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Ronal Watrianthos
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Professional Organization - Ikatan Ahli Informatika Indonesia (IAII) / Indonesian Informatics Experts Association Jalan Jati Padang Raya No. 41 Jati Padang Pasar Minggu 12540 South Jakarta - Indonesia http://iaii.or.id/
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INDONESIA
Journal of Systems Engineering and Information Technology
Published by Organisasi Profesi IKATAN AHLI INFORMATIKA INDONESIA
ISSN : -     EISSN : 2829310X     DOI : https://doi.org/10.29207/joseit.*
Core Subject : Science,
Computer Science & IT
International Journal of Systems Engineering and Information Technology (JOSEIT) is an international journal published by Ikatan Ahli Informatika Indonesia (IAII / Association of Indonesian Informatics Experts). The research article submitted to this online journal will be peer-reviewed. The accepted research articles will be available online (free download) following the journal peer-reviewing process. The language used in this journal is English. JOSEIT is a peer-reviewed, blinded journal dedicated to publishing quality research results in Computers Engineering and Information Technology but is not limited implicitly. All journal articles can be read online for free without a subscription because all journals are open-access.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 40 Documents
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An Optimal Solution to the Overfitting and Underfitting Problem of Healthcare Machine Learning Models Anil Kumar Prajapati Anil; Umesh Kumar Singh
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 2 (2023): September 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v2i2.5460

Abstract

In the current technological era, artificial intelligence is becoming increasingly popular. Machine learning, as the branch of AI is taking charge in every field such as healthcare, the Stock market, Automation, Robotics, Image Processing, and so on. In the current scenario, machine learning and/or deep learning are becoming very popular in medical science for disease prediction. Much research is underway in the form of disease prediction models by machine learning. To ensure the performance and accuracy of the machine learning model, it is important to keep some basic things in mind during training. The machine learning model has several issues which must be rectified duration of the training of the model so that the learning model works efficiently such as model selection, parameter tuning, dataset splitting, cross-validation, bias-variance tradeoff, overfitting, underfitting, and so on. Under- and over-fitting are the two main issues that affect machine learning models. This research paper mainly focuses on minimizing and/or preventing the problem of overfitting and underfitting machine learning models.
A Survey of Approaches for Designing Course Timetable Scheduling Systems in Tertiary Institutions Musa, Usman Bala; Oyelakin, Akinyemi Moruff
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 1 (2024): March 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i1.5609

Abstract

Scheduling the course schedule in tertiary institutions is a complex and crucial task. Past studies have pointed out that when scheduling is performed effectively, it influences students' learning experiences, faculty workloads, and overall institutional efficiency. It has also been argued that in the allocation of courses, classrooms, and faculty members, various constraints, preferences, assumptions, dependencies, and objectives must be taken into consideration. This article reviewed different approaches that have been employed in designing course schedule scheduling systems with particular reference to tertiary institutions. Relevant articles were sourced from notable research repositories using identified keywords. The articles obtained were categorized according to the different methods that were used to solve the scheduling problems of course schedules in higher institutions. The review evaluated how each approach addresses the challenges in course time table scheduling. Thereafter, the paper discussed the advantages, limitations, and suitability of these scheduling techniques time-tabling. Additionally, real-world implementations in various tertiary institutions are mentioned. By discussing the strengths and weaknesses of different methodologies in this work, this survey is believed to be a valuable resource for future studies in the area of course scheduling in tertiary institutions.
A Bibliometric Analysis of Health-Based Gamification Jawaril Haq Al-Azkiya; Dhea Maulida Rahmah
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 1 (2024): March 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i1.5629

Abstract

Gamification, the application of game design elements in non-game contexts, has emerged as a promising strategy to promote positive behavior change. Although gamification has shown potential in various domains, including healthcare, a comprehensive analysis of the existing literature is needed to map research trends, influential works, and future directions in the field of health-based gamification. This study used a bibliometric analysis approach, using PoP software for data extraction, VOSviewer for visualization, and Mendeley for reference management. Relevant publications on health-based gamification were identified through a comprehensive search across multiple databases. The extracted data was analyzed to examine temporal trends, thematic clusters, influential authors, and citation patterns. The analysis revealed a steady growth in articles related to health-based gamification, with contributions from researchers in different disciplines. The key thematic groups included gamification applications in mHealth, physical activity interventions, serious games, and adherence. Influential authors and highly cited studies were identified, highlighting foundational work and seminal contributions. This bibliometric study offers a comprehensive overview of the health-based gamification literature, underscoring its interdisciplinary nature and diverse research topics. The findings highlight the potential of gamification in promoting positive health behaviors and facilitating patient engagement. Identified research gaps and emerging trends provide valuable information for future studies, fostering innovation and collaboration in integrating gamification principles into healthcare interventions.
Comparative Analysis of Muslim Clothing Sales Predictions Using the C4.5 Method and Linear Regression Alpa Gustiana
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 1 (2024): March 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i1.5678

Abstract

This study aims to develop a sales data prediction model using the machine learning method. Sales are an important indicator in the business world because they can provide information about company performance, market trends, and support better decision-making. However, accurate and reliable prediction of sales data is often a complex challenge. In this study, the researchers collected historical sales data from Farhan Stores that included information about time, product, category, and price. This study also aims to apply data mining techniques to predict sales of Muslim clothes at Farhan stores using the C4.5 algorithm and the linear regression algorithm. The prediction method is used in this study and the calculations are performed using Google Collab. The results of the research that was conducted to predict sales of robes and shirts at Farhan Stores show that the best-selling item during the sales period from January to July 2022 was Sabiyan robes, which were the most sold item or can be said to be the Best Seller item at Farhan Stores. In this study, the parameters MAE (Mean Absolute Error), MSE (Mean Squared Error), and the R2 score are used to evaluate prediction performance. In the linear regression algorithm, the MAE value is 43,633.21, the MSE value is 4,005,924,352.66, and the R2 score is 0.94. Whereas in the C4.5 algorithm, the MAE value was 44,823.96, the MSE value was 50,233,775.14, and the R2 score was 0.94.
Summarization and Classification of Sports News using Textrank and KNN Falahah
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 1 (2024): March 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i1.5706

Abstract

The news summary process is critical in the news analysis process. However, there are frequently barriers to the summary process, such as the large number of news articles and the requirement for news classification. The goal of this study is to develop a news summary and categorization model that will be extremely valuable in the news analysis process. Textrank is the suggested summarizing approach, and KNN will be utilized for news classification. The resulting model can be used to automatically summarize and group news, making content analysis easier. Sports news will be used as the study object from July to August 2023, and the supervised category will be used to identify whether the news comprises sports news in three branches, soccer, badminton / tennis, or basketball. Classification is carried out using the KNN algorithm by training the model using 500 categorized news data. Modeling using k = 3 and k = 5 shows that the precision is around 0.9866 and 0.9666 respectively. The model's implementation on unknown text demonstrates that the model can properly predict text categories as long as the news content falls into the three specified categories, but fails for news content that does not fall into these categories.
Web Mining for Enhanced Academic Visibility and Engagement Analysis Based on Visitor Data Yuhefizar, Yuhefizar; Putra, Roni
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 1 (2024): March 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i1.5713

Abstract

With online platforms that transform scholarly communication, academic journals must strategically amplify their digital footprint. This study demonstrates the value of using web analytics and time series modeling to uncover nuanced online readership trends and rhythms. Using the case of the Review of Rekayasa Sistem dan Teknologi Informasi/System Engineering and Information Technology (RESTI) Journal website's 2023 visitor data, we employ visual and ARIMA time series analysis to delineate engagement patterns aligned with academic cycles. The results reveal pronounced seasonal fluctuations, with the participation peaking in October and November, coinciding with increased research dissemination. Fitting an ARIMA model to daily new visitor data indicates positive autocorrelations, suggesting that the engagement effects persist on days. The model provides a predictive baseline for evaluating outreach initiatives. The study offers strategic information on aligning content planning with reading engagement rhythms. At the methodological level, the integration of data mining, predictive modeling, and information retrieval techniques establishes a versatile framework for investigating evolving scholarly communication dynamics in the digital age. The study also emphasizes meticulous data preparation and model diagnostics. The analytical approach presented provides actionable intelligence on trends in the use of academic portals online. This has far-reaching implications for journals seeking to strategically enhance their digital presence amidst increasing competition. With the proliferation of electronic resources, these techniques will only grow in importance for assessing and amplifying the impact of online scholarly platforms.
Public IP Efficiency and Data Center Security Enhancement with Reverse Proxy Implementation Sandy Bukhari, Achmad; Iqbal, Mohammad
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 2 (2024): Sep 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i2.5948

Abstract

With the increasing frequency of cyber-attacks, the trend of national cybersecurity traffic anomalies reached 976,429,996 incidents in 2022. Additionally, the world is now facing the fact that the supply of Public IPv4 addresses available for allocation is diminishing. IPv4 uses 32-bit addressing, which provides only over 4 billion unique IP addresses. By conducting research using two methods, namely a server without a reverse proxy and a server with an applied reverse proxy, it was found that implementing NGINX with a reverse proxy can lead to savings in public IPv4 addresses. Regardless of the number of servers, only one public IPv4 address is needed, which reduces the number of IPs required and also prevents cyber-attacks on the server. Testing with DNSChecker and whatismyipaddress showed that after applying the reverse proxy with NGINX, the application server could not be identified or accessed by external parties. Only the reverse proxy server was accessible to outsiders. As the number of applications increases, which directly correlates with the need for public IPv4 addresses, the study's results show that applying a reverse proxy with NGINX in a data center can overcome the limitations of public IPv4 addresses. As the number of virtual machines and applications grows, a single public IPv4 address applied to the reverse proxy server suffices. Thus, implementing a reverse proxy with NGINX allows multiple servers to use just one public IPv4 address.
Solution of the Data Load Issue in Business Intelligence Tools: QlikView Live Case Study Kumar Prajapati, Anil; Yogesh Mishra; Saral Nigam; Pradeep Lakhare
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 2 (2024): Sep 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i2.6064

Abstract

To stand in the marketplace it very essential to extract and deal with high-performance and usable data for every industry. The traditional ways are too slow and they are not suitable for the current scenario. This document gives knowledge about the new trends in technology which used for the benefit of business in terms of analysis and reporting. The document contains a live case study of the problem faced by an organization and a holistic evaluation of how to overcome it with the help of new technology. The primary objective of this case study is to minimize the ideal time for accessing and/or extracting files from different sources in the QlikView Platform.
Evaluating E-Government Adoption in Rural Digital Transformation: A UTAUT Model Application in Indonesian Smart Village Initiative Yuhefizar; Raemon Syaljumairi; Ervan Asri; Roni Putra
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 2 (2024): Sep 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i2.6136

Abstract

This research examines the implementation of electronic-based government systems (SPBE) in Nagari Batipuah Ateh, West Sumatra, Indonesia, analyzing the adoption patterns and challenges of digital transformation in rural governance. Through the application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this study investigates how traditional rural communities adapt to and accept e-government services while maintaining their cultural identity and social structures. The study employed a mixed-method approach, combining quantitative surveys of fifty participants with qualitative insights from fifteen in-depth stakeholder interviews conducted over twelve months. The research focused on measuring key UTAUT constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions, while also examining the role of community dynamics in technology adoption. Findings reveal significant correlations between social influence and behavioral intention (r = 0.72), highlighting the crucial role of community leadership in technology acceptance. While 52% of users demonstrated advanced digital literacy, 26% required substantial support for basic system navigation, leading to the emergence of effective community-based support networks. The study identified a 65% increase in adoption rates among initially hesitant users through these informal support systems, and a 58% higher sustained engagement when implementing phased approaches with community feedback integration. The research contributes to understanding rural digital transformation by demonstrating how e-government services can be successfully implemented while preserving cultural integrity. The results suggest that successful rural digital governance requires more than technological solutions, demanding careful attention to social dynamics and cultural contexts.
Optimizing Student Admission Process Through EPPMB Raemon Syaljumairi; Asri, Ervan; Sarmiadi
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 3 No 2 (2024): Sep 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i2.6264

Abstract

The research addresses the challenges of managing multiple admission channels in higher education institutions through a comprehensive web-based solution. Following Design Science Research Methodology, we developed and implemented a multi-layer architecture that integrates various admission processes while maintaining system security and performance. The system handles six distinct admission channels, including Recognition of Prior Learning (RPL), Applied Master's Program, and industry partnership programs. Results demonstrate significant improvements in operational efficiency, with a 65% reduction in application processing time and error rates below 0.1%.

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