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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Intechno Journal : Information Technology Journal
Robert Marco
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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EVALUASI KEMATANGAN TATA KELOLA INFRASTRUKTUR TI MENGGUNAKAN COBIT 2019 DAN AHP UNTUK ROADMAP TRANSFORMASI DIGITAL (STUDI KASUS: STMIK PONTIANAK): EVALUATION OF IT INFRASTRUCTURE GOVERNANCE MATURITY USING COBIT 2019 AND AHP FOR A DIGITAL TRANSFORMATION ROADMAP (CASE STUDY: STMIK PONTIANAK) Viorel Andriy Zico; Robert Marco
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7831

Abstract

Digital transformation is crucial for an institution so it requires the support of good technology governance, so that all operations can run effectively. STMIK Pontianak is a university that wants to carry out digital transformation, addressing the problems of unintegrated information systems, limited infrastructure capacity, suboptimal system change control, and immature knowledge documentation. The purpose of this study is to illuminate the maturity level of IT infrastructure governance using the COBIT 2019 framework, then the results are explained again using the MCDM method, namely AHP, and the results of the AHP will be used to create a digital transformation roadmap, which is used as a guide so that STMIK Pontianak can carry out digital transformation in a directed manner. This study uses a descriptive approach, and from the results of the initial analysis and based on initial data received from interviews and questionnaires, as well as identification of design factors, the capability level of STMIK Pontianak is at level 1.57, which is included in the repeatable stage. The processes with the lowest capabilities consist of BAI04 Managing Availability and Capacity, BAI06 Managing Change, and BAI08 Managing Knowledge. The AHP analysis indicates that the main focus for improvement is BAI04 with a weighting value of 0.521, followed by BAI06 with a weighting value of 0.312 and BAI08 with a weighting value of 0.167. Based on these findings, the digital transformation plan is structured in three phases: a short phase to increase infrastructure capacity, a medium phase to control change and system integration, and a long phase for knowledge management and continuous improvement. This study proves that the use of a combination of COBIT 2019 and AHP can be an effective strategic foundation to support digital transformation in higher education institutions.  
An Advanced Deep Learning Approach for Automatic Disease Recognition and Classification in paddy leaf disease detection Robert Marco; Alva Hendi Muhammad; Nur Aini; Yana Hendriana
Intechno Journal : Information Technology Journal Vol. 7 No. 2 (2025): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i2.2482

Abstract

Purpose: Accurate detection of paddy leaf diseases is essential to ensure optimal crop yield and effective disease management. Methods/Study design/approach: In this study, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and an Attention mechanism for paddy leaf disease classification using the Paddy Doctor dataset. The CNN layers extract spatial features from leaf images, the LSTM captures contextual relationships between these features, and the Attention mechanism emphasizes the most relevant patterns for accurate classification. Result/Findings: Experimental results show that the proposed CNN+LSTM+Attention model achieves 95.5% accuracy, 98.12% precision, 98.3% recall, and 0.994 macro AUC, outperforming a simple CNN-3 layer while offering competitive performance compared to state-of-the-art architectures such as ResNet34 and Xception. Novelty/Originality/Value: These results demonstrate that the proposed model is highly effective in detecting paddy leaf diseases with minimal false negatives, providing a reliable and practical solution for automated paddy disease monitoring systems
Co-Authors Alva Hendi Muhammad Nur Aini Viorel Andriy Zico Yana Hendriana