cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ramdani Dwi Pamuji
Contact Email
jocsit.publine@gmail.com
Phone
+6285945340977
Journal Mail Official
jocsit.publine@gmail.com
Editorial Address
Jl. Tawak-tawak No.5 Karang Sukun, Kel. Mataram Timur, Kec. Mataram, Kota Mataram - NTB, Indonesia 83121
Location
Kota mataram,
Nusa tenggara barat
INDONESIA
Journal of Computer Science and Information Technology
Published by Lembaga Publikasi Ilmiah Nusantara
ISSN : -     EISSN : 3090787X     DOI : https://doi.org/10.70716/jocsit
Core Subject : Science,
Computer Science & IT
Journal of Computer Science and Information Technology (JOCSIT) is a scientific journal in computers that contains research results and literature studies, managed by Lembaga Publikasi Ilmiah Nusantara. JOCSIT journal provides a platform for researchers, academics, professionals, practitioners and students to embed and share knowledge in the form of empirical and theoretical research papers, case studies, literature reviews and book reviews related to computer science and information technology research, and or related to it with a range of themes such as Biomedical Application Computer Network and Architecture, Data Mining, E-Business, E-Commerce, E-Government E-Learning, Embedded Systems, Environmental Systems, Fuzzy Logics, Genetic Algorithms, Geographic Information System, High-Performance Computing, Human-Computer Interaction, Image Processing, Internet of Things (IoT), Computer Vision, Information Security, Information Retrieval, Modeling System and Control, Mobile Technology, Neural Networks, Pattern Recognition, Remote Sensing, Robotics, Signal Processing, Smart Home, Smart Sensor Networks. This journal will process all receipts of the script in a double-anonymized review by Bestari partners.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2025): Journal of Computer Science and Information Technology, September 2025" : 5 Documents clear
Analisis Performa Jaringan 5G dengan Metode MIMO Satriawan, Hamdani
Journal of Computer Science and Information Technology Vol. 1 No. 2 (2025): Journal of Computer Science and Information Technology, September 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i2.252

Abstract

This study aims to analyze the performance of 5G networks with the implementation of Multiple Input Multiple Output (MIMO) technology through a simulation approach using NS-3 software. Evaluations were conducted on three antenna configurations, namely SISO (1x1), MIMO 2x2, and MIMO 4x4, in two environmental scenarios: urban and rural. The parameters analyzed include throughput, latency, and signal-to-noise ratio (SNR). The simulation results show that the MIMO 4x4 configuration provides the most significant performance improvement compared to SISO, with an average throughput increase of up to 40%, a latency reduction of 25%, and an SNR increase of up to 10 dB. In addition, MIMO is proven to be able to maintain stable performance in dense traffic conditions and high interference, especially in urban environments. Better channel efficiency is also achieved through the utilization of spatial multiplexing and beamforming. These results strengthen the evidence that MIMO is a key technology in the development of 5G networks, especially in addressing the needs of high-speed communications and real-time services. This study concludes that MIMO technology has great potential to be widely implemented in 5G networks in Indonesia, and is an important foundation for the implementation of future communication systems that are smarter, faster, and more reliable.
Penerapan Teknologi Internet of Things (IoT) untuk Monitoring Kualitas Udara dalam Ruangan Aswaldi, Haikal
Journal of Computer Science and Information Technology Vol. 1 No. 2 (2025): Journal of Computer Science and Information Technology, September 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i2.255

Abstract

Indoor air quality plays a crucial role in the health and comfort of occupants, especially in enclosed environments such as homes, offices, and public spaces. Decreased air quality can lead to various health problems, including allergies, respiratory problems, and decreased work productivity. With the advancement of technology, the Internet of Things (IoT) has become an innovative solution for real-time air quality monitoring. This research aims to design and implement an IoT-based air quality monitoring system capable of detecting environmental parameters such as temperature, humidity, carbon dioxide (CO₂) concentration, and dust particles (PM2.5). This system consists of environmental sensors integrated with a microcontroller and connected to a cloud platform for online data storage and visualization. Test results show that the system can provide accurate data and is responsive to changes in air conditions. The system is also equipped with an automatic notification feature that alerts users when air quality decreases below normal thresholds. With this approach, users can take early preventative measures against health risks caused by exposure to polluted air. This research shows that the application of IoT in air quality monitoring is very effective and can be applied on a household and commercial scale.
Sistem Deteksi Wajah Berbasis Deep Learning Menggunakan Convolutional Neural Network (CNN) Anwar, Khairil
Journal of Computer Science and Information Technology Vol. 1 No. 2 (2025): Journal of Computer Science and Information Technology, September 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i2.258

Abstract

Face detection is an important technology in the field of pattern recognition and digital image processing that is widely used in various applications such as security systems, surveillance, and identity recognition. This study aims to develop a deep learning-based face detection system using the Convolutional Neural Network (CNN) architecture. CNN was chosen because of its ability to automatically extract important features from images through an in-depth training process. This system is designed using a diverse facial dataset to ensure model generalization to various lighting conditions, viewing angles, and facial expressions. The training process is carried out by applying data augmentation and parameter optimization techniques to improve detection accuracy. The evaluation results show that the developed CNN model is able to detect faces with a high level of accuracy, and demonstrates stable performance against input variations. The advantage of this method lies in its ability to detect faces in real-time with low latency and resilience to background noise. With the results obtained, this system is expected to be applied in various computer vision-based applications such as automatic attendance systems, intelligent surveillance, and biometric authentication. This research contributes to the development of more reliable and efficient face detection technology with a modern deep learning approach.
Penerapan Algoritma Deep Learning untuk Deteksi Dini Penyakit dari Citra Medis Munadi, Muhammad
Journal of Computer Science and Information Technology Vol. 1 No. 2 (2025): Journal of Computer Science and Information Technology, September 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i2.259

Abstract

Advances in artificial intelligence technology, particularly in the field of deep learning, have made significant contributions to medical image processing for the early detection of various diseases. This study aims to apply deep learning algorithms, specifically Convolutional Neural Networks (CNNs), in the process of classifying and identifying diseases through the analysis of medical images such as X-rays, MRIs, and CT scans. The use of CNNs enables automatic and efficient feature extraction, thereby improving the accuracy of disease detection compared to conventional methods. The dataset used consists of thousands of medical images that have been manually classified by medical professionals. The model training process was carried out using transfer learning techniques using pre-trained architectures such as VGG16 and ResNet50. Performance evaluation was carried out by measuring the values of accuracy, precision, recall, and F1-score. The results showed that the developed CNN model was able to achieve a detection accuracy level of up to 95.3% on the test dataset. The application of this technology is expected to support computer-based diagnostic systems (CDI) as an aid for medical personnel in clinical decision-making. In addition, this system has the potential to accelerate the screening process and reduce the risk of misdiagnosis. These findings indicate that deep learning technology has enormous potential for improving the quality of healthcare services, particularly in the areas of disease prevention and early detection more effectively and efficiently.
Penggunaan Cryptography dalam Keamanan Pesan Digital Setiawan, Adi Bima
Journal of Computer Science and Information Technology Vol. 1 No. 2 (2025): Journal of Computer Science and Information Technology, September 2025
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/jocsit.v1i2.261

Abstract

The rapid development of information technology has facilitated digital communication processes, but on the other hand, it increases the risk of data leakage and misuse. One solution widely used to secure digital messages is the application of cryptography technology. Cryptography plays a crucial role in maintaining the confidentiality, integrity, and authenticity of information by converting messages into a format that cannot be read without the appropriate key. This study aims to analyze the use of cryptographic methods in maintaining the security of digital messages, with a focus on symmetric and asymmetric encryption techniques. The research method used is a literature review from various scientific sources and simulation implementation to test the effectiveness of cryptographic algorithms against cyberattacks. The results show that symmetric cryptography has a higher processing speed, while asymmetric cryptography excels in key management and authentication. The combination of the two through a hybrid encryption mechanism has been proven to increase the level of security without sacrificing performance. The proper application of cryptography not only protects messages from unauthorized access but also supports trust in digital communication. This study recommends the integration of modern cryptographic algorithms such as AES and RSA in digital messaging applications to achieve optimal protection.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 1 No. 3 (2025): Journal of Computer Science and Information Technology, December 2025 Vol. 1 No. 2 (2025): Journal of Computer Science and Information Technology, September 2025 Vol. 1 No. 1 (2025): Journal of Computer Science and Information Technology, June 2025