Article Per Year (5 Year)
p-Index From 2020 - 2025
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All Journal Journal of Computer Networks, Architecture and High Performance Computing
Djarot Hindarto
Prodi Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional Jakarta, Indonesia
Computer Science & IT Education

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Information System Design at FGH Stores with Unified Modelling Language Djarot Hindarto; Mochamad Hariadi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i2.2702

Abstract

This project aims to develop and execute a proficient information system at FGH Stores, to enhance sales performance and improve customer satisfaction. This study centers on incorporating information technology into routine business activities, intending to devise strategies that facilitate seamless consumer interactions and aid store management in effectively handling inventory and customer data. The research process includes the examination of business needs, the formulation of system architecture, the creation of user interfaces that prioritize ease of use, and the integration of databases. The resultant information system facilitates consumer registration as members, reduces browsing of product catalogs, and enables efficient execution of purchases. Furthermore, implementing shop management systems enables enhanced inventory monitoring, efficient customer data management, and improved responsiveness to consumer requests. This study assesses the effects of information systems on enhancing sales and operational efficiency by conducting data collection and analysis before and after deployment. The findings indicated that implementing information systems effectively enhanced the efficiency of the sales process and improved the customer experience, yielding substantial advantages for the growth of retail establishments. This study offers valuable insights into the possible utilization of information technology within the retail industry while also contributing to the comprehension of the favorable effects that information systems integration may have on corporate expansion and customer satisfaction.
The application of Neural Prophet Time Series in predicting rice stock at Rice Stores Djarot Hindarto; Ferial Hendrata; Mochamad Hariadi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i2.2725

Abstract

Efficient inventory management and consistent rice supply are pivotal for the sustainability of small-scale food stalls. This research introduces an innovative approach to address this challenge through the Neural Prophet algorithm. By synergizing neural networks with additive regression models, the Neural Prophet captures intricate temporal patterns and trends within rice sales data. Our study evaluates the Neural Prophet's effectiveness in predicting rice sales, specifically for essential food vendors. Leveraging historical sales data from June 2022 to April 2023, the algorithm incorporates seasonality and trends and integrates external events, such as holidays, to heighten prediction precision. Our findings underscore the Neural Prophet's remarkable prowess in forecasting rice sales at primary food kiosks, adeptly discerning data trends and fluctuations, culminating in reliable future sales projections. The model boasts compelling performance metrics: MAE = 12.90, RMSE = 15.80, and Loss = 0.0313. Beyond its technical merits, this research carries significant practical implications, empowering proprietors and suppliers of basic food stalls to streamline inventory management, avert stockouts, and curtail overstocking by harnessing the precision of rice demand forecasting facilitated by the Neural Prophet algorithm.
Co-Authors Ferial Hendrata Mochamad Hariadi