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All Journal JUITA : Jurnal Informatika Jurnal Teknologi Informasi dan Pendidikan
I Wayan Rizky Wijaya
Politeknik Negeri Bali
Computer Science & IT Control & Systems Engineering Education Engineering

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Agile-Based Field Internship Information System for Academic Administration Digitalization Made Pradnyana Ambara; I Made Agus Oka Gunawan; I Wayan Rizky Wijaya
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1069

Abstract

This study presents the development of a web-based internship management information system (PKL) aimed at improving the efficiency, transparency, and accountability of internship administration and supervision processes in the Department of Information Technology, Politeknik Negeri Bali. The system was developed using the Agile methodology to allow iterative and adaptive development, supported by the CodeIgniter 4 framework for lightweight and modular implementation. Core system features include online registration, internship letter submission, document uploads, supervision logs, and lecturer monitoring tools, all integrated to facilitate collaboration among students, supervisors, and administrators. The system’s performance was evaluated using black-box testing, which confirmed valid results across all major functional requirements, ensuring technical reliability. Furthermore, a usability assessment using the System Usability Scale (SUS) involving 20 respondents produced a score of 84.50, categorized as excellent, indicating high user satisfaction, ease of use, and efficiency. These findings demonstrate that the system not only fulfills its functional objectives but also enhances the digital transformation of vocational academic services, offering a replicable and scalable model for other educational institutions aiming to modernize their internship management processes.
Classification Brain Tumor in HyperparameterOptimization of VGG-16 Model and Data Augmentation Analysis Putu Desiana Wulaning Ayu; I Gede Teguh Satya Dharma; I Wayan Rizky Wijaya; Made Agus Oka Gunawan; Ni Putu Eka Apriyanthi; Civica Moehaimin Dhewanty
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.27675

Abstract

This Advancements in computational technology have driven the development of Deep Learning, particularly Convolutional Neural Networks (CNN), in the classification and recognition of digital images. This research focuses on the classification of MRI brain tumor images using the VGG-16 architecture. The primary challenges include gradient vanishing and overfitting due to a small dataset. The objective of the study is to evaluate the performance of the model with various data augmentation techniques and to assess the impact of different dataset compositions (90:10 and 70:30) for training and testing. Two model configurations are used: Model A with 4096 neurons and Model B with 128 and 64 neurons in the first two Dense layers, respectively. The tested augmentation techniques include rotation, flip, Zoom , and their combinations. The results indicate that rotation and Zoom augmentations provide the best performance for both models and dataset compositions. Model A (90:10) achieved an accuracy of 96% with rotation and 92% with Zoom, while Model B (90:10) achieved 94% with rotation and 98% with Zoom. For the 70:30 composition, Model A achieved 94% (rotation) and 90% (Zoom ), while Model B achieved 95% (rotation) and 96% (Zoom ). This research provides valuable insights into optimizing VGG-16 architecture for brain tumor classification using limited datasets.
Co-Authors Civica Moehaimin Dhewanty I Gede Teguh Satya Dharma I Made Agus Oka Gunawan Made Pradnyana Ambara, Made Pradnyana Ni Putu Eka Apriyanthi Putu Desiana Wulaning Ayu