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All Journal International Journal Of Science, Technology & Management (IJSTM)
Aini Nasution, Fitri
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Implementation of Decision Tree Method to Predict Customer Interest in Internet Data Packages Surya, Rezki; Dar, Muhammad Halmi; Aini Nasution, Fitri
International Journal of Science, Technology & Management Vol. 5 No. 4 (2024): July 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i4.1155

Abstract

The use of smartphones is on the rise, making internet packages vital in everyday life. In Indonesia, mobile operators offer a variety of data packages to meet customer needs. Understanding preferences is important to making the right decisions when providing products that meet customer needs. However, not all packages are suitable for all customers. Telkomsel is one of the providers that can deliver consistent signals and a wide range of data packages, but it still has a relatively high price. The study uses decision tree methodology to analyze citizens' preferences about Telkomsel services, comparing the relative cost of data packages with other services. This research will use surveys with samples from different communities to determine the representativeness of the results and provide strategic recommendations for Telkomsel to improve customer satisfaction. The research methods employed in this study included data collection, preprocessing, data division, model design, prediction results, and result evaluation. The results showed accuracy levels of 98.7%, precision of 100%, recall of 98.7%, specificity of 100%, and an F1-score of 99.3%. This study demonstrates the effectiveness of the decision tree model in predicting customer interest in Telkomsel services. Despite some limitations, the findings provide valuable insights that Telkomsel can use to develop more effective marketing strategies.
Comparison of Machine Learning Algorithms in Public Sentiment Analysis of TAPERA Policy Sihombing, Eklesia; Halmi Dar, Muhammad; Aini Nasution, Fitri
International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i5.1164

Abstract

The rapid development of information technology has changed the way people interact and express their opinions on public policies, including the People's Housing Savings (Tapera) policy in Indonesia. People now primarily express their views openly on social media platforms like Twitter, generating a substantial amount of text data for analysis to understand public sentiment. However, the main challenge in this sentiment analysis is determining the most effective machine learning algorithm for classifying public opinion with high accuracy. This study aims to compare the performance of three machine learning algorithms, namely Naïve Bayes, Support Vector Machine, and Random Forest, in analyzing public sentiment towards the Tapera policy. This study analyzes public comment data obtained from Twitter. We measure the accuracy of each algorithm to determine its optimal performance in sentiment classification. The research method consists of several stages, starting with data collection, text preprocessing to clean and prepare data, and then applying the three algorithms to analyze sentiment. The results showed that Naïve Bayes had the highest accuracy of 69.17%, followed by Support Vector Machine with an accuracy of 68.42%, and Random Forest with an accuracy of 66.17%. This shows that Naïve Bayes is the most effective algorithm to use in sentiment analysis of public comments related to the Tapera policy, especially in the context of complex text data from social media. The conclusion of this study is that Naïve Bayes is superior in classifying public sentiment towards the Tapera policy compared to Support Vector Machine and Random Forest. As a result, this study makes a significant contribution to selecting the most appropriate machine learning algorithm for public sentiment analysis towards public policy, which in turn can help the government understand and respond to public perceptions more effectively.
Implementation of the K-Means Clustering Method in Clustering Poor Population in Bandar Kumbul Village, Labuhanbatu Regency Maharani, Aulia; Juni Yanris, Gomal; Aini Nasution, Fitri
International Journal of Science, Technology & Management Vol. 6 No. 1 (2025): January 2025
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v6i1.1209

Abstract

Poverty is one of the crucial social problems in rural areas. The problem of poverty in rural areas is increasingly in the spotlight because of its broad impact on the community's economic and social sustainability. The varying levels of poverty require an appropriate analytical approach to design effective intervention programs. In an effort to understand and address this problem, this study uses the K-Means Clustering method to group the poor population. We use K-Means clustering to identify and group hamlets based on their poverty levels. This study aims to categorize the hamlets in Bandar Kumbul Village into multiple clusters according to their poverty levels, thereby identifying which hamlets necessitate more focused attention. The research methods used include collecting data on the number of poor people from 2013 to 2022 in each hamlet, data preprocessing, applying the Elbow method to determine the optimal number of clusters, and applying the K-Means Clustering algorithm to group the hamlets. The results of the study show that there are three main clusters with different characteristics. Cluster 0 includes Hutaimbaru and Mailil Julu hamlets with high poverty levels. Cluster 1 only includes the Pasir Sidimpuan hamlet, which has medium poverty levels. Cluster 2 includes Aek Mardomu, Bandar Kumbul, Mailil Jae, Sidodadi, and Singga Mata hamlets with low poverty levels. Variations in distance from the cluster center indicate significant differences in the distribution of poverty in each hamlet. The K-Means Clustering method is effective in identifying and grouping hamlets based on poverty levels, providing useful insights for the government and stakeholders to design more targeted intervention programs. Clusters with high poverty levels require immediate intervention, while clusters with medium and low poverty levels require maintenance and support to prevent an increase in poverty. This study provides a strong foundation for decision-making and policies to reduce poverty levels in Bandar Kumbul Village more effectively and sustainably.
Co-Authors Halmi Dar, Muhammad Juni Yanris, Gomal Maharani, Aulia Muhammad Halmi Dar Sihombing, Eklesia Surya, Rezki