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All Journal Bulletin of Computer Science Research BEES: Bulletin of Electrical and Electronics Engineering
Puspita Wanny
Universitas Pembangunan Panca Budi, Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis Sentimen Ulasan Pengguna Aplikasi DANA pada Google Play Store Menggunakan TF-IDF dan Naïve Bayes Puspita Wanny; Muhammad Iqbal
BEES: Bulletin of Electrical and Electronics Engineering Vol 7 No 1 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bees.v7i1.10083

Abstract

The DANA application is a digital wallet service widely used by the public to support various digital financial transaction activities. The high number of users results in numerous reviews on the Google Play Store containing various responses, experiences, and opinions regarding the quality of the DANA application service. However, the large number of review data makes the manual process of identifying and grouping opinions less effective and takes a relatively long time. This study aims to analyze and classify the sentiment of DANA application user reviews on the Google Play Store into positive, negative, and neutral categories and to determine the performance of the algorithm used in the classification process. The solution implemented is sentiment analysis using a text mining approach and Natural Language Processing (NLP) to process user reviews automatically. The research data was obtained through a scraping process and resulted in 3,509 DANA application user reviews. The data then went through preprocessing stages including cleaning, case folding, normalization, tokenizing, stopword removal, and stemming. Then, sentiment labeling and word weighting were carried out using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The data was then divided into 80% training data and 20% testing data. Classification was then performed using the Multinomial Naïve Bayes algorithm. Model performance was evaluated using a Confusion Matrix with Accuracy, Precision, Recall, and F1-Score metrics. The results showed that the Naïve Bayes model produced an Accuracy value of 80.48%, Precision of 76.75%, Recall of 80.48%, and F1-Score of 78.13%. These results indicate that the combination of the TF-IDF method and the Naïve Bayes algorithm is capable of classifying the sentiment of DANA app user reviews with quite good performance and can be used to help obtain an overview of user perceptions of the DANA app based on reviews provided on the Google Play Store
Analysis of Food Distribution Efficiency Clustering Based on Cost and Delivery Time Using the K-Means Algorithm Muhammad Syahputra Novelan; Solly Aryza; Puspita Wanny
Bulletin of Computer Science Research Vol. 6 No. 6 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i6.1345

Abstract

Food distribution efficiency is an important factor in maintaining supply chain performance, particularly in controlling transportation costs and ensuring timely delivery. Inefficiencies in distribution may lead to increased operational expenses and delays in delivering food products to their destinations. This study aims to analyze food distribution efficiency by grouping distribution activities based on transportation costs and delivery time using the K-Means clustering algorithm. The research stages consist of data collection, data preprocessing, feature selection, data standardization, determination of the optimal number of clusters, implementation of the K-Means algorithm, and cluster evaluation. Transportation cost and delivery time are used as the main variables to identify distribution patterns and classify distribution activities into efficiency groups. The resulting clusters are intended to distinguish distribution activities with relatively efficient, moderate, and less efficient characteristics, providing a basis for identifying operational patterns that require improvement. Data visualization is also used to support the interpretation of clustering results and facilitate comparisons among groups. The experimental results, based on 330 simulated food distribution records, produced three clusters. Cluster 1 consisted of 110 records, with an average distribution cost of IDR 176,363 and an average delivery time of 2.83 hours. Cluster 2 contained 111 records, with an average distribution cost of IDR 338,472 and an average delivery time of 5.83 hours. Cluster 3 comprised 109 records, with an average distribution cost of IDR 535,296 and an average delivery time of 9.49 hours. The clustering evaluation yielded a Silhouette Score of 0.679 and a Davies–Bouldin Index of 0.444. This study is expected to provide an analytical framework for supporting data-driven decisions in food distribution management. The proposed approach can assist distribution managers in evaluating cost and delivery-time patterns and identifying opportunities to improve operational efficiency. The validity of the findings depends on the quality of the operational data and the evaluation of the resulting clusters.
Co-Authors Muhammad Iqbal Muhammad Syahputra Novelan Solly Aryza