Journal Global Technology Computer

Perancangan dan Penerapan Sistem Pendukung Keputusan Pemilihan Supplier Terbaik Menggunakan Metode Analytical Hierarchy Process

2025-12-15T09:19:02+07:00

This study aims to develop a decision support system for the supplier selection process using the Analytical Hierarchy Process method. The main problem addressed in this research is that the supplier selection process has been subjective and lacks transparency, which may affect the quality of procurement. The Analytical Hierarchy Process is applied to evaluate supplier alternatives based on several criteria, including price, quality, availability, and reputation. Each criterion is assigned a priority weight through pairwise comparisons, and the total scores are calculated to determine supplier rankings. The results of this study indicate that the method is effective in assisting decision-making in a more objective and structured manner. The main contribution of this research is the development of a decision support system that improves the efficiency and accuracy of supplier selection and supports optimal supply chain management.

Penerapan Metode Profile Matching pada Sistem Pendukung Keputusan Penentuan Karyawan Teladan

2025-12-15T09:19:12+07:00

The development of Industry 4.0 encourages companies to manage human resources (HR) more effectively, efficiently, and objectively, especially in the employee performance appraisal process. Manual assessments often face obstacles, such as subjectivity, inefficiency, and limitations in data processing. This study aims to develop a web-based Decision Support System (DSS) using the Profile Matching method to assist in the selection process of exemplary employees. The Profile Matching method is used to compare actual employee values with ideal profiles through the stages of GAP calculation, GAP weighting, determination of Core Factors (CF) and Secondary Factors (SF), and final ranking. The research was conducted through problem identification, literature study, determination of ideal criteria and weights, employee data collection, manual calculations, system development with UML and database design, web application implementation, as well as functional testing and result validation. Based on the test results, the system produces values and rankings that are consistent with manual calculations, proving that the applied algorithm runs correctly and in accordance with the profile matching method procedure. In addition, the system's ranking results are also in line with HRD evaluations, making the system suitable for use as a decision-making tool. Web-based implementation improves accessibility, efficiency, and transparency in data management. Overall, this research contributes to the use of information technology to improve the quality of employee performance management and support HR development strategies in the digital era.

Penerapan Algoritma Machine Learning Random Forest untuk Prediksi Risiko Konversi Sindrom Terisolasi Klinis Menjadi Multiple Sclerosis

2025-12-15T09:19:31+07:00

Clinically Isolated Syndrome (CIS) is an initial neurological episode potentially developing into Multiple Sclerosis (MS), a chronic neurodegenerative disorder of the central nervous system. Early detection of risk factors for CIS to MS conversion is crucial for supporting timely medical interventions and slowing down disease progression. This study aims to develop a risk prediction model for CIS to MS conversion using a Machine Learning algorithm, comprehensively evaluate the model's performance, and implement it as a web-based clinical decision support system. The research employs a machine learning approach utilizing the Random Forest Classifier to predict the conversion risk using the public dataset Conversion Predictors of CIS to Multiple Sclerosis. The dataset comprises 273 patients with clinical variables including demographics, initial symptom characteristics, Magnetic Resonance Imaging (MRI) findings across various brain regions and the spinal cord, and Oligoclonal Bands (OCB) test results. The methodology involved addressing class imbalance using weight adjustments, cross-validation, and implementing a custom threshold of 0.57 to minimize false positives, ensuring clinical diagnostic safety. Test results demonstrate that the Random Forest model achieved optimal performance with an Accuracy of 81.82%, an F1-Score of 0.82, and an Area Under the Curve (AUC) of 0.9140, indicating excellent discriminative capability. Feature Importance analysis revealed that Oligoclonal Bands (OCB), Initial Symptoms (specifically sensory and visual disturbances), and MRI lesions (especially Periventricular) are the most influential predictors. The model is subsequently implemented into a web-based prediction system to facilitate interactive risk assessment by medical professionals. This implementation serves as an accurate and explainable prototype of a Clinical Decision Support System.

Implementasi Enterprise Resource Planning Odoo Menggunakan Metode Analisis Proses Bisnis As-Is dan To-Be

2025-12-24T10:08:19+07:00

The implementation of an Odoo-based Enterprise Resource Planning system is considered a relevant solution to address operational problems faced by small and medium-scale retail businesses that still rely on manual systems. This study aims to analyze existing business processes, identify operational issues, and design as well as evaluate the implementation of an Odoo-based Enterprise Resource Planning system to improve operational efficiency and data management accuracy in grocery retail businesses. The research methods employed include field observation, structured interviews, and documentation studies to obtain a comprehensive understanding of the Sales, purchasing, and inventory management processes that are currently conducted manually. Business process analysis was carried out using the As-Is and To-Be approach to identify gaps between the existing conditions and the ideal conditions supported by an integrated system. The results indicate that the implementation of Odoo ERP using the Sales, Inventory, and Purchase modules successfully automates the recording of Sales and purchasing transactions, updates inventory data in real time, and integrates all operational activities into a centralized system. The implemented system effectively reduces the risk of data entry errors, improves inventory data accuracy, and accelerates the generation of operational reports that can be accessed in real time through the dashboard. System evaluation shows that inter-module integration functions properly and supports faster, data-driven decision-making. However, the study also reveals that optimal system utilization requires additional user training and continuous assistance to ensure that all ERP features can be fully utilized.

The The Prototype of An Automated Temperature Control System for Broiler Chicken Coops Based on Arduino and The Internet of Things (IoT)

2025-12-24T10:37:24+07:00

This study aims to design and implement an Internet of Things (IoT)–based automated temperature control prototype for broiler chicken housing using an Arduino Uno and ESP32 microcontroller architecture. The proposed system integrates a DHT22 temperature–humidity sensor, relay modules, a cooling fan, and a heating lamp to regulate the coop microclimate based on predefined temperature thresholds. The Arduino Uno functions as the primary data acquisition and control unit, reading environmental parameters and executing actuator control logic, while the ESP32 handles wireless communication by transmitting sensor data to a MySQL database via WiFi at a configurable interval of 5 seconds. A simple yet effective control algorithm is applied, in which the fan is activated when the temperature exceeds 30.8 °C, while the heating lamp operates based on a scheduled brooding cycle. Experimental results demonstrate that the system is capable of reading temperature data in real time, responding appropriately to environmental changes, and reliably storing data in the database. The main contribution of this research lies in the development of a low-cost, dual-microcontroller IoT prototype that combines real-time monitoring, automatic temperature control, and flexible data logging, making it suitable for small to medium scale broiler farming applications and serving as a foundation for future enhancements involving humidity-based control.

Penerapan Sistem Informasi Akuntansi, Tata Kelola, dan Kompetensi Manusia: Analisis Pengaruhnya terhadap Ketepatan Waktu Pelaporan Keuangan dengan Metode Regresi Data Panel

2025-12-24T10:56:44+07:00

This study aims to analyze the effect of Accounting Information Systems (AIS) implementation on the quality of financial reports in automotive and component sub-sector companies listed on the Indonesia Stock Exchange (IDX) for the 2019-2023 period. Unlike previous studies that relied on perception data, this study utilizes secondary data sourced from company annual reports to provide more objective evidence. Financial report quality is proxied by audit delay (timeliness), while AIS is measured through the disclosure of Enterprise Resource Planning (ERP) implementation. The moderating variable, Internal Control, is proxied by the frequency of Audit Committee meetings, and HR Competence is proxied by the financial educational background of the Board of Directors. Using panel data regression analysis (via Chow, Hausman, and LM tests), the results show that companies disclosing the use of ERP-based AIS have a significantly higher level of reporting timeliness. Furthermore, moderation effect testing proves that this positive relationship between AIS and timeliness is strengthened by effective internal control and competent human resources at the director level. These findings indicate that technology requires robust governance and adequate human competence to produce quality and timely financial reports. The research implications highlight the importance of a holistic approach to technology investment, where strengthening oversight and improving HR quality must go hand in hand with information system implementation.

Implementasi Enterprise Resource Planning Berbasis Odoo untuk Meningkatkan Efisiensi Operasional Menggunakan Analisis Deskriptif Komparatif

2025-12-29T10:19:55+07:00

The rapid advancement of information technology requires companies, including Micro, Small, and Medium Enterprises (MSMEs), to manage their business processes in an integrated manner to enhance operational efficiency, improve data processing accuracy, and enhance the quality of managerial decision-making. The utilisation of integrated information systems has become a crucial factor in supporting the sustainability and competitiveness of MSMEs amid an increasingly complex business environment. CV Anak Rimba, a manufacturing company engaged in the production of processed food products, continues to face various operational challenges, particularly due to the implementation of manual and fragmented data recording systems across departments. These conditions result in inaccuracies in calculating the Cost of Goods Manufactured (COGM), inconsistencies in raw material and finished goods inventory data, and delays in the preparation of financial reports required by management. Therefore, this study aims to implement an Enterprise Resource Planning (ERP) system based on Odoo as a solution to integrate production processes, inventory management, and financial recording in a unified system. The research method employed is a case study with a comparative descriptive analysis approach, utilising system simulation with dummy data. The configured modules include Manufacturing, Inventory, Purchase, Sales, and Accounting. The results indicate that the implementation of an Odoo-based ERP is able to automate transaction recording, calculate COGM in real-time, synchronise inventory data, and generate accurate and timely financial reports. Thus, the implementation of an Odoo-based ERP system is proven to be effective in improving operational efficiency and data transparency at CV Anak Rimba.

Analisis Kinerja Algoritma Naive Bayes dalam Klasifikasi Data pada Pasien Tuberkulosis Berbasis Data Mining

2025-12-31T03:23:58+07:00

Tuberculosis (TB) is one of the infectious diseases that remains a major public health problem in Indonesia, particularly at the primary healthcare level such as public health centers. The increasing amount of patient data stored in health information systems requires effective analytical methods to support accurate and efficient decision-making. This study aims to analyze the performance of the Naive Bayes algorithm in classifying tuberculosis patient data. The dataset used in this research was obtained from medical records of TB patients and non-TB patients, which were processed through several preprocessing stages, including data cleaning, data integration, data transformation, and normalization to ensure data quality. The data were then divided into training and testing datasets for classification purposes. The Naive Bayes algorithm was implemented to classify patient status based on selected clinical and demographic attributes. Model performance was evaluated using a confusion matrix and several evaluation metrics, including accuracy, precision, recall, and F1-score. The experimental results show that the Naive Bayes algorithm achieves satisfactory performance in classifying tuberculosis patient data and demonstrates good efficiency when applied to real-world healthcare data. However, the algorithm still has limitations related to the assumption of independence among attributes, which may affect classification accuracy. The findings of this study are expected to contribute to the development of a decision support system that can assist healthcare professionals at public health centers in performing early classification and analysis of tuberculosis patient data more effectively and efficiently.