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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 35 Documents
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Analysis and Implementation of Six Sigma in the Design of an Information System at the Bank Indonesia Canteen North Sumatra Feby Paulina; Yoshida Sary
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/tsabit61

Abstract

The conventional cafeteria services at the Representative Office of Bank Indonesia in North Sumatra Province encounter various problems, such as long queues, order recording errors, and limited access to menu information, all of which affect customer satisfaction. This study aims to analyze and implement the Six Sigma method in the design of a web-based cafeteria information system to improve service quality. Using the DMAIC (Define, Measure, Analyze, Improve, Control) framework as the core of the Six Sigma approach and the waterfall model for system development, the system was built with features such as online ordering, real-time menu information, and digital payment integration. The evaluation results showed a Defects Per Million Opportunities (DPMO) value of 93,000 and a Sigma Level of 2.82, indicating a moderate improvement in service quality. The system also achieved a customer satisfaction rate of 90.6%, although some aspects such as order waiting time estimation and ease of use still require improvement. Overall, the implementation of Six Sigma has proven effective in analyzing and enhancing the cafeteria service process through an integrated information system, contributing to better operational efficiency and user experience.
Comparison of Random Forest and Multiple Linear Regression Algorithms in Predicting Daily Drug Expenditure: A Case Study At Bambuan Pharmacy Muammar Farhan; Zuli Agustina Gultom
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/tsabit62

Abstract

Accurate drug inventory management is essential for pharmacies to avoid shortages or excess stock. This study aims to compare the performance of Multiple Linear Regression (MLR) and Random Forest Regression (RFR) in predicting daily drug sales at Apotek Bambuan. The dataset consists of sales records from 2022–2024, which were preprocessed and divided into training and testing sets. Both models were evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² Score. The results show that Random Forest provides higher prediction accuracy with lower error values compared to MLR, although MLR remains useful for interpreting the contribution of predictor variables. Therefore, Random Forest is recommended for daily drug sales prediction due to its superior accuracy, while MLR offers advantages in model interpretability.
Web-Based Gradient Boosting Machine Implementation for Student Success Data Classification at Muhammadiyah Elementary School in East Medan Afdolly Akbar Khaidir Siregar; Halim Maulana
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/tsabit63

Abstract

The rapid advancement of data-driven education has enabled schools to utilize machine learning to identify factors influencing student success. This study presents the development and implementation of a web-based Gradient Boosting Machine (GBM) model for classifying student success data at Muhammadiyah Elementary School in East Medan. The proposed system aims to assist educators in evaluating student performance through predictive analytics that integrates academic, behavioral, and attendance data. The research methodology includes data preprocessing, feature selection, and model training using the GBM algorithm due to its robustness in handling non-linear relationships and reducing classification errors through iterative boosting. The web-based application is designed with an interactive interface, allowing teachers and administrators to input, analyze, and visualize student performance patterns easily. The evaluation results indicate that the GBM model achieves high classification accuracy, outperforming traditional algorithms such as Decision Tree and Logistic Regression. This system not only provides accurate predictions of student performance levels but also generates actionable insights for improving learning outcomes and academic interventions. The research contributes to the integration of machine learning and educational management by demonstrating how predictive modeling can be operationalized in real-time through a web-based platform to support data-informed decision-making in Muhammadiyah schools.
Ai-Based Web Application Design For Photographic Image Quality Optimization Through Digital Image Filtering Method Ibnu Pribudianto; Al-Khowarizmi
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/tsabit64

Abstract

Image quality in photography is often disrupted by factors such as poor lighting or incorrect focus, resulting in blurry images. This study aims to enhance image sharpness using digital image filtering methods, namely Unsharp Masking, High-Pass Filtering, and Sobel Filter. These methods are tested to evaluate their effectiveness in clarifying image details. The study also develops a web-based application powered by AI to help users edit images without requiring technical skills. A quantitative experimental method with a descriptive approach is used, and evaluation is conducted using PSNR, SSIM, and user questionnaires. The results show that the application of sharpening methods can significantly improve the quality of photographic images, and integration into a web platform provides easy access for the general public. This application is expected to be a practical solution for photographers, editors, and general users to obtain high-quality images efficiently and quickly.
Design and Implementation of Multi-Segment LAN Infrastructure for Computer Laboratories Andi Zulherry; Muhammad Gunawan; Mhd. Basri
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/tsabit91

Abstract

Educational computer laboratories require a reliable and well-structured network infrastructure to support learning activities and efficient resource management. However, many laboratory networks are still implemented using a single network segment, which can lead to high broadcast traffic and reduced network performance as the number of connected devices increases. This study proposes the design and implementation of a multi-segment Local Area Network (LAN) infrastructure based on institutional needs in an educational computer laboratory environment. The proposed network architecture consists of four laboratory rooms with a total of 160 computers, where each laboratory operates within a different IP network segment while remaining interconnected through routing mechanisms. Network devices such as the MikroTik RB750Gr3 hEX router are used to manage gateway functions, DHCP services, and network address translation (NAT) for internet connectivity. The implementation is evaluated through connectivity tests between laboratory networks and internet access tests. The results show that all laboratory networks successfully communicate with each other without packet loss and demonstrate low latency values, indicating stable network performance. In addition, internet connectivity tests confirm that all laboratory networks can access external resources reliably. These findings demonstrate that the proposed multi-segment LAN infrastructure improves network organization, scalability, and manageability within educational computer laboratory environments.
Emotion Recognition System for Game Addiction Users Using FACS and Image Feature Extraction on Android Roslinda Tanjung; Yohanni Syahra
Tsabit Journal of Computer Science Vol. 3 No. 1 (2026): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit76

Abstract

Purpose: Online game addiction negatively impacts users’ mental health and social behavior. This study aims to develop an Android-based emotion recognition system using Facial Action Coding System (FACS) with image feature extraction to detect users’ emotions in real-time. Design/Methods/Approach: The system integrates OpenCV for face detection, dlib for facial landmark identification, and TensorFlow Lite for emotion classification. Testing was conducted through black box testing, confusion matrix evaluation, and performance analysis. Findings/Results: The system successfully recognized basic negative emotions (anger, frustration, sadness, displeasure) with adequate accuracy (87%) and operated effectively on Android devices. Conclusions: Integrating FACS with image feature extraction provides a non-intrusive solution to help users recognize and manage emotions, contributing to digital mental health research and practice.
Development of an Android-Based Plant Care Simulation Game Using the Finite State Machine Method and Time-Based Growth Algorithm M Rayyan Al Fariz Prasetya; Al-Khowarizmi
Tsabit Journal of Computer Science Vol. 3 No. 1 (2026): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit77

Abstract

The advancement of digital technology has created new opportunities for delivering educational content in an interactive manner, particularly on the topic of plant growth cycles, which are often difficult to understand theoretically through traditional learning methods. This study aims to develop an Android-based plant care simulation game utilizing the Finite State Machine (FSM) method and the Time-Based Growth algorithm, which can serve as an alternative medium for both entertainment and education. The FSM method is implemented to manage structured transitions between plant growth phases, while the Time-Based Growth algorithm regulates the duration of each growth stage based on time and in-game interactions, such as watering and fertilizing. The development process follows the Multimedia Development Life Cycle (MDLC), which consists of the stages of concept, design, material collecting, assembly, testing, and distribution. The implementation results indicate that the system is capable of dynamically representing the plant growth cycle through mechanisms involving plant health (HP), water levels, a delay-death phase, and growth acceleration through fertilizer usage. Based on testing results, the application functions according to the design specifications and provides an interactive and engaging learning experience.
Implementation of the Naïve Bayes Algorithm in Credit Card Approval Classification at Bank Central Asia (BCA) Surya Lesmana; Firahmi Rizky
Tsabit Journal of Computer Science Vol. 3 No. 1 (2026): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit80

Abstract

Credit card approval is a financial facility that allows a person or business entity to borrow money to purchase products and repay it within a mutually agreed time. Researchers used a credit card approval dataset (Clean Data) of 200 data with 6 atributes. In conducting the analysis, researchers used the WEKA tools. In this study the author wants to classify the data to predict whether or not it is appropriate to grant a credit card. The method used is the Naïve Bayes Algorithm method. In this study, the data was split by 60:40, 70:30, and 80:20. The author also obtained different accurasy result, namely in the Weka data Testing tool 20% using the use Training Set obtained the highest accuracy of 87.68% compared to the lowest accuracy in the 30% testing data using the use Training Set, which is 80.60% The Naïve Bayes Algorithm can be said to be one of the effective Algorithm both in the terms of calculations and final results where the test can be used as a basis for credit card approval.
Implementation of Argon2i Algorithm in A Gamification-Based Habit Tracker Application for Account Security on Android Platform Isnainy Rodiah; Khairuddin Nasution; Rachmat Aulia
Tsabit Journal of Computer Science Vol. 3 No. 1 (2026): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit99

Abstract

The rapid penetration of smartphones in Indonesia has driven the growth of the productivity application ecosystem, one of which is the gamification-based habit tracker application. However, this type of application indirectly collects users' sensitive daily routine data, while the majority of local applications still store passwords in plaintext form or use traditional hashing algorithms such as MD5 and SHA-256, which are vulnerable to GPU- and ASIC-based brute-force attacks. This research aims to design and implement the Argon2 algorithm (Argon2i variant) as an account security system for a gamification-based Android habit tracker application. The system was built using the Kotlin language, the Jetpack Compose framework, MVVM architecture, and Jetpack DataStore as local storage, with a layered security scheme (Dual-Layer Security) integrating Firebase Authentication as the first layer and an Argon2i-based App Lock as the second layer. The Argon2i algorithm was configured with a memory cost parameter of 64 MB (m=65536), a time cost of 2 iterations (t=2), and 1 parallel lane (p=1), producing a strong hash value resistant to specialized hardware exploitation. Functional testing using the Black Box Testing method across two iterations showed that all test scenarios (100%) passed after the debugging process, proving that the cryptographic system successfully secured user credentials without disrupting the application's stability and speed. This research produces a technical blueprint for integrating Argon2i password hashing into Android applications as a reference standard for account security on mobile devices.
Implementation Of Internet of Things (Iot) In Smart Garbage Isyatur Rodhiah
Tsabit Journal of Computer Science Vol. 3 No. 1 (2026): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit101

Abstract

The increasing volume of waste in Indonesia is a major environmental challenge due to poor source segregation. This study develops a Smart Garbage system based on IoT and computer vision to classify waste into metal, organic, and inorganic categories with real-time monitoring. The system integrates an ESP32 microcontroller, inductive proximity sensor, webcam, Teachable Machine, Python, and the Blynk platform. The sensor detects metal waste while image classification distinguishes organic and inorganic waste. Results are sent to ESP32 via serial communication to control the sorting mechanism. Testing evaluated detection accuracy, confidence threshold, lighting conditions, and communication delay. The inductive sensor achieved 100% accuracy for metal detection. The optimal confidence threshold was 0.8 with 90% classification accuracy. The average confidence score reached 0.89 under good lighting and latency averaged 4.26 seconds. The Blynk platform enabled real-time monitoring. Overall IoT and computer vision improve waste sorting efficiency and system performance effectiveness overall improved.

Page 3 of 4 | Total Record : 35

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