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Jurnal Elektronika dan Teknik Informatika Terapan
Published by Politeknik Kampar
ISSN : 29880866     EISSN : 29880874     DOI : 10.59061
Core Subject : Science,
Computer Science & IT
Bidang Ilmu Komputer dan Informatika Bidang Ilmu Internet Of Thinks Bidang Ilmu Mikrokontroller Bidang Ilmu Animasi dan Multimedia
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 100 Documents
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Integrasi Artificial Intelligence pada Arsitektur Backend Next.js dan PostgreSQL di DISKOMINFO Kalimantan Barat Fauzan Asrin; Muhammad Bayu Prasetyo; Khairul Hafidh
Jurnal Elektronika dan Teknik Informatika Terapan Vol. 4 No. 2 (2026): Juni: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i2.1528

Abstract

This study examines the implementation of a web-based backend system using Next.js and PostgreSQL, integrated with Artificial Intelligence, within the context of the West Kalimantan Communication and Informatics Agency (Diskominfo). The system is designed to replace manual data management processes—previously reliant on spreadsheets and disparate documents—which frequently resulted in delayed updates, synchronization difficulties, and inefficient reporting. Implementation efforts focused on developing a structured API, user authentication, role-based authorization, project data management, AI integration for analysis and automated summarization, and real-time notification support. The technologies employed include Next.js 15, React 18, TypeScript 5, Tailwind CSS, PostgreSQL (via Supabase), NextAuth.js, Supabase Realtime, and Google Gemini AI (via Firebase Genkit). A structured design approach was adopted to ensure effective management of workflows, data structures, and component relationships. The design results indicate that the system has the potential to enhance efficiency, access security, and data management quality within the West Kalimantan Diskominfo environment.
Analisis Usability Website Oceanography ITB Hub Menggunakan System Usability Scale Berdasarkan Karakteristik Pengguna Kahfi Gunardi; Iwan Pramesti Anwar; Suhadi Parman; Gina Khayatun Nufus; Layli Hardiyanti
Jurnal Elektronika dan Teknik Informatika Terapan Vol. 4 No. 2 (2026): Juni: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i2.1529

Abstract

Oceanography ITB Hub is a scientific dashboard that presents real-time meteorological and oceanographic observation data from an Automatic Weather Station (AWS) installed at the Marine Technology and Coastal Research Center (MTCRC) pier, Cirebon. As a dashboard with high information density and technical terminology, usability becomes a critical aspect, particularly because its users come from diverse academic backgrounds. This study aims to measure the usability level of the website using the System Usability Scale (SUS) and to analyze differences in usability perception based on user characteristics. The study involved 68 respondents divided into five groups: STMKG Lecturers, Science and Technology Lecturers, STMKG Students, Science and Technology Students, and Social Science Students. Data were analyzed using descriptive statistics, the Kruskal–Wallis test, and the Dunn–Bonferroni post hoc test. The results show a mean SUS score of 67.13, classified as Grade D, Marginal Acceptability, and an “OK” Adjective Rating, with all items proven valid and the instrument reliable (Cronbach's Alpha = 0.851). The Kruskal–Wallis test revealed a statistically significant difference in usability across user groups (H = 17.126; p = 0.0018), with STMKG Students rating usability significantly higher than Science and Technology Students (p = 0.0059) and Social Science Students (p = 0.0040). Item-level analysis identified learnability as the main weakness, particularly users' need to learn extensively or seek help before being able to use the website effectively. These findings emphasize the importance of considering user characteristics in the usability evaluation of scientific dashboards and provide a basis for interface improvement recommendations grounded in Nielsen's Usability Heuristics
Deteksi Penyakit Daun Padi Berbasis Deep Learning untuk Pertanian Presisi: Studi Komparatif ResNet-18 dan Arsitektur CNN Putrama Alkhairi; Harly Okprana; Andri Nofiar. Am
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 3 No. 4 (2025): Desember: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v3i4.1532

Abstract

Rice plant diseases can reduce agricultural productivity and yield quality, highlighting the need for rapid and accurate identification methods to support precision agriculture. This study evaluates four deep learning architectures—ResNet-18, VGG-16, MobileNetV2, and Inception V3—for rice leaf disease classification using digital images. The dataset underwent preprocessing and augmentation before training, while transfer learning with ImageNet pre-trained weights was applied. Models were trained using CrossEntropyLoss and Adam optimizer with a learning rate of 0.0001 for 10 epochs. Performance was evaluated using accuracy, precision, recall, F1-score, AUC, and confusion matrix. Results demonstrate that the proposed ResNet-18 achieved the best overall performance, obtaining 96.94% accuracy, 100% precision, 95.45% recall, 96.18% F1-score, and 1.00 AUC. Inception V3 and MobileNetV2 showed competitive performance, whereas VGG-16 achieved relatively lower recall. These findings indicate that ResNet-18 provides effective feature representation and stable learning, demonstrating strong potential for rice leaf disease classification and future integration into mobile, field-camera, and IoT-based precision agriculture systems.
Deteksi Cacat Otomatis Komponen Industri Menggunakan Faster R-Cnn Berbasis Pembelajaran Mendalam Anggit Suryopratomo; M. Syafaruddin Mahaputra; Jayadi
Jurnal Elektronika dan Teknik Informatika Terapan Vol. 4 No. 3 (2026): September: Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK )
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i3.1533

Abstract

Abstrak. Inspeksi cacat pada komponen industri menjadi tahapan kritis yang secara langsung menentukan kualitas produk akhir. Metode manual masih memiliki keterbatasan dalam hal konsistensi dan efisiensi, sementara pendekatan konvensional berbasis machine vision menghadapi kendala pada kompleksitas tekstur serta variasi pencahayaan. Penelitian ini membangun sistem deteksi cacat otomatis menggunakan Faster R-CNN dengan backbone ResNet-101 dan Feature Pyramid Network (FPN). Dataset yang digunakan terdiri dari 2.500 citra komponen industri dengan empat kelas: goresan, retakan, inklusi, dan normal. Data augmentation diterapkan untuk meningkatkan robustness model terhadap variasi kondisi operasional. Evaluasi pada data pengujian independen menghasilkan mAP@0,5 sebesar 96,8%, presisi 97,2%, recall 96,1%, dan F1-score 96,6%. Kecepatan inference mencapai 0,15 detik per citra, yang menunjukkan kelayakan untuk implementasi inspeksi near-real-time. Temuan ini memperkuat bahwa Faster R-CNN merupakan solusi efektif untuk otomasi inspeksi kualitas komponen industri.
Evaluasi Penerimaan dan Usability Aplikasi SIBERGO Menggunakan Technology Acceptance Model dan SUS Layli Hardiyanti; Kahfi Gunardi; Ardi Susanto; Muhammad Iszul Wilsa; Sokid
Jurnal Elektronika dan Teknik Informatika Terapan Vol. 4 No. 2 (2026): Juni: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i2.1539

Abstract

As a mobile academic information system, SIBERGO supports students in carrying out their daily campus routines. To find out both how usable the application is and how willing students are to adopt it, this study combines two complementary instruments — the System Usability Scale (SUS) and the Technology Acceptance Model (TAM). Responses were gathered from 257 student users through an online questionnaire, then processed via descriptive statistics, validity and reliability checks, Pearson correlation, and Partial Least Squares-Structural Equation Modeling (PLS-SEM). On the 0-100 SUS scale, the application averaged 57.97, a Marginal, D-grade result that points to usability still below what the industry generally considers acceptable. Under TAM, five of the six proposed paths were statistically significant (p<0.05); notably, Perceived Ease of Use (PEOU) drove both Perceived Usefulness (PU) and Attitude Toward Using (ATU) at coefficients of 0.598 and 0.455. The direct PU-to-Behavioral Intention (BI) path, by contrast, was not significant (coefficient 0.068; p=0.357), leaving ATU to act as a full mediator between the two. A subsequent correlation analysis found PEOU to be the construct most closely tied to SUS scores (r=0.589; p<0.001), reinforcing agreement between the two evaluation methods. In short, students find SIBERGO reasonably straightforward to learn, yet its broader usability — system stability and feature consistency above all — has room to improve before daily use can be considered optimal.
Sistem Cerdas Pelacakan Individu Berbasis YOLO dan Deep SORT untuk Pengawasan Keamanan Kampus Elvi Rahmi; Eva Yumami; Isna Yulia
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 4 No. 3 (2026): September: Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK )
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i3.1541

Abstract

Digital transformation in vocational higher education requires the integration of intelligent technology into campus security systems. This study develops an individual tracking system based on CCTV video using YOLOv8 for object detection and Deep SORT for identity tracking. The system detects people, assigns unique IDs, records movement automatically, and visualizes tracking data through a heatmap and an interactive Streamlit dashboard. Testing was conducted using an uploaded video and CCTV footage. The first scenario produced 1,371 detection records from 47 frames and 36 unique tracks, with an average confidence of 0.458. The CCTV scenario produced 715 records from 60 frames and 14 unique tracks, with an average confidence of 0.486. Overall evaluation yielded 97.9% precision, 100% recall, 99.0% F1-score, and 97.9% accuracy. The results show that the integration of YOLOv8 and Deep SORT can support consistent multi-object tracking and informative real-time visualization for data-driven campus security monitoring.
Implementasi Keamanan Website Alfarouq Tour Travel Berdasarkan Temuan Kerentanan Owasp Zap Ahmad Suhenri Lubis; Nurmi Hidayasari; Zuliar Efendi
Jurnal Elektronika dan Teknik Informatika Terapan Vol. 4 No. 2 (2026): Juni: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i2.1547

Abstract

Websites used for tourism services process personal and transaction information, making security evaluation important to reduce the risk of misuse and data exposure. This study implements security improvements on the Alfarouq Tour Travel website based on vulnerability findings identified using OWASP Zed Attack Proxy (ZAP). The research uses an applied experimental approach consisting of an initial security scan, classification and analysis of findings, planning and implementation of remediation, re-scanning, and comparison of conditions before and after remediation. The initial scan using OWASP ZAP 2.16.1 on 25 April 2025 found no High-risk alerts, but identified three Medium-risk alert types with 57 instances: Absence of Anti-CSRF Tokens (1), Content Security Policy Header Not Set (30), and Missing Anti-clickjacking Header (26). Remediation was implemented through CSRF token protection and request-flow adjustment, Content Security Policy configuration, and X-Frame-Options together with the frame-ancestors directive. The re-scan on 30 December 2025 showed that all three Medium-risk alert types were no longer detected, reducing Medium instances from 57 to 0. Six Low-risk and six Informational alert types remained. The findings indicate that remediation based on ZAP results met the study’s success indicator for the three targeted Medium-risk findings within the tested scope, while further assessment is still required for residual findings and system areas not reached by the scan.
Prediksi Jumlah Penduduk Malaysia Berdasarkan Faktor Demografi dan Etnis Menggunakan Metode Random Forest Nurkholis Nurkholis; Andri Nofiar. Am; Hayatul Khairul Rahmat; Rahmad Akbar
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 3 No. 4 (2025): Desember: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v3i4.1550

Abstract

Accurate population prediction is essential for development planning, particularly in multiethnic countries such as Malaysia, yet conventional statistical approaches generally do not explicitly incorporate ethnic composition. This study aims to develop a population prediction model for Malaysia using a Random Forest Regressor that accounts for demographic factors and the composition of six ethnic groups. Historical data from 16 Malaysian states spanning 1981–2025 (673 state-year observations) were processed through feature engineering, including population lag variables, growth rate, and ethnic proportions, yielding 21 independent variables. The model was trained using a time-aware train-test split (80% training, 20% testing) and validated with 5-fold time series cross-validation. Results on the test set (2020–2025) showed strong performance, with R² = 0.9769, RMSE = 259 thousand people, MAE = 97 thousand people, and sMAPE = 3.17%. Feature importance analysis revealed that the previous year’s population (pop_lag1) and gender composition were the most dominant predictors. Projections indicate Malaysia’s population will reach approximately 38.5 million by 2030. These findings demonstrate that Random Forest with demographic-ethnic features can produce more accurate population predictions, providing a data-driven policy support tool for the Malaysian government.
Fish Check: Sistem Berbasis Convolutional Neural Network untuk Menilai Kelayakan Konsumsi Ikan Laut di Desa Weru, Kecamatan Paciran, Kabupaten Lamongan: Fish Check: A Convolutional Neural Network-Based System for Assessing the Edibility of Marine Fish in Weru Village, Paciran District, Lamongan Regency Santi Febrianti; Aisyah Nur Nabila; Ratih Berliana; Nuke Amalia; Rudy Suryadi; Riyan Bagus Prihandanu
Jurnal Elektronika dan Teknik Informatika Terapan Vol. 4 No. 2 (2026): Juni: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i2.1564

Abstract

Conventional assessment of fish freshness remains subjective, resulting in inconsistent evaluations. Advances in computer vision and Convolutional Neural Networks (CNNs) offer an opportunity to develop automated and objective approaches for assessing fish freshness through mobile devices. This study developed Fish Check, a smartphone-based application that utilizes the MobileNetV2 architecture to classify the freshness status of marine fish from digital images captured using a smartphone camera. A dataset comprising 2,400 marine fish images was collected. Image acquisition was conducted under varying lighting conditions, camera angles, and object positions to represent practical field conditions. The proposed approach involved image preprocessing, data augmentation, transfer learning-based model training, and performance evaluation using Accuracy, Precision, Recall, F1-score, and a Confusion Matrix. The optimized MobileNetV2 model was subsequently integrated into a React Native mobile application using TensorFlow Lite to enable efficient real-time inference directly on smartphones. The experimental results showed that the proposed model achieved an Accuracy of 96.39%, Precision of 96.11%, Recall of 96.74%, and F1-score of 96.42% demonstrate that the model can effectively distinguish between fresh and non-fresh marine fish. Overall, Fish Check provides a practical approach for real-time fish freshness assessment by combining a locally collected marine fish image dataset with an efficient deep learning architecture optimized for mobile deployment.
Pengembangan Website Menggunakan Astrojs dan Directus di Lumintu Logic Zardai Alghifari; Yudi Prayudi
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 4 No. 3 (2026): September: Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK )
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v4i3.1534

Abstract

The increasing need for digital preservation of cultural heritage has encouraged the development of web-based digital heritage platforms that are accessible, scalable, and efficient in managing cultural collections. However, many existing digital heritage systems still face challenges related to content management flexibility, accessibility, and system performance. This research aims to design and evaluate a web-based digital heritage information system that supports cultural collection preservation and public access through a lightweight modern web architecture approach. The study employed a Design Science Research (DSR) approach consisting of requirement analysis, system design, implementation, and evaluation stages. The proposed system integrates a headless content management system with server-side rendering architecture to improve content scalability and website performance. The implementation process focused on digital collection management, content accessibility, and efficient information retrieval for users. System evaluation was conducted through functional testing and usability analysis to measure system performance and user accessibility. The evaluation results indicate that the developed system is capable of providing efficient cultural collection management, faster page rendering performance, and positive usability feedback from users. This research contributes to the development of lightweight and scalable digital heritage information systems by integrating modern web technologies with flexible cultural content management to support digital preservation and public dissemination of cultural heritage information.

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