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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 26 Documents
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Search results for , issue "Vol. 9 No. 1 (2025): Issues July 2025" : 26 Documents clear
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.
Android-based Detection of Melon Leaf Diseases Using Convolutional Neural Network and TensorFlow Syahputri, Rahmalia; Winarto; Trisnawati, Sherli; Taufik
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.v9i1.14542

Abstract

Melon productivity in Indonesia has experienced a significant decline due to leaf diseases, while manual detection performed by farmers remains subjective, time-consuming, and highly dependent on individual experience. To address this issue, this study aims to develop a mobile-based melon leaf disease detection system utilizing a Convolutional Neural Network (CNN) architecture integrated into the Tani Cerdas Android application via the TensorFlow framework. The dataset consists of 250 images of melon leaves categorized into five classes: healthy, aphids, fusarium wilt, leaf caterpillars, and unknown. Data were collected from two different melon farms employing distinct cultivation methods and processed through the machine learning life cycle, including data cleaning, manual labeling using one-hot encoding, splitting into 80% training and 20% validation sets, model training, and performance evaluation. The CNN model was trained for 11 epochs using ReLU and Softmax activation functions and a dropout rate of 0.2 to reduce the risk of overfitting. Training results achieved an accuracy of 91.5% with a loss value of 0.313, while model validation reached 71.9% accuracy. The ROC-AUC evaluation indicated excellent classification performance in most classes (AUC 0.99–1.00), although performance in the fusarium wilt class remained lower (AUC 0.87). Deployment of the model into the Tani Cerdas application achieved an average field accuracy of 86.33%. This study demonstrates the effectiveness of CNN and TensorFlow integration in supporting rapid and independent detection of melon leaf diseases via mobile devices, offering potential for the development of similar systems for other horticultural commodities.
Analysis of Moodle E-Learning Server Optimization with Load Balancing Technology using Round Robin and Leastconn Algorithms Ihsan, Ihsan; Lesmideyarti, Dwi; Kango, Riklan
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.v9i1.14658

Abstract

As web services and applications become increasingly complex and user demand grows—especially with the rising number of students—the need for a reliable E-Learning system becomes critical. At Politeknik Negeri Balikpapan, the current E-Learning platform operates on a single server, leading to slow response times and potential server downtime under high traffic conditions. This study addresses the issue by implementing load balancing using two algorithms: Round Robin and Least Connection, across three web servers and one separate database server. Testing was conducted using Apache JMeter with 1000 requests per 10 seconds. Results show that the Least Connection algorithm outperformed Round Robin, achieving an average response time of 155.8ms, compared to 184.2ms. Compared to the single-server setup, the load-balanced system showed significant improvements in response time, error rate, concurrency, availability, upstream, and downstream metrics. CPU load was also reduced due to traffic distribution across multiple servers. This demonstrates that server resource optimization via load balancing can significantly enhance the overall performance of E-Learning services. These findings provide a strong foundation for more efficient and scalable IT infrastructure development and support better decision-making in managing high-demand educational platforms in the future
Web-Based Job Portal System with WhatsApp Integration for Interview Invitation and Verification Automation Ade, Ade Agung Kurniawan; Suri, Riko Muhammad; Maharani , Putri Decelia
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.v9i1.14690

Abstract

In the digital era, job searches increasingly rely on online platforms that provide real-time job vacancy information. However, the conventional recruitment process still faces various obstacles, such as delays in information to applicants, the amount of administrative costs used by applicants in preparing documents to apply for jobs, vulnerable to fraudulent locker information from fake companies which of course this is detrimental and has an impact on job applicants. WhatsApp as the most popular communication media in Indonesia is a strategic opportunity in answering these challenges. This research aims to develop a digital-based job vacancy application with an integrated WhatsApp-based automatic verification interview invitation system and offer applicants the convenience of applying for a job and the ease of the Company in managing job applicant data to be more effective and efficient. This study is on the Instagram account @infokerjambi job vacancy media in Jambi Province with 50.1 thousand followers and 1.2 million profile impressions. Based on data from BPS Jambi Province, the Open Unemployment Rate (TPT) is 4.45. The Waterfaal model method used in this research and the test results show that the system is able to speed up the information process of sending interview invitations, reduce the potential for information delays because the system presents interview invitation features to wa applicants, applications and applicant emails in real time and increase security and trust in the recruitment process. For applicants, this system provides easy access to valid and real-time interview information, thus reducing the risk of fraud from verified companies. For companies, this system improves the management of applicant data management and administrative efficiency to reach a wider range of candidates.
Clustering Analysis to Identify Stunting Vulnerability Areas in North Aceh District Using the Fuzzy C-Means Algorithm Muhammad Ridha; Nurdin; Maryana
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.v9i1.14892

Abstract

Stunting is a serious public health issue that poses a long-term threat to the quality of human resources. North Aceh Regency is one of the regions with a relatively high prevalence of stunting, requiring targeted and effective intervention strategies. This study aims to classify regions based on their level of stunting vulnerability to support data-driven decision-making. The Fuzzy C-Means (FCM) clustering algorithm was selected due to its ability to handle data with flexible membership degrees, making it suitable for complex classification tasks. The data used in this research were obtained from the North Aceh Health Office for the year 2023 and include variables such as the number of children recorded in the E-PPGBM system, newly entered children in 2023, and the percentages of stunting, wasting, and underweight across 32 subdistricts. The research process involved data collection, literature review, system design and implementation using the Python programming language, and analysis of clustering results. The findings reveal that the 32 subdistricts can be grouped into three main clusters: high vulnerability (13 subdistricts), medium vulnerability (6 subdistricts), and low vulnerability (13 subdistricts). These clusters facilitate the visualization and identification of priority areas requiring more focused stunting interventions. In conclusion, the FCM algorithm proved effective in clustering regions based on stunting-related data. The implication of this study is to provide a foundation for local governments in formulating more efficient and targeted stunting intervention strategies according to the vulnerability level of each area.
Reliability Analysis of the Circulating Water Pump Instrumentation System Using the FMEA Method at PT PLN Nusantara Power UP Tenayan Wahyudhi Alfitrah; Jufrizel; Dian Musrsyitah; Aulia Ullah
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.v9i1.14921

Abstract

PT PLN Nusantara Power UP Tenayan in Pekanbaru operates a power plant that relies on the Circulating Water Pump (CWP) as a vital part of the cooling system. Based on the results of field observations and interviews, failures in the CWP instrumentation system can cause downtime and reduce operating efficiency, but reliability studies are still limited. This study aims to analyze the reliability of the CWP instrumentation system using the Failure Mode and Effect Analysis (FMEA) method. Data were obtained through field observations and technician interviews, then analyzed based on Severity, Occurrence, and Detection parameters. The analysis identified eight main components, with Risk Priority Number (RPN) values all below the 200 threshold. Based on the results of the FMEA calculation, the limit switch component has the highest RPN value of 160 with the potential for downtime reaching 2 to 3 hours per occurrence. The application of FMEA is proven effective to reduce the risk of failure by 25% based on estimated technical evaluation and failure history.
Sentiment Analysis of Public Opinion on Online Gambling Through Social Media Using Convolutional Neural Network D. Diffran Nur Cahyo; Handayani, Rizky; Budhi Lestari, Verra; Febriani, Siska
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.v9i1.15024

Abstract

Online gambling has become a serious social issue due to its easy accessibility through digital platforms, requiring effective policy interventions. This study analyzes public sentiment toward online gambling by examining 10,000 YouTube comments using a Convolutional Neural Network (CNN) algorithm. Data were collected via the YouTube API and underwent preprocessing steps including text cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was performed using a lexicon-based approach, with data transformed through Word2Vec embedding and balanced using oversampling techniques. The CNN model, consisting of embedding, convolutional, pooling, and dense layers, achieved an impressive accuracy of 99.10%, outperforming traditional machine learning methods. Sentiment was categorized into positive, neutral, and negative, with the majority of comments reflecting positive sentiment, indicating public support for efforts to combat online gambling. WordCloud visualizations highlighted dominant themes and frequently used terms. This study demonstrates the effectiveness of CNN in analyzing unstructured social media data and offers valuable insights for policymakers. Future research should explore hybrid architectures such as CNN-LSTM and expand datasets by including other platforms like Twitter, Instagram, and TikTok to enhance generalization and address broader social challenges.

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