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Indah Purnama Sari
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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 19 Documents
 1   2 
A Simulation-Based Analysis of Dual-Counter Service Efficiency and Profitability Laila, Sintya; Azizah, Nur; Prasetyo, M. Aditya; Hasibuan, Indah Larasati
Tsabit Journal of Computer Science Vol. 1 No. 2 (2024): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit40

Abstract

Queue systems play a critical role in service industries, affecting customer satisfaction and operational performance. This study investigates the optimization of a dual-counter queue system consisting of regular and express counters, each with distinct service rates, operational costs, and profit margins. Using simulation modeling, the study aims to evaluate system performance, identify profit-maximizing strategies, and provide actionable insights for service management. The methodology involves simulating customer arrivals, service rates, and operational costs over a 60-minute period. Key metrics analyzed include the number of customers served, total operational costs, and net profit for each counter. The results reveal that express counters, despite higher operational costs, generate greater net profit per customer compared to regular counters. The findings underscore the importance of strategic resource allocation and cost-benefit analysis in queue management systems. This research contributes to the field by addressing a gap in the application of simulation for dual-counter systems, providing a framework for optimizing service operations across various industries. Further research is recommended to explore additional variables, such as customer preferences and dynamic arrival rates, to enhance the robustness of the simulation model. The purpose of the study entitled "A Simulation-Based Analysis of Dual-Counter Service Efficiency and Profitability" is most likely to analyze the effectiveness and efficiency of the service system in a dual-counter environment.
Design of IoT Based Electrical Parameter Monitoring System using NodemCu V3 and PZEM-004T V3 Sensor Rangkuti, Tegar Hilmansyah; Martiano, Martiano
Tsabit Journal of Computer Science Vol. 2 No. 1 (2025): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit42

Abstract

This research aims to design an electrical parameter monitoring system based on the Internet of Things (IoT) using NodeMCU V3 and the PZEM-004T V3 sensor. The system is designed to monitor various electrical parameters in real-time, including voltage, current, power, power factor, frequency, and energy consumption with high accuracy. Data collected by the PZEM-004T sensor is transmitted to Blynk via a Wi-Fi connection facilitated by NodeMCU V3. Users can access this information through Blynk, enabling efficient energy consumption management. By integrating IoT technology, this system provides a practical solution for monitoring and controlling electricity usage in household environments. The implementation of this system is expected to help users optimize energy consumption and effectively reduce electricity costs.
Development of Babinsa 0205/TK Performance Monitoring System using Copras Complex Proportional Assessment Method Sundari, Razdnie Rhaka; Amrullah, Amrullah
Tsabit Journal of Computer Science Vol. 2 No. 1 (2025): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit43

Abstract

This study aims to develop a performance monitoring system for Babinsa 0205/TK utilizing the COPRAS (Complex Proportional Assessment) method. COPRAS, a Multi-Criteria Decision Analysis (MCDA) technique, allows for the evaluation of performance based on various criteria such as discipline, initiative, and communication skills. The system is designed to provide an objective and proportional assessment of Babinsa performance by assigning weights to each criterion and calculating the overall score. The development of this system has demonstrated improved accuracy in performance evaluation and enhanced transparency, providing a solid foundation for decision-making in performance management. The implementation of this system is expected to enhance the effectiveness of monitoring, accountability, and performance improvement for Babinsa, thereby supporting better execution of duties at the village level.
Comparison of Random Forest and XGBOOST Methods on Weather in North Sumatera Sibuea, Royhan Umri; Maulana, Halim
Tsabit Journal of Computer Science Vol. 2 No. 1 (2025): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit44

Abstract

Accurate weather forecasting is crucial for various sectors, including agriculture, transportation, and disaster management. The weather data used includes variables such as humidity, temperature, and wind speed collected from weather stations across North Sumatra. The Random Forest method is an ensemble algorithm based on decision trees known for its ability to handle overfitting and provide accurate results. On the other hand, XGBoost is a boosting technique that improves model performance through iterative learning, correcting errors made by previous models. Research results show that both methods have their respective advantages in terms of accuracy and prediction speed. The Random Forest method yields a Root Mean Squared Error (RMSE) of 0.753732 and a Coefficient of Determination (R²) of 0.736315. In contrast, XGBoost shows a slightly lower RMSE of 0.737818 and a higher R² of 0.747332. It is concluded that XGBoost performs slightly better in minimizing prediction errors (RMSE) and improving model fit to the data (R²) compared to Random Forest.
Web-Based Final Assignment Monitoring Information System Design Simanjuntak, Pastima; Jayadi, Akhmad; Lase, Devi Chrisman
Tsabit Journal of Computer Science Vol. 2 No. 1 (2025): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit45

Abstract

This study aims to design a web-based final assignment monitoring information system in the Business Administration Study Program, Malikussaleh University. This is motivated by various problems in the process of submitting and managing final assignments which are still done manually, such as administrative errors and the slow process of submitting final assignments by students. To overcome these problems, a web-based monitoring information system was developed using the CodeIgniter framework, Bootstrap, and an integrated MySQL database. This system is equipped with several main features, including monitoring student and lecturer data, recording guidance progress, managing activity schedules, and real-time notifications. The implementation results show that the system has succeeded in automating the final assignment administration process, facilitating monitoring guidance progress, and increasing the effectiveness of communication between students and supervisors. With this system, it is expected to provide a structured and efficient solution in managing final assignments.
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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