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All Journal Jurnal Riset Teknik Komputer Jurnal Riset Sistem Informasi
Ayu Elisya Natama Sianturi
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Computer Science & IT Education Industrial & Manufacturing Engineering Other

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PERANCANGAN DATA WAREHOUSE E-COMMERCE MENGGUNAKAN POSTGRESQL PADA PUBLIC DATASET OLIST Arron Mosses Jhon Hadi; Ayu Elisya Natama Sianturi; Andri Wijaya
Jurnal Riset Sistem Informasi Vol. 3 No. 1 (2026): Januari : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/8dwt6j74

Abstract

This study designs a PostgreSQL-based data warehouse using the Olist public dataset to address fragmented and unstructured e-commerce transactional data. The research process includes ETL (Extract, Transform, Load), data cleaning and standardization, table consolidation, and the development of a star schema consisting of a sales fact table and multiple dimension tables. OLAP analysis reveals key patterns such as annual sales trends, top product categories, seller performance, preferred payment methods, and customer geographic distribution. The results demonstrate that the data warehouse improves analytical efficiency and provides strategic insights to support business intelligence in the e-commerce environment
PREDIKSI PENERIMAAN MAHASISWA MENGGUNAKAN NEURAL NETWORK BERBASIS RAPIDMINER PADA DATA GRADUATE ADMISSION Ayu Elisya Natama Sianturi; Arron Mosses Jhon Hadi; Andri Wijaya
Jurnal Riset Teknik Komputer Vol. 2 No. 4 (2025): Desember : Jurnal Riset Teknik Komputer (JURTIKOM)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/q509nv83

Abstract

This study aims to predict graduate admission outcomes using a Neural Network approach implemented in RapidMiner. The dataset was processed through a series of stages, including data cleaning, normalization, and model training, to ensure optimal learning quality. Model performance was assessed using the Root Mean Square Error (RMSE) metric. The resulting RMSE score of 0.054 indicates a low level of prediction error and demonstrates that the constructed model performs reliably. These findings highlight the potential of Neural Networks as an effective analytical tool for estimating student admission likelihood with higher accuracy and supporting data-driven decision-making in the selection process.
Co-Authors Andri Wijaya Arron Mosses Jhon Hadi