cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota medan,
Sumatera utara
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 436 Documents
 40   41   42   43   44 
Classification of Oranges Based on Their Quality Using the YOLOv5 Algorithm muldayani, wahyu; Ali Rizal Chaidir; Sumardi; Dodi Setiabudi; Aabid Nabhaan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16255

Abstract

Indonesia, as an agrarian country, has a wide variety of horticultural commodities, one of which is mandarin orange (Citrus reticulata). Post-harvest handling, particularly the sorting process based on fruit ripeness and defects, plays an important role in maintaining product quality and market value. However, manual sorting is considered inefficient because it is repetitive, highly dependent on operator subjectivity, and prone to inconsistency. Several studies report those manual methods can result in classification error rates exceeding 20% and longer processing times compared to computer vision-based systems. This study develops an automatic citrus fruit quality classification system using the YOLOv5 algorithm. The dataset consists of 703 citrus fruit images captured directly using a webcam under varying lighting intensities and color conditions, and is divided into 80% training data and 20% testing data. The classification is performed into three quality categories: ripe, unripe (green), and rotten oranges, based on the visual characteristics of the fruit peel. Experimental results show that a training configuration with 300 epochs, a batch size of 40, and warm white bright lighting conditions achieves the best performance. Real-time testing on 15 citrus fruits yields an average accuracy of 78.2%, indicating the potential of the proposed system as an initial sorting aid, despite limitations related to lighting conditions and the amount of test data.
Design and Engineering of an AI-Enabled Mobile Microlearning Application Integrating Short-Form Video and Learning Analytics for Vocational Soft Skills Development Rosdiana; Hadiyana, Rizky Wahyu; Putra, Fikri Adi; Khair, Rizaldy
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16292

Abstract

The rapid growth of mobile technologies has reshaped how learning systems are designed, deployed, and evaluated, particularly in vocational education contexts. From a Mobile Software Engineering perspective, learning platforms must address constraints such as short interaction cycles, heterogeneous devices, scalability, and real-time analytics. This study focuses on the design and engineering of an AI-enabled mobile microlearning application that integrates short-form video, learning analytics, and LMS services to support vocational students’ soft-skills development. The proposed system is engineered as a mobile-first application with modular micro-content (60–180 seconds), rule-based personalization, and event-driven analytics to capture user interaction patterns. A Research and Development approach using the ADDIE framework is adopted, with emphasis on the software design, architecture, and prototyping stages. Validation involves expert review of system usability, content–software alignment, and limited pilot testing with end users. The results demonstrate that a mobile-engineered microlearning system can achieve high completion rates, acceptable latency under concurrent access, and effective analytics-driven feedback loops. The study contributes a practical mobile software engineering artefact and design insights for AI-enabled learning applications in vocational education.
Multi-Detection System Using Faster R-CNN for Fish Species Classification and Quality Assessment on Android Faza, Sharfina; Lubis, Arif Ridho; Meryatul Husna; Rina Anugrahwaty; Muhammad Rafif Rasyidi; Romi Fadillah Rahmat
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16374

Abstract

Species identification and quality assessment of fish in trade still rely on manual visual observation, which is subjective and requires specialized expertise. This method's limitations make it difficult for consumers to distinguish species with similar morphology and accurately assess fish quality, which can lead to inappropriate purchasing decisions. This research develops a multi-detection system based on Faster R-CNN with VGG16 backbone for fish species classification and quality assessment simultaneously on Android platform. The system uses convolutional layers to extract visual features from input images, Region Proposal Network for fish object detection and localization, and fully connected layers for simultaneous classification of species and quality levels. The research dataset consists of 3,000 images of five fish species (gourami, tilapia, nile tilapia, snapper, and pomfret) with four quality levels, divided into 2,400 training images and 600 testing images. The trained model is converted to TensorFlow Lite format for implementation on Android devices. Test results show the multi-detection system achieves 92% accuracy in fish species classification and quality assessment, demonstrating the effectiveness of the Faster R-CNN approach for multi-detection applications in the Android-based fisheries sector.
Optimizing Real-Time Weather Data and Information Services with Frameworks and Application Programming Interfaces Yanti, Nur; Ihsan; Fathur Zaini Rachman; Dwi Lesmideyarti; Mikail Eko Prasetyo
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16399

Abstract

The need for fast, accurate, and secure weather information is a major challenge in BMKG public services. This research aims to develop a web-based information system capable of presenting real-time weather, climate, and flight forecast data, as well as supporting transaction services and digital data distribution at the BMKG Balikpapan Meteorological Station. The method employed is Research and Development (R&D), utilizing a Waterfall model in conjunction with the Network Development Life Cycle (NDLC). The research stages include needs analysis, system design, implementation using a web framework, and integration of Application Programming Interface (API) for real-time data synchronization. The system utilizes PostgreSQL as a database and the SMTP protocol for sending verification emails. Key features developed include login and registration with layered verification, uploading proof of payment, and automatic sending of documents to user emails. With a responsive interface design and good data integration, this system improves the efficiency, security, and transparency of public services. The results of Black Box Testing on six items were declared successful. Additionally, system quality testing, conducted among 17 respondents through six questions using a Likert scale, yielded an average of 95.08%, indicating a very good category. This system supports the digital transformation. These findings imply that the developed system has the potential to be adapted to other sectors, such as health and agriculture, to support more efficient and secure digitalization of public services
Design and Field Evaluation of a Smart-Contract FinTech Model for MSME Financial Accountability and Transparency in Medan Surbakti, Eka Wulandari; Hasibuan, Siska; Almadany, Khairunnisa; Khair, Rizaldy
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16411

Abstract

Digital transformation requires Micro, Small, and Medium Enterprises (MSMEs) to enhance financial transparency and accountability in order to support Indonesia’s digital economic growth. This research is motivated by the limitations of traditional MSME accounting systems, which are still dominated by manual record-keeping, vulnerable to data manipulation, and characterized by limited access to digital accounting technologies. These conditions may reduce trust from business partners and financial institutions. Blockchain-based smart contract technology offers an innovative solution by enabling transparent, automated, and immutable financial transactions. This study aims to design a smart contract–based digital accounting system suitable for implementation by MSMEs in Medan City. The research adopts a Research and Development (R&D) approach combined with Design Science Research (DSR). The research stages include user needs identification, system design using the Solidity programming language, prototype development on the Ethereum Testnet, and testing through MSME transaction scenarios. System evaluation was conducted through functional testing and end-user interviews, revealing significant improvements in key financial accountability indicators, along with a high system usability score (SUS = 77.12) and strong adoption intention. The proposed system is expected to deliver a real-time, automated, and tamper-resistant accounting model that strengthens MSME financial accountability and competitiveness within the digital economy ecosystem..
Yolov12N: Implementation and Measuring an Ingredient-Detection Recipe App for Household Food-Waste Reduction Suwarno, Suwarno; Jackson, Jackson; Deli, Deli
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16494

Abstract

Household food waste remains pervasive, driven by suboptimal meal planning and the underuse of available ingredients, with particularly acute impacts in Indonesia. This study presents a mobile application using YOLOv12n for multi-ingredient detection that translates recognized items into actionable recipes, and it evaluates user acceptance through the Technology Acceptance Model. On this implementation, the detection module attains mAP at 0.5 of 0.579 and mAP from 0.5 to 0.95 of 0.331. This study implements a Flutter application with YOLOv12n multi-ingredient detection integrated with TheMealDB and observes 219 users. The instrument validity is established and reliability is strong. While, Structural Equation Modelling supports 3 hypotheses, meanwhile Perceived Usefulness to Behavior Intention is not significant, indicating an indirect pathway via attitude. In conclusion, the solution is feasible for daily use, and strengthening perceived usefulness and ease of use appears to be a promising route to increase adoption and help reduce household waste.
Digital Marketing and Consumer Engagement for Traditional Handwoven Products through a Web-Based Retail System Muhammad Zamroni Uska; Wirasasmita, Rasyid Hardi; Firdaus, Rona; Kholisho, Yosi Nur; Jamaluddin, Jamaluddin
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16500

Abstract

Traditional handwoven products often struggle to reach wider markets due to the continued reliance on conventional selling practices, which limits visibility and reduces opportunities for consumer engagement in digital retail environments. This study aims to develop and evaluate a web-based retail platform that supports the digital marketing of traditional handwoven products and enhances consumer interaction. A Research and Development approach was employed, integrating essential retail features such as product catalog management, ordering processes, user administration, and interaction interfaces. The platform was evaluated through expert validation to assess content relevance, functional testing using automated tools to ensure system accuracy, performance assessment to examine system efficiency, and usability testing involving 25 users to measure ease of use and consumer acceptance. The results demonstrate that the platform performs reliably, achieves complete functional accuracy, and exhibits high usability, indicating strong approval among users. Performance testing also shows that the platform operates efficiently and is suitable for real retail use. The implications of this study suggest that web-based retail platforms can strengthen digital marketing practices, expand market accessibility for traditional artisans, and contribute to improved consumer experience within craft-based retail sectors.
Comparative Analysis of Naïve Bayes and K-Nearest Neighbor for Lexicon-Based Emotion Classification of Paxel App User Reviews Salsabilah, Azka; Vitianingsih, Anik Vega; Cahyono, Dwi; Lidya Maukar, Anastasia; Zangana, Hewa Majeed
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16516

Abstract

The rapid growth of app-based delivery services has increased the importance of understanding user emotions as an indicator of service quality. User reviews on digital platforms provide valuable insights into customer perceptions, satisfaction levels, and service-related issues. This study aims to compare the performance of Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in classifying user emotions related to the Paxel application. The dataset was collected from Google Play Store and X (Twitter) using web scraping techniques and subsequently processed through text pre-processing stages, including case folding, tokenization, and stopword removal. Emotion labels were assigned using the NRC Indonesian Emotion Lexicon, while feature extraction was performed using the TF-IDF method. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied prior to model training. Experimental results show that the Naïve Bayes model achieved the highest overall accuracy of 90.83% with a weighted F1-score of 0.90, while the KNN model obtained an accuracy of 81.21% and a weighted F1-score of 0.77. Both models performed well in identifying happy, sad, and neutral emotions, whereas anger remained the most challenging class to classify. Overall, Naïve Bayes demonstrated more consistent and reliable performance for sentiment analysis tasks..
Enhancing Oil Palm Leaf Disease Classification using a Pruned SqueezeNet Architecture Nugraha Rahmadan Diyanto; Muhathir; Fadlisyah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16524

Abstract

The SqueezeNet architecture is known to be effective but possesses a considerable number of parameters, which can be optimized using pruning—a compression technique that significantly reduces model parameters without sacrificing accuracy. This research aims to apply the L2-Norm based pruning method to the SqueezeNet architecture and compare its performance (accuracy and efficiency) against the default SqueezeNet model for classifying four classes of oil palm leaf diseases. The study used a primary dataset of 4,000 images, divided into training (70%), validation (20%), and testing (10%) sets. The SqueezeNet architecture was pruned using L2-Norm structured pruning with a uniform distribution at rates from 10% to 50%, followed by fine-tuning. The results show that the default SqueezeNet achieved 97.50% accuracy with 724,548 parameters. Significantly, a 10% pruning rate actually increased the accuracy to a high of 99.25% while simultaneously reducing the parameters to 579,036. Overly aggressive pruning, such as 40%, drastically decreased accuracy to 93.25%. It is concluded that the 10% pruning rate is the most optimal, proving that this method not only makes SqueezeNet lighter but also more effective. This 10% pruned model is highly suitable for application implementation due to its enhanced efficiency. Future research is recommended to validate these findings using a more diverse dataset and to test the model on actual edge devices.
IoT System for Monitoring Protection IED Switch Status Abdullah, Muhammad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16535

Abstract

The reliability of protection systems in transmission networks depends on the readiness of devices, particularly the switch status that governs trip functions during a fault. In many substations, monitoring is still manual, conducted via panel checks or photos, which can lead to delays and misconfigurations, especially after maintenance. This risks protection unreadiness, causing operational delays or trip failures. This research develops an IoT-based system to monitor the status of protection switches, ensuring real-time readiness. The system reads IED coil registers via Modbus TCP, processes data using Node-RED, and displays results on a Node-RED UI dashboard. Results show the system reads switch status with 100% communication success. Status changes are detected in 0.2–0.3 seconds, matching the 0.5-second polling interval. Tests conducted on four IED test scenarios demonstrated full conformity between the IED status and the dashboard. All five protection channels per IED were read without discrepancies, and the dashboard handled parallel IED updates without data loss. This demonstrates that the system can replace slow and error-prone manual methods. This IoT system enhances protection reliability and supports efficient subsystem management. The implication is that this digital monitoring can be implemented without additional SCADA infrastructure.

Page 43 of 44 | Total Record : 436

 40   41   42   43   44 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol. 9 No. 2 (2026): Issues January 2026 Vol. 9 No. 1 (2025): Issues July 2025 Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy Vol. 8 No. 2 (2025): Issues January 2025 Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024 Vol. 8 No. 1 (2024): Issues July 2024 Vol. 7 No. 1 (2023): Issues July 2023 Vol. 6 No. 2 (2023): Issues January 2023 Vol 6, No 1 (2022): Issues July 2022 Vol 5, No 2 (2022): Issues January 2022 Vol 5, No 1 (2021): EDISI JULY 2021 Vol 4, No 2 (2021): EDISI JANUARY 2021 Vol 4, No 1 (2020): ---> EDISI JULI Vol. 4 No. 1 (2020): ---> EDISI JULI Vol 3, No 2 (2020): EDISI JANUARI Vol 3, No 1 (2019): EDISI JULI Vol 2, No 2 (2019): EDISI JANUARI Vol 2, No 2 (2019): EDISI JANUARI Vol 2, No 1 (2018): EDISI JULI Vol 2, No 1 (2018): EDISI JULI Vol 1, No 2 (2018): Edisi Januari Vol 1, No 2 (2018): Edisi Januari Vol 1, No 1 (2017): Edisi Juli Vol 1, No 1 (2017): Edisi Juli More Issue