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All Journal CCIT (Creative Communication and Innovative Technology) Journal Journal of Applied Data Sciences Automotive Experiences
Untung Rahardja
University of Raharja, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering

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User Satisfaction of Artificial Intelligence Air Quality Detection: UTAUT2 Approach Untung Rahardja; Putri Marewa Oganda; Sri Watini; Nesti Anggraini Santoso
CCIT (Creative Communication and Innovative Technology) Journal Vol 17 No 2 (2024): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v17i2.3236

Abstract

The Artificial Intelligence (AI)-based air quality detection application is a technology that can assist the public in monitoring the air conditions around them. This application provides information on pollution levels, health implications, and recommendations for appropriate actions based on air quality. However, the usage of this application is still constrained by various factors that influence user satisfaction. This study aims to examine the impact of elements within the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model on user satisfaction with AI-based air quality detection applications. The UTAUT2 model comprises 9 constructs. This research employs an online survey method with a sample of 150 respondents who have used AI-based air quality detection applications. Data were analyzed using the PLS-SEM (Partial Least Square Structural Equation Modeling) technique using SmartPLS4. The research findings indicate that only Performance Expectancy and Behavioral Intention significantly influence usage intention and behavior of the application. These findings highlight the critical role of user intention and performance expectations in determining usage behavior and user satisfaction. The practical implications and theoretical of this study, including recommendations for application developers and future researchers, are further discussed in this research.
Short-Term Prediction of Bus Station Fleet Number Using a Combination of BiLSTM Models Joko Siswanto; Ainun Rahmawati; Untung Rahardja; Nanda Dwi Putra; Muhammad Iman Nur Hakim; Tito Pinandita; Ilham Bagus Prasetyo
Automotive Experiences Vol. 8 No. 1 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.13402

Abstract

Predicting the number of bus station fleets requires a holistic approach, using sophisticated data analysis techniques and appropriate predictive modeling. Short-term predictions of bus station fleet numbers are proposed based on the best MAPE evaluation values ”‹”‹from the comparison of the Bi-LSTM, BiLSTM-CNN, BiLSTM-Transformer, BiLSTM-Informer, and BiLSTM-Reformer models. The dataset used is in the form of a CSV consisting of 6 types of arrivals and departures of the Giwangan City Yogyakarta type A bus station fleet from 01/01/2021 to 09/30/2023. The best prediction model was found in BiLSTM-Transformers based on a MAPE value of 0.2211 with a relatively fast time (00:00:52) compared to BiLSTM, BiLSTM-CNN, BiLSTM-Informer, and BiLSTM-Reformer. The BiLSTM-Transformer model can short-term predict 6 types of fleet arrivals and departures at the bus station in the next 30 days. The peak of the bar and curve is at 0 which means the proposed prediction model is very accurate. There is 1 strong positive correlation, 2 weak positive correlations, 2 strong negative correlations, 8 weak negative ones, and 2 uncorrelated ones. Prediction results can be used to support short-term decision making in fleet planning and management based on the dynamics of community mobility.
Predictive and Analytics using Data Mining and Machine Learning for Customer Churn Prediction Chandra Lukita; Lalu Darmawan Bakti; Umi Rusilowati; Asep Sutarman; Untung Rahardja
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.131

Abstract

This research aims to predict and analyze customer churn using Data Mining and Machine Learning methods. The background of this research is based on the importance of understanding the factors that influence customer decisions to churn, as well as improving the effectiveness of customer retention strategies in a business context. The method used in this research involves the use of a customer bank dataset that includes information about customers who left in the past month, services registered by customers, customer account information, and demographic info about customers. The factors most influential to churn were identified through heatmap analysis, including MonthlyCharges, PaperlessBilling, SeniorCitizen, PaymentMethod, MultipleLines, and PhoneService. This research compares the performance of several machine learning algorithms, including Random Forest, Logistic Regression, Adaboost, and Extreme Gradient Boosting (XGBoost), to predict customer churn. Accuracy metrics and confusion matrix results are used to evaluate the performance of these algorithms. The results showed that XGBoost proved to be the best algorithm in predicting customer churn with high accuracy. The factors that have been correctly identified do not provide missed precision, showing a significant influence on customer churn decisions. The novelty and uniqueness of this research lies in focusing on the factors that have the most influence on customer churn and comparing the performance of machine learning algorithms. This research provides more specific and relevant insights for companies in developing effective customer retention strategies. However, this research has some limitations. One of them is the use of a dataset limited to a customer bank, so the generalizability of the findings of this research may be limited to that business context. In addition, other factors that are not the focus of this research may also contribute to the prediction of customer churn.
Co-Authors Ainun Rahmawati Asep Sutarman Chandra Lukita Ilham Bagus Prasetyo Joko Siswanto Lalu Darmawan Bakti Muhammad Iman Nur Hakim Nanda Dwi Putra Nesti Anggraini Santoso Putri Marewa Oganda Sri Watini Tito Pinandita Umi Rusilowati