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Detection of Indonesian wildlife sales and promotion through social media using machine learning approach Lestarini, Dinda; Rusdy, Taufiqurrahman; Iriyani, Silfi; Raflesia, Sarifah Putri
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5418

Social media is one of the communication media that is widely used in the digital era as it is today. The use of social media allows people who are far apart to communicate and exchange media, both voice, video, and images quickly and even in real-time. In the past, the sale of protected animals was mostly done on the black market, usually involving a supply chain between sellers that usually existed in traditional markets or certain communities. With the existence of social media, the trend in conducting transactions and promoting wild animals has shifted from traditional to modern thanks to the support of existing technology. Protected wild animals are of concern to the local government or the global world to protect their existence. Therefore, this research proposes a machine learning (ML) based approach to detect the promotion and sale of wild animals on social media. The implementation of Naïve Bayes classifier (NBC) has a high accuracy in detecting trade in wild animals on social media with an accuracy value of 86. The implementation of ML-based approach is expected to produce new technology that allows authorities to know and monitor social media in order to reduce the sale and promotion of protected wildlife.

Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media X Ahmad Syakir Muliana; Dinda Lestarini; Sarifah Putri Raflesia
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4592

The objective of the research is to examine the public opinion regarding the 2024 Indonesian election results by applying Naïve Bayes to social media data obtained from platform X of Twitter. A dataset comprising 2,500 election-related tweets was obtained by web scraping and then subjected to tokenization, stopword elimination, stemming, and TF-IDF weighting for preprocessing. The application of the Synthetic Minority Oversampling Technique (SMOTE) was attempted to mitigate class imbalance. The performance of the Naïve Bayes model was assessed using Stratified K-Fold Cross-Validation. The model achieved an average accuracy of 66.90% on the test set and 80% during cross-validation. The results demonstrate successful categorization of positive sentiment, although the model encountered difficulties in precisely detection of negative and neutral sentiments. The results underscore significant consequences for policymakers and political parties in formulating effective communication strategies. Further study is advised to investigate sophisticated algorithms to improve the accuracy of sentiment classification, namely in detecting neutral sentiments.

Assessing User Experience of ChatGPT Website Employing the User Experience Questionnaire (UEQ) Dila Okta Dwi Putri; Dinda Lestarini
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4566

One form of information technology advancement is the use of AI in website development, one of which is ChatGPT website. ChatGPT has a poor user experience in various aspects, so it is necessary to evaluate the user experience on the ChatGPT website using User Experience Questionnaire (UEQ). The attractiveness variable obtained a positive evaluation value of 1.503. The perspicuity variable obtained a positive evaluation value of 1.661. The efficiency variable obtained a positive evaluation value of 1.615. The dependability variable obtained a positive evaluation value with an overall average value of 1.286. The stimulation variable obtained a positive evaluation value of 1.182. The novelty variable obtained a positive evaluation value of 0.942. The ChatGPT website has shown good quality because it has a positive evaluation value from user assessments. However, in the attractiveness, dependability, and novelty variables, there are still several items that get neutral ratings. So product improvements are still needed to increase user satisfaction.

Analyzing Co-Authorship Networks in Indonesian PTN-BH Institution Through Social Network Analysis Firdaus, Firdaus; Nurmaini, Siti; Darmawahyuni, Annisa; Rachmatullah, Muhammad Naufal; Raflesia, Sarifah Putri; Lestarini, Dinda
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v14i1.1265

This study involved an examination of bibliographic information from Indonesia. Our approach centered on utilizing social network analysis to explore the co-authorship relationships among Indonesian authors, focused on the co-authorship network within the context of authors affiliated with Indonesian state universities known as "PTN-BH," which specialize in higher education and legal studies. To conduct our analysis, we gathered publication data from the Scopus database, spanning a time frame from 1948 to 2020. The primary methodology entailed constructing a graph composed of nodes and edges, representing the co-authorship connections among these authors. By employing the Louvain method, we were able to identify prominent communities within this graph. We carried out a comprehensive analysis at both macro and micro levels, involving measurement techniques tailored to these perspectives. Through this approach, we revealed and examined the collaboration patterns among authors associated with PTN-BH institutions, as illuminated by the co-authorship network analysis.

Analyzing Co-Authorship Networks in Indonesian PTN-BH Institution Through Social Network Analysis Firdaus, Firdaus; Nurmaini, Siti; Kurniawan, Anggy Tyas; Darmawahyuni, Annisa; Naufal, Muhammad; Raflesia, Sarifah Putri; Lestarini, Dinda
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

This study involved an examination of bibliographic information from Indonesia. Our approach centered on utilizing social network analysis to explore the co-authorship relationships among Indonesian authors, focused on the co-authorship network within the context of authors affiliated with Indonesian state universities known as "PTN-BH," which specialize in higher education and legal studies. To conduct our analysis, we gathered publication data from the Scopus database, spanning a time frame from 1948 to 2020. The primary methodology entailed constructing a graph composed of nodes and edges, representing the co-authorship connections among these authors. By employing the Louvain method, we were able to identify prominent communities within this graph. We carried out a comprehensive analysis at both macro and micro levels, involving measurement techniques tailored to these perspectives. Through this approach, we revealed and examined the collaboration patterns among authors associated with PTN-BH institutions, as illuminated by the co-authorship network analysis.

Implementation of the K-Means Algorithm for Customer Churn Segmentation in Developing Bank Marketing Strategies Rahmadiana, Reva Nur; Lestarini, Dinda
SISTEMASI Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5341

Customer churn, or the loss of banking clients, represents a major challenge in the banking industry due to its potential to cause significant financial losses. This study aims to segment customers based on characteristics that influence their churn risk using the K-Means algorithm. The data used in this research is secondary data consisting of 9,763 customer records from a bank customer churn dataset obtained via the Kaggle platform. The data processing follows the CRISP-DM framework. Clustering was conducted using RapidMiner, and performance was evaluated using the Davies-Bouldin Index to determine the optimal number of clusters (K). The results indicate that the optimal number of clusters is K = 4. Centroid analysis revealed that balance and estimated salary are the primary variables contributing to cluster formation. Cluster 1 and Cluster 3 had the highest number of churned customers. Cluster 1 consisted of customers with high balances but low salaries, while Cluster 3 included customers with both high balances and high salaries. These findings suggest that a high balance does not necessarily guarantee customer loyalty, and that income level plays an important role in preventing churn. Based on the analysis, recommended strategies include providing financial education and loyalty programs for customers in Cluster 1, and offering exclusive services and personalized approaches for those in Cluster 3. This study demonstrates that the K-Means algorithm is effective in producing relevant customer segmentation, serving as a valuable foundation for developing more targeted and efficient marketing strategies.

Analyzing Co-Authorship Networks in Indonesian PTN-BH Institution Through Social Network Analysis Firdaus; Nurmaini, Siti; Kurniawan, Anggy Tias; Darmawahyuni, Annisa; Rachmatullah, Muhammad Naufal; Raflesia, Sarifah Putri; Lestarini, Dinda
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

This study involved an examination of bibliographic information from Indonesia. Our approach centered on utilizing social network analysis to explore the co-authorship relationships among Indonesian authors, focused on the co-authorship network within the context of authors affiliated with Indonesian state universities known as "PTN-BH," which specialize in higher education and legal studies. To conduct our analysis, we gathered publication data from the Scopus database, spanning a time frame from 1948 to 2020. The primary methodology entailed constructing a graph composed of nodes and edges, representing the co-authorship connections among these authors. By employing the Louvain method, we were able to identify prominent communities within this graph. We carried out a comprehensive analysis at both macro and micro levels, involving measurement techniques tailored to these perspectives. Through this approach, we revealed and examined the collaboration patterns among authors associated with PTN-BH institutions, as illuminated by the co-authorship network analysis.

Comparison of Support Vector Machine and Random Forest Algorithms in Sentiment Analysis of the JMO Mobile Application Via Mariska, Inneke; Meiriza, Allsela; Lestarini, Dinda
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10764

JMO Mobile is a digital service application that enables the public to access employment-related information and benefits. User reviews serve as a valuable resource for evaluating service quality, yet systematic sentiment analysis on this application remains limited. This study aims to classify the sentiment of user reviews and compare the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms. A total of 41,673 reviews were collected through web scraping, then preprocessed through text cleaning, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. The reviews were categorized into positive, negative, and neutral sentiments, and divided into training and testing datasets with an 80:20 ratio. The choice of SVM and RF was based on their proven effectiveness in text classification tasks, with SVM excelling in handling high-dimensional data and RF recognized for its stability in producing reliable results. Model evaluation was conducted using accuracy as the primary metric. The findings indicate that Random Forest achieved an accuracy of 86.15 percent, slightly outperforming SVM at 86.06 percent. While SVM showed superior performance in identifying positive sentiment, Random Forest demonstrated greater consistency across classifications. Overall, Random Forest is considered more suitable for sentiment analysis of public service application reviews. This study contributes an automated approach to understanding user perceptions and offers a reference for selecting classification algorithms in similar cases.

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