernetes performance under various load scenarios in production environments.
RICE DISEASE RECOGNITION USING TRANSFER LEARNING XCEPTION CONVOLUTIONAL NEURAL NETWORK Muslikh, Ahmad Rofiqul; Setiadi, De Rosal Ignatius Moses; Ojugo, Arnold Adimabua
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1529

Abstract

As one of the major rice producers, Indonesia faces significant challenges related to plant diseases such as blast, brown spot, tugro, leaf smut, and blight. These diseases threaten food security and result in economic losses, underscoring the importance of early detection and management of rice diseases. Convolutional Neural Network (CNN) has proven effective in detecting diseases in rice plants. Specifically, transfer learning with CNN, particularly the Xception model, has the advantage of efficiently extracting automatic features and performing well even with limited datasets. This study aims to develop the Xception model for rice disease recognition based on leaf images. Through the fine-tuning process, the Xception model achieved accuracies, precisions, recalls, and F1-scores of 0.89, 0.90, 0.89, and 0.89, respectively, on a dataset with a total of 320 images. Additionally, the Xception model outperformed VGG16, MobileNetV2, and EfficientNetV2.
Co-Authors Achmad Nuruddin Safriandono Akbar, Ismail Amelisa, Eka Puspita Roro Arif Saivul Affandi De Rosal Ignatius Moses Setiadi Delviana Muti Dian Kristiawan Nugroho Edi Subiyantoro Elan, Melania Seindang Fachrudin, Mochamad Rizal Fandi Yulian Pamuji Firnando, Fadel Muhamad Heru Agus Santoso Imanulloh, Satrio Bagus Islam, Hussain Md Mehedul Jehadu, Yustina Vania Ghaisani Jutono Gondohanindijo, Jutono Khamdani, Ahmad Rizal Luh Putu Ratna Sundari Marcus, Ronald David Mardiana Andarwati Mustofa, Fachrul Ojugo, Arnold Adimabua Pradana, Akbar Ganang Pravisya, Raihan Ihza Rizza Muhammad Arief Swalaganata, Galandaru Syahroni Wahyu Iriananda, Syahroni Wahyu Warto - Wijaya, Nantalira Niar Yulian Pamuji, Fandi