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All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH
Felix Andreas Sutanto
Universitas Stikubank, Semarang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Data Mining to Predict Gojek's Consumer Satisfaction Level Using Naive Bayes Algorithm Rudika Rahman; Felix Andreas Sutanto
IJISTECH (International Journal of Information System and Technology) Vol 6, No 5 (2023): February
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i5.276

Abstract

Gojek is an application that is very popular and in demand as a means of transportation because it is practical and fast. Consumer satisfaction is where the expectations, desires and needs of consumers are met. To assess whether the company provides quality service to consumers, it is necessary to evaluate consumers to determine the level of consumer satisfaction when using the Gojek application. This study aims to build a system for predicting satisfaction levels from Gojek Driver services to consumers using the Naive Bayes algorithm, as well as to determine the level of accuracy in classifying customer satisfaction using Gojek services. questionnaire is the method used in collecting data on Gojek consumer satisfaction. In this study, 120 questionnaires were distributed to respondents, namely Gojek service users, and these questionnaires would later become training data. Researchers use the survey method as a direct observation of the process of using Gojek services to identify the services provided to consumers. Researchers use the waterfall method as a system development model. This model is the oldest software development model paradigm, and the most widely used. The process of calculating the accuracy of the system uses the Naive Bayes method by testing based on training data taken from the questionnaire. The calculation results on the level of accuracy obtained from the training data are equal to 88.9%. The calculation is processed and divided by the system as much as 70% training data and 30% testing data or as many as 84 training data and 36 testing data. This consumer satisfaction prediction system can assist an admin in determining the classification of consumer satisfaction with web-based Gojek services by applying the Naive Bayes method. In this study, researchers only calculated the level of accuracy and predictive value, for further research it is hoped that they can try to calculate the precision value and recall value calculations.
Data Mining to Predict Gojek's Consumer Satisfaction Level Using Naive Bayes Algorithm Rudika Rahman; Felix Andreas Sutanto
IJISTECH (International Journal of Information System and Technology) Vol 6, No 5 (2023): February
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i5.276

Abstract

Gojek is an application that is very popular and in demand as a means of transportation because it is practical and fast. Consumer satisfaction is where the expectations, desires and needs of consumers are met. To assess whether the company provides quality service to consumers, it is necessary to evaluate consumers to determine the level of consumer satisfaction when using the Gojek application. This study aims to build a system for predicting satisfaction levels from Gojek Driver services to consumers using the Naive Bayes algorithm, as well as to determine the level of accuracy in classifying customer satisfaction using Gojek services. questionnaire is the method used in collecting data on Gojek consumer satisfaction. In this study, 120 questionnaires were distributed to respondents, namely Gojek service users, and these questionnaires would later become training data. Researchers use the survey method as a direct observation of the process of using Gojek services to identify the services provided to consumers. Researchers use the waterfall method as a system development model. This model is the oldest software development model paradigm, and the most widely used. The process of calculating the accuracy of the system uses the Naive Bayes method by testing based on training data taken from the questionnaire. The calculation results on the level of accuracy obtained from the training data are equal to 88.9%. The calculation is processed and divided by the system as much as 70% training data and 30% testing data or as many as 84 training data and 36 testing data. This consumer satisfaction prediction system can assist an admin in determining the classification of consumer satisfaction with web-based Gojek services by applying the Naive Bayes method. In this study, researchers only calculated the level of accuracy and predictive value, for further research it is hoped that they can try to calculate the precision value and recall value calculations.
Co-Authors Rudika Rahman