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INDONESIA
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
Published by Universitas Medan Area
ISSN : 25496247     EISSN : 25496255     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
JURNAL TEKNIK INFORMATIKA, JITE (Journal of Informatics and Telecommunication Engineering) is a journal that contains articles / publications and research results of scientific work related to the field of science of Informatics Engineering such as Software Engineering, Database, Data Mining, Network, Telecommunication and Artificial Intelligence which published and managed by the Faculty of Informatics Engineering at the University of Medan Area .
Arjuna Subject : -
Articles 412 Documents
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Grouping of Tourism Locations in Indonesia Using Distance Variations in the K-Means Algorithm Farida, Juni Irsan; Lubis, Andre Hasudungan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14528

Abstract

Indonesia is home to a diverse range of tourist destinations, yet the classification and mapping of these locations remain a challenge in tourism management. This study aims to cluster tourist destinations in Indonesia by applying the K-Means algorithm with three distance metric variations: Euclidean Distance, Manhattan Distance, and Canberra Distance. The dataset was sourced from public data repositories and underwent preprocessing steps, including data normalization. The optimal number of clusters was determined using the Elbow Method, while the clustering results were evaluated using the Silhouette Score and Davies-Bouldin Index. The findings indicate that Manhattan Distance produced the highest Silhouette Score (0.321463), suggesting superior clustering performance compared to the other two metrics. The results of this study provide valuable insights for stakeholders in formulating strategic tourism promotion and infrastructure development efforts.
Enchancing Brain Tumor Disease Classification via SqueezeNet Architecture Integrated with Group Convolution Gultom, William; Muhathir, Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14552

Abstract

Brain tumor classification using MRI images is a major challenge in medical image processing, particularly when facing imbalanced data between classes. This imbalance often leads to model bias toward the majority class and reduces sensitivity to the minority class—patients with tumors. This study aims to analyze the impact of applying Group Convolution techniques to the VGG19 and SqueezeNet architectures to enhance both computational efficiency and classification accuracy. A quantitative experimental approach was employed, implementing Convolutional Neural Networks (CNNs) using the PyTorch framework. The dataset includes two classes, “Yes” (with tumor) and “No” (without tumor), organized into Train, Validation, and Test folders. The models were evaluated by comparing the performance of standard architectures with modified versions integrating Group Convolution. Experimental results show that SqueezeNet with Group Convolution achieved up to 90% accuracy, outperforming the original model. Additionally, the model exhibited significantly improved sensitivity to the minority class, indicating better performance under imbalanced conditions. These findings suggest that Group Convolution enhances not only computational efficiency but also classification capability. Therefore, this technique is applicable in developing automated diagnostic systems. Future research is encouraged to combine Group Convolution with methods such as attention mechanisms to achieve more optimal and reliable classification results.
Improving the Accuracy of Coffee Leaf Disease Detection Using Squeezenet and Simam Fadli, MHD. Fajar Alry; Muhathir, Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14557

Abstract

Early detection of coffee leaf diseases such as leaf rust and Phoma is essential due to its direct impact on crop productivity and quality. Recent studies have shown that lightweight CNN architectures like SqueezeNet are effective for deployment on resource-constrained devices, though they still face limitations in classification accuracy for complex disease types. This study aims to improve the accuracy of coffee leaf disease classification by integrating the SqueezeNet architecture with the SimAM attention module, which enhances feature representation without significantly increasing model complexity. A quantitative experimental approach was used, employing an open-source dataset of coffee leaf images that was augmented and categorized into three classes: healthy leaves, leaf rust, and Phoma. The models were evaluated using accuracy, precision, recall, and F1-score metrics. Results show that integrating SimAM into SqueezeNet increased the model’s accuracy from 81% to 84%. The most significant improvements were observed in the leaf rust and Phoma classes, with F1-scores rising from 0.70 to 0.79 and from 0.73 to 0.76, respectively. Additionally, the AUC score improved to 0.91. These results demonstrate that SimAM integration effectively enhances classification performance, though challenges remain in distinguishing classes with visually similar features. Further research is recommended to implement more aggressive data augmentation and regularization techniques to improve model generalization.
Application of MobileNetV2 Architecture with SIMAM for Automatic Detection of Diseases on Mango Leaves Simanjuntak, Juan; Muhathir, Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14612

Abstract

Early detection of diseases in mango plants is crucial for improving crop yields and reducing economic losses for farmers. This study proposes the use of the MobileNetV2 architecture integrated with the Simple Attention Module (SIMAM) to enhance the accuracy of disease detection on mango leaves. MobileNetV2 was chosen for its computational efficiency, particularly on mobile devices, while SIMAM was utilized to strengthen the model’s focus on important visual features that represent disease symptoms on the leaves. The dataset used in this research consists of 3,000 images of mango leaves categorized into three classes: Capnodium, Colletotrichum, and Healthy Leaves. The model was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the MobileNetV2 + SIMAM model achieved high performance, with an accuracy of 0.9833, precision of 0.9841, recall of 0.9833, and F1-score of 0.9833. With its combination of computational efficiency and high classification accuracy, this model has strong potential for implementation in mobile applications to assist farmers in detecting mango leaf diseases quickly, accurately, and practically in the field.
Comparison of Support Vector Machine (SVM) and Naïve Bayes Algorithm Performance in Analyzing Garuda Bird Design Sentiment in IKN Moh Hafiz Raja Pratama , Munthe; Andre Hasudungan , lubis
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14830

Abstract

The Government's policy in moving the Indonesian Capital City (IKN) is considered controversial, this has given rise to various responses from the public, especially on social media X. This research aims to analyze tweet sentiment related to IKN and compare the two algorithms. In this experiment, we succeeded in collecting 5128 tweet data regarding IKN in the X application, the total amount of IKN data was classified into positive sentiment as 2598 1659 negative data and sentiments. Research objectives, methods used, main results, and implications. This research aims to measure public sentiment towards the design of the Garuda bird as the main symbol of the Indonesian Capital City (IKN) by using a comparison of the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in analyzing the sentiment of the Garuda bird design in the IKN. main results, for example: the proportion of positive, negative and neutral sentiment, as well as the factors that most influence sentiment. Implications of research results for government, designers and society.
Classification Of Interest In Sports Using The Naive Bayes Classifier Method Lumbantobing, Martin; Novita, Nanda
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.15054

Abstract

Interest in sports must be done from an early age and requires the right method to achieve the desired results, namely by using the Naive Bayes method. The purpose of this study was to explore the sports interests of students of SDN 3 Tarutung with the Naive Bayes method. The subjects of this study were students in grades 4, 5, and 6 of SDN Jenggolo Tuban who liked sports using data collection methods, namely the Naive Bayes method, Observation, Interviews and practical sports tests based on sport search, namely Height (TB), Sitting height (TD), Weight (BB), Arm span (RL), Tennis ball throw and catch (LTBT), Basketball throw (LBB), Vertical jump (LT), Agility run (LK), 60 meter sprint (L60M), and multi-stage run (MFT). The Naive Bayes modeling system is carried out in two phases, namely training data and testing data. The results of the study obtained were the results of the classification of each student who was talented in sports (football, volleyball, badminton, sprinting, swimming) or not talented in sports (football, volleyball, badminton, sprinting, and swimming). The results of the testing data showed that 7 students were talented in football, 2 students were talented in volleyball, 1 student was talented in badminton, 5 students were talented in sprinting, and 3 students were talented in swimming.
Optimization of Feature Extraction in Images Using Variants of Decomposition Algorithms Hutagalung , Fatma Sari; Siregar, Farid Akbar; Al-Khowarizmi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.12705

Abstract

This research aims to optimize the feature extraction process in digital images using two decomposition algorithms, namely Haar and Riyad. Feature extraction is an important step in digital image processing, used to extract significant information from images for applications such as pattern recognition, medical image analysis, and surveillance systems. Haar and Riyad algorithms are tested on three types of images: grayscale, color, and texture. Results show that Haar's algorithm excels in processing speed with an average time of 121.67 ms, making it ideal for real-time applications. In contrast, the Riyad algorithm showed higher feature detection accuracy, achieving an average of 93.33% on complex images, despite requiring a longer processing time of 154 ms. This research shows that the selection of a feature extraction algorithm should consider the type of image and the application needs. Haar's algorithm is suitable for real-time surveillance applications, while Riyad is more suitable for in-depth analysis such as on medical images. The significant contribution of this research is that it provides insight into the trade-off between speed and accuracy, and opens up opportunities to develop hybrid methods that combine the advantages of both algorithms to create more efficient and effective image processing solutions.
Classification Of Outstanding Students Using Support Vector Machine (SVM) Based on Data Mining Riduan Syahri; Desi Puspita
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.13191

Abstract

This research aims to classify outstanding students at the Pagar Alam Institute of Technology using the Support Vector Machine (SVM) algorithm based on data mining. Early identification of outstanding students is crucial for supporting potential development and institutional decision-making. Historical data from 245 students from the 2016 to 2018 cohorts were utilized, encompassing course grades and Cumulative Grade Point Average (CGPA). The research process included data preprocessing such as normalization and splitting the data into 80% training data and 20% testing data. The SVM model was implemented with a Radial Basis Function (RBF) kernel and parameters C=1.0 and gamma=0.1. Evaluation results show that the model achieved an overall accuracy of 89.80% on the testing data. The model's performance was further validated through a confusion matrix (9 True Positives, 1 False Negative) and a classification report indicating good precision and recall for both classes. Furthermore, an Area Under the Curve (AUC) value of 0.93 signifies the model's excellent discriminative ability. This study contributes by providing an effective classification tool for identifying outstanding students, which can serve as a basis for the institution to design more targeted development and recognition programs.
Comparison of Random Forest, K-Nearest Neighbors, Decision Tree, and Neural Network for Predicting Rainfall Fariyani, Fariyani; Sunarno; Iqbal; Upik Nurbaiti; Ian Yulianti
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.13638

Abstract

Erratic rainfall due to climate change has significant impacts on the environment, agriculture and economy. To mitigate these impacts, a reliable rainfall prediction model is needed. Erratic rainfall due to climate change affects various sectors of life, so a reliable prediction model is needed to support data-based decision making. This study aims to compare the performance of Random Forest, k-Nearest Neighbors (kNN), Decision Tree, and Neural Network algorithms in predicting rainfall using observation data from the Citeko Meteorological Station. The data used include weather parameters such as temperature, humidity, and air pressure. The analysis was carried out using Orange software to evaluate the accuracy, precision, and computation time of each model. The results showed that Random Forest had the highest accuracy, while Neural Network showed consistent performance on more complex datasets. The kNN algorithm gave good results with the optimal number of neighbors, but was less efficient on large datasets. Decision Tree was easier to interpret but was prone to overfitting. This study provides insight into the most appropriate algorithm for rainfall prediction based on the characteristics of the data available.
Simulation of the Single Sign-On Method for Service Provider Applications: A Case Study of Bhayangkara University Surabaya Sudrajat, Vicinthia Veren; Adityo, R.Dimas; Arizal, Arif
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.13750

Abstract

The authentication system at Bhayangkara University Surabaya is still traditional, where users must have separate accounts for each service. This condition causes inefficiency, administrative burden, and security risks due to managing multiple credentials. This study aims to design and simulate a single authentication system based on Single Sign-On (SSO) to improve efficiency and ease of user access to campus digital services. The system was developed with an iterative approach using JSON Web Token (JWT) and RESTful API technology. The simulation was carried out by testing two applications, namely Identity Provider (IdP) and Service Provider (SP), which interact in a single authentication scenario. Three types of testing were carried out: (1) simulation of the login flow and SP access after authentication at the IdP, (2) compatibility testing between Android devices (multi-device), and (3) RESTful access performance testing, including response time, throughput, and token validity. The results show that the SSO system is able to centrally integrate campus services, accelerate authentication, and maintain access security. The average response time was recorded below 1.5 seconds, even when tested on 20 devices simultaneously. The implementation of SSO has been proven to improve operational efficiency and simplify user identity management. This system contributes to an improved user experience and can be replicated by other educational institutions with similar needs.

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