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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
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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 6 Documents
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Search results for , issue "Vol. 1 No. 3 (2023): September - December 2023" : 6 Documents clear
Sentiment Analysis of the Use of Digital Banking Service Applications On Google Play Store Reviews Using Naïve Bayes Method Amalia Nur Soliha; Tb Ai Munandar; Muhammad Yasir
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 3 (2023): September - December 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v1i3.40

Abstract

The development of the financial system is characterized by the emergence of digital banking service applications that are widely circulated and can be accessed for free. With so many applications, users often feel confused in choosing which applications are safe to use. Before downloading an application on the Google Play Store, users will usually look at ratings and reviews first. However, the title of the best application cannot be pinned if only seen from the rating and number of downloads. This research was conducted to analyze sentiment on user reviews of digital banking service applications on the Google Play Store using the NBC (Naïve Bayes Classifier) method. Research using the NBC algorithm produced an accuracy value of 81% on the classification of Allo Bank reviews and 78% on the classification of Line Bank reviews
Clustering of Child Nutrition Status using Hierarchical Agglomerative Clustering Algorithm in Bekasi City Ozzi ardhiyanto; Muhammad Salam Asyidqi; Ajif Yunizar Pratama Yusuf, S.Si, M.Eng; Dr. Tb. Ai Munandar, S.Kom., MT
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 3 (2023): September - December 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v1i3.42

Abstract

Clustering infant nutrition based on weight, height, and age is a data analysis method used to group infant nutritional status based on these characteristics. The research on clustering infant nutrition aims to analyze whether there are still many infants in the area with insufficient or excessive nutrition, and to identify groups of infants requiring special attention regarding their nutritional intake. In the analysis of infant nutrition clustering, data on weight, height, and age of infants are collected and then grouped based on similarities in body height and weight at certain ages. The method used in this research is hierarchical clustering, which can help in grouping the data. Clustering analysis can help understand how infants' feeding patterns vary based on their weight, height, and age. The results of research on clustering infant nutrition based on weight, height, and age can provide valuable insights for nutrition experts, pediatricians, and community health workers in developing appropriate intervention programs to improve infant feeding patterns and meet their nutritional needs. Additionally, the results of clustering infant nutrition can also be used to identify groups of infants requiring special attention regarding their nutritional needs, thus minimizing the risk of malnutrition and unhealthy growth in infants.
Overcoming Challenges and Unlocking the Potential: Empowering Small and Medium Enterprises (SMEs) with Data Analytics Solutions Brandy, Susan
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 3 (2023): September - December 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v1i3.47

Abstract

In today's data-driven business landscape, Data Analytics (DA) has emerged as a vital tool for organizations to extract insights from their existing data, enabling informed decision-making. While large enterprises have wholeheartedly embraced DA as a strategic asset for operational enhancement, SMEs have been comparatively slower in adopting these transformative solutions. To remain competitive and surpass their rivals, SMEs must recognize the significance of harnessing their data assets effectively to drive decision-making processes. This research aims to delve into the challenges hindering the adoption of DA among SMEs, particularly focusing on issues such as inadequate information infrastructure and limited awareness of the benefits that DA can offer. Furthermore, this study investigates the implementation of data analytics as a practical solution to address these challenges, providing a comprehensive analysis of both the advantages and disadvantages associated with DA adoption in the SME context. By shedding light on the untapped potential of data analytics, this research aims to empower SMEs and equip them with the necessary tools to thrive in today's digitally-driven era of business.
Addressing Inconsistent Sales and Determining Continent-Specific Discounts: A Case Study of CeilBrakes Furniture Retail Company Baghati, Huyanah
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 3 (2023): September - December 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v1i3.50

Abstract

This paper highlights the primary business issues faced by CeilBrakes Furniture Retail Company, namely inconsistent sales across different continents. While Europe demonstrates strong sales, North America, Australia, Asia, and Africa exhibit varying levels of mediocre and low sales in terms of the quantity of products ordered. Additionally, determining appropriate discount percentages for each continent poses a challenge. To address the issue of inconsistent sales, the company aims to enhance advertising efforts, improve customer relationships, and analyze trends in continents with low sales (excluding Europe). For determining continent-specific discounts, a solution involves identifying continents with low sales and offering higher discounts accordingly. In the case of Australia, which experiences low sales, CeilBrakes should provide more significant discounts. Despite Asia and Africa already having high discounts, their sales remain low. This situation calls for considering factors beyond retail price reductions, such as cultural influences. To facilitate easier viewing and analysis, a dashboard has been developed to visualize the aforementioned issues. The dashboard enables the identification of trends and patterns, empowering the company to make informed decisions in addressing the observed problems.
Development and Analysis of a Unified Mobile App for Coffee Shop Operations and Ordering Experience: A Proposal Review Maulana, Waleed
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 3 (2023): September - December 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v1i3.52

Abstract

This study extends the exploration of ordering apps in the context of coffee shop owners, specifically focusing on the utilization of popular apps like Grabfood and Foodpanda. With the increasing number of coffee shops adopting ordering apps, there arises a clear necessity for a coffee-focused app that can effectively address the unique demands of establishments. The objective of this study is to conduct a comprehensive review of a mobile app specifically designed to streamline the process of ordering coffee in advance, with a paramount emphasis on ensuring its reliability. By developing an app that caters to the specific needs of coffee shops, both owners and customers can benefit greatly. The app will serve as a dedicated platform, connecting coffee enthusiasts with quality coffee shops, while offering a seamless and convenient ordering experience. By providing a high-quality ordering system that encompasses the full range of customization options for beverages, the developed app is expected to significantly enhance the customer experience and ultimately boost sales for the coffee establishments listed on the platform. With a focus on reliability, the app will enable coffee shop owners to efficiently manage orders, minimize errors, and improve overall operational efficiency. Moreover, by fostering a user-friendly interface and intuitive design, the app will engage customers and encourage them to explore new coffee shops, further promoting the growth of the coffee industry. This study will contribute to the existing body of knowledge by highlighting the importance of tailored ordering apps for coffee shops and providing insights into the development and implementation of such apps. The findings will be valuable not only for coffee shop owners seeking to enhance their business operations but also for app developers looking to cater to the specific needs of the coffee industry. Ultimately, the study aims to bridge the gap between technology and the coffee business, fostering innovation and growth in the ever-evolving digital landscape.
Utilizing Linear Regression for Predicting Sales of Top-Performing Products Pratama, matthew
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 3 (2023): September - December 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v1i3.92

Abstract

PT Ajidarma Delta Medika is a company engaged in the sale of medical devices in the city of Bekasi. This company markets a variety of medical device products. Judging from the large number of consumer requests for medical device products based on sales data for the last 3 years, predictions are needed for the best-selling product sales, in order to facilitate the company in planning the supply of stock. To find out the best-selling medical device product sales, data prediction techniques are used with the Linear Regression algorithm. By using the Linear Regression algorithm, the results are obtained to predict the best-selling sales of several products sold at PT Ajidarma Delta Medika. This research produces an accuracy value with the MAPE formula for predicting the best-selling product sales of 14.2%. This shows that the linear regression method is good at predicting sales of medical devices in the following year.

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