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InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan
Published by Universitas Islam Sumatera Utara
ISSN : 25407597     EISSN : 25407600     DOI : -
Core Subject : Science,
Computer Science & IT
Merupakan jurnal yang dikelola oleh program studi teknik informatika Universitas Islam Sumatera Utara (UISU), jurnal ini membahas ilmu dibidang Informatika dan Teknologi jaringan, sebagai wadah untuk menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan ilmu informatika. InfoTekjar terbit 2 kali dalam setahun yaitu pada bulan maret dan september, terbitan pertama bulan september 2016. Artikel yang masuk akan diterima oleh editor untuk kemudian diteruskan ke editor bagian dan diteruskan lagi ke reviewer untuk di review artikel nya. Waktu review maksimal dilakukan selama 4 minggu.
Arjuna Subject : -
Articles 331 Documents
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Design of a Virtual Based English Learning Application System Reality and Augmented Reality Hasibuan, Ahmad Riady; Syafrayani, Putri Rizki; Khairani, Suci
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 8, No 2 (2024): InfoTekJar Maret
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v8i2.9894

Abstract

Textbook-based learning media makes the learning atmosphere less attractive for students, especially for elementary school students. This causes the transfer of knowledge to be hampered. Advances in information technology have penetrated the world of education which applies information technology as a tool in teaching and learning activities such as visual animations including augmented reality technology. The augmented reality of technology in English learning applications is a solution to attract the interest of elementary school children. This application has two main features, namely learning and quizzes. First, students are given an introduction to objects around the house in English, then asked to work on questions in an augmented reality and virtual reality technology environment. This application carries out several processes which include reading marker symbols using a camera, then carrying out a pre-processing stage, namely the segmentation process for comparing marker symbols. If the marker symbol is an image that is similar to the reference data, the recognition image will be used to display the 3 dimensions of the object. The trial results show that the features of this application work well, and students perceive it as helpful to have this application for learning English.
Attedance System with Face Recognition Based on Raspberry Pi using Viola Jones Algorithm Hakim, Arief Rahman
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 8, No 2 (2024): InfoTekJar Maret
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v8i2.9898

Abstract

In the process of recording attendance at the company, there are many ways that can be used, such as using an attendance card, employee ID (Identity) and fingerprints. However, the existing methods still have the potential for fraud to occur, such as entrusting attendance cards or ID cards to other co-workers, manipulating time on attendance cards, doubling fingerprints etc., so we need another more precise method such as face recognition. This method was chosen because it is considered the most appropriate and very effective than other methods. This study builds a presence system using Raspberry Pi as an image processing controller, a camera to capture facial images and an ultrasonic sensor to detect the presence or absence of people, then the system will perform face recognition using Viola Jones algorithm, followed by validation where the input data will be matched with the data contained in the database and the resulting output in the form of attendance records used for recapitulation. After testing, accurate detection results were obtained when taking the right employee face data that with perpendicular position to the camera so that attendance can be recorded
Implementation of a Mobile-Based Expert System for Anesthesia Type Recommendation Using Bayes’ Theorem Siregar, Siti Julianita
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 1 (2025): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i1.13011

Abstract

The selection of anesthesia type is a critical stage in surgical procedures that must consider the patient’s clinical condition and risk level. Inappropriate anesthesia selection may increase perioperative complications. This study aims to implement a mobile-based expert system to recommend anesthesia types using the Bayes Theorem based on the American Society of Anesthesiologists (ASA) classification. The system was developed by constructing a knowledge base consisting of anesthesia classifications, patient symptom data, and their relationships. System evaluation was conducted by comparing the system’s recommendations with expert decisions. The results indicate that the expert system provides accurate anesthesia recommendations. These findings demonstrate that the Bayes Theorem is effective in handling uncertainty in clinical data, and the mobile-based expert system can serve as a decision support tool for anesthesia selection.
Implementasi YOLO V8 Dengan Pemanfaatan Apple M2 Untuk Deteksi Perilaku Merokok hamidah, syarifah syifa; Erzed, Nixon
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 9, No 1 (2024): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v9i1.9713

Abstract

Indonesia is one of the countries facing serious problems related to the high number of smokers. Active smokers have a high risk of contracting various serious diseases, such as heart disease, cancer, respiratory diseases, and others. Additionally, exposure to tobacco smoke also has adverse effects on passive smokers, who are often individuals around them who do not smoke but are affected by it. Conventional methods for detecting smokers are often inefficient and require significant manual intervention, thus necessitating a technological solution for automatic and real-time detection to support the enforcement of anti-smoking regulations. Therefore, this research aims to detect smoking behavior using the You Only Look Once (YOLO) version 8 method on Apple M2. YOLO V8 was chosen for its capability in fast and accurate object detection, while the Apple M2 supports real-time processing. The training results showed an accuracy rate of 91.6%, precision of 96.4%, recall of 90.4%, and an F1-Score of 93.2%. During the inference stage, the Apple Neural Engine (ANE) was able to process 21-25 frames per second (fps), demonstrating good capability for real-time object detection. The combination of YOLO V8 and Apple M2 proved effective for detecting smokers in public areas, offering an efficient and effective innovative solution, supporting the creation of a smoke-free environment in Indonesia, and showing great potential for the application of edge computing in similar applications in the future.
STARTUP E-COMPLAINT DENGAN INTEGRASI API UNTUK AKSES TANPA UNDUH PADA ONLINE SHOP Setiani, Indah Nur; Anisa N W, Hana Fitri; Sundari, Jeni
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 9, No 1 (2024): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v9i1.9723

Abstract

The development of the times that demands everything to be more efficient and effective has prompted various companies to develop their online systems. Along with global trends, online shopping in Indonesia is also becoming more popular. Customer satisfaction in online shopping is a determining factor in their decision to make a repeat purchase. Dissatisfaction can increase customer turnover and the cost of acquiring new customers. A common problem faced by businesses is the quality of customer service, especially in handling complaints manually which is no longer relevant in the midst of technological developments. This research aims to create an online customer complaint system that facilitates the handling of complaints efficiently, and simplifies the administration of reports and documentation. The system uses API technology to ensure an effective interface for admins and customers. The Waterfall method is used in the development of this application to minimize bugs and detect errors. The results of the study show that a web-based complaint application that is connected to a mobile application in real-time can provide ease of access and increase customer trust in online shop services.
Early Detection of Diabetes Using a Machine Learning Model Based on Laboratory Data Hakim, Arief Rahman; Br Tarigan, Yuni Franciska; Margolang, Khairul Fadhli
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 1 (2025): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i1.12810

Abstract

Diabetes mellitus is a chronic disease whose prevalence continues to increase worldwide, with a projected number of sufferers reaching 643 million by 2030. Early detection of diabetes is crucial to prevent serious complications such as cardiovascular disease, kidney failure, and nerve damage. This study aims to compare the performance of four machine learning algorithms (Random Forest, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors) in detecting diabetes based on clinical parameters, and to identify the most significant predictor variables. The study uses the Pima Indians Diabetes dataset consisting of 768 samples with 8 predictor variables (number of pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, and age). Data is divided into a training set (70%) and a testing set (30%) using stratified sampling. Data preprocessing includes handling missing values, feature scaling using StandardScaler, and handling imbalanced data using the SMOTE technique. Performance evaluation uses accuracy, precision, recall, F1-score, and Area Under Curve (AUC-ROC) metrics. Results show that the Random Forest model achieves the best performance with an accuracy of 81.8%, precision of 79.2%, recall of 78.5%, F1-score of 78.8%, and AUC of 0.88. Support Vector Machine achieves an accuracy of 78.0%, Logistic Regression 76.0%, and K-Nearest Neighbors 74.5%. Feature importance analysis identifies glucose (28.5%), BMI (19.8%), and age (16.5%) as the most significant predictors in diabetes detection. The Random Forest model produces 17 false negatives and 12 false positives from 231 testing samples. The study concludes that Random Forest is the most effective algorithm for early diabetes detection with good accuracy and superior interpretability through feature importance.
Penerapan Algoritma Boyer-Moore pada Aplikasi Glosarium Kesehatan Amanda, Shelya; Pardede, Doughlas; Ichsan, Aulia
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 1 (2025): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i1.12886

Abstract

Pencarian istilah medis dalam glosarium kesehatan merupakan kebutuhan penting bagi mahasiswa, tenaga medis, dan masyarakat umum untuk memahami terminologi kesehatan. Namun, proses pencarian yang kurang efisien dapat memperlambat akses informasi. Penelitian ini membahas penerapan algoritma Boyer-Moore dalam aplikasi glosarium kesehatan guna meningkatkan efisiensi pencarian istilah medis. Metode penelitian meliputi studi literatur, analisis kebutuhan, perancangan sistem, implementasi, serta pengujian performa pencarian. Hasil implementasi menunjukkan bahwa algoritma Boyer-Moore lebih cepat dibandingkan metode pencarian sederhana (naïve search), dengan pengurangan jumlah perbandingan karakter dan waktu eksekusi hingga 50% pada dataset uji berisi 1000 istilah medis. Kesimpulan dari penelitian ini adalah bahwa algoritma Boyer-Moore efektif digunakan dalam aplikasi glosarium kesehatan karena mampu mempercepat proses pencarian istilah medis dan meningkatkan pengalaman pengguna.
Development and Evaluation of Digital Image-Based Tomato Leaf Disease Classification Model Using Transfer Learning Rasyid, Muhammad; Riyadi, Sugeng; Daniel, Irwan
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 1 (2025): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i1.11915

Abstract

Leaf diseases in tomato plants (Solanum lycopersicum), including Early Blight, Late Blight, and Leaf Mold, can cause substantial reductions in crop yield if not detected at an early stage. Conventional manual detection methods are constrained by limitations in speed, consistency, and accuracy, particularly under field conditions. This study proposes a tomato leaf disease classification framework leveraging a transfer learning approach, in which the Inception V3 architecture functions as a feature extractor and the Random Forest algorithm serves as the classifier. The dataset employed comprises four categories of tomato leaf images—Early Blight, Late Blight, Leaf Mold, and Healthy—which were stratified into training (80%) and testing (20%) subsets. All images were resized to 299×299 pixels, normalized, and subjected to optional data augmentation. Feature representations were extracted from the Global Average Pooling layer of Inception V3 pretrained on the ImageNet dataset and subsequently input into a Random Forest classifier with hyperparameters optimized via grid search. Experimental evaluation demonstrated that the proposed model achieved an accuracy of 94.3%, surpassing the performance of a conventional CNN (89.2%) and a Random Forest classifier without transfer learning (76.5%). The confusion matrix analysis revealed the highest classification performance for the Healthy and Late Blight categories, whereas the Leaf Mold category exhibited a higher misclassification rate due to its visual symptom similarity to Early Blight. The findings of this research indicate that a hybrid methodology combining deep learning-based feature extraction and classical machine learning algorithms is highly effective for agricultural image classification in scenarios with limited datasets. Furthermore, the proposed approach holds significant potential for integration into web- or mobile-based decision support systems, enabling rapid and accurate plant disease detection in practical agricultural settings.
Prediksi Viralitas Hoaks Menggunakan Explainable Machine Learning Pardede, Doughlas; Lubis, Muhamad Sayid Amir Ali; Limas Ptr, Agus Fahmi
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 1 (2025): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i1.13089

Abstract

The spread of hoaxes on social media has become a systemic threat, potentially triggering opinion polarization, mass panic, and disruption of social stability. Previous research has primarily focused on hoax detection through classification, while predictive efforts to anticipate the extent of their spread remain limited. This study aims to develop a machine learning model to predict the propagation level of hoax content on social media (low, medium, high) and identify the most influential factors contributing to its virality. The dataset was collected from TurnBackHoaks and MAFINDO repositories, comprising 2,500 Indonesian-language hoax contents published throughout 2022-2023. Feature extraction included TF-IDF-based text features and sentiment analysis, temporal features (upload time), and early engagement features (number of likes, shares, comments within the first hour). Three algorithms were compared: Logistic Regression, Random Forest, and XGBoost, with class imbalance handled using SMOTE. The results showed that XGBoost achieved the best performance with a macro average F1-score of 0.82, outperforming Random Forest (0.79) and Logistic Regression (0.70). SHAP analysis revealed that early engagement (shares and likes within the first hour) was the most dominant predictor, followed by content emotionality and nighttime uploads. The model demonstrated high sensitivity to the high-spread class (recall 0.85), indicating its potential for integration into early warning systems by social media platforms and fact-checking organizations. This research contributes to the development of predictive approaches in disinformation mitigation and the strengthening of digital literacy in Indonesia.
Simulasi Serangan Man-in-the-Middle Menggunakan Virtualbox dan Wireshark di Jaringan Wi-Fi Publik Perpustakaan UIN Ar-Raniry Putra, Fitra Akbar Eka; Sulaiman, Oris Krianto
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 10, No 1 (2025): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v10i1.11939

Abstract

Penelitian ini mensimulasikan serangan Man-in-the-Middle (MitM) pada jaringan Wi-Fi publik Perpustakaan UIN Ar-Raniry menggunakan lingkungan virtual (VirtualBox) dan analisis trafik dengan Wireshark. Teknik yang digunakan berfokus pada ARP spoofing untuk mengalihkan trafik klien melalui mesin penyerang sehingga lalu lintas dapat dipantau dan dianalisis. Hasil simulasi menunjukkan bahwa MitM berbasis ARP spoofing berhasil mengintersepsi paket tertentu dan membuka peluang kebocoran informasi apabila koneksi tidak terlindungi (misalnya non-HTTPS/tanpa VPN). Sebagai pembanding, uji flooding (DoS) dengan hping3 dalam lingkungan virtual tidak secara signifikan menurunkan kualitas layanan karena keterbatasan antarmuka virtual dan adanya proteksi bawaan perangkat jaringan. Temuan ini menegaskan pentingnya penerapan praktik pengamanan berlapis pada jaringan publik serta edukasi pengguna.

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