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All Journal JUITA : Jurnal Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Teknologi Informasi dan Pendidikan Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
I Wayan Rizky Wijaya
Politeknik Negeri Bali
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy 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.
Weighted ANOVA and Mutual Information for Enhanced Intrusion Detection System I Gede Teguh Satya Dharma; I Wayan Rizky Wijaya; I Made Agus Oka Gunawan; Made Pradnyana Ambara
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2448

Abstract

The rapid escalation in the sophistication of network attacks has exposed the limitations of traditional Intrusion Detection Systems (IDS). While machine learning has shown great promise in enhancing IDS performance, its success often hinges on the effectiveness of feature selection. Standard feature selection techniques, however, struggle in cybersecurity applications due to the highly imbalanced nature of network traffic datasets. In such settings, minority attack classes—though critical—are often overshadowed by majority classes, leading to reduced detection of rare intrusions. To address this challenge, we propose a hybrid feature selection framework that integrates Analysis of Variance (ANOVA) and Mutual Information (MI) with a novel class-frequency weighting mechanism. This weighting scheme adjusts the relevance score of each feature according to the distribution of classes, ensuring that features associated with rare attacks are more strongly emphasized during the selection process. We evaluate our method on the UNSW-NB15 dataset using a Support Vector Machine classifier. The results show that our approach achieves substantial gains in recall for underrepresented classes while simultaneously reducing feature dimensionality and maintaining efficiency. By improving the visibility of features tied to minority attacks, the proposed framework provides a more balanced and reliable solution for modern IDS. This contribution advances the detection of rare but impactful threats and highlights a scalable pathway for building more resilient cybersecurity defenses.
Evaluasi Usability Aplikasi Absensi Pegawai Menggunakan System Usability Scale : Studi Kasus di Pemerintah Provinsi Bali I Made Agus Oka Gunawan; I Gede Teguh Satya Dharma; I Wayan Rizky Wijaya
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v15i1.19367

Abstract

This study aims to evaluate the usability of a government employee attendance application using the System Usability Scale (SUS) method. A questionnaire-based survey involving 26 respondents was used in this study. Data were collected through a SUS questionnaire consisting of 10 statements with a five-point Likert scale, then analyzed using the SUS score calculation. This study also mapped SUS items into four usability aspects: effectiveness, efficiency, learnability, and user satisfaction. The results show that the application has a good level of usability and is in the Good category with a Grade B and Acceptable based on a SUS score of 74,62. The results of the measurement of four usability aspects show that the effectiveness aspect obtained a score of 3,13, the learnability aspect obtained a score of 3,03, the efficiency aspect obtained a score of 2,81 and the satisfaction aspect obtained a score of 2,79. These findings indicate that while the application is functionally sound, improvements to the user experience, based on usability, are still needed. The results of this study also provide an empirical basis that can be used as a reference in developing employee attendance applications that are more oriented towards user needs and experience.
Perbandingan Weighted Product Dengan AHP+WP Dalam Penentuan Pegawai Berprestasi Di Universitas XYZ I Made Agus Oka Gunawan; I Wayan Rizky Wijaya; I Gede Teguh Satya Dharma
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3067

Abstract

The selection of outstanding employees is an important aspect of human resource management in higher education, requiring objective and accurate evaluation. This study compares two multi-criteria decision-making methods: Weighted Product (WP) and a combination of Analytic Hierarchy Process with WP (AHP+WP) in ranking high-performing staff at XYZ University. The evaluation uses Mean Absolute Percentage Error (MAPE) to measure the closeness of the method’s results to expert-based manual assessments. WP produced a MAPE of 16.86%, while AHP+WP yielded only 9.17%, indicating higher accuracy. The Wilcoxon signed-rank test resulted in a p-value of 0.0002 (< 0.05), indicating a significant difference. AHP+WP is superior due to its systematic and consistent weighting mechanism, compared to the more subjective WP. These findings align with previous studies recommending hybrid methods. AHP+WP is considered more appropriate for official implementation in employee performance assessment systems to support fair, transparent, and data-driven evaluation.Kata kunci: AHP; Weighted Product; outstanding employee; MAPE; Wilcoxon.   AbstrakPemilihan pegawai berprestasi merupakan bagian penting dalam manajemen sumber daya manusia di perguruan tinggi, yang memerlukan evaluasi objektif dan akurat. Penelitian ini membandingkan dua metode pengambilan keputusan multikriteria: Weighted Product (WP) dan kombinasi Analytic Hierarchy Process dengan WP (AHP+WP) dalam menentukan peringkat pegawai berprestasi di Universitas XYZ. Evaluasi menggunakan Mean Absolute Percentage Error (MAPE) untuk mengukur kedekatan hasil metode dengan penilaian manual oleh pakar. WP menghasilkan MAPE sebesar 16,86%, sementara AHP+WP hanya 9,17%, menunjukkan akurasi lebih tinggi. Uji Wilcoxon signed-rank memberikan nilai p = 0,0002 (< 0,05), mengindikasikan perbedaan signifikan. AHP+WP unggul karena mekanisme pembobotannya yang sistematis dan konsisten, dibandingkan WP yang lebih subjektif. Temuan ini sejalan dengan studi sebelumnya yang merekomendasikan metode hybrid. AHP+WP dinilai lebih layak untuk diimplementasikan secara resmi dalam sistem penilaian pegawai berprestasi, guna mendukung evaluasi kinerja yang adil, transparan, dan berbasis data. 
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