cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yohanes Bowo Widodo
Contact Email
ybowowidodo@gmail.com
Phone
+6285718767171
Journal Mail Official
ojslppmumht@gmail.com
Editorial Address
Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Mohammad Husni Thamrin Kampus A Universitas Mohammad Husni Thamrin Jl. Raya Pondok Gede No. 23-25, Kramat Jati, Jakarta Timur 13550
Location
Kota adm. jakarta timur,
Dki jakarta
INDONESIA
Jurnal Teknologi Informatika dan Komputer
Published by Universitas Mohammad Husni Thamrin
ISSN : 26569957     EISSN : 26228475     DOI : https://doi.org/10.37012/jtik
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal Teknologi Informatika dan Komputer merupakan salah satu jurnal berbasis Open Journal System (OJS) yang dikelola oleh Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) Universitas Mohammad Husni Thamrin (UMHT) yang berisi artikel-artikel dengan topik Teknologi Informasi yang menampung karya ilmiah para dosen Perguruan Tinggi di Indonesia. Diharapkan jurnal ini mampu memberikan motivasi dan kontribusi ilmiah bagi perkembangan ilmu pengetahuan dan teknologi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 706 Documents
 67   68   69   70   71 
YOLO in Suspicious Human Activity Recognition for Intelligent Environmental Security Systems: A Review Widodo, Yohanes Bowo; Sibuea, Sondang; Agustino, Rano
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3243

Abstract

The rapid growth of intelligent environmental security systems has intensified the need for accurate and real-time suspicious human activity recognition. Computer vision techniques, particularly deep learning–based object detection models, have emerged as key enablers in addressing these challenges. Among them, You Only Look Once (YOLO) has gained significant attention due to its high detection speed, end-to-end architecture, and suitability for real-time surveillance applications. This review paper presents a comprehensive analysis of the application of YOLO-based models in suspicious human activity recognition for intelligent environmental security systems. It examines the evolution of YOLO architectures, their adaptations for activity and behavior analysis, and their integration with surveillance frameworks. The review further discusses commonly used datasets, performance evaluation metrics, and comparative results reported in existing studies. In addition, key challenges such as occlusion, varying illumination, complex backgrounds, privacy concerns, and computational constraints are highlighted. Finally, the paper outlines future research directions, including hybrid models, multi-modal data fusion, edge-based deployment, and explainable AI, to enhance the robustness and reliability of YOLO-driven security systems. This review aims to provide researchers and practitioners with a structured understanding of current advancements and open issues in YOLO-based suspicious human activity recognition.
Zero-Day Attack Detection Using Autoencoder and XGBoost Rohman, Mujibbur; Dharmayanti
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3248

Abstract

Advances in information and communication technology have significantly impacted progress in various sectors, but they have also given rise to increasingly complex network security threats. Cyberattacks such as Distributed Denial of Service (DDoS), ransomware, and software vulnerability exploits continue to increase year after year. Signature-based Intrusion Detection Systems are often ineffective in identifying novel cyberattacks since they rely solely on previously known attack patterns. To address this limitation, this study proposes a hybrid approach that integrates Autoencoders, including Dense and Memory-Augmented variants, with Extreme Gradient Boosting (XGBoost) to enhance zero-day attack detection using the UNSW-NB15 dataset. The research methodology encompasses data exploration, preprocessing with a split-before-transform strategy to prevent information leakage, Autoencoder training to model normal network behavior, reconstruction error computation for anomaly detection under both fixed and adaptive thresholding, and the utilization of these errors as input features for XGBoost classification. Experimental results demonstrate that adaptive thresholding improves F1 performance compared to fixed thresholds, while the hybrid Autoencoder–XGBoost integration achieves a significant performance boost. The proposed model consistently obtained F1 scores above 0.80 and PR-AUC values exceeding 0.81 with a balanced trade-off between precision and recall. These findings confirm that the hybrid approach is more effective, consistent, and adaptive in detecting intrusions, particularly zero-day attacks, and highlight its potential as a robust framework for advancing network security in dynamic threat environments.
Application of Artificial Neural Network in Estimating Harvest Time of Lettuce and Spinach Plants in Nutrient Film Technique Hydroponic System Munawar, Zilfa Agustina; Insany, Gina Purnama; Kharisma, Ivana Lucia
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3261

Abstract

Hydroponic farming using the Nutrient Film Technique (NFT) system is widely implemented due to its efficiency in nutrient management and water use. Spinach and lettuce are leafy commodities widely cultivated using this system because they have a relatively short growth cycle and high economic value. However, determining harvest time is still often done manually based on experience, potentially leading to inaccurate decisions that impact the quality and quantity of production. This study aims to develop a prediction model for harvest time for hydroponic spinach and lettuce plants based on Artificial Neural Network (ANN) by utilizing environmental and physiological parameters of the plant. The parameters used include water temperature, air humidity, light intensity, pH, Electrical Conductivity (EC), and plant age. The dataset used consists of 1,200 observation data of NFT hydroponic cultivation results from January to July 2025. The data went through a preprocessing stage in the form of cleaning, normalization, and dividing training data and test data with a ratio of 80:20. The ANN model was built using the backpropagation method with training parameter optimization. Data was obtained from plant growth monitoring, then normalized and divided into training and test data. Test results showed a prediction accuracy of 92.8% based on MAPE, MAE, and R-squared. This model was implemented in a Streamlit-based web application to facilitate farmer use, making harvest timing more objective, measurable, and data-driven.
Comparative Analysis of Indihome and Starlink Network Performance at the Banda Aceh City Office Ookla Azmy, M Ulul; Firmansyah
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3263

Abstract

The availability of a fast, stable, and reliable internet network is a crucial factor in supporting office operational activities in the digital era. This study aims to analyze and compare the performance of the IndiHome and Starlink networks in the Banda Aceh City Office environment using the Ookla Speedtest application as the main measuring tool. The research method used is a quantitative approach with a comparative descriptive design. Data collection was carried out through direct network speed testing and the distribution of Likert-based questionnaires to 10 IndiHome office staff. The evaluation approach in this study focused on measuring internet network service quality parameters, such as access speed and delay to objectively assess the performance of IndiHome and Starlink services. Parameters analyzed include download speed, upload speed, latency, network stability, weather effects, ease of installation, and service costs. The results showed that the IndiHome network achieved a success rate of 96%, while the Starlink network achieved 95%, both of which are included in the very feasible category. However, based on Ookla testing results and user perceptions, the IndiHome network demonstrated more stable and consistent performance, even though both services have the same speed capacity of 300 Mbps. These findings indicate that the IndiHome network is more recommended as the primary internet connectivity solution to support office operational needs in Banda Aceh.
A Comparative Study of DenseNet-201 and Swin Transformer for Malignant and Benign Skin Lesion Classification Hidayat, Dahlan; Musyafa, Ahmad; Handayani, Murni
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3265

Abstract

Skin cancer has a high global prevalence, underscoring the need for accurate and efficient early detection systems to support screening. This study presents a comparative analysis of DenseNet-201 and Swin Transformer for binary classification of malignant and benign skin lesions using the BCN20000 dataset, which contains 12,413 dermoscopic images. The proposed workflow includes image preprocessing and augmentation, transfer learning-based model training, and evaluation under a 5-fold stratified cross-validation protocol. Performance is assessed using Accuracy, Precision, Sensitivity (Recall), F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). In addition, computational efficiency is examined in terms of parameter count, model size, and training time. Across five folds, DenseNet-201 achieved 88.05% Accuracy, 88.90% Precision, 89.48% Sensitivity, 89.17% F1-score, and 94.73% AUC, whereas Swin Transformer achieved 87.42% Accuracy, 89.77% Precision, 87.06% Sensitivity, 88.39% F1-score, and 94.33% AUC. A paired t-test at α = 0.05 indicated no statistically significant performance difference between the two models. Model interpretability was investigated using Grad-CAM for DenseNet-201 and EigenCAM for Swin Transformer to verify that predictions were driven by lesion-relevant regions. Overall, the results suggest that both architectures are suitable candidates for dermoscopic image-based skin lesion screening support systems, including teledermatology applications.
User Interface and User Experience Design for Character Development System Web Application at State Vocational School 56 Jakarta Based on Prototypes with The User Centered Design Method Rifki, Miftahudin; Yasin, Verdi; Sianipar, Anton Zulkarnain
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3266

Abstract

In the digital era, information technology plays a crucial role in supporting various activities, including education. One increasingly developed technology implementation is a web-based student violation recording system. This system not only simplifies the process of recording and monitoring violation data but can also be integrated with student character development programs. At SMK Negeri 56 Jakarta, character development is a crucial part of the educational process, but its implementation still faces various challenges, particularly regarding the effectiveness and efficiency of digital data recording. This research aims to design the User Interface (UI) and User Experience (UX) for a prototype-based character development system web application using the User-Centered Design (UCD) method. This method is used to ensure that the application design truly centers on the needs of the user, in this case the Student Affairs Team. Through this approach, the application is expected to present an easy-to-use, engaging interface and provide a positive user experience. The result of this research is a web application prototype design that can display historical student violation data, facilitate the development process more efficiently, and present interactive data visualizations to assist the school in analyzing student character development. Therefore, this application is expected to support the increased effectiveness of character development at SMK Negeri 56 Jakarta and become a digital solution that adapts to the needs of modern education.

Page 71 of 71 | Total Record : 706

 67   68   69   70   71 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 2 (2025): Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 1 (2025): Jurnal Teknologi Informatika dan Komputer Vol. 10 No. 2 (2024): Jurnal Teknologi Informatika dan Komputer Vol. 10 No. 1 (2024): Jurnal Teknologi Informatika dan Komputer Vol 9, No 2 (2023): Jurnal Teknologi Informatika dan Komputer Vol. 9 No. 2 (2023): Jurnal Teknologi Informatika dan Komputer Vol. 9 No. 1 (2023): Jurnal Teknologi Informatika dan Komputer Vol 9, No 1 (2023): Jurnal Teknologi Informatika dan Komputer Vol 8, No 2 (2022): Jurnal Teknologi Informatika dan Komputer Vol. 8 No. 2 (2022): Jurnal Teknologi Informatika dan Komputer Vol 8, No 1 (2022): Jurnal Teknologi Informatika dan Komputer Vol. 8 No. 1 (2022): Jurnal Teknologi Informatika dan Komputer Vol 7, No 2 (2021): Jurnal Teknologi Informatika dan Komputer Vol. 7 No. 2 (2021): Jurnal Teknologi Informatika dan Komputer Vol. 7 No. 1 (2021): Jurnal Teknologi Informatika dan Komputer Vol 7, No 1 (2021): Jurnal Teknologi Informatika dan Komputer Vol 6, No 2 (2020): Jurnal Teknologi Informatika dan Komputer Vol 6, No 1 (2020): Jurnal Teknologi Informatika dan Komputer Vol. 6 No. 1 (2020): Jurnal Teknologi Informatika dan Komputer Vol. 5 No. 2 (2019): Jurnal Teknologi Informatika dan Komputer Vol 5, No 2 (2019): Jurnal Teknologi Informatika dan Komputer Vol 5, No 1 (2019): Jurnal Teknologi Informatika dan Komputer Vol. 5 No. 1 (2019): Jurnal Teknologi Informatika dan Komputer Vol. 4 No. 2 (2018): Jurnal Teknologi Informatika dan Komputer Vol 4, No 2 (2018): Jurnal Teknologi Informatika dan Komputer Vol 4, No 1 (2018): Jurnal Teknologi Informatika dan Komputer Vol. 4 No. 1 (2018): Jurnal Teknologi Informatika dan Komputer Vol 3, No 2 (2017): Jurnal Teknologi Informatika dan Komputer Vol. 3 No. 2 (2017): Jurnal Teknologi Informatika dan Komputer Vol 3, No 1 (2017): Jurnal Teknologi Informatika dan Komputer Vol. 3 No. 1 (2017): Jurnal Teknologi Informatika dan Komputer More Issue