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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Informatika dan Teknik Elektro Terapan JOIV : International Journal on Informatics Visualization International Journal of New Media Technology Jurnal ULTIMA InfoSys Indonesian Journal of Electrical Engineering and Computer Science Journal of Mutidisciplinary Issues
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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Enterprise Resource Planning (ERP) Evaluation and Implementation: A Case Study Yosevine, Prisca; Oetama, Raymond Sunardi; Setiawan, Johan; Princes, Elfindah
Journal of Multidisciplinary Issues Vol 1 No 1 (2021): Journal of Multidisciplinary Issues
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.883 KB) | DOI: 10.53748/jmis.v1i1.10

Abstract

Objective – To understand the success rate of ERP in the company by using the Ifinedo method and provide proposals that can improve ERP implementation in the company based on the unfulfilled Ifinedo method. Methodology – This research uses Quantitative method research distributed to 50 end users at Indoporcelain using surveys and interviews. Findings – The research found one point that is less valued in the company, namely vision and mission factors in organizational variables compared to other factors. Therefore, proposals in this sector are indispensable in order to increase the success of ERP implementation in the company. Furthermore, lack of IT support due to the management’s ignorance has made the ERP implementation did not reach the optimum performance expected. Novelty – By measuring the success rate of ERP in the company, the company can know how the success rate of ERP implementation in its company. The company can make corrections and quality improvements to existing ERP systems based on proposals with unmet Ifinedo method.
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 Gold Membership Classification Using Machine Learning Christiano Tjokro, Vincencius; Oetama, Raymond Sunardi; Prasetiawan, Iwan
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2023

Abstract

The main challenge in loyalty programs is selecting customers with limited funding. To address it, we explore various machine learning-based classification models. This study aims to enhance the effectiveness of a marketing strategy that promotes gold membership to customers with prior transaction history. Previously, much research applied decision trees, random forests, and logistic regression for classification, but gradient boosting is still unpopular. However, in this study, the Gradient Boost algorithm exhibits the best performance among these models, achieving an impressive accuracy of around 88%. This result underscores the model's capability to classify customers, thereby suggesting its potential to significantly enhance the marketing strategy's effectiveness. The analysis identifies crucial features that influence the model's predictive capabilities. Notably, the recency of the last visit, the number of transactions involving wine and meat, marital status, and the number of offline store transactions are identified as influential factors. Leveraging machine learning techniques enables the automation of the customer selection process, facilitating the attraction of a more extensive customer base. By targeting those customers most likely to respond positively to the gold membership offer, efficient resource allocation can be achieved. This research provides valuable insights and practical recommendations for implementing an effective marketing strategy under resource constraints. Combining machine learning algorithms and feature identification enables efficient targeting of potential customers, maximizing the impact of the gold membership offering. Implementing the findings of this study could lead to increased customer acquisition and improved overall business performance.
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.
Enhancement of Coronary Heart Disease Prediction using Stacked Long Short Term Memory Cinthiya, Cinthiya; Oetama, Raymond Sunardi
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 1 (2023): Juni 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i1.9707

Abstract

The high incidence of death caused by coronary heart disease has become a global concern in the world of health, where patients with coronary heart disease are no longer only adults and the elderly, yet there are now so many cases of coronary heart disease experienced by underage patients. As a result, it is critical to be able to prevent and reduce the number of instances. One of them is the ability to predict a person's risk of coronary heart disease so that patients can be treated and provided early therapy. The risk of coronary heart disease will be predicted in this study utilizing Stacked long short-term memory algorithms. By appling this algorithm, the accuracy of 81.3% from previous study can be increased to 91.8% by this study. 
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.
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 Angelie Tania, Valencia Eurelia Christiano Tjokro, Vincencius Cinthiya, Cinthiya Lala, Yohanes Brian Caesaryano Lvina, Kimberly Nurdiyansyah, Dudi Prasetiawan, Iwan Princes, Elfindah Yosevine, Prisca