Garuda - Garba Rujukan Digital

PENGEMBANGAN MEDIA PEMBELAJARAN MENGENAI ORGAN JANTUNG MENGGUNAKAN TEKNOLOGI AUGMENTED REALITY BERBASIS ANDROID Aryo Nur Utomo; Dinda Lutfiyah
JURNAL REKAYASA INFORMASI Vol 10 No 2 (2021): JURNAL REKAYASA INFORMASI Vol 10 No 2 Oktober 2021
Publisher : PROGRAM STUDI SISTEM INFORMASI INSTITUT SAINS DAN TEKNOLOGI NASIONAL (ISTN)

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Interactive learning media is currently in great demand and has been widely applied to deliver subject matter, but in the health sector, it is still very minimal. This final project was made to create interactive learning media, especially regarding learning in the health sector. The 3D interactive learning media application using Augmented Reality Technology based on Android is designed and developed using 3D software, namely Blender 2.91.2, and supporting software for Augmented Reality, namely Unity 2017. The development of this media produces multimedia applications in the form of 3D-based interactive learning media. . This 3D-based learning media is presented in a simulation of Learning Media regarding the Heart Surface, Cardiovascular System, and Handling of Attacks and Cardiac Arrest. Keywords: Augmented Reality, Blender , Unity, Learning, Heart, Android

PENERAPAN CUSTOMER RELATIONSHIP MANAGEMENT (CRM ) PADA TOKO URBAN TRAFFIC BERBASIS WEB Aryo Nur Utomo; Reza Christiando Purba
JURNAL REKAYASA INFORMASI Vol 11 No 1 (2022): JURNAL REKAYASA INFORMASI
Publisher : PROGRAM STUDI SISTEM INFORMASI INSTITUT SAINS DAN TEKNOLOGI NASIONAL (ISTN)

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In a business, the customer is a very important factor. For that, it is necessary to build a good relationship between the company and the customer. One of the obstacles faced by cake shops today is how to obtain and provide information from and to customers quickly. To meet the need for information that is fast, accurate, and has a wide reach, both for customers and the store, a web-based customer information system is needed that can be accessed online by customers. This web-based customer information system is integrated with a database that will store the required data. The results of the implementation of the implementation of the Customer Relationship Management method make it easy for customers to find out what products are sold and make product orders quickly. The system that will be designed focuses on helping customers to get information about products and companies, and helping customers in the online purchasing process. Information System Application of CRM Concepts on Web-Based Urban Traffic built using PHP and MySQL Database Key words : customer, PHP, MySQL Database, Information Sistem, CRM Concept, Urban Traffic Store

PENGGUNAAN METODE LEXICON UNTUK ANALISIS SENTIMEN PADA ULASAN APLIKASI KAI ACCESS DI GOOGLE PLAY STORE Aryo Nur Utomo; Rahma Dwi Wahyuni
JURNAL REKAYASA INFORMASI Vol 11 No 2 (2022): JURNAL REKAYASA INFORMASI Vol 11 No 2 Oktober 2022
Publisher : PROGRAM STUDI SISTEM INFORMASI INSTITUT SAINS DAN TEKNOLOGI NASIONAL (ISTN)

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Google play store is a digital service platform that functions as an official store that allows users to search and download applications. Google play store also has a feature where users can submit reviews and ratings for apps and digital content. PT. Kereta Api Indonesia abbreviated PT. KAI was launched, namely KAI Access. KAI Access is an application on the Google Play Store that is useful in the process of selling tickets online by utilizing internet technology. However, the review on the KAI Access application is only a sentence without a specific meaning, for that an analysis is needed that can divide classes as user sentiment. This study aims to determine the sentiment analysis of user review data on the KAI Access application on the Google Play Store using the lexicon method. Lexicon is a method used to classify application user opinions into 3 classes, namely, positive, negative, and neutral sentiments. The results showed that the reviews of users of the KAI Access application had negative sentiments, with the highest number and percentage of negative sentiment data, namely 24700 data and a percentage of 60.01%. Keywords: KAI Access, Google Play Store, Sentiment Analysis, Lexicon

Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models Linda Wahyu Widianti; Adhitio Satyo Bayangkari Karno; Hastomo, Widi; Aryo Nur Utomo; Dodi Arif; Indra Sari Kusuma Wardhana; Deon Strydom
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10364

The quick growth of the banking sector is reflected in the rise in the number of banks. In addition to the intense competition among banks for new customers, efforts to keep existing ones are essential to minimizing potential losses for the company. To ascertain whether customers will leave the bank or remain customers, this study will employ churn forecasts. A 1,750,036-customer demographic dataset, which includes data on bank customers who have left or are still customers, is used in the training process to compare five machine learning technology models in order to investigate the improvement of binary classification prediction accuracy. These models are Decision Tree, Random Forest, Gradient Boost, Cat Boost, and Light Gradient Boosting Machine (LGBM). According to the study's results, LGBM performs better than the other four models since it has the highest recall and accuracy and the fewest False Negatives. The LGBM model's corresponding accuracy, precision, recall, f1 score, and AUC are 0.8789, 0.8978, 0.8553, 0.8758, and 0.9694. This demonstrates that, in comparison to traditional methods, machine learning optimization can produce notable advantages in churn risk classification. This study offers compelling proof that sophisticated machine learning modeling can revolutionize banking industry client retention management.

Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models Linda Wahyu Widianti; Adhitio Satyo Bayangkari Karno; Hastomo, Widi; Aryo Nur Utomo; Dodi Arif; Indra Sari Kusuma Wardhana; Deon Strydom
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10364

The quick growth of the banking sector is reflected in the rise in the number of banks. In addition to the intense competition among banks for new customers, efforts to keep existing ones are essential to minimizing potential losses for the company. To ascertain whether customers will leave the bank or remain customers, this study will employ churn forecasts. A 1,750,036-customer demographic dataset, which includes data on bank customers who have left or are still customers, is used in the training process to compare five machine learning technology models in order to investigate the improvement of binary classification prediction accuracy. These models are Decision Tree, Random Forest, Gradient Boost, Cat Boost, and Light Gradient Boosting Machine (LGBM). According to the study's results, LGBM performs better than the other four models since it has the highest recall and accuracy and the fewest False Negatives. The LGBM model's corresponding accuracy, precision, recall, f1 score, and AUC are 0.8789, 0.8978, 0.8553, 0.8758, and 0.9694. This demonstrates that, in comparison to traditional methods, machine learning optimization can produce notable advantages in churn risk classification. This study offers compelling proof that sophisticated machine learning modeling can revolutionize banking industry client retention management.

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