TF-IDF Decision Matrix to Measure Customers’ Satisfaction of Ride Hailing Mobile Application Services: Multi-Criteria Decision-Making Approach

Dina, Nasa Zata; Triwastuti, Ria; Silfiani, Mega

doi:10.3991/ijim.v15i17.22509

“…Categorical data processing requires TF-IDF weighting before determining the optimal number of clusters using the elbow method. TF-IDF is obtained by multiplying term frequency (TF) with inverse document frequency (IDF) [17]. Inverse document frequency will identify that the occurrence of a term with a high frequency in data or documents is significant.…”

Section: Methodsmentioning

confidence: 99%

Identifying Improvement Strategic from User Application Reviews Group Using K-Means Clustering and TF-IDF Weighting

Istiqomah,

Widodo,

Mufid

et al. 2023

Int. J. Artif. Intell. Res

0

View full text Add to dashboard Cite

PT ABC is one of the companies that provide online ticket-purchasing facilities amidst the rise of the digitalization era. So, companies need to see how application users complain as a form of evaluation and improvement. The rating results given by application users show a score of 3.3 from 172,000 reviews. The review results that will be examined are user reviews from January 2022 to April 1, 2023, which is more or less the last year of user comments. This research aims to form a review group using K-Means Clustering, the Elbow method, TF-IDF weighting, and analysis of review improvement strategies. The Elbow method is used to determine the optimal number of clusters so as not just to use assumptions. The success of the Elbow method in processing categorical data can be supported by assigning weights based on word frequency sequences using TF-IDF. The research analysis results show the formation of 4 clusters, with two tending to have negative sentiment, one neutral sentiment, and one positive sentiment. Mapping is carried out on each cluster to find out the characteristics of each cluster and possible causes of reviews, as well as providing solutions and strategies as a form of improvement. The problem of negative reviews appearing in each review group is different. It can be corrected with the proposed strategies, such as improving the appearance of features at the registration, ordering, and payment stages, adding payment methods, and carrying out regular system maintenance.

show abstract

Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century

Mah¹,

Skalna²,

Muzam³

et al. 2022

JITDW

View full text Add to dashboard Cite

FinTech is a digital innovation technology that aims to educate and enable the world on how to create utility values in every activity. Natural language processing is one of the umbrella systems that has unite other innovative technologies behind FinTech. Technological system drivers regarded as "ABCDE" of FinTech consist of Artificial intelligence, Blockchain, Cloud computing, big data, and the internet of things. As communication took the lead in the second half of the 21st century, most companies are moving remotely, leading to much-needed innovation in FinTech. The study presents how natural language processing enables different technologies and advances the technology behind FinTech. The study aims to identify areas of the modern world that can be transformed into a source of finance using the FinTechs drivers of "ABCDE”. The study observed FinTech as a digital Economics that integrates with different aspects of modern technology to create utility values. The study uses the 5 C's of credit as the source of finance for innovative ideas and the 5 P's of marketing as innovative network to reach ultimate FinTech utility values. Results based on the demand and supply analysis indicate that a combination of 5 C's and 5 P's is the bond behind FinTech with the support of the drivers of "ABCDE". Also that the increased demand for goods and services in every economy indicates a fall in the request for credit and vice versa. The study concluded that a well-structured 5 C's and 5 P's is the best route to FinTech technology which is financial freedom to the world.

show abstract

Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century

Mah¹,

Skalna²,

Muzam³

et al. 2022

JITDW

2

0

View full text Add to dashboard Cite

FinTech is a digital innovation technology that aims to educate and enable the world on how to create utility values in every activity. Natural language processing is one of the umbrella systems that has unite other innovative technologies behind FinTech. Technological system drivers regarded as "ABCDE" of FinTech consist of Artificial intelligence, Blockchain, Cloud computing, big data, and the internet of things. As communication took the lead in the second half of the 21st century, most companies are moving remotely, leading to much-needed innovation in FinTech. The study presents how natural language processing enables different technologies and advances the technology behind FinTech. The study aims to identify areas of the modern world that can be transformed into a source of finance using the FinTechs drivers of "ABCDE”. The study observed FinTech as a digital Economics that integrates with different aspects of modern technology to create utility values. The study uses the 5 C's of credit as the source of finance for innovative ideas and the 5 P's of marketing as innovative network to reach ultimate FinTech utility values. Results based on the demand and supply analysis indicate that a combination of 5 C's and 5 P's is the bond behind FinTech with the support of the drivers of "ABCDE". Also that the increased demand for goods and services in every economy indicates a fall in the request for credit and vice versa. The study concluded that a well-structured 5 C's and 5 P's is the best route to FinTech technology which is financial freedom to the world.

show abstract

TF-IDF Decision Matrix to Measure Customers’ Satisfaction of Ride Hailing Mobile Application Services: Multi-Criteria Decision-Making Approach

Cited by 3 publications

References 20 publications

Identifying Improvement Strategic from User Application Reviews Group Using K-Means Clustering and TF-IDF Weighting

Identifying Improvement Strategic from User Application Reviews Group Using K-Means Clustering and TF-IDF Weighting

Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century

Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century

Contact Info

Product

Resources

About