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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Informatika dan Teknik Elektro Terapan Journal of Information System JOIV : International Journal on Informatics Visualization International Journal of New Media Technology Jurnal ULTIMA InfoSys Journal of Information Systems and Informatics JISA (Jurnal Informatika dan Sains) Indonesian Journal of Electrical Engineering and Computer Science Journal of Mutidisciplinary Issues IJISCS (International Journal of Information System and Computer Science)
Oetama, Raymond Sunardi
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science

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Nusantara capital city sentiment analysis using support vector machine and logistic regression Angelie Tania, Valencia Eurelia; Oetama, Raymond Sunardi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1708-1721

Abstract

The decision to move position the capital city of Indonesia to East Kalimantan has drawn people’s opinions, both pro and con, among the public, especially ahead of the presidential and vice-presidential elections. Discussions relevant to the relocation and construction of the capital city are increasingly crowded on social media, especially Twitter or X. This research aims to determine public sentiment regarding the development of the national capital to help the government and policymakers improve communication strategies, evaluate existing policies, and make more informed decisions based on public feedback. Public sentiment related to developing the Capital city of the Nusantara, including the presidential palace, toll road, and government offices, is analyzed. Support vector machine (SVM) and logistic regression (LR) algorithms are utilized for the sentiment classification. The results reveal that the SVM performs better in classifying sentiments in X data relevant to developing the Capital city of Nusantara, achieving an average accuracy of 91.97%.
The Effect of Video Games Towards the Students' Academic Performance Lala, Yohanes Brian Caesaryano; Oetama, Raymond Sunardi; Lvina, Kimberly
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3638

Abstract

Video games have remained popular ever since the video game industry boom in the 1980s. It has remained popular, especially for children, teenagers, and adults. However, video games have also sparked controversies among the population. Concerns have been raised regarding the harmful effects of video games, particularly regarding addictions. Video games have been accused by many of being the cause of lowering academic performance. Therefore, this study aims to explore the relationship between video games and the overall academic performance of university students in depth. We applied several statistical methods using a questionnaire, which 100 university students filled out. The insights uncovered from this study may help determine if and how much video games affect the academic performance of university students.
DEVELOPING SALES FORCE AUTOMATION PROTOTYPE AT INDONESIAN FURNITURE TRADING COMPANY Oetama, Raymond Sunardi
IJISCS (International Journal of Information System and Computer Science) Vol 7, No 3 (2023): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v7i3.1537

Abstract

Information technology plays a crucial role in enhancing company performance and driving business growth. Maintaining good customer relationships is essential, and Customer Relationship Management focuses on building customer commitment. Sales Force Automation systems automate sales processes and manage customer interactions. PT. Maju Jaya Kreasindo, a furniture company in East Jakarta, aims to automate its sales force and centralize operational information through a web-based Sales Force Automation system using Electronic Customer Relationship Management. The research adopts the "Prototyping" method, offering a shorter development duration and flexible implementation. Visual Studio Code and Xampp, along with PHP and MySQL, are utilized as development tools. The result is a Sales Force Automation system based on Electronic Customer Relationship Management that assists PT. Maju Jaya Kreasindo in automating its sales force. The design successfully automates tasks, supported by Electronic Customer Relationships and the prototyping method. The Sales Force Automation system passed the functionality evaluation, achieving high user acceptance test scores, and indicating its effectiveness in meeting user requirements.
Enhancing Sales Strategies In Prime Market Retail Business Using Tuned Gradient Boosting Nurdiyansyah, Dudi; Oetama, Raymond Sunardi; Prasetiawan, Iwan
ULTIMA InfoSys Vol 15 No 1 (2024): Ultima Infosys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v15i1.3595

Abstract

In the retail sector, comprehending customer behavior and employing effective customer segmentation is pivotal for refining marketing strategies and augmenting profits. This study delves into predictive modeling for customer segmentation at Prime Market, a prominent retail entity. The research initially yields a classification error rate of 25.10% by employing Gradient Boosting for customer classification. However, through meticulous parameter tuning, this rate dramatically improves to 8.6%, achieving an impressive accuracy of 91.4%. This refined model furnishes invaluable insights into Prime Market's customer segments, enabling the customization of marketing tactics and strategic business approaches. Armed with these insights, Prime Market can make data-driven decisions to enhance customer segmentation accuracy, better comprehend customer preferences, and pinpoint potential avenues for revenue growth. Leveraging advanced data analytics and predictive modeling empowers Prime Market to maintain a competitive edge and deliver its clientele a personalized, gratifying shopping experience.
UNVEILING CHURN PREDICTION AT BANK IVORY Oetama, Raymond Sunardi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3s1 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3s1.3394

Abstract

The banking industry faces significant challenges in tackling customer churn within its credit card services. Customer churn refers to the situation where customers discontinue using a bank's services and migrate to another financial institution. To proactively address this critical issue, the present research endeavors to predict customer attrition in credit card services. To achieve this goal, the study extensively employs the CRISP-DM framework and diligently compares the performance of two predictive models, namely Gradient Boosting and Random Forest. The research endeavors to identify potential churn customers by analyzing crucial variables, including customer age, marital status, gender, income category, credit limit, and total transactions. The preferred modeling approach, determined based on the lowest misclassification rate, serves as a vital component of the research's analytical process. Remarkably, the research findings unequivocally demonstrate the superior performance of the Gradient Boosting model, which attains a misclassification rate of 0.1118 in predicting customer attrition. 
Co-Authors Amri, Mahfudz Andersen, James Angelie Tania, Valencia Eurelia Angellin, Kevina Anggina, Rhauma Syira Christiano Tjokro, Vincencius Cinthiya, Cinthiya Enzelin, Lola Naomi Jevelin, Jevelin Lala, Yohanes Brian Caesaryano Lvina, Kimberly Nurdiyansyah, Dudi Prasetiawan, Iwan Princes, Elfindah Rwanda, Rwanda Setiawan, Johan Setiawan, Jovanka Suryajaya Yosevine, Prisca