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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 617 Documents
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Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance Wahyudi, Mochamad; Sintagel br Sianipar, Imeldi; Pujiastuti, Lise; Solikhun, Solikhun; Kurniawan, Deny
ILKOM Jurnal Ilmiah Vol 17, No 1 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i1.2363.44-53

Abstract

Acquired Immunodeficiency Syndrome (AIDS) caused by the Human Immunodeficiency Virus (HIV) is one of the deadliest infectious diseases in the world. Understanding its spread and epidemiological characteristics is crucial for developing and preventing more effective treatments. This study uses the K-Medoids method with a quantum computing approach to predict AIDS based on clinical and demographic data. K-Medoids is chosen to group large amounts of data using a clustering technique that determines the center point (medoid) of each cluster, minimizing the overall distance between data in a cluster. The Manhattan distance is used because it is easier to process data. The quantum computing approach is used to overcome the limitations of classical computing when processing large-scale medical data. This study shows that the application of quantum algorithms to the K-Medoids method allows for faster and more accurate predictions in the diagnosis of AIDS. The tests carried out showed that the prediction accuracy of classical and quantum methods was comparable, namely 85%. The results support the great potential of quantum computing to improve the efficiency of medical predictions. The research involves converting data into quantum format, processing it with the K-Medoids algorithm, and evaluating its performance based on metrics such as intercluster distance and computation time. The research will also identify patterns and risk factor for the spread of AIDS that can be used to develop more effective health interventions. The conclusion of the research is that integrating the K-Medoids techniques can only increase the speed of data processing but also provide competitive accuracy compared to traditional techniques. This research opens up new possibilities in medical data analysis, especially when managing large and complex data sets. The bottom line is that these findings can help make better medical decisions and strategically support AIDS prevention and treatment efforts.
Blockchain-Based Diploma Authentication System: A Design Science Approach Using Smart Contracts and Ganache Haryati, Haryati; Vernanda, Dwi
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3224.69-84

Abstract

Academic credential fraud poses a critical challenge to Indonesian higher education, with approximately 30% of job applicants providing false academic qualifications while conventional verification processes require 2–4 weeks with significant administrative costs. This research addresses the gap where 77% of blockchain education research remains conceptual by proposing and evaluating a four-layer blockchain system architecture for academic diploma authentication. Using Design Science Research Methodology (DSRM), the study designs and implements a layered architecture comprising a Presentation Layer (React 18.2.0 with client-side SHA-256 hashing), Application Layer (Node.js 18.20.8 with Web3.js), Data Layer (PostgreSQL 14.5 for off-chain metadata), and Blockchain Layer (DiplomaValidator smart contract in Solidity 0.8.19 on Ganache 2.7.1). The architectural design enforces separation of concerns, enabling tamper-evident credential storage through immutable on-chain hash registration and trustless public verification through zero-gas view functions. Comprehensive evaluation through 38 functional tests, performance benchmarking, security auditing, and integration testing demonstrates 100% pass rate across all categories. Performance metrics show registration in 15.23 ms (240,082 gas units) and verification in 9.47 ms at zero gas cost, achieving 51.81 TPS throughput. Security audit yields 95/100 with zero high or medium vulnerabilities. The primary contribution of this research is a formally documented four-layer blockchain architecture for academic credential authentication validated through DSRM providing a replicable architectural model and quantified performance baselines for the Computer Science community and Indonesian higher education institutions considering blockchain adoption
Detection of Drivers Drowsiness on Four-Wheeled Vehicles using the Haar Cascade Algorithm and Eye Aspect Ratio Maukar, Maukar
ILKOM Jurnal Ilmiah Vol 17, No 1 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i1.2362.1-11

Abstract

One of the most common types of threats to four-wheeled vehicle drivers is microsleep. Microsleep is a condition in which a person's loss of attention or consciousness due to a state of fatigue or drowsiness. In general, microsleep lasts for a short duration, about a fraction of a second to a full 10 seconds. One way to modify the driver's sleepy condition is to form a drowsiness detection system through the extraction of facial feature points. The extraction of facial feature points refers to 68 predictor landmarks with detection in the eyes and facial movements of the driver in the form of poses with the determination of the angle threshold of changes in the position of the face while driving which indicates a state of drowsiness. This study implements the use of the Haar Cascade Classifier algorithm in detecting the drowsiness of four-wheeled vehicle drivers and the Eye Aspect Ratio of the points that form the eyes using Euclidean Distance. In detecting the eye index on the face predictor landmarks uses the dlib python library to detect objects, face detection, and face landmark detection. This study also uses the Face Detector library to create a face detector object and a Landmark Predictor. The test results showed that the detection system was 98.33% accurate with the condition of facial features that could still be identified by the system even though the difference in face distance with the webcam acquisition tool was far away. This detection system is also able to detect driver drowsiness with an average time duration of less than 5 seconds with a distance of up to 50 meters.  The system detects drowsiness quickly with a notification in the form of a warning in the form of an alarm sound, which is very important in order to reduce the number of accidents due to drowsiness.
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.
Optimization of Text Emotion Classification through the Combination of ITC Smoothed and Linear Models Garonga, Melki; Rangga Punne, Mc Rore; Damayanti, Irene Devi
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.2954.1-16

Abstract

This research investigates four feature extraction techniques TF-IDF, Smoothed TF-IDF, Inverse Term Counting (ITC), and ITC Smoothed to determine how effectively they enhance text-based emotion classification when working with imbalanced datasets. The study also seeks to pinpoint the most effective pairing between feature extraction methods and classification algorithms. Its key contributions include a methodical side-by-side comparison of these lesser-examined TF-IDF variations and demonstrating empirically that linear models handle class imbalances with considerable resilience. The analysis drew upon an Indonesian Twitter dataset comprising 4,132 tweets, categorized into six unequally distributed emotional states: anger, fear, joy, love, sadness, and neutrality. These four feature extraction approaches were assessed using five distinct classifiers: Naive Bayes, Logistic Regression, SVM, Random Forest, and KNN. Performance was measured through accuracy, precision, recall, and F1-score. Findings indicate that linear classifiers, specifically Logistic Regression and SVM, delivered superior performance, achieving accuracy rates between 93.71% and 94.44%. These models consistently outperformed both probabilistic and distance-based algorithms regardless of the feature extraction method applied. Interestingly, the impact of smoothing proved context-dependent. While applying smoothing to both TF-IDF and ITC boosted the performance of linear models over their unsmoothed counterparts, it paradoxically reduced accuracy for the standard ITC method. This outcome questions the widely held belief that smoothing universally enhances model performance. The combination of Logistic Regression with the unITC Smoothed method yielded the peak accuracy of 94.44%. The study offers actionable guidance, suggesting the pairing of Logistic Regression with ITC as a highly effective strategy for text-based emotion classification. It also contributes theoretically by underscoring the particular aptitude of linear models for managing high-dimensional text data within imbalanced class contexts
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.
A Hybrid Deep Learning–Machine Learning Approach for the Identification of Active Compounds in Blumea balsamifera (Sembung Leaves) Kusnaeni, Kusnaeni; Prihatin, Prihatin; Rahmatullah, Rahmatullah; Hafid, Mega Sartika; Nisardi, Muhammad Rifki; Nurmalasari, Nurmalasari; Andy B, Afif Budi
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3195.165-179

Abstract

Blumea balsamifera (sembung) is a medicinal plant with well-documented antibacterial, anti-inflammatory, and analgesic properties. However, the systematic identification of its bioactive compounds remains a significant challenge due to the complexity and high dimensionality of LC–MS (Liquid Chromatography–Mass Spectrometry) data. This study aims to develop a robust computational framework for automated compound identification using a hybrid modeling approach.A hybrid model integrating Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) was employed to enhance feature extraction and classification performance. The LSTM component was utilized to capture sequential dependencies in spectral data, while XGBoost performed optimized classification through gradient boosting. This integration enables efficient handling of complex spectral patterns and improves predictive accuracy.The proposed model achieved an accuracy of 91%, demonstrating strong performance in classifying and identifying bioactive compounds. Feature importance analysis identified several key compounds contributing to the model predictions, including Luteolin-7-methyl-ether, Umbelliferone, Blumeatin, Dihydroquercetin-7,4′-dimethylether, Chrysosplenol C, Blumealactone B, and Blumeaene E. These compounds are associated with known pharmacological activities, supporting the therapeutic relevance of B. balsamifera.The proposed hybrid LSTM–XGBoost framework provides an effective and scalable approach for LC–MS-based compound identification. This method reduces analytical complexity, enhances classification reliability, and offers a data-driven strategy for accelerating phytochemical research and bioactive compound validation
Utilization of Deep Learning YOLO V9 for Identification and Classification of Toraja Buffalo Breeds Manga', Abdul Rachman; Herawati, Herawati; Purnawansyah, Purnawansyah
ILKOM Jurnal Ilmiah Vol 17, No 1 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i1.2349.12-19

Abstract

This study aims to develop and evaluate a buffalo breed detection system that supports the cultural practices of the Toraja community, particularly in the context of the Rambu Solo’ ceremony. The ceremony places significant importance on the types of buffaloes used, as each breed symbolizes different social statuses and cultural meanings. In response to the need for an accurate and efficient identification method, this research utilizes the YOLOv9 (You Only Look Once version 9) deep learning model to detect and classify Toraja buffalo breeds. A dataset comprising 2,656 annotated images was used, representing five distinct buffalo categories: bongga sori, bonga ulu, moon, saleko, and todi. The images were collected from both field documentation and online sources. The YOLOv9 model was trained across 90 epochs, aiming to achieve high accuracy in breed detection and classification. The evaluation results demonstrate the model's strong performance, achieving a precision of approximately 0.9 and a recall of 0.8. These metrics indicate the model's ability to correctly identify the buffalo breeds with a high degree of reliability. However, during the training process, certain patterns of overfitting and underfitting were observed, suggesting that the model's performance could still be improved. These issues can potentially be addressed by increasing the volume and diversity of training data, applying data augmentation techniques, and fine-tuning hyperparameters to achieve a more balanced generalization. Overall, the findings show that YOLOv9 is a promising tool for supporting cultural preservation through technology by automating the identification of buffalo types used in traditional ceremonies. This system can assist in maintaining the accuracy and consistency of buffalo classification according to local customs. Future research is recommended to explore broader datasets, compare alternative object detection algorithms, and develop an integrated application for practical field use.
Comparative Performance Analysis of Modified VGG16 and Slim-CNN for Arabica Coffee Bean Defect Classification Ardian, Yusriel; Astawa, I Nyoman gede Arya; Irawan, Novta Danyel; Pradnyana, I Putu Bagus Arya; Sulistyo, Agung
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3244.85-96

Abstract

Defect detection in Arabica coffee beans is a critical aspect of quality control, particularly for export-oriented commodities that require consistent visual standards and uniform quality across production batches. Black and partial-black defects are known to significantly affect market value, quality perception, and sensory characteristics. Meanwhile, manual inspection processes remain vulnerable to evaluator subjectivity and inter-operator inconsistency.This study aims to conduct a comparative analysis between a Modified VGG16 architecture and Slim-CNN for detecting these two defect categories using a deep learning-based Convolutional Neural Network (CNN) approach. The dataset consists of 4,080 high-resolution images of Arabica green coffee beans captured using a 24.2 MP macro camera under controlled lighting conditions to minimize shadows and visual distortion. To preserve the natural characteristics of the defects, minimal data augmentation was applied using cropping and 15-degree rotation techniques. The Modified VGG16 architecture was simplified by reducing the complexity of the fully connected layers, integrating batch normalization, and applying dropout to enhance training stability and computational efficiency. Slim-CNN was employed as a lightweight comparative model with fewer parameters and lower memory requirements, making it suitable for resource-constrained deployment scenarios. Four training schemes were evaluated using variations in learning rate and epoch number to assess configuration impacts on performance. Experimental results show that Modified VGG16 achieved the highest test accuracy of 86.7% at a learning rate of 0.001 with 3 epochs, demonstrating a strong balance between training and validation accuracy. Slim-CNN exhibited shorter training time and lower computational complexity, although with slightly lower classification accuracy compared to Modified VGG16. These findings highlight a trade-off between classification performance and computational efficiency in selecting CNN architectures for coffee bean defect detection. Although the results demonstrate potential for industrial automatic classification systems, further validation using larger datasets and more comprehensive evaluation schemes is required to improve model generalization. This study contributes to the development of a more measurable, adaptive, and efficient deep learning-based coffee quality inspection system to support agro-export industry requirements.
Decision Support System on Independent Curriculum Learning Models with Artificial Intelligence at Islamic Universities Efriyanti, Liza; As'ad, Ihwana
ILKOM Jurnal Ilmiah Vol 17, No 1 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i1.2082.74-85

Abstract

The design of curricula in Islamic universities frequently encounters difficulties in addressing the evolving needs of students, industry demands and the distinctive integration of Islamic values. Conventional methodologies are inadequate in their capacity to adapt to the evolving needs of the modern educational landscape. Furthermore, the integration of artificial intelligence (AI) in this domain remains underdeveloped, with many instances overlooking the crucial role of religious principles and institutional characteristics. This study addresses this gap by developing a Decision Support System (DSS) using Mamdani type 1 fuzzy logic, with the objective of assisting in determining an independent curriculum learning model tailored to Islamic higher education. The system incorporates a number of input variables, including student needs, industry requirements, institutional characteristics and data analysis. The output variables include an evaluation of the suitability of the learning model and a recommendation as to the most appropriate model. To illustrate, in situations where student needs are high, industry demands are moderate, institutional characteristics are high, and data analysis is moderate, the recommended model places an emphasis on balancing theoretical knowledge with practical application, while also aligning with Islamic values.  The validation of this AI-based model, utilizing 2023 historical data from five Islamic universities in West Sumatra, yielded an average Mean Absolute Error (MAE) of 0.64, thereby demonstrating good predictive accuracy. The integration of AI in this system facilitates data-driven decision-making, thereby enhancing the relevance and adaptability of the curriculum. It has the potential to improve the quality of education, support balanced student learning outcomes, and ensure alignment with Islamic principles, making it a transformative tool for curriculum development in Islamic higher education.

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