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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
EEG Signal Classification Using Low Temporal Information in Virtual Reality Environments I Made Artha Agastya; Robert Marco; Mohamad Firdaus
Intechno Journal : Information Technology Journal Vol. 6 No. 1 (2024): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i1.1698

Abstract

Purpose: This research systematically compares the performance of K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM) in recognizing emotional and cognitive states from EEG data in a virtual reality (VR) environment. It aims to identify the model with the highest accuracy for each participant. Methods: EEG data were collected from four channels (TP9, AF7, AF8, TP10) with a data range of 0.0 - 1682.815 µV and a sampling rate of 2 Hz. The sampling rate is shallow compared to the standard EEG datasets. Features extracted included statistical measures (mean, standard deviation, skewness, kurtosis) and Hjorth parameters (activity, mobility, complexity), classifier (SVM, RF, KNN). Each classifier’s performance was evaluated using accuracy, indicating the proportion of correctly classified instances. Result: RF achieved the highest average accuracy but showed more significant variability. SVM demonstrated a high median accuracy with consistent performance, as indicated by a narrow interquartile range (IQR) and few outliers. KNN exhibited the lowest median accuracy and highest variability, suggesting sensitivity to data characteristics and parameters. These findings highlight RF’s potential for consistent performance with careful tuning and SVM’s reliability. Novelty: The research’s novelty lies in its personalized performance analysis, evaluating each model’s accuracy individually for participants. This tailored approach reveals the best-performing model for each person, emphasizing customized machine-learning applications in VR-EEG systems. The study’s detailed, participant-specific evaluation enhances emotion and cognitive state recognition precision, advancing individualized VR therapeutic interventions and cognitive research methodologies.
EEG Emotion Recognition using Deep Neural Network (DNN) in Virtual Reality Environments I Made Artha Agastya; Robert Marco; Dini Oktarina Dwi Handayani
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1903

Abstract

Purpose: The purpose of this study is to explore the integration of EEG technology with virtual reality (VR) systems to enhance therapeutic interventions, improve cognitive state recognition, and develop personalized immersive experiences. Specifically, it investigates the classification of EEG signals in a VR environment using machine learning models and identifies the most effective methods for individual-level analysis.Methods: The study utilized EEG data collected from 31 participants using the Muse 2016 headset, with electrodes positioned according to the 10-20 international system. EEG signals were analyzed for features such as statistical metrics (mean, median, standard deviation, skewness, and kurtosis) and Hjorth parameters (activity, mobility, complexity). Machine learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM), were evaluated for their performance in classifying emotional and cognitive states in a VR environment. Result: The results indicate that the Deep Neural Network (DNN) outperformed SVM and KNN models, achieving the highest average classification accuracy. SVM demonstrated consistent performance, with accuracy values consistently above 0.8 across subjects, while KNN showed greater variability and lower overall performance. DNN's architecture, incorporating two hidden layers with ReLU activation and a softmax output layer, demonstrated superior capability in modeling complex EEG patterns. The findings emphasize the effectiveness of DNN in handling high-dimensional and non-linear data, particularly for multi-class classification tasks.Novelty: This study is novel in its focus on personalized machine learning model performance in a VR-EEG setup. Instead of a one-size-fits-all approach, it emphasizes individualized analysis, identifying the most effective model for each participant.
Co-Authors Alva Hendi Muhammad Dini Oktarina Dwi Handayani I Made Artha Agastya Mohamad Firdaus Nur Aini Viorel Andriy Zico Yana Hendriana