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Indah Purnama Sari
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ibctsabitjournal@gmail.com
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Jl. Batang Kuis - Lubuk Pakam Gg. Cempaka Dusun III No. 3, Tanjung Sari, Batang Kuis, Kab. Deli Serdang Sumatera Utara
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Tsabit
Published by Ilmu Bersama Center
ISSN : -     EISSN : 30628504     DOI : https://doi.org/10.56211/tsabit
Core Subject : Science,
Computer Science & IT
Tsabit Journal of Computer Science is open to researchers and experts in the field of Computer Science. This journal functions as a forum for disclosing research results both conceptually and technically related to computer science. Tsabit journal of computer science is published twice a year, namely in June and December. Submitted manuscripts will be accepted by the editor and then checked for similarities with the Turnitin application. The review process is carried out using Double Blind Peer Review. Manuscripts received are expected to relate to new technologies and current issues. Please read the Guidelines and Template for this journal carefully. Authors who wish to send their manuscripts to the Tsabit Journal of Computer Science editorial team must comply with the writing guidelines. Tsabit Journal of Computer Science accepts manuscripts on the topics Software Engineering, Media, Game and Mobile Technologies, Data Mining, Information Security, Image Processing and Pattern Recognition, Natural Language Processing, Smart City, Expert System, Decision Support System, Cloud Computing, Digital Forensics , Artificial Intelligence, Machine Learning, Computational Intelligence, Computer Networking and other study topics relevant to Computer Science.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 4 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2025): December Edition" : 4 Documents clear
Comparative Analysis of the Performance of VGG16 and ResNet50 Architectures in Multi-Class Classification of Rice Plant Diseases Based on Convolutional Neural Networks (CNN) Aditya, Krisna; Basri, Mhd.
Tsabit Journal of Computer Science Vol. 2 No. 2 (2025): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit55

Abstract

Rice plant diseases significantly affect crop productivity and food security, making early and accurate disease detection essential for effective agricultural management. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN), have demonstrated strong potential in image-based plant disease classification. This study presents a comparative analysis of the performance of VGG16 and ResNet50 architectures for multi-class classification of rice plant diseases using CNN-based approaches. A dataset of rice leaf images representing multiple disease classes and healthy conditions was collected and preprocessed through image resizing, normalization, and data augmentation to enhance model generalization. Both pre-trained models were fine-tuned using transfer learning to adapt them to the rice disease classification task. Model performance was evaluated using standard metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that both architectures achieve high classification performance; however, ResNet50 demonstrates superior accuracy and better generalization capability compared to VGG16, particularly in handling complex disease patterns and intra-class variations. Meanwhile, VGG16 offers a simpler architecture with faster convergence and lower computational complexity. The findings of this study provide insights into the selection of appropriate CNN architectures for rice plant disease classification and support the development of intelligent decision support systems in precision agriculture.
Detecting Potential Dangers in Elderly Bathrooms using a PIR-Based Notification System and Magnetic Sensor Ananda, Dwi Arfi; Siregar, Farid Akbar
Tsabit Journal of Computer Science Vol. 2 No. 2 (2025): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit57

Abstract

Bathrooms represent one of the most hazardous environments for elderly individuals due to the high risk of falls, prolonged inactivity, and delayed emergency response. Early detection of potentially dangerous situations is therefore crucial to improve safety and reduce injury risks. This study proposes a notification system for detecting potential dangers in elderly bathrooms using a Passive Infrared (PIR) sensor and a magnetic door sensor. The PIR sensor is utilized to monitor human presence and movement patterns, while the magnetic sensor detects door status to identify abnormal conditions, such as prolonged bathroom occupancy or lack of movement after entry. The system is designed to automatically trigger notifications to caregivers when predefined risk conditions are detected. The proposed system was implemented using a microcontroller-based platform and evaluated through a series of controlled experiments simulating typical and abnormal bathroom usage scenarios. Performance evaluation focused on detection accuracy, response time, and system reliability. The experimental results indicate that the system is capable of effectively identifying potentially dangerous situations and delivering timely alerts to caregivers. The integration of PIR and magnetic sensors provides a simple, low-cost, and non-intrusive solution for enhancing elderly safety in domestic environments. This research demonstrates the potential of sensor-based notification systems to support assisted living and improve the quality of care for elderly individuals.
Implementation of Deep Learning using the Convolutional Neural Network (CNN) Method to Improve Attedance List Lestari, Wirna; Khair, Rizaldy
Tsabit Journal of Computer Science Vol. 2 No. 2 (2025): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit58

Abstract

Efficient and accurate employee attendance recording is a vital aspect of human resource management, including within the Faculty of Computer Science and Information Technology, Universitas Muhammadiyah Sumatera Utara (FIKTI UMSU). This study focuses on enhancing the efficiency of the attendance system through the application of Deep Learning techniques, particularly the Convolutional Neural Network (CNN), which serves to automatically detect and recognise faces from visual data. The web-based application developed in this research employs programming languages such as Python, HTML, PHP, CSS, and JavaScript, with MySQL as the database system, and is designed to support two user roles: administrator and end-user. The findings indicate that the implementation of the CNN method enables real-time image processing, reduces the potential for fraud in manual attendance, and improves the accuracy and efficiency of attendance recording. Based on testing, the application functions effectively, provides a user-friendly interface, and is capable of delivering reliable automated attendance documentation.
Development of a Decision Support System to Determine Best-Selling Menu Canteen Employees of the Bank Indonesia Representative Office in North Sumatra Province using the Topsis Method Adhari, M. Rizki; Basri, Mhd.
Tsabit Journal of Computer Science Vol. 2 No. 2 (2025): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit60

Abstract

The availability of accurate sales information is essential for supporting managerial decision-making in institutional food services. At the Bank Indonesia Representative Office in North Sumatra Province, determining the best-selling menu for employee canteen services is still largely based on manual evaluation, which may lead to inefficiencies and subjective judgments. This study aims to develop a Decision Support System (DSS) to identify the best-selling canteen menu using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The system evaluates menu alternatives based on multiple criteria, including sales volume, price, menu availability, and employee preferences. Data were collected from historical sales records and questionnaires distributed to canteen employees. The TOPSIS method was applied to rank menu alternatives by calculating their relative closeness to the ideal positive and ideal negative solutions. The DSS was implemented as a computerized system to facilitate data processing, ranking, and visualization of decision results. The results show that the proposed system is able to objectively determine the best-selling menu and provide consistent rankings compared to conventional methods. The developed DSS improves accuracy, efficiency, and transparency in menu evaluation, thereby supporting better planning and inventory management for the employee canteen. This study demonstrates that integrating multi-criteria decision-making methods into a DSS can effectively enhance decision quality in institutional food service management.

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