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Jiko (Jurnal Informatika dan komputer)
Published by Universitas Khairun
ISSN : 26148897     EISSN : 26561948     DOI : -
Core Subject : Science,
Computer Science & IT
Jiko (Jurnal Informatika dan Komputer) Ternate adalah jurnal ilmiah diterbitkan oleh Program Studi Teknik Informatika Universitas Khairun sebagai wadah untuk publikasi atau menyebarluaskan hasil - hasil penelitian dan kajian analisis yang berkaitan dengan bidang Informatika, Ilmu Komputer, Teknologi Informasi, Sistem Informasi dan Sistem Komputer. Jurnal Informatika dan Komputer (JIKO) Ternate terbit 2 (dua) kali dalam setahun pada bulan April dan Oktober
Arjuna Subject : -
Articles 241 Documents
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DEVELOPING AN IT INFRASTRUCTURE MODEL FOR ENHANCING DIGITAL LITERACY THROUGH WEB-BASED LEARNING: A COMPREHENSIVE FRAMEWORK Sulianta, Feri; Rumaisa, Fitrah; Puspitarani, Yan; Violina, Sriyani; Rosita, Ai
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8761

Abstract

In today's rapidly evolving educational landscape, there is a growing need to develop an IT infrastructure model that can effectively support web-based learning environments to enhance digital literacy. The proposed model offers a comprehensive framework for educational institutions to integrate digital technologies into their curricula seamlessly. Key elements of the model include essential hardware, user-friendly software, and advanced security measures, each playing a vital role in creating a seamless, secure, and efficient digital learning experience. This study explores the dynamic interactions among these components and their collective influence on fostering a conducive and productive web-based learning environment. By addressing the need for reliable infrastructure, scalable solutions, and robust security protocols, the model provides a holistic approach to improving digital literacy in educational contexts. The research underscores the critical role of a well-structured IT infrastructure in supporting digital education, offering actionable insights and recommendations for implementation. Moreover, it emphasizes that a well-developed IT infrastructure is foundational for the long-term success of web-based learning programs, enabling institutions to meet diverse learner needs, adapt to technological advances, and ensure sustainability in the digital education landscape.
DETECTION OF THE SIZE OF PLASTIC MINERAL WATER BOTTLE WASTE USING THE YOLOV5 METHOD Karyanto, Dony Dwi; Indra, Jamaludin; Pratama, Adi Rizky; Rohana, Tatang
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8535

Abstract

The use of plastic bottles for various needs is increasingly massive, especially in consumption needs such as mineral water bottles. The use of plastic bottles is used to reduce costs and be effective in maintaining the quality of mineral water, but its impact can affect natural conditions if not managed properly. Plastic bottle waste if left buried in the ground will have difficulty expanding, which can cause environmental pollution. Therefore, we can take advantage of technology to sort plastic bottle waste using a camera based on the size of plastic bottles. Differentiating the size of bottles aims to distinguish the economic value when exchanged at the waste bank. This technology utilizes object detection and recognition functions such as the YOLO (You Only Look Once) method. YOLO is a detection method that is a development of the CNN (Convolutional Neural Network) algorithm. By using YOLOv5, we can detect objects in the form of plastic bottle waste of various different sizes. To maximize object detection according to size, data annotation is done by creating a Bounding Box on each dataset according to its size. The test was carried out with several different distance configurations including 40cm, 80cm and 1m. Detection results using YOLOv5 produce up to 84% accuracy in real-time.
EVALUATING HYBRID NEURAL NETWORK ARCHITECTURES FOR PREDICTING SLEEP DISORDERS FROM STRUCTURED DATA Airlangga, Gregorius
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7873

Abstract

The accurate diagnosis of sleep disorders is crucial for effective treatment and management, yet current methods often rely on subjective assessments and are not always reliable. This research examines the efficacy of various neural network architectures, including dense networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and innovative hybrid models, in predicting sleep disorders from structured health data. Our study focuses on comparing the performance of these models using metrics such as accuracy, precision, recall, and F1 score across a dataset comprising 400 individuals with detailed sleep and lifestyle data. Our findings demonstrate that while traditional models like dense networks and CNNs for structured data yield robust results, hybrid models, particularly the CNN-Transformer, significantly outperform others. This model effectively integrates convolutional layers with Transformer’s attention mechanisms, excelling in handling complex data interactions and providing superior predictive accuracy with an F1 score and accuracy reaching as high as 0.91. Conversely, RNN models, designed to capture temporal data dependencies, showed less efficacy, underscoring the importance of model selection aligned with data characteristics. This suggests that for datasets not exhibiting strong temporal features, models leveraging spatial relationships or advanced attention mechanisms are more suitable. This study not only advances our understanding of neural network applications in medical diagnostics but also highlights the potential of hybrid models in enhancing diagnostic accuracy. These insights could lead to significant improvements in the early detection and treatment of sleep disorders, thereby enhancing patient outcomes and contributing to the broader field of medical informatics.
HOW EDUCATIONAL GAME CAN IMPROVE THE PLAYER’S METACOGNITIVE SKILLS Dzaky, Dedi Imaduddin; Hartanto, Rudy; Fauziati, Silmi; Bierig, Ralf
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.6363

Abstract

Gamification is a basic concept of game mechanics which apply into non-game environments. Games as joyful playing is widely used in daily life, including in education. Game was assembled by fundamental elements which regulate not only how it was played but also what is the final goal of their mechanics. When a game is operated; the player's cognitive, affective, and psychomotor aspects are involved. Involving three domains was integrated into an environment which stimulated the cognitive dimension. By using more complicated mechanics it will cultivate the metacognitive of players. This paper investigated how educational games can improve the player’s metacognitive skill. Investigating was done by theoretically analysing. The invention of this work is that educational games can improve a player's metacognitive which consist of several stages, namely goal setting and planning, selection and strategy selection, monitoring and evaluation, organization and self regulation, and attention. So, learning the effort of clearing stages of Cat Mario would encourage students to memorize knowledge and pattern of question so they could clear the question and learn new stuff through metacognitive skills that are obtained by their gaming experiences.
COMPARISON OF ROBUSTNESS TEST RESULTS OF THE EYE ASPECT RATIO METHOD AND IRIS-SCLERA PATTERN ANALYSIS TO DETECT DROWSINESS WHILE DRIVING Aditia, Risky; Sriani, Sriani
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.9239

Abstract

Traffic accidents caused by driver drowsiness are a leading factor in fatal road incidents. This study introduces a computer vision-based drowsiness detection system utilizing two methods: Eye Aspect Ratio (EAR) and Iris-Sclera Pattern Analysis (ISPA). The EAR method measures the eye aspect ratio to determine whether the eyes are open or closed. This involves calculating the vertical distance between specific landmark points on the eyelids and comparing it to the horizontal distance between points on the eye. A decrease in this ratio serves as an early indicator of drowsiness. The ISPA method employs symmetry analysis between the iris and sclera. This approach relies on the visual pattern formed when the eyes are open, where the sclera appears symmetrically distributed around the iris. During this process, eye images are processed to extract iris and sclera features, which are then analyzed for symmetry to detect signs of drowsiness. The study evaluates the reliability of both methods under varying conditions, such as changes in lighting, viewing distances, head movements, and the use of eyeglasses. The results show that the EAR method achieved an accuracy of 83.33% in distance testing, indicating its effectiveness in stable lighting environments. In contrast, the ISPA method achieved an accuracy of 59.25% under low and variable lighting conditions and proved more reliable for detecting the eyes of users wearing glasses.
ANALYSIS OF FUZZY C-MEANS IN PERSONALITY CLUSTERING BASED ON THE OCEAN MODEL Pamput, Jessicha; Dillah, Salsa; Muthmainnah, Aindri; Surianto, Dewi Fatmarani
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8369

Abstract

Personality is the pattern of an individual's behavior in daily life, reflected in their thoughts, feelings, and actions. The Big Five Personality Traits Model, known as OCEAN, helps to understand the complexity of human personality through five main traits. The identification and classification of personality, particularly among students, impacts academic performance, personal development, anxiety levels, and risky behaviors. Collaboration between educators, mental health professionals, and career advisors is crucial to creating an educational environment that supports students' holistic development. The Fuzzy C-Means (FCM) method is used to identify students' personalities with adequate accuracy. This study adopts the OCEAN model with FCM to efficiently identify and classify students' personalities. Data were obtained from 142 respondents, resulting in 27% of respondents being classified in cluster 1, 21% in cluster 2, 18% in cluster 3, 16% in cluster 4, and 18% in cluster 5. This study has important implications for students, educators, and educational institutions to understand that learning patterns, social interactions, and decision-making processes can be influenced by an individual's personality.
PRIMARY QUERY ANALYSIS ON SQL DATABASE RESTRUCTURING IN GEOGRAPHIC INFORMATION SYSTEMS Ilyas, Ridwan; Witanti, Wina; Syarafina, Fildzah
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8565

Abstract

Database restructuring is a crucial process aimed at enhancing data management and access efficiency by modifying the existing data structure. This research focuses on improving a Geographic Information System (GIS) for taxation by migrating and restructuring an inefficient and redundant database. The study conducts a comparative performance evaluation of the old and restructured databases using benchmarking tests with varying numbers of threads and ramp-ups. The results reveal a significant increase in average throughput (24.60%) following the restructuring, indicating a substantial improvement in the database's data processing capacity. However, there is also an average increase in response time (21.65%), suggesting a trade-off between enhanced throughput and slower response times. This increase in response time indicates that while the system can handle more data, it requires more time to process each query. Overall, the restructured database demonstrates enhanced performance and efficiency, though further optimization is necessary to achieve consistent throughput across different workloads and to mitigate the increased response times
CLASSIFICATION OF JAVANESE NGLEGENA SCRIPT USING COMPLEXVALUED NEURAL NETWORK Rahmawati, Adinda Aulia; Muhaimin, Amri; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7808

Abstract

Javanese script is one of the traditional scripts in Indonesia used by the Javanese people. The Javanese script used in Javanese spelling basically consists of 20 main characters (nglegena), namely from the Ha to Nga script. Javanese script has very high value, the uniqueness of the script is one thing that must be preserved. However, widespread use of Javanese script has declined as technology has developed. In this context, one of the problems that arises is the difficulty in automatically recognizing and classifying the Javanese Nglegena script. Therefore, the use of computational methods to automatically classify the Nglegena Javanese script is very important. This research compares 2 methods for classifying Javanese Nglegena script, namely Complex-Valued Neural Network (CVNN) and Convolutional Neural Network (CNN). This research aims to compare the best accuracy between CVNN and CNN. In this study, the Complex-Valued Neural Network method had a higher average accuracy, namely 96.332% and a loss of 0.1834. Meanwhile, the CNN method has an average accuracy of 93.72% and a loss of 0.4254. Artificial intelligence-based Javanese Nglegena script classification technology can help people to recognize the Javanese Nglegena script, especially in the fields of education and culture.
SPERM ABNORMALITY CLASSIFICATION USING MULTI-PURPOSE IMAGE EMBEDDING AND CLASSICAL MACHINE LEARNING Adinugroho, Sigit; Sari, Yuita Arum; Kurniawan, Wijaya; Arwan, Achmad
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8938

Abstract

Since sperm cells have big impact for human welfare in terms of reproduction, there are many studies have been done. In this case, we are attracted to enrich the method in determining the morphological properties of them using machine learning. Most study about it is done using 2-steps action that are feature extraction which is continued by classification. In our work, we aimed to lower the complexity by using image embedding as a general-purpose feature extractor that requires no training. For feature extraction using image, it is found that RGB has better performance compared to grayscale if we want to use Support Vector Machine (SVM). Meanwhile, when a comparation is done between SVM, random forest, Multi-Layer Perceptron (MLP), Naïve Bayes, and k-Nearest Neighbour (kNN) for classification process, MLP shows the best performance among them which is around 85%. Moreover, our proposed method has low complexity indicated by the training time around one and a quarter minute s for the most accurate method, compared to hours of training time in similar methods.
CLASSIFICATION OF BONE FRACTURES IN THE WRIST AND HAND USING DENSENET AND XCEPTION Nusantara, Michelle Swastika Bianglala; Wonohadidjojo, Daniel Martomanggolo
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 1 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i1.9201

Abstract

This study aims to apply Convolutional Neural Network (CNN) using DenseNet and Xception to classify fracture in the wrist and hand bones, while utilizing transfer learning to enhance model's performance. Accurate diagnosis and successful treatment of bone fractures depend on early identification, which lowers the likelihood of long-term issues such avascular necrosis or non-union. The research utilized data from two publicly available musculoskeletal radiography datasets and employed deep learning techniques with the Keras framework. DenseNet was selected for wrist image analysis due to its dense connectivity, which preserves information from previous layers, while Xception was chosen for hand bone image analysis because of its ability to identify complex patterns using depthwise separable convolutions. Transfer learning was implemented to accelerate training and improve accuracy. The DenseNet model achieved a test accuracy of 97.5% for wrist classification, while the Xception model reached 92% accuracy for hand bone classification. By tailoring CNN architectures to specific radiographic images and employing transfer learning, this study demonstrates significant potential for improving diagnostic precision in clinical situations. Furthermore, the findings can support medical personnel in detecting bone fractures more efficiently and accurately, ultimately expediting clinical decision-making and improving patient care.

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