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All Journal JINAV: Journal of Information and Visualization Bigint Computing Journal
Bimantara, Muhammad Dhuha
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Mathematics

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Text Mining Sentiment Analysis on Mobile Banking Application Reviews using TF-IDF Method with Natural Language Processing Approach Bimantara, Muhammad Dhuha; Zufria, Ilka
JINAV: Journal of Information and Visualization Vol. 5 No. 1 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav2772

Abstract

As part of its efforts to improve digital banking services, PT Bank Rakyat Indonesia (Persero) Tbk (BRI) has launched a mobile banking application called BRImo. This move is in line with the global trend where financial institutions are increasingly focusing on digitalization to meet the evolving needs of customers who demand faster and more efficient accessibility to banking services. BRImo comes as an innovative solution to provide a better banking experience to BRI customers. This research was conducted to find out the reviews of the BRImo application on the App markets google playstore, In BRImo mobile banking's efforts to remain competitive with other mobile banking applications, understanding positive and negative reviews from users is very important. The fundamental issue that must be addressed is how to analyze positive reviews to strengthen the advantages of the BRImo app and identify negative reviews to address weaknesses that may hinder its competitiveness. This research was conducted to find out the reviews of the BRImo application on the App markets google playstore, In BRImo mobile banking's efforts to remain competitive with other mobile banking applications, understanding positive and negative reviews from users is very important. The method used in the calculation is TF-IDF and NLP approach and the calculation of SVM algorithm is trained using training data. The calculation results show that the model has an accuracy of 92%. or Precision Score of about 92%, Recall Score has 100% and F1 Score has 0.95 or approximately 95%.
Implementation of HSV Imagery with K-Nearest Neighbor for Classification of Maturity Levels in Tomatoes Aulia, Lailatul Husna; Azhari, Fazar; Bimantara, Muhammad Dhuha
Bigint Computing Journal Vol 1 No 2 (2023)
Publisher : Ali Institute of Reseach and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/bigint.v1i2.779

Abstract

The tomatoes used use tomatoes (Lycopersicum esculentum Mill) which is a type of horticultural plant. One type is plum tomatoes. The process of classifying tomato ripeness is carried out manually through direct visual observation. However, this is very difficult to do because it is inconsistent. Therefore, relevant features are needed to classify tomato maturity levels based on HSV features using the KNN method. The method used in classification is K-Nearest Neighbor, this algorithm requires features to build the model. The feature used is HSV feature extraction. Based on the results of research tests carried out, it proves that Euclidean distance k=2 has a percentage value of 85%. Based on the level of accuracy, the color feature k=2 shows the best k value in classifying tomato ripeness levels measuring 400x400 pixels. To achieve a high level of accuracy, image processing time should be less
Co-Authors Aulia, Lailatul Husna Azhari, Fazar Ilka Zufria