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All Journal Journal Of Informatics And Busisnes
Septi Putri Azzahra
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Computer Science & IT Economics, Econometrics & Finance

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Analisis Dan Perancangan Data Warehouse Untuk Pengelolaan Stok Barang Pada Cv Aneka Artha Niaga Septi Putri Azzahra; Yohanes Agung Apriyanto; Andri Wijaya
Journal Of Informatics And Busisnes Vol. 1 No. 2 (2023): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v1i2.375

Abstract

CV Aneka Artha Niaga is a developing company which operates in the field of kassemergud vesmuving distributor. Distributor means an actor who distributes finished products from factory production in the form of packaged snacks and drinks to shops that have registered as customers. As a business that operates in the distribution sector, stock management is an important factor in the success of selling the products they offer. From availability to distribution to customers. The large amount of data that is managed makes it difficult for CV Aneka Artha Niaga to process data analysis and make decisions. This research aims to overcome existing problems at CV Aneka Artha Niaga by implementing a data warehouse with the ITL (Extract, Transform, Load) process. The method used is Kimball's nine steps. Data was collected through field observations, interviews, and the company's ERP system. The result of this research is the implementation of a data warehouse which can be the answer to the problems and information that CV Aneka Artha Niaga needs.
Analisis Ulasan Produk Amazon Menggunakan Random Forest Sentimen dan Probabilistic Retrieval Model Septi Putri Azzahra; Afifah Azzahra; Yohanes Agung Apriyanto; Andri Wijaya
Journal Of Informatics And Busisnes Vol. 2 No. 4 (2025): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v2i4.2070

Abstract

In the digital era, product reviews on e-commerce platforms such as Amazon have become an important source of information for consumers and sellers. This study develops a system for sentiment analysis of reviews using the Random Forest algorithm and relevant information retrieval with a TF-IDF-based probabilistic model. The data used includes 568,454 product reviews from Amazon, which are processed through data cleaning, tokenization, lemmatization, and feature extraction stages. Sentiments are classified into positive, negative, and neutral. The Random Forest model shows reliable performance with precision, recall, and F1-score of 0.878. The probabilistic search system successfully sorts relevant reviews with a high level of accuracy, which is evaluated using the Mean Average Precision (MAP) metric of 0.878. The results of this study provide significant contributions to improving the e-commerce user experience and supporting data-driven decision making. The approach used opens up opportunities for further research in the fields of natural language processing and machine learning.
Co-Authors Afifah Azzahra Andri Wijaya Yohanes Agung Apriyanto