cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
M Rhifky Wayahdi
Contact Email
technolabsindonesia@gmail.com
Phone
+6281396692946
Journal Mail Official
technolabsindonesia@gmail.com
Editorial Address
Jl. Umar No. 26A, Kel. Glugur Darat 1, Kec. Medan Timur, Medan, Sumatera Utara.
Location
Kota medan,
Sumatera utara
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 84 Documents
 5   6   7   8   9 
Implementation of Security on Library System Login Using a Combination of AES and RSA Cryptography Siregar, M. Zaki Musaid
Journal of Technology and Computer Vol. 2 No. 4 (2025): November 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of information technology has encouraged library systems to shift from manual methods to digital-based platforms. While this transformation improves efficiency, it also raises security risks in the login process, which handles sensitive user data. This research aims to implement login security using a combination of the Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms. AES-128 is used to encrypt passwords, while RSA is used to encrypt the AES output before transmission to the server. The research method includes requirements analysis, security architecture design, algorithm implementation, and testing through login simulations and traffic analysis using Wireshark. The results show that the combination of AES and RSA effectively protects user credentials and prevents unauthorized access to the transmitted data. The conclusion of this study is that the combined cryptographic approach provides dual protection for library system authentication. Further development is suggested by integrating secure communication protocols such as HTTPS and exploring modern public-key cryptographic algorithms.
Artificial Intelligence-Based Hydroponic Plant Disease Detection System (Lactuca sativa) Wayahdi, M. Rhifky; Ruziq, Fahmi; Nurhajijah, Nurhajijah
Journal of Technology and Computer Vol. 2 No. 4 (2025): November 2025 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Hydroponic cultivation of lettuce (Lactuca sativa) offers high water efficiency, yet productivity is frequently compromised by rapid disease spread and nutrient imbalances. Traditional manual monitoring is labor-intensive, time-consuming, and prone to subjective diagnostic errors, often leading to delayed interventions. This study aims to develop an automated, real-time disease detection system by integrating Deep Learning algorithms with an Internet of Things (IoT) architecture. The proposed method utilizes an optimized One-Stage Object Detector based on the YOLO framework, specifically designed for efficient deployment on edge computing devices. The model was trained and validated on a diverse dataset encompassing healthy plants, tip-burn, leaf spot, and nutrient deficiencies, employing rigorous data augmentation to ensure robustness against indoor lighting variability. Experimental results demonstrate that the system achieves a Mean Average Precision (mAP@0.5) of 94.8%, significantly outperforming conventional Support Vector Machine (SVM) approaches and standard detectors. The model maintains high detection accuracy even under complex background conditions. In conclusion, this research provides a viable, low-latency solution for precision agriculture, enabling growers to automate plant health monitoring and effectively minimize crop losses.
Real-Time Classification of Hydroponic Vegetable Types on Mobile Devices Using Lightweight Deep Learning Models Wayahdi, M. Rhifky; Ruziq, Fahmi; Nurhajijah, Nurhajijah
Journal of Technology and Computer Vol. 1 No. 4 (2024): November 2024 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Hydroponic cultivation requires precise monitoring to ensure crop quality and productivity, yet manual identification of vegetable varieties and their growth status remains labor-intensive and prone to error. This study aims to develop a real-time, mobile-based classification system for hydroponic vegetables using lightweight Deep Learning models optimized for edge computing. The proposed method evaluates two distinct architectures, MobileNetV3 and YOLO-Nano, trained via transfer learning on a dataset comprising major hydroponic crops such as Lettuce, Pak Choy, Mustard Greens, and Cherry Tomatoes. Experimental results demonstrate that while YOLO-Nano offers superior inference speed (~55 FPS), MobileNetV3 achieves a significantly higher classification accuracy of 96.4% while maintaining a real-time performance of ~35 FPS on standard mobile hardware. The study concludes that MobileNetV3 provides the optimal balance between accuracy and computational efficiency for handheld agricultural applications. This research contributes a scalable, low-cost solution for smart farming, enabling producers to perform rapid, on-site digital inventory and quality assessment without reliance on internet connectivity.
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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).

Page 8 of 9 | Total Record : 84

 5   6   7   8   9 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer Vol. 2 No. 4 (2025): November 2025 - Journal of Technology and Computer Vol. 2 No. 3 (2025): August 2025 - Journal of Technology and Computer Vol. 2 No. 2 (2025): May 2025 - Journal of Technology and Computer Vol. 2 No. 1 (2025): February 2025 - Journal of Technology and Computer Vol. 1 No. 4 (2024): November 2024 - Journal of Technology and Computer Vol. 1 No. 3 (2024): August 2024 - Journal of Technology and Computer Vol. 1 No. 2 (2024): May 2024 - Journal of Technology and Computer Vol. 1 No. 1 (2024): February 2024 - Journal of Technology and Computer