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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 24 Documents
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Search results for , issue "Vol. 9 No. 2 (2026): Issues January 2026" : 24 Documents clear
Classification of Bougainvillea Plant Types Using Convolutional Neural Network Algorithm Fauzi Rachman; Iwan Lesmana; Nugraha, Nunu
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.v9i2.15354

Abstract

Bougainvillea is one of the most popular ornamental plants, featuring a variety of types with morphological characteristics that often appear very similar. This resemblance frequently complicates the conventional identification process, particularly for sellers and buyers at Rabiku Florist. This study aims to develop an Android application capable of automatically classifying different bougainvillea types using a Convolutional Neural Network (CNN) algorithm. The system is developed using the Rapid Application Development (RAD) methodology, leveraging the MobileNetV2 architecture and integrating it with the TensorFlow Lite framework to ensure compatibility with mobile devices. The application is designed to identify five types of bougainvillea using digital images captured via the device’s camera or selected from the user’s gallery. Based on implementation results, the system demonstrates strong classification performance and delivers accurate information to users. This application is intended to serve as a practical and user-friendly tool for both the general public and businesses in accurately identifying bougainvillea species.Keywords: Image Classification, Bougainvillea, Convolutional Neural Network, MobileNetV2, Android.
Combined Barker-M-Sequence Coded LFM for High-Performance Subarray-MIMO Radar Applications Iqbal, Akhmad; Tahcfulloh, Syahfrizal; Antonius, Antonius; Juliannanda, Rizkyandi; Nurrahmansyah, Arya
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.v9i2.15837

Abstract

Subarray-Multiple-Input Multiple-Output (SMIMO) radar is an advanced technology that integrates the advantages of phased-array and MIMO radars to enhance target detection resolution. A key challenge in SMIMO implementation lies in improving velocity resolution without compromising spectral efficiency, while maintaining accurate target detection capability under high sidelobe levels and inter-channel interference. This study proposes a novel approach—Combined Barker-M-Sequence Coded LFM—in which the LFM signal is phase-modulated using a hybrid code formed by concatenating a Barker sequence (length 11) and an M-sequence (length 7). Simulation results show that the proposed signal achieves a Peak Sidelobe Ratio (PSLR) of −20.83 dB, significantly outperforming LFM-Barker (−8.45 dB) and LFM-M-sequence (−16.3 dB). It also delivers a velocity resolution of 0.95 m/s and a range resolution of 225 m, representing a 38% improvement over standard LFM. Moreover, under SNR = −5 dB, the system achieves a SINR gain of 4.7 dB relative to LFM-M-sequence. This approach enables more efficient waveform utilization in modern radar applications—such as air surveillance, military defense, and autonomous vehicles—particularly in challenging environments characterized by low SNR, multipath propagation, and high clutter.
Anomaly Detection in Cloud Device-Based Information Technology Infrastructure Using Isolation Forest Algorithm ., Andi Zulherry; Imam Riadi; Rusydi Umar
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

Abstract

Cloud device-based information technology infrastructure generates large volumes of operational data that are dynamic and heterogeneous, increasing the complexity of monitoring and anomaly detection processes. Conventional rule-based approaches and supervised learning methods are often less effective due to limited labeled data and their inability to detect newly emerging anomaly patterns. Therefore, this study aims to apply and evaluate the Isolation Forest algorithm as an anomaly detection method for cloud device-based information technology infrastructure. The research data consist of system and network performance metrics, including CPU usage, memory utilization, disk activity, and network traffic collected from a cloud environment. The research stages include data preprocessing, normalization, and feature selection to improve data quality and model performance. The Isolation Forest algorithm is implemented using an unsupervised learning approach, where anomalies are identified based on the algorithm’s ability to isolate data points that exhibit characteristics deviating from the majority of normal data. Model performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics, while parameter optimization is conducted using the grid search method to obtain the best configuration. The results indicate that the Isolation Forest algorithm is able to detect anomalies effectively, achieving high accuracy and a good balance between precision and recall. The model with optimal parameters demonstrates improved performance by reducing detection errors compared to the baseline configuration. Thus, the Isolation Forest algorithm can serve as a reliable and scalable solution to support monitoring activities and enhance the reliability of cloud infrastructure.
Identifikasi Sentimen pada Data Teks Media Sosial Melalui Pendekatan Pembelajaran Terawasi Bahit, Muhammad; Sari, Yuslena; Baskara, Andreyan Rizky; Wijaya, Eka Setya; Yunus, Harry Pratama; Armelia, Alysa
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.v9i2.16032

Abstract

Analisis sentimen pada data teks media sosial menjadi penting untuk memahami opini publik, sehingga penelitian ini bertujuan untuk mengidentifikasi sentimen pada data teks media sosial melalui pendekatan pembelajaran terawasi. Dataset yang digunakan terdiri dari tweet dan ulasan produk yang telah dilabeli sentimen positif maupun negatif. Proses penelitian dilakukan melalui beberapa tahapan, yaitu prapemrosesan data (Removal of Stopwords, Lemmatization and Word Stemming, Spell Correction), Ekstraksi Fitur (N-Grm, Word count dan Tf-Idf Vectorizer) serta penerapan algoritma Multinomial Naive Bayes, dan Support Vector Machine (SVM). Hasil penelitian menunjukkan bahwa penghapusan stopwords menurunkan kinerja model, sehingga tetap menggunakan stopwords. Stemming dan lemmatization juga tidak memberikan pengaruh terhadap kinerja model, sedangkan spell correction dapat meningkatkan akurasi sekitar 2% tetapi dengan trade-off waktu komputasi yang tinggi. Pada tahap ekstraksi fitur, TF-IDF menghasilkan akurasi yang lebih tinggi dibandingkan Word Count. Algoritma Multinomial Naive Bayes menghasilkan akurasi sebesar 79,73% dengan AUC-ROC sebesar 0,85. Sedangkan SVM dengan kernel linear mendapatkan hasil terbaik dengan akurasi 82% dan AUC-ROC 0,88, lebih tinggi daripada RBF kernel yang hanya mencapai akurasi 77,79% dan AUC-ROC 0,82. Hasil penelitian ini dapat disimpulkan bahwa SVM dengan kernel linear lebih sesuai untuk klasifikasi teks berdimensi tinggi.
Integrating Blockchain-Based Smart Contracts for Digital Certification: A Micro-Credentials Model for Vocational Higher Education Rahmah, Maulidya; Br Lubis, Ika Rahmadani; 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.v9i2.16045

Abstract

The rapid advancement of the digital industry requires vocational education in Indonesia to undergo transformation, particularly in providing competency validation systems that are efficient, adaptive, and trustworthy. In reality, however, competency certification processes in many vocational institutions are still conducted manually and tend to be bureaucratic, limiting their ability to respond to the dynamic needs of industry. This condition may reduce graduates’ competitiveness and widen the skills gap between vocational education and the labor market. Micro-credentials have emerged as an innovative approach to recognizing competencies in a modular, flexible, and industry-oriented manner. Nevertheless, their implementation still faces significant challenges, especially in terms of validation speed, reliability, and transparency. To address these challenges, this study develops a micro-credential–based competency validation model integrated with blockchain technology through the use of smart contracts at Politeknik LP3I Medan. This research adopts a Research and Development (R&D) approach based on the Borg and Gall model, including needs analysis, learning module design, system development, limited trials, expert validation, and effectiveness evaluation. Alpha testing involving 15 students demonstrates a system success rate of 95%, with an average verification time of 14.4 seconds. Usability evaluation indicates that the system is user-friendly and well accepted.
Development of a Smart IoT Dashboard for Sustainable River Water Quality Monitoring in Ciujung River Mariestiara Putri, Salsanabila; Mujiburohman, Mujiburohman; 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.v9i2.16063

Abstract

The water quality of the Ciujung River in Serang has experienced a significant decline due to domestic and industrial waste pollution, directly affecting public health and environmental sustainability. Current monitoring systems remain largely manual and lack responsiveness, resulting in delayed and less data-driven pollution management. This study aims to develop an Internet of Things (IoT)-based water quality monitoring system integrated with an intelligent dashboard to support sustainable environmental programs. The proposed system monitors key water quality parameters, including pH, temperature, turbidity, and total dissolved solids (TDS), in real time. The methodology includes designing a microcontroller-based sensor prototype, integrating data communication modules (LoRa/GSM), processing data via a cloud server, and implementing interactive visualization through a web-based dashboard. Furthermore, the system features an early warning mechanism when water parameters exceed environmental quality thresholds. Field trials are conducted at several strategic points along the Ciujung River to evaluate data acquisition reliability, connectivity stability, and sensor accuracy. The expected outcome is an efficient, responsive, and adaptive monitoring system that supports data-driven decision-making in river water management and reinforces commitments to sustainable development.
Integrating Automatic Stock Monitoring and Digital Inventory Systems for MSMEs A Mobile Application Approach (Case Study in Serang City, Indonesia) Arizta Putri , Rezty; Mariestiara Putri , Salsanabila; Mardiah, Hayatul; 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.v9i2.16111

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Indonesia continue to face inefficiencies in inventory management due to manual stock recording, data inconsistency, and delays in operational decision-making. In Serang City, these challenges often lead to stockouts, excess inventory, and limited business scalability. This study aims to develop and evaluate a mobile-based automated inventory management system that supports real-time stock monitoring and decision-making for MSMEs. The research employs a Research and Development (R&D) approach integrated with the Agile-Scrum methodology, encompassing problem identification, user requirement analysis, system design, prototype development, functional testing, and usability evaluation. Functional validation was conducted using black box testing, while system usability was assessed using the System Usability Scale (SUS) involving 15 MSME users. The results indicate that all core system functions achieved a 100% success rate, including automated stock recording, cloud-based data synchronization, real-time notifications, and dashboard analytics. The usability evaluation produced an average SUS score of 82.5, classified as Excellent, indicating high user acceptance and ease of use. These findings demonstrate that the proposed system effectively improves inventory accuracy, operational efficiency, and decision-making quality, contributing to MSME digital transformation in developing regions.
Design and Implementation of a Blockchain-Based Smart Barcode System to Enhance Supply Chain Traceability of Traditional Golok Ciomas Craftsmanship Solihin; Dodi; Idris, Iswandi
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.v9i2.16196

Abstract

Golok Ciomas, a traditional weapon deeply rooted in Indonesian heritage, holds both cultural and economic significance for local micro-enterprises. However, the expansion of its market reach is hindered by limited supply chain transparency, inadequate product traceability, and the absence of authentication mechanisms. This study presents the design and implementation of a smart QR-based tracking system to enhance supply chain visibility and prevent counterfeiting. Employing a mixed-methods approach—combining participatory field observation with web-based software prototyping— framework embeds dynamic QR codes at every production stage, from raw material sourcing to end distribution. Pilot testing was conducted with selected blacksmiths and local traders in Banten Province. The results demonstrate that platform successfully increases information transparency, verifies product authenticity, and expands digital marketing reach. Compared to traditional manual records, the smart barcode platform significantly reduces data fragmentation and facilitates efficient access without requiring high-end infrastructure. This research contributes to the digital empowerment of heritage-based micro-enterprises while preserving product authenticity. Future improvements include blockchain integration and mobile-responsive features to extend usability. Framework serves as a scalable model for other cultural craftsmanship sectors seeking to modernize without compromising their artisanal identity.
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.

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