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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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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.
Enhancing Security and Efficiency of an IoT-Based Diesel generator Using AES Encryption Ikhsan Yuda Pratama; lindawati, Lindawati; Eka Susanti, Eka Susanti
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.v9i1.15044

Abstract

The Automatic Transfer Switch (ATS) system plays a crucial role in ensuring seamless power transfer between the main power source (PLN) and the backup generator (Diesel Generator), which requires real-time monitoring. PT Kereta Api Indonesia (Persero) utilizes diesel generators at strategic stations such as Kramasan Station to maintain operational continuity. This study aims to design and develop a monitoring system based on the Internet of Things (IoT) using the ESP8266 microcontroller connected to Firebase as a cloud storage platform, along with a mobile application for remote control and monitoring. The system is equipped with the PZEM-004T sensor to measure electrical parameters (voltage, current, power, and frequency), the HC-SR04 ultrasonic sensor to monitor fuel levels, and a DC voltage sensor to assess battery conditions. Data security is enhanced through encryption using the Advanced Encryption Standard (AES) algorithm before transmission to the cloud, and decrypted on the user application. Experimental results show that the system operates optimally, with relative error averages of 0.44% for voltage, 7.5% for current, 1.32% for power, and 4.00% for fuel level. The novelty of this research lies in the integration of an IoT-based ATS monitoring system with AES encryption for securing cloud data transmission, as well as the implementation of a multi-sensor approach in a single integrated and industry-applicable system. Therefore, the system has proven to be effective in improving the efficiency, security, and reliability of automatic diesel generator monitoring, and holds potential for broader industrial-scale implementation.
SIMANKEL: A Web-based Information System for the Efficiency of Village Administration Muhammad Zamroni Uska; Arianti , Baiq Desi Dwi; Nafisah, Khufatun; Wirasasmita , Rasyid Hardi; Kholisho, Yosi Nur; Amri, Zaenul
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.v9i1.15055

Abstract

Administrative services in Sekarteja Village are still carried out manually through physical recording, which causes data irregularity, risk of losing archives, and limited access to information for the community. This research aims to develop and evaluate the feasibility of a web-based Kelurahan Administration Management Information System (SIMANKEL) as a solution to these problems. System development uses the Research and Development (R&D) method with a waterfall process model that includes requirements analysis, design, implementation, testing, and documentation. The system was developed using PHP Native, MySQL as a database, and the user interface was designed based on UI/UX principles using Visual Studio Code. System evaluation was conducted by referring to three aspects of the ISO 9126 software quality standard, namely functionality, usability, and efficiency. The test results show that all functions run according to specifications (100%), the usability aspect obtained a feasibility score of 91% from 20 respondents (excellent category), and the efficiency aspect shows that the system load time is in the good category based on testing using GTMetrix. These findings indicate that SIMANKEL is feasible to use as an information system for administrative services in Sekarteja Village and has the potential to increase the effectiveness, efficiency, and transparency of pub services.
Implementation of Random Forest Algorithm for Early Detection of Heart Health Using IoT and MAX30102 Sensors Matondang, Edo Hasudungan; Br Pintubatu, Sindy; Simamora, Johan Nopanzhe; Br Karo, Indi Jianna
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.v9i1.15062

Abstract

Heart disease is one of the leading causes of death in the world, including in Indonesia, which ranks second after stroke. Early detection is essential to reduce the risk of serious complications and death from cardiovascular disorders. This study aims to design an Internet of Things (IoT)-based early detection system for heart health that is integrated with MAX30102 sensors and Random Forest algorithms to classify heart rate conditions. Biometric data in the form of heart rate (BPM), blood oxygen level (SpO₂), and activity condition features (rest, light exercise, stress) were collected from 150 respondents. This data collection was validated by comparing the results using ECG devices by medical personnel. Pre-processing is done through data cleansing, category variable encoding, and feature extraction (BPM variability, PPG amplitude). The classification model was developed with the Random Forest 100 decision tree and tested with 5-fold cross validation. The results showed that the system was able to achieve an average accuracy of 93% with a standard deviation of 0.03, as well as an accuracy per fold of 93%, 93%, 97%, 93%, and 87%. The classification results are in line with the ECG data of medical personnel, indicating that this system is reliable enough for the early detection of normal or abnormal heart conditions. The study concluded that the integration of IoT and Random Forest is effective as a real-time, cost-effective, and supporting early detection of heart health, especially in remote areas. Advanced development is suggested to expand activity data and add biometric features to improve classification accuracy.
Implementation and Evaluation of 5G Standalone Network Using Open5GS, srsRAN, and USRP B210 for Research Purposes Noviansyah, Noer Ramadhon; Aryanti, Aryanti; Sopian Soim
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.v9i1.15064

Abstract

This study aims to implement and evaluate an Open Source-based 5G Standalone (SA) network using Open5GS as the Core Network, srsRAN as the Radio Access Network (RAN), and USRP B210 as a Software Defined Radio (SDR) device. A commercial smartphone was used as the User Equipment (UE) to test end-to-end network connectivity and performance. The research method includes software installation, network parameter configuration, system integration, as well as connectivity and performance testing based on ITU-R IMT-2020 standards. The test results show that all network elements were successfully integrated, as indicated by the successful registration and authentication of the UE and the establishment of a data session. Performance testing recorded a downlink throughput of 55 Mbps, uplink throughput of 15 Mbps, latency of 33 ms, jitter of 8.9 ms, and 0% packet loss. Although some performance parameters did not meet the minimum ITU-R IMT-2020 standards, the system proved operable as an independent Open Source and SDR-based solution for experimental purposes in a laboratory environment. Future work should focus on optimizing the backhaul connection, conducting multi-UE testing, and simulating mobility and handover scenarios to assess system performance in large-scale and real-world deployments.

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