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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 50 Documents
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A Comprehensive Exploration of Text, Web, Social Media, and Geospatial Analytics for Informed Decision Making Tenya, Yureni
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 2 (2024): May - August 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i2.148

Abstract

Text Analytics is the process of turning unstructured text data into useful information for analysis, to gauge consumer opinions, feedback, and product evaluations. It also provides search functionality, entity modelling, and emotional analysis to enable fact-based decision making. Analysis of website visitor behaviour is done using Web Analytics. The number of websites and users on the internet is growing daily. It is the process of tracking website information that can help to improve the web application features and evaluate the behaviour of users. By performing social media analytics on social media for example twitter, Instagram, Facebook. Data such as likes, comments, shares and saves can be obtained and analyse to know how the society think about the product. Geospatial analysis is used to make visualizations that include maps, graphs, statistics that show data according to geographic location. This is important to be analysed as it tells which area or country has the highest product sold or lowest subscription of the service. To make the data much easier for human brain to analyses or to make a conclusion, data visualization design is the process of putting all data information collected into a visual context. For instance, graph or map. The main objectives of data visualization are to make it simpler or easier to spot the outliers and patterns trends in big data sets. There are a variety of clustering processes or techniques available to arrange the data efficiently to its related data. The clustering process that is used in data mining is presented in this work.
Analyzing the Impact of Online Learning on Higher Education: A Text Analytics Approach Asplangyi, Gulam Ruti
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 2 (2024): May - August 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i2.149

Abstract

Amidst the relentless upheaval caused by the ongoing Covid-19 pandemic, the higher education landscape finds itself compelled to pivot towards internet-mediated learning modalities. This shift, while necessary for continuity, has engendered profound repercussions for students, educators, and administrative staff alike. Foremost among the concerns is the discernible impact on student learning outcomes and academic performance. Studies, such as those conducted by Brookings and The University of Chicago, underscore the alarming projections of learning loss and escalating failure rates within this context. Bloom, a prominent higher education institution grappling with the tumult of the pandemic, has witnessed a palpable decline in average grades since its onset. Recognizing the imperative to stem this tide and foster informed decision-making, Bloom endeavors to harness the power of text analytics. Through the systematic analysis of unstructured textual data sourced from diverse channels—ranging from social media platforms to educational websites—Bloom endeavors to unveil underlying patterns, discern actionable insights, and drive strategic interventions. This article presents a comprehensive framework delineating Bloom's foray into text analytics, elucidating the attendant challenges, proposed solutions, and anticipated implementation strategies. By delving into the nuances of managing unstructured textual data and navigating the complexities thereof, this endeavor seeks to empower Bloom with the tools and insights requisite for optimizing academic performance and mitigating the deleterious effects of the pandemic.
LQ45 Stock Price Forecasting: A Comparison Study of Arima(p,d,q) and Holt-Winter Method Santosa, Raden Gunawan; Chrismanto, Antonius Rachmat; Raharjo, Willy Sudiarto; Lukito, Yuan
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 2 (2024): May - August 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i2.150

Abstract

The Holt-Winter method and ARIMA(p,d,q) are two frequently used forecasting techniques. When using ARIMA, errors are expected to be connected with earlier errors because it is based on data correlation with prior data (autoregressive) (moving average). The Holt-Winter model comes in two different forms: Multiplicative Holt-Winter and Additive Holt-Winter. No one has ever attempted to compare combined time series and cross-section data, despite the fact that there has been a great deal of prior study on ARIMA and Holt-Winter. In a combined time-series and cross-section dataset, the accuracy rates of Holt-Winter and ARIMA(p,d,q) will be compared in this study. LQ45 stock prices are used because they track the performance of 45 stocks with substantial liquidity, sizable market caps, and solid underlying businesses. The Mean Absolute Percentage Error (MAPE) method is used to gauge accuracy. This study contributes to MAPE exploration by using a Boxplot diagram from cross-sectional data. With the Boxplot diagram, we can see the MAPE spread, the MAPE's center point, and the presence of outliers from the MAPE of LQ45 stock. According to the findings of this empirical study, the average error rate for predicting LQ45 stock prices using ARIMA is 7,0390%, with a standard deviation of 7,7441%; for multiplying Holt-Winter, it is 29,3919%, with a standard deviation of 25,7571%; and for additive Holt-Winter, it is 18,0463%, with a standard deviation of 18,3504%. Apart from numerical comparisons, it can also be seen visually, based on the Boxplot diagram, that the MAPE of ARIMA(p,d,q) is more focused than Holt-Winter. In addition, in terms of accuracy distribution, it can be seen that the MAPE accuracy of the ARIMA method produces four outliers. Based on the MAPE accuracy rate, we conclude that Holt-Winter has a bigger error based on the MAPE value than ARIMA(p,d,q) at forecasting LQ45 stock prices.
Descriptive Analysis Of K-Means and Apriori Methods To Find Promotion Strategies For University Bhayangkara. Sultan Bacharuddin Yusuf hidayat; Tb. Ai. Munandar
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 3 (2024): September - December 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i3.151

Abstract

The increasing number of higher education institutions in Indonesia has intensified competition between universities. Universitas Bhayangkara Jakarta Raya (Ubhara Jaya) must develop an effective marketing plan to stand out. This study used segmentation and associative analysis on 2023 student enrolment data. The Apriori algorithm identified patterns in student preferences for study programmes, while the K-Means method categorized students based on demographics and family income. Three income-based clusters were identified: C0 ‘Already stable’ (IDR 1,000,000 - 2,500,000), C1 ‘Focus on promotion strategy’ (IDR 20,000,000), and C2 ‘Maximise promotion again’ (IDR 5,000,000 - 10,000,000). The Davies-Bouldin Index (DBI) indicated k=5 as the optimal cluster number, but k=3 was adequate with a minimal score difference. The most popular programmes were Communication Science and Management, with high support and confidence values. This data helps Ubhara Jaya manage study programme demand and room availability. Combining K-Means and Apriori algorithms is expected to enhance data segmentation[1] and support effective marketing strategies, aiding strategic decisions in higher education marketing.
Big Data Analytics and Business Intelligence in Business Marketing: A Review Duong, Vacharasip
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 3 (2024): September - December 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i3.162

Abstract

The aim of this paper is to conduct an extensive study on big data analytics and business intelligence (BI) in marketing that is within the academic research sphere. Research gaps were identified and development for future research on the study was highlighted. A systematic review based on literature which related academic articles indexed in Web of Science and Scopus databases was used. The articles reviewed were based on certain features like theoretical and conceptual characterization; data source; research topic; type and size of data; data analysis techniques and methods used in data collection. The research outcome indicates that there is an increase in the marketing research with analytical technique applies to large quantity of data. However, this research area is limited in scope and methodologies and presents several gaps. A conceptual framework that will help in detecting important business challenges and relate the domain of big data and business intelligence to marketing is missing. This study contributes to exploring systematically the awareness of marketers working in big data and business intelligence.
Tacit Knowledge Mapping for Business Intelligence Analysis Janna, Alisha Barqha
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 3 (2024): September - December 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i3.163

Abstract

The tacit knowledge in a higher institution, especially in university libraries, contains a series of intuition and inspiration that a librarian arises in exploring solutions to the various problems. Thus, limited sources of knowledge or information is a critical factor in the failure to provide accurate information. The main problem of the BI system is to capture tacit knowledge and use tacit knowledge as one of the data sources for data analysis to enhance the analytic results. The unstructured data can define as tacit knowledge in the form of data and information presented in the Knowledge Management System (KMS), and the cognitive business use both structured and unstructured data with highly sophisticated analytical techniques to identify, evaluate, and recommend a business plan of actions. The idea of being able to capture knowledge from different sources can be very beneficial to the BI system. This paper explored the solution to extracting tacit knowledge from librarians in order to enhance the data sources to be used in the BI by exploring the library's academic services, which use much tacit knowledge for answering questions with the requirement of data analysis as online or offline queries.
Hadoop Ecosystem Enhances Data Analytics for Music Streaming: A Case Study of User Behavior in the Last FM Dataset Elizade, Akkord
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 3 (2024): September - December 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i3.166

Abstract

This paper proposed a big data pipeline to analyze user behavior on Last.fm which aims to make data-driven recommendations for improving user engagement and attracting new users. The comprehensive analysis of user behavior in the music streaming industry using the Hadoop ecosystem and data analytics techniques. Specifically, the study focuses on Last.fm, a popular music streaming platform that collects large amounts of user activity data. The article proposes a new data pipeline utilizing Hadoop Distributed File System (HDFS) for data storage and Apache Pig for data transformation, leading to improved data preprocessing and analysis. Various analyses are conducted, including identifying the most listened to artists, top users based on song consumption and social connections, artist popularity by tags, and the most recently tagged artists. The findings provide valuable insights into user preferences, current trends, and opportunities for enhancing the recommendation algorithm and user engagement. The article concludes by offering recommendations for personalized marketing strategies and curated playlists to increase user satisfaction and revenue.
DSS Decision Support System for Best Employee Evaluation Using the SMART Algorithm. Handayani, Dwipa; Rasim, Rasim
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 3 (2024): September - December 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i3.167

Abstract

The use of information technology has permeated all areas, including employee performance evaluation, which plays a crucial role in company management. The success of a company is largely dependent on human resources, which are considered valuable assets due to their strong link to employee performance. This study focuses on the application of the SMART Algorithm in a Decision Support System designed for the evaluation of the best employees at PT Raharja Jaya Mandiri Bekasi. The main objectives of the study are to develop an employee evaluation system, apply the SMART Algorithm to make the evaluation process more objective, and improve the security and reliability of evaluation data through a digital system. The research employs the SMART Algorithm to evaluate and select the best option based on predetermined goals and criteria. This approach is implemented using the waterfall model of system development. The results of the study indicate that the implementation of a web-based Decision Support System utilizing the SMART Algorithm enhances the accuracy and efficiency of employee performance evaluations at PT Raharja Jaya Mandiri Bekasi. Moreover, it reduces subjective bias in the decision-making process and ensures that the best employees are chosen based on measurable and transparent criteria.
The MAPE Analysis of Arima (p,d,q) on LQ45 Stock Price to Determine Training Data Period Santosa, Raden Gunawan; Chrismanto, Antonius Rachmat; Lukito, Yuan; Raharjo, Willy Sudiarto
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 3 (2024): September - December 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i3.168

Abstract

Most of the research using the Arima (p,d,q) focused on the accuracy of prediction results. Unlike other research, this work examines the training data period suitable for modeling ARIMA (p,d,q) in stock prices. Due to the volatile movement of stocks, the number of training data is assumed to affect the LQ45 prediction results. This research used five kinds of training data, including daily data for up to 5 years. With these five types of data series, the Arima (p,d,q) was made for LQ45 stocks. The prediction was conducted for two months after obtaining the model 5 data series of LQ45 stocks. Two months of data were used for January and February 2021 prediction test data. The results of this prediction were compared with the test data to produce the MAPE value. Based on the observations and calculation results, the most suitable stock to use the Arima (p,d,q) was ASII. In 5 years, the stocks produced the lowest MAPE value of 0.05%. Relatively stable LQ45 stocks with no change in the Arima (p,d,q) using four consecutive data series were ACES, CTRA, INTP, MIKA, and TLKM. Based on the MAPE value analysis performed in this study, we concluded that the best period to use the Arima (p,d,q) for LQ45 stocks is two years, with a median error rate of only 6.0091%.
Enhancing Film Genre Classification Using FastText Embeddings, Bidirectional GRU (BiGRU), and Attention Mechanisms Muhammad Fairuzabadi; Munandar, Tb Ai
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 3 (2024): September - December 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i3.169

Abstract

This research aims to enhance the classification of film genres using advanced natural language processing techniques. By integrating FastText embeddings with Bidirectional Gated Recurrent Units (Bi-GRU) and attention mechanisms, the proposed model addresses the limitations of existing methods that struggle with capturing both local and global dependencies within textual data. The model's performance is evaluated on a dataset from IMDb, demonstrating its capability to predict film genres from textual descriptions accurately. Key contributions include the development of a robust model architecture that effectively handles out-of-vocabulary words and contextual nuances, implementing regularization techniques such as DropConnect to improve generalization, and using advanced embeddings to enhance semantic representation. The results indicate significant improvements in genre classification accuracy, particularly for frequent genres, showcasing the model's potential for practical applications in media content analysis. Future work will address data imbalance and explore more sophisticated architectures to enhance performanc.

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