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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 8 Documents
 1 
Search results for , issue "Vol 17, No 1 (2025)" : 8 Documents clear
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.
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.
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.
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.
Implementation Of Deep Learning Using Convolutional Neural Network Method In A Rupiah Banknote Detection System For Those With Low Vision Akhiyar, Dinul; Tukino, Tukino; Defit, Sarjon
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.v17i1.2253.34-43

Abstract

The application of deep learning in various sectors continues to grow due to its ability to provide efficient and effective solutions to complex problems. One significant implementation is in object detection, such as identifying Indonesian rupiah banknotes. This innovation aims to assist individuals with visual impairments in using money more effectively. At present, visually impaired individuals rely on conventional methods, such as identifying banknotes by touch, folding them in specific ways, or seeking assistance from others. However, these methods are often time-consuming, prone to error, and lack practicality in everyday situations. In this project, a system was developed using the Convolutional Neural Network (CNN) architecture combined with the YOLO (You Only Look Once) algorithm. YOLO is renowned for its speed and accuracy in real-time object detection, making it an ideal choice for detecting banknotes in moving images. The training dataset included 1,260 images, and the model underwent 7,000 iterations during training. As a result, the system achieved a high mean Average Precision (mAP) score of 97.65%, demonstrating its robustness and precision. For validation, 140 test images were utilized, which yielded an impressive mAP value of 97.5%. To further evaluate the system's reliability, tests were conducted under varying conditions, such as banknotes with creases, folds, or different lighting scenarios. These tests resulted in an mAP score of 88%, showcasing the system's adaptability to real-world conditions. This system provides significant benefits for individuals with visual impairments by offering a practical, efficient, and accurate solution for recognizing banknotes. With this technology, visually impaired users can interact with currency independently, reducing their reliance on others and traditional, less practical methods. This innovation not only enhances their autonomy but also fosters inclusivity in financial transactions. By integrating this system into mobile applications or wearable devices, its accessibility and usability can be further improved, paving the way for a broader societal impact.
Refining the Performance of Neural Networks with Simple Architectures for Indonesian Sign Language System (SIBI) Letter Recognition Using Keypoint Detection Amir, Nur Hikma; Dewa, Chandra Kusuma; Luthfi, Ahmad
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.v17i1.2522.64-73

Abstract

The diversity of non-verbal communication styles among persons with disabilities in Indonesia highlights the urgent need for technological solutions that support accessibility in both workplace settings and social contexts. This study proposes a novel approach to improving neural network performance through the use of simple architectures for recognizing Indonesian Sign Language (SIBI) letters M and N, by applying keypoint detection while accounting for hand size variations (17–22 cm). Four models were evaluated: YOLOv5 based on image detection, as well as VGG-16, Attention, and Multi-Layer Perceptron (MLP) developed using keypoint detection. The evaluation was conducted in real-time, taking into account accessories such as rings, watches, and gloves, as well as varying lighting intensities to simulate real-world user environments. The novelty lies in the integration of keypoint detection into lightweight architectures, which significantly improves accuracy and resilience against visual disturbances (noise). The MLP model achieved the best performance, with an accuracy of 94% for M and 93% for N, outperforming more complex approaches such as YOLOv5, which showed a significant drop in accuracy under disturbed conditions. The integration of VGG-16 with Attention resulted in underfitting, emphasizing that complexity does not always correlate with effectiveness. These findings underscore the potential of lightweight models to enhance technological accessibility for the disabled community across various social and professional domains.
Evaluation of Multi-Class Classification Performance Lung Cancer Through K-NN and SVM Approach Saputra Troy, Muh. Indra Endriartono; Jabir, Sitti Rahmah; Anraeni, Siska
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.v17i1.2464.27-33

Abstract

Lung cancer is one of the deadliest diseases in the world with a mortality rate of 25% of all cancer-related deaths in 2021. Lung cancer is a lung disease caused by genetic changes in respiratory epithelial cells, resulting in uncontrolled cell proliferation. In an effort to improve diagnosis and treatment, this study proposes an approach for multiclass performance evaluation using K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms based on 2024 data. in this study KNN is implemented conventionally while SVM applies 2 kernel processes, namely Linear and Polynominal. The data used is 1000 rows and uses 24 variables with a ratio of 70% training data and 30% testing data, the data in this study includes important information such as medical history, diagnostic test results, and clinical characteristics of patients. this study aims to determine which algorithm has the best performance by looking at the final results based on accuracy in identifying lung cancer data. Based on the research and discussion of SVM and KNN performance evaluation, the SVM algorithm produces an accuracy of 98.28%, surpassing the accuracy of the KNN algorithm of 97.25%. Therefore, the results show that the SVM algorithm is superior to the KNN algorithm. The KNN and SVM methods were implemented for multi-class classification of lung cancer, allowing identification of various subtypes of lung cancer with optimal accuracy.
Tackling Attendance Analysis: Unraveling Employee Patterns using K-means Clustering for Workforce Optimization Nur Khusna, Arfiani; Efendi, Wisdah; Hidayati, Nur Arina
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.v17i1.2309.54-63

Abstract

This study aims to apply the K-Means Clustering method using employee attendance data. The background of this research problem is to improve the understanding and management of employee attendance by identifying similar attendance patterns in different groups. Employee attendance impacts their morale, sense of responsibility, discipline, cooperation with supervisors or colleagues, and their level of productivity. The K-means Clustering method divides employees into groups based on their attendance patterns, to create groups with similar attendance characteristics. This research has important benefits in decision-making related to human resource management, scheduling, and employee performance evaluation. The results of the study were measured using the Silhouette Coefficient, with a value of 0.3140272065284342, which shows a moderate level of accuracy in separating groups based on attendance patterns. Furthermore, the study also achieved a 100% truth value, signifying the success of consistent and accurate grouping. The main contribution of this research is the use of the K-Means Clustering method as an effective tool in analyzing the attendance of employees and providing valuable insights into managing employee attendance by understanding existing attendance patterns.

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