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All Journal Journal Of Informatics And Busisnes Jurnal Sains dan Teknologi Jurnal Ilmu Komputer dan Informatika
Andri Wijaya
Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Katolik Musi Charitas
Agriculture, Biological Sciences & Forestry Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mathematics Physics Transportation

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Perancangan Rencana Strategis Sistem Informasi Pada PT XYZ Leni Kusneti; Bima Aprianto S; Stenilaus A; Andri Wijaya
Journal Of Informatics And Busisnes Vol. 2 No. 3 (2024): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v2i3.2007

Abstract

This study develops a strategic plan for Information Systems/Information Technology (IS/IT) at PT XYZ, a distribution company in the plastic packaging sector. The primary objective of the research is to enhance operational efficiency and expand market reach through the optimal utilization of information technology. The methodology employed includes SWOT analysis, PESTLE, and the McFarlan Grid to evaluate the company's internal and external conditions and define the IS/IT implementation strategy. The findings highlight the need for additional features in the Enterprise Business System (EBS) to record retail transactions, the implementation of an e-commerce platform to broaden market access, and the adoption of automation technology in logistics processes to boost productivity. Moreover, the study recommends developing a Customer Relationship Management (CRM) system to strengthen relationships with business partners. A systematically designed five-year implementation roadmap ensures that this strategy supports the company's digital transformation and enhances its performance in the global market.
Implementasi dan Evaluasi Sistem Pencarian Informasi Ulasan Restoran India Menggunakan Algoritma VSM Anggitta Ratu; 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.
Penerapan Data Mining untuk Memprediksi Kelulusan Mahasiswa Menggunakan Decision Tree Angelus Galang Shevchenko; Wianti Maharani; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

This study aims to apply data mining techniques to predict student graduation using the C4.5 Decision Tree algorithm in the Information Systems Study Program, Faculty of Science and Technology, Musi Charitas Catholic University. The data used in this research consist of academic records of students from 2018 to 2020, including Grade Point Average per semester (GPA) and Cumulative Grade Point Average (CGPA). The research method follows the Knowledge Discovery in Database (KDD) stages, namely data selection, preprocessing, transformation, data mining, and interpretation. Model development and evaluation were conducted using RapidMiner software with the 10-Fold Cross Validation method. The results indicate that Semester 8 GPA is the most influential attribute in determining student graduation status, followed by Semester 4 GPA as a supporting indicator. The generated decision tree model achieved an accuracy rate of 75.68%, indicating a good predictive performance. These findings demonstrate that the C4.5 Decision Tree algorithm can serve as an effective decision-support tool for early detection of students at risk of delayed graduation, thereby assisting academic institutions in improving on-time graduation rates and academic management quality.
Perancangan Data Warehouse Penjualan Minimarket XYZ Menggunakan Kimball Modeling dan Analisis OLAP Ketut Agus Wikananda; Muhammad Raka Nur Habibi; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Minimarkets generate large volumes of transaction data every day that have not been fully utilized for business analysis. This study aims to design a minimarket sales data warehouse using the Kimball method and to conduct OLAP analysis using RapidMiner. The steps carried out include identifying requirements, designing a dimensional model based on a star schema, performing ETL processes, and conducting OLAP analysis. Sales data are processed into a fact table and several dimension tables. The analysis results indicate that the data warehouse can display sales information multidimensionally based on time, product, and region, which is useful for managerial decision-making.
Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Menentukan Stok Smartphone Berdasarkan Pola Harga Penjualanan Juni Lapita Hasugian; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Intensifying competition in the smartphone retail sector encourages businesses to utilize sales data more effectively to support strategic decision-making, especially in inventory planning. In many cases,sales records are primarily used for routine administrative purposes and are not thoroughly analayzed to uncover sales trends that can guide stock prioritization.This research focuses on the application of data mining techniques to determine smartphone stock priorities by analyzing sales patterns. The dataset used in this study consists of smartphone sales records obtained from a retail store, incorporating attributes such as product pricing and sales volume. The research process includes data preprocessing stages, namely data cleaning and normalization, followed by the implementation of the K-Means clustering algorithm. Through the clustering process, smartphone products are categorized into several groups that reflect high, moderate, and low sales performance. The findings indicate that the K-Means-based data mining approach is effective in identifying sales patterns and classifying products according to their sales levels. The resulting clusters serve as a valuable reference for establishing stock priorities, improving inventory management efficiency, and supporting strategic decision-making in smartphone retail operations. Consequently, the application of data mining techniques offers an effective approach to enhancing inventory control in smartphone retail businesses.
Analisis Pola Kepuasan Pengunjung Amanzi Waterpark Palembang Menggunakan Algoritma K-Means Clustering Septia Angelika Gettin Daely; Aloisius Egi Sanjaya; Andri Wijaya
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Palembang's tourism sector increasingly relies on online reviews as visitor satisfaction indicators, yet the large volume of unstructured review data complicates manual analysis. This study aims to analyze visitor satisfaction patterns at Amanzi Waterpark Palembang using K-Means Clustering algorithm on 1,812 Google Maps reviews collected through web scraping techniques. The analytical process includes text preprocessing, TF-IDF weighting, TruncatedSVD dimensionality reduction, and clustering with k=5. Research findings identify five visitor experience segments: Family Recreation (12.4%, rating 4.69), General Positive Reviews (8.9%, rating 4.55), Cleanliness & Comfort (7.1%, rating 4.60), Mixed Reviews & Complaints (67.5%, rating 3.99), and English Language Reviews (4.1%, rating 4.57). Critical findings reveal that 67.5% of reviews fall into the cluster with the lowest rating, dominated by complaints regarding pool water cleanliness, operational system complexity, and perceived high prices. Service quality inconsistency is identified through differing cleanliness sentiments across clusters, indicating standards not consistently maintained especially during peak visit periods . This research provides practical contributions in the form of strategic recommendations for cleanliness improvement, payment system simplification, and quality control consistency, while academically enriching the literature on text mining applications in Indonesia's tourism sector.
Co-Authors Aloisius Egi Sanjaya Andronikus G Angelus Galang Shevchenko Anggitta Ratu Bima Aprianto S Juni Lapita Hasugian Ketut Agus Wikananda Leni Kusneti Leni Kusneti Muhammad Raka Nur Habibi Septia Angelika Gettin Daely Stenilaus A Thomas Filikano Wianti Maharani