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M Rhifky Wayahdi
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INDONESIA
Journal of Technology and Computer (JOTECHCOM)
Published by PT. Technology Laboratories Indonesia
ISSN : -     EISSN : 30480477     DOI : -
Core Subject : Science,
Computer Science & IT
The Journal of Technology and Computer (JOTECHCOM) brings together researchers, academics (faculty and students), and industry practitioners to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote cross-disciplinary and cross-domain collaboration. JOTECHCOM aims to integrate all scientific disciplines, such as computer science, information systems, informatics, information technology, data science, databases, artificial intelligence, data mining, decision support systems, expert systems, and other related disciplines. This journal is published by PT. Technology Laboratories Indonesia (TechnoLabs) Publisher division. Accepted papers will be available online (free open access).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 11 Documents
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Search results for , issue "Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer" : 11 Documents clear
Minimizing Cigarette Smoke Exposure Based on IoT Using Automatic Filtration and MQ-2 Sensor Sinuraya, M.Fahriza; Sinuraya, M. Fahriza; Irvan, Irvan
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

This study designs and implements an Internet of Things (IoT)-based system to minimize cigarette smoke exposure in enclosed spaces. The system uses an MQ-2 sensor integrated with an ESP32 microcontroller, along with automatic fan-based filtration and a HEPA + activated carbon filter. The method used is research and development (R&D) through the design, implementation, testing, and analysis stages. The results show that the MQ-2 sensor is able to detect cigarette smoke concentrations in real time and transmit data to the Thinger.io platform. An LED traffic light indicator system is used to show air quality status, while an automatic relay controls the fan to speed up the filtration process. The trial showed that the system was able to reduce cigarette smoke levels by more than 70% and provide warning notifications to users. Thus, this system has the potential to increase awareness of active smokers and protect passive smokers in the household environment.
Designing a Web-Based Online Sales System for The Krakatau Inderapura Minimarket Ramadhan, Fandy; Syahputra, Dinur
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

The use of information technology in the business world is growing rapidly, including in online sales systems. Krakatau Inderapura Minimarket, as a micro-business engaged in the sale of various daily necessities in the Inderapura area, recognizes the need to adapt technology to expand its market reach and improve customer service. This study aims to design and develop a web-based online sales system that allows customers to make purchases easily and efficiently. The system development method used is the waterfall method with stages of requirements analysis, system design, implementation, testing, and maintenance. This system is designed to allow customers to view a list of available products, place orders online, select payment methods, and track order status in real-time. In addition, the system also provides administrative features for minimarket managers to manage inventory, view sales reports, and manage store profiles. The results of this study show that the implementation of a web-based online sales system at Minimarket Krakatau Inderapura can increase ease of access for customers when shopping, improve the operational efficiency of minimarkets, and provide a better overall shopping experience for customers.
Implementation of The Backpropagation Algorithm to Improve the Effectiveness of Artificial Neural Network Models in Classifying Flooding Attacks Wijaya, Brata; Faisal, Ilham
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Flooding attacks such as UDP flood, SYN flood, and ICMP flood can disrupt network stability, requiring an effective early detection system. This study aims to build a classification model using artificial neural networks (ANN) with the backpropagation method to distinguish between normal traffic and flooding attacks. Data was collected through simulation in VirtualBox with Kali Linux as the attacker and Windows 10 as the target, and captured using Wireshark. The results of training and testing both libraries showed differences in performance between the two libraries. The PyTorch model produced a prediction accuracy of 94% for normal networks and SYN floods, and 100% for UDP floods and ICMP floods, with a total accuracy of 97%. In contrast, the TensorFlow model achieved an accuracy of 77% for normal networks, 80% for UDP floods, 95% for SYN floods, and 100% for ICMP floods, with a total accuracy of 88%. The comparison of the two models shows that a simple Multi Layer Perceptron neural network with the backpropagation method using the PyTorch library is quite effective in classifying flooding attacks.
A Comparative Study of Decision Tree and Neural Network Algorithms for Stroke Risk Prediction Nur JB, Salwa; Nadita, Lola Astri; Fachrurazy, Fachrurazy; Hidayati, Sri
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Stroke is one of the leading non-communicable diseases that causes high mortality and long-term disability, making early risk prediction an important public health issue. In Indonesia, the increasing prevalence of stroke highlights the need for data-driven approaches to support early detection and prevention efforts. This study aims to compare the performance of Decision Tree and Neural Network algorithms in predicting stroke risk using health-related data. The research method employs a publicly available stroke prediction dataset obtained from Kaggle consisting of 5,111 records. Data preprocessing was conducted to handle missing values and prepare the dataset for modeling, followed by data splitting into training and testing sets using an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the Decision Tree model achieved higher overall accuracy of 80.72%, but demonstrated low recall for the stroke class. In contrast, the Neural Network model produced a lower accuracy of 69.37% but achieved a high recall of 82%, indicating better sensitivity in detecting stroke cases. These findings reveal a trade-off between overall accuracy and sensitivity in both models. It can be concluded that Neural Network is more suitable for stroke risk prediction when early detection is prioritized, while Decision Tree is preferable for achieving higher general classification accuracy.
Comparative Machine Learning Analysis for Sentiment Classification of Sumatra Disaster 2025 Alfarizi, Nauval; Lydia, Prima; Novelan, Muhammad Syahputra; Putra, Adi; Sinurat, Satria
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Indonesia is highly vulnerable to natural disasters due to its geological position, resulting in extensive disaster-related news coverage that shapes public sentiment. This study presents a comparative machine learning analysis for sentiment classification of online news related to natural disasters in Sumatra during December 2025. The dataset was collected through web scraping from two major Indonesian news portals, like CNN Indonesia and Detik, and categorized into three sentiment classes: negative, neutral, and positive. Sentiment classification was conducted using Naive Bayes, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) algorithms. The results demonstrate that Naive Bayes achieved accuracy values of 0.57 on the CNN Indonesia dataset and 0.61 on the Detik dataset. However, its performance was highly biased toward the dominant negative class, as indicated by low macro-average F1-scores of (0.24) and (0.39). In contrast, SVM showed the most balanced performance by reducing class bias, achieving accuracies of (0.68) and (0.67) with macro-average F1-scores of (0.51) and (0.59), respectively. KNN demonstrated moderate performance, with accuracy values of 0.60 and 0.59, but remained less effective than SVM in handling minority sentiment classes.
Event-Driven Intrusion Detection and Response Automation Using n8n Workflow Platform Alfarizi, Nauval; Rivaldi, Rivaldi
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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This study introduces a server security monitoring system that uses events to detect SSH brute-force attacks. It uses automatic log analysis and sends real-time alerts. To test how well the system works, an experiment was conducted simulating attacks against an SSH service (port 22) without a firewall. Three different situations were tested: normal access, detecting unusual activity, and high-stress attacks. Under normal conditions, the system saw very little traffic: 233 packets, an average of 19 packets per second, and 38 kbps, indicating little impact and no false alarms. As the attacks grew more intense, network traffic increased significantly, reaching 96,997 packets and 76.5 MB of data during high-stress attacks, with an average speed of 1,132 kbps. All 500 brute-force attempts were found and recorded. By combining automated workflows with real-time Telegram alerts, administrators can get timely warnings. The results show that the system is effective, can handle large amounts of data, and is dependable for real-time SSH attack detection and server security monitoring.
Comparison of Random Forest and Naïve Bayes Classifier Methods for Monkeypox Classification Aprilia, Katharina Tyas; Sitorus, Irwansyah Putera; Ridha, Muhammad Rasyid; Novelan, Muhammad Syahputra
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Monkey Pox is a disease caused by a virus with the genus orthopoxvirus that can infect humans. The initial symptoms of this disease are the appearance of lumps due to swollen lymph nodes, muscle pain, fever, feeling tired and weak. Although similar to Chickenpox, Monkey Pox is clinically difficult to distinguish from other smallpox diseases. This study aims to classify Monkey Pox disease using the "Monkey-Pox PATIENTS Dataset". Classification of Monkey Pox disease is done using Random Forest and Naïve Bayes methods. Random Forest produces higher accuracy than Naïve Bayes in classifying Monkey Pox disease, which is 69.24% with a k-fold value of 5 and the number of trees 64 using an unbalanced dataset with 6 attributes. While Naïve Bayes produces an accuracy of 68.56% using a dataset without balancing with 8 attributes (k-fold=5, kernel=Gaussian) and 9 attributes (k-fold=3 and 10, kernel=Gaussian).
Design of an Automatic Trash Can Based on Ultrasonic Sensors and Arduino at LP3I Medan Polytechnic Tembusai, Zoelkarnain Rinanda; Santoso, Ahmad Imam; Butarbutar, Firma Deni
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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This study aims to design, develop, and test a prototype of an automatic trash bin based on HC-SR04 ultrasonic sensors and Arduino Uno R3 microcontrollers to improve cleanliness, efficiency, and awareness of waste management at the LP3I Polytechnic in Medan. This system is designed to detect the presence of users at a distance of less than 30 cm using an ultrasonic sensor, which then triggers the SG90 servo motor to automatically open the trash can lid and display information on capacity status and full warnings via a 16x2 LCD screen. After a 2-second delay, the lid will close again to prevent the spread of odors and maintain environmental hygiene. The prototype was developed through a research approach that included direct observation in the campus area, literature studies from reliable sources, and system design with hardware and software integration. Testing was conducted at strategic locations such as rest areas and campus corridors for 10 days, involving 100 trials with 10 daily tests per location. The test results showed a system effectiveness rate of 92%.
Designing an Interactive Educational Game for Class IV Students of Cahaya Azhari Primary School Gunawan, Syahrizal Ilham; Purwawijaya, Ellanda; Rambe, Aripin
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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One of the main problems in mathematics learning at the elementary level, especially in fourth grade, is the low learning motivation of students. Game-based learning using interactive media is believed to enhance students’ engagement and understanding through appealing visuals and enjoyable game mechanics. This study aims to design and develop an interactive educational game based on GameMaker Studio 2 as a mathematics learning medium for fourth-grade students at SD Cahaya Azhari, aligned with the learning outcomes of the Merdeka Curriculum. The research employed the Research and Development (R&D) method with stages including needs analysis, design, development, implementation, and evaluation. Research instruments consisted of expert validation, student and teacher response questionnaires, and pretest–posttest assessments. Validation results showed an excellent category (material 94.5%, media 96.2%). Student and teacher responses reached 92.8% and 94.1%, respectively. Effectiveness testing indicated an improvement in learning outcomes with an N-Gain value of 0.78 (high category). Thus, the developed educational game is declared feasible and effective as an engaging and interactive mathematics learning medium for fourth-grade students.
Optimising Digital Marketing Using Marketplaces to Increase Onion Sales at Wale Warehouse Pangaribuan, Muhammad Alfin; Harahap, Baginda; Siregar, Muhammad Furqon
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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This study aims to analyse and describe the digital marketing optimisation strategies implemented by Gudang Wale to increase onion sales through the Facebook marketplace. This study also identifies effective digital marketing strategies and reveals the obstacles faced in implementing digital marketing in this marketplace. The approach used is descriptive qualitative with data collection techniques through in-depth interviews with 8 informants consisting of the owner of Gudang Wale, senior management, IT staff, digital marketing staff, operational staff, and regular consumers. Informants were determined using purposive sampling techniques. The results show that the digital marketing optimisation strategy is not yet fully effective, with the main obstacles including limited technological knowledge, inadequate IT infrastructure, and unattractive product displays. Although the use of the Facebook marketplace influences consumer behaviour, fierce competition and difficulty in finding products are challenges for consumers. Barriers to digital marketing optimisation impact the effectiveness of sales growth. Therefore, Gudang Wale needs to improve its digital strategy by utilising more advanced technology and enhancing the appeal of its content on the Facebook marketplace to overcome existing barriers and significantly increase onion sales.

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