Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal SYSTEMIC RESEARCH: Systemic and Information Technology Journal
Candra Mecca Sufyana
Information Systems, Piksi Ganesha Polytechnic, Bandung, Indonesia

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementation of Research and Community Service Information System (SIMLITABMAS) at LPPM Politeknik Piksi Ganesha Based on Laravel Framework Reyhan Asissrazi Sabian; Candra Mecca Sufyana; Ratnanto Aditiarno
SYSTEMIC RESEARCH: Systemic and Information Technology Journal Vol. 1 No. 2 (2026): January 2026
Publisher : Greenation Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/systemic.v1i2.60

Abstract

The management of data and information related to research and community service (LITABMAS) at LPPM Politeknik Piksi Ganesha frequently faces obstacles due to manual processes and difficulties in tracking project progress. This research adopts a case study approach to describe the implementation of the Research and Community Service Information System (SIMLITABMAS) based on the Laravel Framework. The primary objective is to present an efficient, centralized solution. The development of SIMLITABMAS utilizes the Rapid Application Development (RAD) methodology, which emphasizes iterative development and responsiveness to user feedback. The Laravel Framework was selected for its structured Model-View-Controller (MVC) architecture, reliable security features, and rich package ecosystem. The implementation of SIMLITABMAS successfully created an integrated platform to manage various stages of LITABMAS activities, ranging from proposal submission to result reporting. It is expected that this system can improve the effectiveness, efficiency, and transparency in the management of research and community service at LPPM Politeknik Piksi Ganesha.
Product Recommendation Information System Using the Apriori Algorithm on Sales Transaction Data of Shoe Store X Devie Hartanti; Candra Mecca Sufyana; Ratnanto Aditiarno
SYSTEMIC RESEARCH: Systemic and Information Technology Journal Vol. 1 No. 2 (2026): January 2026
Publisher : Greenation Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/systemic.v1i2.62

Abstract

The advancement of information technology has encouraged retail business owners to utilize transaction data as a basis for business decision-making, particularly in determining sales strategies and product recommendations. Shoe Store X possesses continuously growing sales transaction data; however, this data has not yet been optimally utilized to identify customer purchasing patterns. This study aims to design a product recommendation information system using the Apriori algorithm applied to the sales transaction data of Shoe Store X. The Apriori algorithm is used to discover patterns of relationships between products based on support and confidence values, thereby generating association rules that reveal the tendency of products to be purchased together. The research method employed includes the collection of sales transaction data, analysis of system requirements, system design, implementation of the Apriori algorithm, and testing of the recommendation results. The system developed is expected to help the store provide product recommendations to customers, formulate promotional strategies, arrange product placement, and improve sales effectiveness. The result of this research is an information system capable of processing transaction data into more targeted and user-friendly product recommendation information for store owners. With this system, Shoe Store X can utilize historical sales data as a basis for more effective, efficient, and data-driven decision-making.
Co-Authors Devie Hartanti Ratnanto Aditiarno Reyhan Asissrazi Sabian