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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 287 Documents
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IMAGE CLASSIFICATION OF VINE LEAF DISEASES USING COMPLEX-VALUED NEURAL NETWORK Putri, Irma Amanda; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
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.v7i1.7809

Abstract

Leaf diseases are a serious challenge in the agricultural industry affecting crop quality and yield especially in grapevines. Early recognition and classification of grape leaf diseases is crucial to enable farmers to take appropriate preventive measures in maintaining the health of their crops. The research utilized an innovative approach based on Complex-Valued Neural Network (CVNN) to address the problem. Using Complex-Valued Neural Network (CVNN) this research seeks to identify and classify grape leaf diseases through a series of experiments. A total of 100 images divided into 4 classes namely Black Rot, ESCA, Leaf Blight, and Healthy were collected to train the model. The results show that the trained CVNN model successfully achieved a training accuracy of 100% and a testing accuracy of 97%, demonstrating excellent performance in classifying grape leaf diseases. This states that the proposed approach has great potential to be an effective tool in helping growers manage their vineyards more efficiently and effectively. The developed image processing method is expected to be applied in designing a system to perform image classification of diseases on grape leaves.
APPLICATION OF THE K-MEANS AND DECISION TREE ALGORITHMS IN DETERMINING STUDENT ACHIEVEMENT Jevintya, Nandya Rifki; Darussalam, Ucuk; Abdullah, Syahid
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.v7i1.7580

Abstract

Various factors influence student achievement, both internal and external; this makes it difficult for some teachers to detect every student in class. This research aims to determine student achievement in class among students at the SDS Kartika X-6 school. Data comes from SDS Kartika X-6, an elementary school owned by the Indonesian Army. By knowing the factors that influence the determinants of student learning achievement, steps can be taken to improve student learning achievement at SDS Kartika x-6. The methods used in this research are the K-Means algorithm and Decision Tree. This method will be chosen to determine student learning achievement. The process begins by determining clusters using the K-Means algorithm; then a classification process is carried out using a Decision Tree. The number of datasets in this research is 28, and the criteria are gender, mathematics grades, English, natural sciences, religion, class performance, and school achievement. The implementation results show that academic grades, class achievements, and school achievements play a role in determining student achievement for SDS Kartika X-6 students. Meanwhile, 3 clusters were formed: Fairly Good, Good, and Very Good. In the testing stage using the Decision Tree method, prediction accuracy was 71%, with an error of 29.
IMPLEMENTATION MULTIMEDIA DEVELOPMENT LIFE CYCLE IN INTERACTIVE MULTIMEDIA DESIGN FOR TRADITIONAL INDONESIAN MUSIC INSTRUMENTS INTRODUCTION Zuhdi, Aditya Imam; Mustafidah, Zahrotul; Nur Alam, Muhammad Risqi; Irawan, Safira Anggraini
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.v7i1.7640

Abstract

This research aims to address the lack of interactive multimedia-based educational media in introducing traditional Indonesian musical instruments to the public, especially children. The issue arises from the fact that the diversity of traditional musical instruments in Indonesia has not been presented attractively in an interactive media format. Therefore, this study utilizes the Multimedia Development Life Cycle (MDLC) method as a guide in designing and developing interactive multimedia. The MDLC stages, namely Concept, Design, Material Collecting, Assembly, Testing, and Distribution, are implemented to ensure that each step of the system development is well-organized. The results of alpha testing indicate that all features of the interactive multimedia work well. Beta testing, involving 36 respondents, yields a rating of 4.52 out of 5, demonstrating that this interactive multimedia is excellent and suitable for use as a learning media for traditional Indonesian musical instruments. This research addresses the gaps in the presentation of educational information by providing an interesting and effective media, especially in the context of traditional musical instruments. Thus, it is expected that this interactive multimedia can enrich the knowledge of the public, especially children, about the cultural wealth of traditional Indonesian musical instruments.
ARTIFICIAL NEURAL NETWORK MULTI-LAYER PERCEPTRON FOR DIAGNOSIS OF DIABETES MELLITUS Tavares, Ofelia Cizela da Costa; Abidin, Abdullah Zainal; Tempola, Firman
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.v7i1.7743

Abstract

Diabetes Mellitus is a disease caused by an unhealthy lifestyle, so blood sugar is not controlled, causing complications. This disease is one of the most dangerous diseases in the world. Approximately 422 million people worldwide have diabetes, the majority living in low- and middle-income countries, and 1.5 million deaths are caused by diabetes each year. The number of cases and prevalence of diabetes have continued to increase over the last few decades. Artificial Neural Networks are a part of machine learning that can solve various problems. One of them is in terms of disease diagnosis. MLP has the advantage that learning is done repeatedly to create a durable, consistent system that works well. This research aims to implement the Multi-Layer Perceptron Artificial Neural Network method for diagnosing diabetes mellitus and then evaluating the MLP by analyzing precision, recall, f1 score, and calculating accuracy. Next, it is validated with k-fold cross-validation. In the experiment in this study, several scenarios were used, and the best scenario was obtained when using eight input layers, seven hidden layers, one output layer, and 5000 iterations. The experiment results showed that the multi-layer perceptron successfully classified diabetics and non-diabetics by percentage. Precision 77.24%, Recall 72.58%, F1 Score 76.86%, accuracy 75%, and average accuracy 78.01%.
EVALUATING MACHINE LEARNING MODELS FOR PREDICTING SLEEP DISORDERS IN A LIFESTYLE AND HEALTH DATA CONTEXT 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.v7i1.7870

Abstract

Sleep disorders significantly impact public health, but their detection is often complicated by the multifaceted nature of causative factors. This study investigates the efficacy of various machine learning (ML) models in identifying sleep disorders based on comprehensive lifestyle and health data. We employed a dataset comprising 400 individual records with features including demographic information, sleep metrics, lifestyle factors, and health parameters. The dataset distinguished between individuals with no sleep disorder, insomnia, and sleep apnea. We evaluated a broad spectrum of ML models including logistic regression, decision trees, ensemble methods like RandomForest and GradientBoosting, support vector machines, and neural networks. The models' performances were assessed using accuracy, precision, recall, and F1 score metrics. Results indicated that ensemble methods, particularly RandomForest and XGBClassifier, outperformed other models in terms of accuracy, precision, and F1 scores, achieving values as high as 0.93. These methods proved effective in managing the complexity and variability of the dataset, thereby suggesting their robustness in clinical predictive analytics. The study's findings advocate for the use of advanced ensemble techniques in developing diagnostic tools for sleep disorders, highlighting their potential to enhance predictive accuracy and reliability in real-world healthcare settings. Further research is recommended to optimize these models and explore their integration into clinical practice.
ENHANCED NETWORK SECURITY USING ZERO TRUST IN SMART HOME NETWORKS AGAINST MAN-IN-THE-MIDDLE ATTACKS SINGH, BEWIT RAJ; Yusuf, Raka
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.10329

Abstract

The rapid adoption of Internet of Things (IoT) devices in Smart Home environments has increased network vulnerability to internal threats, such as Man-in-the-Middle (MitM) attacks, which traditional security models often fail to address. This study aims to design, simulate, and comparatively analyze the effectiveness of a Zero Trust architecture against a traditional security model in protecting a smart home network from MitM attacks. A comparative experiment was conducted in a GNS3 simulation environment featuring two topologies: a conventional flat network using HTTP and a Zero Trust network implementing microsegmentation via VLANs, Access Control Lists (ACLs), and encrypted HTTPS communication. MitM attacks, specifically ARP Spoofing and packet sniffing, were launched against both scenarios. The results unequivocally show that the traditional network was highly vulnerable, allowing attackers to successfully intercept user credentials in plaintext. In contrast, the Zero Trust architecture completely thwarted the attack; its layered defenses blocked unauthorized traffic and encrypted sensitive data, preventing any credential theft. This research concludes that the Zero Trust model is a significantly more effective and robust security strategy for IoT-based smart homes, providing superior protection against internal threats with minimal performance trade-offs compared to conventional approaches
AN EVALUATION OF THE POWER SUPPORT INTERNET INFRASTRUCTURE OF MAKASSAR CITY IN TELEMEDICINE FRAME Muhammad, Figur; Achmad, Andani; Adnan, Adnan; Mubarak, Abdul; Muis, Abdul
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.v7i1.7785

Abstract

This research aims to find the quality of the internet in Makassar City. It uses a 10 Mbps service from Indihome to support telemedicine. The study is a case study of sending raw MRI image data to the AWS cloud. The research uses a virtual server from the AWS cloud. It stores raw MRI image data. The data will be sent via the FTP client FileZilla. The tests were carried out eight times. They used the quality of service standard formula from TIPHON. The results come from 8 tests. In the tests, MRI image data was sent to the AWS cloud. The results show that the average throughput value was 4.53 Mbps with an index of 4. This result is excellent. Packet loss is low at 0.01% with an index of 4, which is very good. The delay is 1.7 ms with an index of 3, which is good. The jitter is 1.69 ms with an index of 3, which is good. The quality of service test results are based on TIPHON standards. They show that sending Raw MRI image data to the AWS cloud at 10 Mbps from Indihome in Makassar City is good.
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.
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.

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