In today's data-driven business landscape, robust metadata and data documentation practices are essential for enterprises aiming to maximize their data assets. When integrated with Business Intelligence (BI) syste...
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This research investigates sentiment analysis of user reviews for the XYZ e-wallet application on the Google Play Store, which currently holds a low rating of 2.7 out of 5. A preliminary study, including a focus group...
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This study looks on the factors that influence 452 Indonesian customers' adoption of digital insurance. The study investigates the impact of psychological, social, and technical aspects on adoption intentions usin...
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This study aims to investigate the adoption of robo-advisors in the Indonesian insurance sector, focusing on key factors influencing their acceptance, such as trust, anxiety, performance expectancy, preference for hum...
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This study explores sentiment analysis on Indonesian social media data related to stock investments, focused on GOTO stocks. Using the Knowledge Discovery in Database (KDD) methodology, data was collected from the soc...
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This study related phenomena to the adoption of digital insurance services in Jakarta. Analysis of 403 respondents reveals that several factors have no significant impact on individual behavioral intention to use digi...
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In the rapidly urbanizing context of major cities such as Jakarta, the demand for efficient and rapid laundry services poses a considerable challenge, particularly within residential settings like boarding houses or d...
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ISBN:
(纸本)9798350390025
In the rapidly urbanizing context of major cities such as Jakarta, the demand for efficient and rapid laundry services poses a considerable challenge, particularly within residential settings like boarding houses or dormitories in Central Jakarta, where access to communal laundry facilities is markedly constrained. Against this backdrop, laundry enterprises categorized under Micro, Small, and Medium Enterprises (MSMEs) in the service sector have witnessed significant growth. These services typically encompass the collection, washing, drying, folding, and delivery of laundry to the customer's domicile. Nevertheless, the burgeoning proliferation of laundry businesses has precipitated intensified competition within the sector, compelling these enterprises to devise strategies for customer retention and revenue augmentation. This research endeavors to delineate effective strategies and solutions to navigate the competitive landscape of the laundry industry. The study undertakes a meticulous evaluation of various machine learning algorithms, including Decision Tree, Logistic Regression, Naïve Bayes, Support Vector Machine, Random Forest, XGBoost, LightGBM, and CatBoost, based on their performance with unseen data. This evaluation employs a comprehensive set of metrics such as precision, recall, Fl-score, and ROC AUC to determine the efficacy of each model. The findings reveal that the CatBoost Classifier algorithm outperforms others, achieving a training accuracy of 95.9% and a testing accuracy of 85.7%. The novelty of this study lies in its comprehensive comparison of multiple machine learning algorithms in the context of MSMEs in Jakarta's laundry sector, highlighting the superior performance of the CatBoost algorithm. Furthermore, the study contributes by demonstrating the practical application of these algorithms to improve customer retention strategies, enhance service quality, and optimize marketing tactics. Based on these results, the study recommends the implemen
The rise of music streaming services has led to significant challenges in royalty management, particularly affecting independent musicians who face poor royalty payments and a lack of transparency in royalty calculati...
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This research aimed to perform a comparative analysis of the effectiveness of customer support systems and the management of helpdesk tickets, which play a pivotal role in maintaining customer satisfaction and loyalty...
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In this study, a combined approach using the Software Usability Measurement Inventory (SUMI) and the Technology Acceptance Model (TAM) is employed to evaluate an e-commerce application's usability and user accepta...
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