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Ratu, Anggitta
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Computer Science & IT Economics, Econometrics & Finance

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Prototype Data Warehouse Kantor Penilai Publik XYZ Dengan Metode Nine-Step Kimball Ratu, Anggitta; Kusneti, Leni; Wijaya, Andri
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.v1i3.372

Abstract

In the era of information technology, data management has become crucial for organizations. The Public Appraisal Office (KJPP) XYZ in Palembang City, despite providing accurate property analysis, is still limited to the use of hard disks, hard copies, email, and Excel. This results in a lack of integration and efficiency in their operations. Therefore, this research proposes a solution in the form of designing a data warehouse prototype using the Nine-Step Kimball method. This method is employed as a systematic approach in designing and building a data warehouse, with the aim of ensuring effective and relevant development for business needs. The design will organize data into table components, creating regularity and centralization of data to facilitate search and analysis. The inspiration for this research comes from similar implementations in online market transactions and television editorial decisions using the snowflake schema and the successful Nine-Step Kimball method. It is hoped that through this approach, KJPP XYZ can optimize access, analysis, and use of the data.
Implementasi dan Evaluasi Sistem Pencarian Informasi Ulasan Restoran India Menggunakan Algoritma VSM Ratu, Anggitta; Leni Kusneti; Thomas Filikano; Andronikus G; 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.v2i4.2128

Abstract

Online review platforms, such as restaurant search websites and apps, have become a primary source of information for consumers when choosing restaurants. The large number of reviews available provides insights into service quality, food taste, and previous customer experiences. However, the main challenge is managing and extracting relevant information from the diverse and unstructured reviews, which can make it difficult for users to find accurate and relevant information. This study implements an information retrieval system for Indian restaurant reviews using the Vector Space Model (VSM) algorithm to address this challenge. The dataset from Kaggle, containing 10,000 Indian restaurant reviews, was processed through tokenization, stopword removal, stemming, and text normalization. The TF-IDF method was applied for term weighting, and relevance between the user's query and reviews was calculated using VSM. The evaluation results showed a precision of 70.92%, recall of 84.81%, and F1-score of 77.25%, indicating that the system can provide relevant reviews accurately and efficiently. This system could serve as a reference for developing information retrieval systems in the culinary field and other sectors that require effective customer review analysis.
Co-Authors Andri Wijaya Andronikus G Kusneti, Leni Leni Kusneti Thomas Filikano