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Journal : journal of computer science advancements

DESIGN OF A WEB-BASED GOODS DELIVERY INFORMATION SYSTEM WITH API SERVICES AND IOT INTEGRATION AT PT. ESA MANDIRI RUBBER Nadia Natasya Putri; Vany Terisia; Muhajir Syamsu
Journal of Computer Science Advancements Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i3.2415

Abstract

The advancement of information technology has driven various industrial sectors, including manufacturing, to transform toward more efficient and responsive distribution systems. PT. Esa Mandiri Rubber, a rubber manufacturing company, still relies on manual processes in managing goods delivery, resulting in various issues such as delays, distribution errors, and a lack of transparency in tracking. This study aims to design a web-based goods delivery information system integrated with Internet of Things (IoT) technology and API services. The system development method used is the Waterfall method, which consists of five stages: requirement analysis, system design, implementation, testing, and maintenance. The developed system includes delivery recording, real-time tracking, IoT device data integration, and access to information through a web interface. The results of the study show that the designed system successfully replaces the previously used manual processes, enhances distribution effectiveness, and facilitates easier monitoring and reporting. Thus, this system is capable of improving operational efficiency and the quality of logistics services at PT. Esa Mandiri Rubber.
WEB-BASED FINANCIAL MANAGEMENT INFORMATION SYSTEM FOR MSMES USING RAD METHOD Haikal Rizky; Vany Terisia; Muhajir Syamsu
Journal of Computer Science Advancements Vol. 3 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i6.2416

Abstract

Micro, Small, and Medium Enterprises (MSMEs), such as the Sukamurni Cracker Factory, often rely on manual financial recording, a practice prone to human error, data loss, and significant inefficiencies in generating financial reports. This research addresses these challenges by developing a web-based financial management information system tailored to the operational needs of MSMEs. The primary objective was to design and implement a system that improves the effectiveness, efficiency, and accuracy of financial record-keeping. The study employed the Rapid Application Development (RAD) methodology, encompassing requirements planning, user design, rapid construction, and system validation. The resulting system was built using the Laravel framework, PHP programming language, and a MySQL database. Functional validation was conducted through Black Box testing, which confirmed that all system modules—including income and expense tracking, automated report generation, and role-based access control for Admins and Staff—operate as specified. The novelty of this work lies in its practical application of the RAD model to create a user-centric and rapidly deployable solution for a resource-constrained MSME environment. This research provides a functional model for digital transformation in small-scale businesses, demonstrating that a well-designed system can significantly enhance operational efficiency and support strategic decision-making.
STUDENT GRADUATION PREDICTION USING DECISION TREE ALGORITHM WITH CRISP-DM METHOD (CASE STUDY: ITB AHMAD DAHLAN) Kholilah Husni; Elliya Sestri; Vany Terisia
Journal of Computer Science Advancements Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i5.2429

Abstract

On-time graduation is an important indicator of higher education effectiveness; however, delays in student graduation are still observed at ITB Ahmad Dahlan Jakarta. This study develops a student graduation prediction system using the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and the Decision Tree algorithm based on historical academic data. The model was built through six CRISP-DM stages, including problem understanding, data preparation, modeling, and evaluation. Testing results indicate high performance with an Accuracy of 97.44%, Precision of 97.14%, Recall of 100%, and F1-Score of 98.55%. This system has the potential to support strategic decision-making to enhance academic quality through data-driven approaches.