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ILKOM Jurnal Ilmiah
Published by Universitas Muslim Indonesia
ISSN : 20871716     EISSN : 25487779     DOI : -
Core Subject : Science,
Computer Science & IT
ILKOM Jurnal Ilmiah is an Indonesian scientific journal published by the Department of Information Technology, Faculty of Computer Science, Universitas Muslim Indonesia. ILKOM Jurnal Ilmiah covers all aspects of the latest outstanding research and developments in the field of Computer science, including Artificial intelligence, Computer architecture and engineering, Computer performance analysis, Computer graphics and visualization, Computer security and cryptography, Computational science, Computer networks, Concurrent, parallel and distributed systems, Databases, Human-computer interaction, Embedded system, and Software engineering.
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Articles 10 Documents
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Search results for , issue "Vol 17, No 2 (2025)" : 10 Documents clear
Deep Learning Convolutional Neural Networks on Multi Label Image Classification of Torajanese Buffalo Ramadhan, Aslan Poetra; Handayani, Anik Nur; Zaeni, Ilham Ari Elbaith
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2905.162-169

Abstract

Convolutional Neural Networks (CNNs) represent the primary methodology in the advancement of intelligent systems and technologies. The capacity to transition from prediction to categorization establishes CNNs as the primary benchmark in the advancement of deep artificial intelligence. This study use CNN implementation to categorize photos of Torajanese buffalo. The Torajanese buffalo is a distinctive animal species belonging to the Bos bubalis family, integral to the lives and culture of the Torajanese people residing in northern South Sulawesi. This species is integral to the culture, deeply intertwined with several traditional practices of the community. This renders the species distinctive for more investigation. The distinctiveness of the buffalo's style, coloration, and form differentiates them from one another. This study use Convolutional Neural Networks (CNNs) as the primary method to categorize Torajanese buffalo species using head photos and markers derived from local knowledge. This research employs InceptionV3, DenseNet, and Xception as primary architectures, each with variations corresponding to 10, 50, and 100 epochs, therefore enhancing the study. The findings of this investigation indicate that the InceptionV3 architecture has commendable performance across both versions, achieving an average AUC-ROC score of 0.96, although with increased execution time. Nonetheless, the DenseNet architecture demonstrates superior performance in its optimal configuration, achieving flawless accuracy; nonetheless, it incurs the most processing time for the frontal view of the Torajanese buffalo head test case.
Low Power Consumption IoT Weather Monitoring System for Coastal Areas Wiwi, Muhammad Hibrian; Awaluddin, Muhammad; Saharis, Risky
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2456.120-130

Abstract

This research aims to design and develop a real-time weather monitoring system in coastal areas using weather sensors integrated with Internet of Things (IoT) technology. This system wants to provide accurate weather information that fishermen can access directly through web-based platforms or mobile devices. Fishermen can make the right and safe decisions before going to sea, improving fishing activities' safety and efficiency. How to overcome the limitations of traditional weather monitoring methods that are manual, non-real-time, and discontinuous, and how to design systems that can transmit weather data automatically and in real-time in coastal areas that are often difficult to reach by power grids and stable internet. Another essential issue formulated is how to optimize the power consumption of sensor and communication systems so that they can operate efficiently using battery power resources for an adequate period. This study confirms the importance of energy efficiency in battery-based monitoring systems, especially in sensor nodes installed in remote locations without a fixed power source. The test results show the difference between the estimated operating time based on theoretical calculations and the results of simulations or real tests in the field. In idle mode, theoretical calculations estimate the system can last up to about 12.8 hours, but actual simulations show an endurance of about 7.3 hours. Meanwhile, in active mode, the estimated calculation is around 5.5 hours, while the simulation shows a slightly longer endurance, which is about 7.2 hours.
Enhanced Violence Detection in CCTV Using LSTM Hasanudin, Muhaimin; Santoso, Hadi; Wahab, Abdi; Indrianto, Indrianto; Kuswardani, Dwina; Ridlan, Ahmad
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2318.196-202

Abstract

Violence detection in CCTV footage remains a critical challenge for public safety, necessitating automated solutions to overcome human monitoring limitations. This study proposes an LSTM-based framework to improve detection accuracy by analyzing temporal patterns in surveillance videos. Using a dataset of 2,000 videos (1,000 violent/1,000 non-violent), the model extracts spatial-temporal features via optical flow and achieves 93% training accuracy and 91% test accuracy, with a precision of 92% and AUC of 0.94. Results demonstrate significant improvements over traditional methods, particularly in dynamic scenarios, though performance dips for occluded actions or weapon-related violence. The discussion highlights the model’s real-time applicability, computational efficiency (120 ms latency per segment), and alignment with smart city surveillance needs. Limitations include dataset diversity and environmental variability, suggesting future directions in multi-modal data fusion and edge computing. This research advances AI-powered security systems, offering a robust tool for proactive threat detection while underscoring the need for scalable, context-aware solutions.
Sentiment Analysis towards Jokowi Post-Presidential Term Using CNN-BiLSTM with Multi-head Attention on Platform X Setyawan, Muhammad Rizki; Putra, Fajar Rahardika Bahari; Ramadhani, Ardhina
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2843.150-161

Abstract

The development of social media has changed the way the public expresses political opinions, especially regarding the evaluation of President Joko Widodo’s (Jokowi) leadership after his term. Platform X (formerly Twitter) has become the primary source of public opinion data, but the use of informal language and sarcasm makes accurate sentiment analysis challenging. This study creates a sentiment analysis model that uses deep learning with a CNN-BiLSTM structure and a multi-head attention mechanism. The dataset consists of 52,643 tweets that have been labeled and embedded using IndoBERT. To address class imbalance, the SMOTE method was applied to the training data, enabling the model to better learn from minority classes. The results indicate that the model achieves a high accuracy of 98.78%, with an average precision, recall, and F1-score of 0.98. These findings indicate that the model is not only accurate but also reliable in distinguishing each sentiment class. A comparison with other model variants suggests that the complete combination of CNN-BiLSTM and Multi-Head Attention delivers the best performance, although the improvement is relatively small.
SqueezeNet Image Embedding and Support Vector Machine for Recognizing Hand Gestures in Indonesian Sign Language System Islami, Megan Shahra; Jamzuri, Eko Rudiawan
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2476.98-106

Abstract

This research proposes a hand gesture recognition method for the Sistem Isyarat Bahasa Indonesia (SIBI) sign language, integrating SqueezeNet for image feature extraction and Support Vector Machine (SVM) for classification. The study focuses on 24 static gestures representing alphabetic letters, excluding J and Z due to their motion-based representation. The dataset consists of 5280 RGB images (227×227 pixels), with 220 samples per gesture, obtained from a public Kaggle source. SqueezeNet, a lightweight CNN architecture, is used to generate 1000-dimensional feature vectors, which are then classified using an SVM with an RBF kernel (C = 1.0) to effectively handle non-linear decision boundaries. A 10-fold cross-validation was applied without data augmentation to evaluate baseline performance. The proposed method achieved 99.51% classification accuracy, with an average precision of 94.04%, recall of 94.02%, and F1-score of 94.02%. Certain gestures, such as G, H, and Q, achieved near-perfect recognition, while others, like V, presented greater classification challenges with a recall of 80.5%. Compared to existing models such as MobileNet (98% accuracy) and VGG16 (86% accuracy) on the same dataset, the SqueezeNet–SVM combination provides competitive or superior accuracy with significantly reduced computational requirements. These results highlight the method’s potential for real-time integration into mobile or embedded sign language translation applications, bridging communication gaps between the deaf and hearing communities. Future work will focus on improving performance for difficult gestures, applying data augmentation to enhance generalization, and developing a prototype mobile application for real-world testing in relevant environments.
Detection of Persistent vs. Non-Persistent Drugs in Pharmacy Using Decision Tree Classification Based on Gini, Entropy, and Log Loss Criteria Mardewi, Mardewi; Aziz, Firman; Usman, Syahrul; Fuadi Syam, Rahmat
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2585.186-195

Abstract

This study evaluates the performance of Decision Tree methods in classification, utilizing three different criteria: Entropy, Gini, and Log Loss. The objective is to determine which criterion is most effective in achieving high classification accuracy using prescription data from the UCI repository, comprising 3,424 prescription records with 67 variables. The analysis results show that the Entropy criterion delivers the best performance with an accuracy of 79.1%, followed by the Gini criterion at 78%, and the Log Loss criterion at 77.9%. These findings indicate that the Entropy criterion is superior in reducing uncertainty and capturing the underlying data structure, while both Gini and Log Loss criteria also provide competitive, though slightly lower, results. The main contribution of this research is a comparative evaluation of decision tree criteria using real-world prescription data to support accurate classification of medication adherence, which can be beneficial for developing intelligent pharmacy systems. This research offers valuable insights into the effectiveness of various criteria within the Decision Tree method and can aid in selecting the most appropriate criterion for future classification applications.
An Enhanced Mean Value Theorem with Bisection Technique to Elevate User Focus Metrics in Talent Finder Applications Arifin, M Zainal; Wibawa, Aji Prasetya; Safii, Moh; Noertjahyana, Agustinus; Che Pee, Ahmad Naim
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2822.140-149

Abstract

Contemporary digital workplaces face pervasive distractions (e.g., notifications, multitasking), yet talent-assessment systems rarely quantify their impact on attention. To address this gap, we integrate the classical Mean Value Theorem (MVT) with an adaptive bisection algorithm to model user-focus dynamics in talent-matching applications. MVT’s limit-based formulation captures continuous attentional shifts, while the iterative bisection method focus metrics by capturing dynamic attentional shifts through the mean toward optimal focus equilibrium, ensuring temporal continuity and rapid convergence. A controlled experiment involving Universitas Negeri Malang undergraduate students tested the Enhanced Mean Value Theorem–Bisection (EMVT-B) method in four simulated workplace scenarios. Participants selected Focus-oriented options over alternative strengths (Communication, Input, Relator, Adaptability) in approximately 65% of decisions, highlighting moderate yet improvable attentional commitment. Sensitivity analysis indicated that increasing the mean-shift threshold by 0.05 could raise Focus-oriented selections to 72%, emphasizing the method's practical impact. These findings establish EMVT-B as both a diagnostic and prescriptive tool, quantifying attentional stability while providing personalized strategies to enhance user focus. Future research should examine longitudinal applications and broader talent portfolios.
A Hybrid Movie Recommendation System to Address Data Sparsity Using Genre-Based K-Means and Neural Collaborative Filtering Darwis, Herdianti; Syahrir, Firdaus Abrazawaiz; Hayati, Lilis Nur
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2868.203-212

Abstract

Recommendation systems play a crucial role in helping users navigate the overwhelming volume of information on digital platforms. However, conventional Collaborative Filtering (CF) methods often suffer from data sparsity, leading to reduced prediction accuracy and limited recommendation diversity. To address this challenge, this study proposes a hybrid recommendation model that integrates K-Means clustering based on genre, release year, and rating statistics into the Neural Collaborative Filtering (NCF) framework. Unlike previous works that rely on a single dimension like genre or demographics for clustering, our model uniquely combines multiple content-based features. Furthermore, we explicitly integrate the cluster labels as additional embedding features within the NCF framework, enabling more nuanced and context-aware representation learning. Using the MovieLens Latest-Small dataset, our hybrid model significantly outperforms the baseline NCF across all metrics, achieving a Mean Absolute Error (MAE) of 0.6097, a Root Mean Square Error (RMSE) of 0.7946, and improvements in Precision@10 (0.6065) and Recall@10 (0.7063). These findings highlight the effectiveness of our novel, content-aware clustering approach in deep learning recommenders, resulting in more accurate, diverse, and contextually relevant movie suggestions.
Performance Comparison of Ensemble Learning Models for Brain Tumor Detection on Augmented MRI Datasets Titaley, Gilberth Valentino; Rismayanti, Nurul; Handayani, Anik Nur; Ardiansah, Jevri Tri
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2523.86-97

Abstract

Brain tumors are highly fatal diseases, making early detection a critical factor in improving patient survival rates. Magnetic Resonance Imaging (MRI) has become a primary tool in brain tumor diagnosis; however, manual analysis processes are often time-consuming and prone to subjective errors. This study employs a machine learning-based classification model to detect four categories of brain tumors—glioma, meningioma, pituitary, and healthy—with high accuracy. The methods include image segmentation using the U-Net model, which excels in medical image analysis due to its encoder-decoder architecture with skip connections, allowing efficient integration of spatial and contextual information. Features are extracted using HuMoments, known for their invariance to rotation, translation, and scale, ensuring robust spatial pattern representation. Data normalization is conducted using Robust Scaling and L2 Normalization to address outliers and harmonize feature scales, enhancing model performance. The MRI dataset, originally comprising 7,023 images, was augmented to 8,000 images using techniques such as rotation, flipping, and contrast adjustments to improve class balance and minimize overfitting. Three ensemble algorithms—Random Forest, XGBoost, and Stacking—were employed to train the models, with performance evaluation based on accuracy, ROC-AUC, F1-score, and confusion matrix. The results demonstrate that Random Forest achieved the best performance with an accuracy of 72% and an ROC-AUC of 0.91. This study illustrates the potential of machine learning approaches for automated brain tumor diagnosis, with further improvement possible through model optimization and the use of more diverse datasets.
Enhancing Kubernetes-Based Microservices Deployment Efficiency Through DevOps and GitOps Maulana, Irvan; Umar, Rusydi; Yudhana, Anton
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2562.107-119

Abstract

An effective and resilient means to deploy microservices to Kubernetes is an ongoing challenge. This challenge becomes more difficult with ever increasingly complex application architectures. This research explored a DevOps model based on GitOps that integrates ArgoCD and GitLab CI/CD, as a means to create a more effective, resilient, and scalable deployment. Twelve microservices that were deployed in a controlled experimentation format were used in a comparative approach to previous deployment practices that only considered manual deployments. The results show an overall deployment time improvement of 40%. For the deployments that were executed incorrectly, ArgoCD ensures service availability leveraging its self-healing capabilities. During the computation of each run we also experienced system performance in a sustained high-load environment. Upon high demand, we experienced the desired autoscaling behavior requested, which resulted in higher service responsiveness. In comparison to previous studies, this research considered statistical analysis, while also looking at an aspect of real-world orchestration and networking efficiency while adopting Kubernetes. Altogether, this research gives organizations practical advice on how they may optimize their deployment pipelines for efficient, scalable and resilient microservices.

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