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All Journal Jurnal Teknoinfo Jurnal Sisfotek Global Jurnal Algoritma Jurnal Sistem Informasi dan Teknologi (SINTEK) Journal Information & Computer (JICOM) Jurnal Relawan dan Pengabdian Masyarakat REDI Jurnal Pengabdian kepada Masyarakat The Scientific Journal of Information Systems Inovasi Gagasan Abdimas & Kuliah Kerja Nyata Sintek Kuwera
Lukas Umbu Zogara
Universitas Utpadaka Swastika
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : The Scientific Journal of Information Systems

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BIG DATA ANALYTICS FOR HEALTHCARE APPLICATIONS MOBILE CLOUD BASED Umbu Zogara, Lukas; Dai Payon Binti Gabriel, Cecilia
Scientific Journal of Information System Vol. 1 No. 1 (2023): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v1i1.85

Abstract

Mobile devices are increasingly becoming one and more indispensable part of our daily lives, as it facilitates to perform various useful tasks. Mobile cloud integrates mobile and cloud computing to extend the benefits of the cloud itself, and overcome limitations in times of cloud such as limited memory, CPU power, big data analytics technology allows extracting value from data that has four Vs: volume, variety, speed, and honesty. This paper discusses mobile cloud-based healthcare and big data analytics in its application. The conclusion is drawn about the design of healthcare systems using big data and mobile cloud technologies.
Big Data Trade Survey Pricing, Trading, and Data Protection Umbu Zogara, Lukas
Scientific Journal of Information System Vol. 2 No. 1 (2024): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v2i1.97

Abstract

In this era, Big Data is considered as the key to unlocking the growth of people's productivity in daily life. Internet usage, mobile applications and social networks, as well as the internet of things based on smart-grid and so on has an impact on the amount of data collected. The advancement of analytics data provided by machine learning and data mining technology supported by cloud computing, the resulting information will be more useful. Since the advent of Big Data, the dataset has become one type of "new money" in the digital world. Copyright protection mechanisms including digital encryption and watermarking need to be done to maintain data values. To maximize this, an effective container is needed to allow data owners and buyers to trade the data. The focus of the topic is on designing data trading platforms and schemes, supporting effective and efficient data trading, security, and maintaining the privacy of data owners.
DETECT CLASSIFICATION OF EMPLOYEES TENDING TO MOVE WORK WITH THE NAIVE BAYES ALGORITHM Lukas Umbu Zogara
Scientific Journal of Information System Vol. 2 No. 2 (2024): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v2i2.143

Abstract

In recent years due to economic conditions and the uncertain situation of a country, many employees with a certain level of education, work experience and different countries in development and the level of income per capita of a country and several other factors cause many employees to move to a new place. the. Because there are many factors that cause employees to move careers and advances in information technology also make it difficult to predict what factors affect decision-making employees to move to work to new places. Therefore, it is necessary to know what factors and conditions or what are the employees so that they have a tendency to move to work so that the company can prevent, anticipate and immediately seek other solutions early if this condition must occur from its employees. Based on the problems described, this study discusses the classification of employees who have a tendency to change workplaces using the Naive Bayes algorithm. The goal is expected to identify the dominant factors in influencing employees to change workplaces. From the results of the research conducted by Naive Bayes, it was found that 3 dominant factors were influencing, namely the STEM area of expertise, the size of the company size and the level of education as well as the accuracy level of 80.79% and AUC 0.816.
HOW TECHNOLOGY AFFECTING RESEARCHERS IN THE ERA OF GENERATIVE AI Yato, Dhimas Buing Rindi Widra; Zogara, Lukas Umbu; Suharmat, Asep
Scientific Journal of Information System Vol. 3 No. 1 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i1.175

Abstract

In the rapidly evolving research landscape, generative AI is emerging as a transformative force. This study explores the multifaceted impacts of generative AI on researchers across various disciplines. By automating routine tasks, enhancing data analysis, and generating novel hypotheses, AI tools are significantly boosting productivity and opening new avenues for innovation. However, these advancements also present challenges, including ethical considerations, the need for transparency, and the potential for bias in AI-generated results. Moreover, the integration of AI into research demands the development of new skill sets, presenting both opportunities and risks for researchers. Drawing on recent studies, this article provides a comprehensive overview of how generative AI is reshaping the research landscape and highlights the critical dynamics researchers must navigate in this new era.
APPLICATION OF DATA MINING TECHNIQUES TO ANALYZE ATTENDANCE AND IMPROVE THE QUALITY OF CHINESE LEARNING Grace Limiko; Pupista, Orinda; Surahmat, Asep; Umbu Zogara, Lukas
Scientific Journal of Information System Vol. 3 No. 1 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i1.176

Abstract

In the era of globalization, learning Chinese is increasingly important, but challenges such as low student attendance and learning quality are still significant problems. This article discusses the application of data mining techniques as a solution to analyze student attendance and improve the quality of Chinese learning. By collecting and analyzing attendance data from 200 students for one semester, through classification and visualization methods, this article identifies patterns that affect student attendance. The analysis results show that 65% of students who followed the interactive teaching method attended more than 80% of the total meetings, compared to only 40% of students who followed the traditional teaching method. In addition, it was found that 75% of students who received additional material for difficult topics experienced a 20% increase in average test scores compared to pre-intervention scores. Recommendations for improvement were made based on these findings, including adaptation of teaching methods and provision of supplementary materials. Through a case study of an educational institution that has successfully implemented this technique, this article shows that data mining can not only improve student attendance, but also significantly improve the quality of learning. This research is expected to encourage educational institutions to adopt data mining technology in an effort to improve students' learning experience.
A COMPARATIVE REVIEW OF CLUSTERING AND CLASSIFICATION ALGORITHMS FOR BIG DATA ANALYTICS Zogara, Lukas Umbu; Ningrum, Leny
Scientific Journal of Information System Vol. 3 No. 1 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i1.179

Abstract

These days, there's so much data being created all the time. It’s honestly getting hard to keep up.That’s where data mining comes in. Basically, people use it to make sense of all this huge amount ofinformation, and there are two main ways to do it: clustering and classification. I found that there area bunch of algorithms for both, like K-Means, DBSCAN, and Hierarchical Clustering for clustering,and then there’s Decision Tree, Naïve Bayes, SVM, and Random Forest for classification. Each ofthese has its own strengths and weaknesses depending on the data you’re working with. The point ofthis paper was really to see how these algorithms perform and to give people an idea of which onemight work best depending on the situation. What we found is that no algorithm is perfect foreverything. So, choosing the right one really comes down to understanding the data and figuring outwhat you're trying to get out of it.
The Impact of Knowledge Management Systems in Enhancing the Competitiveness of Retail Companies Muttaqi, Fajar; Zogara, Lukas Umbu; Alfaujianto, Moh.; Surahmat, Asep
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.227

Abstract

This study investigates the role of Knowledge Management System (KMS) implementation inenhancing the competitiveness of retail companies, with a specific focus on Lotte Mart Indonesia.Using a qualitative exploratory case study approach, the research collected data through in-depthinterviews, field observations, and company document analysis. The findings demonstrate that KMSaccelerates the flow of information, reduces duplication, and improves operational efficiency, therebyenabling better coordination among departments. Furthermore, KMS facilitates knowledge sharingand collaboration, which supports the development of service innovations and responsive marketingstrategies. Employees reported that the system allows faster access to documents, real-time inventorychecking, and more structured workflows. Beyond operational benefits, KMS contributes tostrengthening customer satisfaction through improved responsiveness and accurate informationdelivery. Additionally, KMS supports the company’s digital transformation by integrating internalsystems such as ERP, CRM, and e-commerce platforms. Overall, KMS functions not only as aknowledge repository but as a strategic enabler of sustainable competitive advantage in the retailsector.
Implementation and Analysis of Multiple Interface Policies through System Feature Visibility on Fortigate FG-60F Alfaujianto, Moh; Muttaqi, Fajar; Surahmat, Asep; Zogara, Lukas Umbu
Scientific Journal of Information System Vol. 3 No. 2 (2025): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v3i2.229

Abstract

Fortigate FG-60F is one of the popular firewall appliances utilized by small and medium-scalenetworks in managing security. However, some of the needed features such as multiple interfacepolicies are not displayed by default on the user interface. This study explores the functionality andeffectiveness of enabling system-feature visibility for easier management of inter-interface policies.Employing an experimental approach, the Fortigate FG-60F device was configured to activate thehidden feature, and subsequently, a set of policy rule scenarios with multiple interfaces wereestablished and tested. The results indicate that supporting system-feature visibility enhancessignificantly the administrator's ability to implement more specific traffic policies that arecommensurate with network topology requirements. Moreover, performance analysis showed nonegative impact on device performance after the implementation of multi-interface policy. Thefindings are expected to serve as a valuable reference for network administrators in optimizingFortigate FG-60F security capabilities by leveraging advanced, previously hidden features
Co-Authors Adiiba Dwikanthi Ahmad Sururi Alfaujianto, Moh Alfaujianto, Moh. Ameliana Ameliana Dai Payon Binti Gabriel, Cecilia Darul Mukminin Fajar Muttaqi Grace Limiko Hasyanta, Gopas Leny Tritanto Ningrum Moh Alfaujianto Muttaqi, Fajar Ningrum, Leny Pupista, Orinda Rindi Widya Yato, Dhimas Buing Sari, Santi Purnama Suharmat, Asep Surahmat, Asep Sutrisno, Gono Tehupuring, Ronald Tobing, Suasana Hisanty Umiyati, Hesti Yato, Dhimas Buing Rindi Widra