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All Journal WIDYAKALA JOURNAL JSAI (Journal Scientific and Applied Informatics) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences
Purnama, Denny Ganjar
Lembaga Penelitian Dan Pengabdian Kepada Masyarakat UPJ
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Analisa Tingkat Kematangan Teknologi Informasi Domain Acquire & Implement : Studi Kasus Universitas Pembangunan Jaya Purnama, Denny Ganjar
WIDYAKALA: JOURNAL OF PEMBANGUNAN JAYA UNIVERSITY Vol 6, No 1 (2019): Urban Development & Urban Lifestyle
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat UPJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (558.437 KB) | DOI: 10.36262/widyakala.v6i1.142

Abstract

Pemanfaatan Teknologi Informasi (TI) yang optimal sangat penting dalam upaya mencapai tingkat kinerja yang maksimal. Untuk meningkatkan kinerja, diperlukan perencanaan pemerolehan atau penerapan layanan TI yang baik. Tingkat pemerolehan dan penerapan layanan TI yang bersifat ad-hoc dan memiliki celah yang cukup besar dengan expected level pada best practice COBIT 4.1 hanya akan menyebabkan terjadinya pemborosan semata. Penelitian yang bersifat deskriptif ini bertujuan untuk mendapatkan gambaran bagaimana kondisi tata kelola pada proses kegiatan TI di Universitas Pembangunan Jaya yang dijadikan sebagai tempat studi kasus. Evaluasi dan analisa dilakukan untuk menganalisis dan mengetahui pemerolehan dan penerapan TI yang digunakan. Evaluasi dan analisa menggunakan framework COBIT 4.1. Domain yang digunakan adalah Acquire & Implement (AI), karena domain ini mengelola pemerolehan dan penerapan TI termasuk pengelolaan perubahannya. Tujuan dari penelitian ini adalah untuk mengevaluasi dan menjadikan hasil analisa sebagai masukan untuk memperbaiki pengelolaan TI yang berjalan pada institusi tersebut.Kata Kunci : Tata Kelola TI, COBIT 4.1, Acquire & Implement
Enhancing Apple Leaf Disease Detection with Deep Learning: From Model Training to Android App Integration Santoso, Cahyono Budy; Singadji, Marcello; Purnama, Denny Ganjar; Abdel, Saimam; Kharismawardani, Aqila
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.507

Abstract

This study presents an innovative approach to enhance apple leaf disease detection using deep learning by comparing three models: ReXNet-150, EfficientNet, and Conventional CNN (ResNet-18). The objective is to identify the most accurate and efficient model for real-world deployment in resource-constrained environments. Utilizing a dataset of 1,730 high-quality images, the models were trained using transfer learning, achieving significant results. ReXNet-150 outperformed other models with an F1-score of 0.988, precision of 0.989, and recall of 0.989. EfficientNet and ResNet-18 demonstrated competitive performances with F1-scores of 0.966 and 0.977, respectively. The integration of the ReXNet-150 model into a TensorFlow Lite-based Android application ensures real-time detection, enabling farmers and researchers to capture or upload images for immediate classification. The findings highlight ReXNet-150's robustness, achieving a test accuracy of 98.9% and minimal misclassification, making it ideal for practical agricultural applications. The novelty lies in bridging advanced deep learning with mobile deployment, addressing real-world constraints. Future work could extend this framework to multi-crop disease detection and real-time video analysis, providing scalable solutions for precision agriculture.
The Analisis Penerapan Tata Kelola TI menggunakan COBIT 5.0 dan COBIT 2019 Purnama, Denny Ganjar; Rufman Iman Akbar
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8417

Abstract

The rapid development of information technology (IT) has affected many sectors, including higher education. Universitas Pembangunan Jaya (UPJ) implemented two versions of COBIT, COBIT 5.0 and COBIT 2019, to improve their IT management. This research aims to compare the effectiveness of the two versions in supporting IT management at UPJ. The research method used is a qualitative approach with a case study involving in-depth interviews, direct observation, and documentation analysis. The data obtained was analyzed using thematic and comparative approaches. The results showed that COBIT 2019 is more flexible, allows customization to the specific conditions of the university, and improves collaboration between departments. Meanwhile, COBIT 5.0 tends to be more rigid and less adaptive to rapid changes. This research concludes that COBIT 2019 is more effective in IT management at UPJ, as it provides greater flexibility in the management of IT resources and response to environmental changes.
Analisis Prediktif Faktor Kematian Balita di Bandung menggunakan Logistic Regression, Random Forest, dan XGBoost Kharismawardani, Aqila; Purnama, Denny Ganjar
TIN: Terapan Informatika Nusantara Vol 6 No 6 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i6.8594

Abstract

The Under-Five Mortality Rate (UFMR) is a crucial issue in Indonesia that requires data-driven interventions. This study aims to develop a predictive model to identify the most influential risk factors for under-five mortality in Bandung City and to compare the performance of three machine learning algorithms. This research utilizes secondary data from the Bandung City Open Data portal for the period 2019-2021. The method employed is a comparative analysis of Logistic Regression, Random Forest, and XGBoost. To address the significant class imbalance in the data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data. The evaluation results show that all three models achieve high accuracy, however, performance on the minority calss (mortality cases) remains challenging, indicated by low F1-scores (0.12 for Random Forest and 0.17 for XGBoost). Nonetheless, the feature importance analysis from the Random Forest model successfully identified 'other causes' (penyebab_LAIN-LAIN), 'fever' (penyebab_DEMAM), and the availability of healthcare professionals (PERAWAT, BIDAN) as the most significant predictors. This study highlights the insight from feature importance in identifying risk factors in imbalanced medical data, providing a basis for more targeted health policy recommendations.
Co-Authors Abdel, Saimam Kharismawardani, Aqila Marcello Singadji Rufman Iman Akbar Santoso, Cahyono Budy