cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ai Munandar
Contact Email
ijitcsa@gmail.com
Phone
+62+6282111152015
Journal Mail Official
ijitcsa@gmail.com
Editorial Address
International Journal of Information Technology and Computer Science Applications (IJITCSA) Sekretariat Jejaring Penelitian dan Pengabdian Masyarakat (JPPM) : Ranau Estate Blok D.3, Kel. Panggungjati, Kp. Pantogan Kec. Taktakan - Kota Serang, Provinsi Banten, e-mail : jitcsa@jejaringppm.org web : www.jejaringppm.org
Location
Kota serang,
Banten
INDONESIA
International Journal of Information Technology and Computer Science Applications (IJITCSA)
Published by Jejaring Penelitian dan Pengabdian Masyarakat
ISSN : 29643139     EISSN : 29855330     DOI : https://doi.org/10.58776/ijitcsa.v1i2
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering
he Journal of Information Technology and Computer Science Applications (JITCSA) is an information technology and computer science publication. Applications from both fields for solving real cases are also welcome. JITCSA accepts research articles, systematic reviews, literature studies, and other relevant ones. Several fields of science that are the focus of JITCSA include information technology and the like, computer science fields, including artificial intelligence, data science, data mining, machine learning, deep learning, and the like. IJITCSA is published three times a year, in January, May, and September. The first issue in January 2023 had eight articles. Focus and Scope International Journal of Information Technology and Computer Science Applications includes scholarly writings on scientific research or review, pure research, and applied research in the field of computer science, information systems, and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. Information systems System Software Artificial Intelligence Computer Architecture Distributed Systems System & Software Engineering Genomics & Bioinformatics Internet and Web AI & Expert systems Software Process and Life Cycle Database Systems Software Testing & Quality assurance Bioinformatics Information Technology Implementation Computing Languages & Algorithms E-commerce & M-Commerce Computer Networks & Communications Computing Systems Control Systems & Engineering Systems Engineering System Security Digital Forensics Data Mining & Machine Learning Data Modeling
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 50 Documents
 1   2   3   4   5 
Prediction Model for Product Stock Procurement Using the Naive Bayes Method Sari, Rafika; Ashardi, Muhammad
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 1 (2025): January - April 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i1.172

Abstract

Product sales circulation and product stock repurchase play a strategic role in meeting customer needs and increasing a company's profits. PT Rotaryana Engineering as a company that provides spare parts for electronic kitchen products in various brands is experiencing rapid dynamics in the sales of its various products. Therefore, a decision-making system is needed regarding spare parts stock policies that will support increased sales and customer satisfaction for the company. This research will model a product stock procurement prediction system using the Naive Bayes method. Sales data for one year will be used to categorize the availability of each product, namely the available category and the not available category. Furthermore, calculations using the Naive Bayes method produce likelihood probability values ​​for each product item which are used to predict and recommend procurement of spare parts stock. This information can be used as a basis for determining priorities for procuring stock of the most popular goods and reducing stock of less popular goods so that the circulation of the company's product stock becomes more efficient and effective.
An Extended Relational Database Model for Interval Probability Set-Valued Attributes Nguyen, Hoa
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 1 (2025): January - April 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i1.174

Abstract

In this paper, we introduce a new probabilistic relational database model as an extension of the classical relational database model for interval probability set-valued attributes to represent and handle uncertain and imprecise information in practice. To develop the new model, we use extended probabilistic values for representing interval probability set-valued relational attributes and the probabilistic interpretation of binary relations on sets for computing uncertain degree of functional dependencies, keys and relations on attribute values, and propose the new combination strategies of extended probabilistic values for building probabilistic relational algebraic operations. A set of the properties of the basic probabilistic relational algebraic operations is also formulated and proven
The Role of Data Analytics in Customer Behavior Analysis and Market Strategy Optimization (A Case Study) Nusrat Fatima, Ayesha
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 1 (2025): January - April 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i1.176

Abstract

In the highly competitive global retail market, understanding customer behavior is essential for sustained business growth and strategic decision-making. Stellar Peak Sports & Adventure, a multinational retailer specializing in sports and outdoor products, is facing operational challenges due to a lack of deep customer insights. This deficiency has resulted in inefficiencies in marketing strategies, inventory management, and overall business performance. To address these issues, the company has enlisted the expertise of a business analyst to conduct a comprehensive customer analysis. This study employs a data-driven approach to identify and analyze key customer attributes, including demographic segmentation, purchasing behaviors, membership trends, and engagement patterns. The business analyst will determine the most relevant data sources, apply statistical and machine learning techniques to extract actionable insights, and develop interactive visualizations through a dashboard, enabling the company to monitor customer trends in real-time. Moreover, the study will critically assess potential risks associated with data analytics, such as data quality issues, biases in predictive modeling, and the challenges of integrating analytics into decision-making frameworks. Through a rigorous evaluation of customer data, this research provides strategic recommendations to optimize marketing campaigns, enhance customer segmentation, improve sales forecasting, and strengthen customer retention strategies. By leveraging advanced analytics, Stellar Peak Sports & Adventure can develop data-driven operational improvements, refine business strategies, and achieve a sustainable competitive advantage in the global sports and outdoor retail industry.
Optimizing Retail Strategy with IBM Retail Data Warehouse (RDW): A Short Review of Data-Driven Approach to Customer-Centric Decision-Making Santos Fernandez, Maria
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 1 (2025): January - April 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i1.178

Abstract

Retail businesses operate in highly competitive and unpredictable environments where customer demands continue to evolve rapidly. Whether catering to niche markets with premium products or competing at scale as mega retailers, businesses must deeply understand their customers and develop strategies that align with their preferences, behaviors, and expectations. Simply recording raw data is insufficient to gain meaningful insights; retail businesses must leverage advanced business intelligence tools to visualize and analyze data effectively. The integration of data mining techniques with business analytics plays a crucial role in extracting actionable intelligence, enabling retailers to enhance customer experiences, optimize inventory management, and improve operational efficiency. This paper explores the IBM Retail Data Warehouse (RDW) as a comprehensive solution for data-driven decision-making in the retail sector. By implementing robust data integration and governance frameworks, businesses can enhance their ability to derive valuable insights, streamline operations, and maintain a competitive edge in the dynamic retail landscape. Ultimately, mastering data-driven strategies through IBM RDW empowers retail businesses to transition from reactive decision-making to proactive, customer-centric approaches, ensuring long-term growth and sustainability in an ever-changing market.
An Economic Analysis of Drug Offences in Thailand : Exploring the relationship between drug offences and economic costs Kyaw, Yoon Nandar; Htike, Zin Me; Wang, Ruyi; Li, Jiaxian
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 1 (2025): January - April 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Thailand faces significant hurdles in combating drug offenses, with more than 74% of itsjail population incarcerated for drug related crimes. Methamphetamine (‘ya ba’)dominated these cases, highlighting the widespread problem of addiction and trafficking.Despite a tripling of government investment on prevention, suppression, and treatmentinitiatives between 2006 and 2016, the public’s understanding of drug laws, rehabilitationchoices, and health dangers remains uncle/ar. The 2021 Narcotics Code contained health-related improvements, such as voluntary treatment and partial decriminalization,indicating a trend towards a more balanced approach. However, these interventions havehad little effect on decreasing systemic burdens and societal stigmas. A comprehensiveplan that includes education, accessible rehabilitation, socioeconomic support, andcompassionate laws is required to reduce the economic and social consequences of drug-related offenses
Integrated Healthcare Database Systems: A Review of Data Warehousing, Storage, and Integration Strategies Ram Suman, Bikash
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 2 (2025): May - August 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i2.181

Abstract

This paper provides a comprehensive review of current database systems and data warehousing technologies within the healthcare sector, emphasizing their roles in supporting forecasting and analytics. The objective is to describe, analyze, and evaluate the key features of these systems, particularly focusing on the essential functions of data storage and data integration in managing complex healthcare data environments. Recognizing that efficient data storage is fundamental to effective database management, the paper examines prevalent challenges within current healthcare systems, including issues related to data infrastructure, security, and interoperability. It further investigates how these challenges impact the reliability and accessibility of data crucial for informed decision-making. In addition to highlighting the difficulties, this review delves into the benefits and drawbacks of various data integration strategies. It discusses how advanced integration techniques can enhance data accuracy, streamline real-time access, and bolster analytical capabilities, while also addressing potential risks such as integration complexity and security vulnerabilities. By synthesizing the latest trends and research in database management, this paper aims to offer valuable insights for healthcare practitioners, IT professionals, and researchers. Ultimately, it seeks to guide the development of more secure, efficient, and resilient data management strategies that can better support healthcare analytics and forecasting in an increasingly data-driven industry.
Agglomerative Spatial Clustering Analysis for Mapping Crime Risk Zone Clusters Munandar, Tb Ai; Ramdhania, Khairunnisa Fadhilla
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 2 (2025): May - August 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i2.197

Abstract

Public safety and order are crucial aspects of social and economic life, especially in densely populated urban areas. High crime rates can undermine the sense of security and quality of life within society. Therefore, a deep understanding of crime distribution patterns is essential for designing effective prevention strategies. This study aims to map crime risk zones in Indonesia using the Agglomerative Clustering method, by integrating socio-economic and demographic variables. This method was chosen for its ability to group data based on similarity of characteristics, making it easier to identify areas with high-risk levels. The results show the formation of four main clusters that reflect crime risk distribution in Indonesia. The first cluster includes several provinces with similar crime patterns, while the other clusters reflect significant differences in crime patterns, particularly in Jakarta, which has very distinct criminal characteristics. This mapping provides valuable insights for the planning of more efficient, data-driven crime prevention policies. The research is expected to provide a strong foundation for policymakers and law enforcement agencies to formulate more targeted strategies to combat crime in Indonesia.
A review of Data Infrastructure for Education: A Proposal for Improved Decision Making in XYZ University Lê Thắng Thục
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 2 (2025): May - August 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i2.200

Abstract

Data is becoming a valuable resource for organisations in a variety of industries today, including education. Educational institutions are constantly gathering massive volumes of data from many sources. XYZ University (XYZU), one of the higher educational institutions, has issues with its current data infrastructure, which slows down its decision-making. The existing system relies on reporting and analytics that are derived directly from operational applications, which leads to data silos and discrepancies. To address these issues, this paper proposes a data architecture that combines data from several source applications to facilitate integrated reporting. The paper explores the background and problems in terms of data storage, management, and use of enterprise data, followed by a problem statement, a discussion of data integration, and a proposed technical architecture.
From Offline to Online: Utilizing Sentiment and Web Analytics to Navigate Retail Transformation Agustin, Bayani Krisanto; Ting, Alon Dakila; Dolores, Althea Keenan
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 2 (2025): May - August 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i2.203

Abstract

The covid-19 pandemic has forced the way businesses run, including the offline apparel store that needs to shift their business to an online shopping platform. This also means that the volume of unstructured data that needs to be analyzed has increased significantly. This unstructured data should be analyzed accurately to help businesses in decision-making and solve their problems such as understanding customer satisfaction levels and evaluating marketing approaches. One way to utilize unstructured data is by using text analytics, the data like customer reviews from the online shopping platform and social media can be integrated and analyzed using a sentiment analysis approach in order to gain a better understanding of customer satisfaction levels. Furthermore, web analytics can also be utilized to evaluate the current marketing approach and how to maximize the marketing strategy for the business.
A Desk Review of The Application of Data Analytic on Tesla Inc. Firmansyah, Mulya
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 2 (2025): May - August 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i2.205

Abstract

Tesla Inc. is frequently regarded as a pioneer in the fields of big data analytics and artificial intelligence. They generate, collect, and analyse a large amount of data every day for a better decision-making for their business and for their self-driving car. However, a little effort was put into marketing. Customer segmentation and customer retention are noticed to be the problems Tesla Inc. should take into consideration. Therefore, the data that is relevant to solve these problems is extracted from various websites and social media platforms by using the text-scrapping technique. Web analytics of Tesla’s official website is studied to analyse the demographic and geographic details of the audiences. Geospatial analysis is also carried out to further analyse the top 5 countries the audiences are coming from. Customers' reviews that were collected undergoes sentiment analysis to determine whether it is positive, neutral, or negative. Text analytics is done in the later stage by gathering all the visualisations into an interactive dashboard and coming up with a possible solution.

Page 5 of 5 | Total Record : 50

 1   2   3   4   5 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2025): May - August 2025 Vol. 3 No. 1 (2025): January - April 2025 Vol. 2 No. 3 (2024): September - December 2024 Vol. 2 No. 2 (2024): May - August 2024 Vol. 2 No. 1 (2024): January - April 2024 Vol. 1 No. 3 (2023): September - December 2023 Vol. 1 No. 2 (2023): May - August 2023 Vol. 1 No. 1 (2023): January - April 2023