Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : IJMST

The Role of Sentiment Analysis in Election Predictions Compared to Electability Surveys Firdaus, Asno Azzawagama; Faresta, Rangga Alif; Yunus, Muhajir
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.1-8.2025

Abstract

Indonesia has just held the voting process for the Presidential Election. This has become a discussion of various media to social media, especially Twitter. However, when making predictions based on social media it will be so difficult if there is no specific technique or method for handling it. The prediction method we found in Indonesia often uses electability surveys in elections, but this research will compare it with sentiment analysis that utilizes social media in data collection. Another novelty is the data used during candidate campaign debates using the Support Vector Machine (SVM) method in class classification. The results obtained show that there are still differences between electability and sentiment, but this is due to several factors such as the amount of data, data objects, data collection time span, and methods. Overall, the SVM method has an accuracy of more than 0.75 on all three candidate datasets, proving that this method can be applied to similar cases.
Classification of Stunting in Toddlers using Naive Bayes Method and Decision Tree Maulana, Adrian; Ilham, Muhammad; Lonang, Syahrani; Insyroh, Nazaruddin; Sherly da Costa, Apolonia Diana; B. Talirongan, Florence Jean; Furizal, Furizal; Firdaus, Asno Azzawagama
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.28-33.2025

Abstract

Child stunting is a health problem that has a major impact on their physical growth and brain development. This study aims to create a model that can predict the risk of stunting using machine learning technology, in order to provide assistance quickly. Using data from 7,573 children, which included information such as age, weight, height gender and breastfeeding status, we tried two methods, Naive Bayes and Decision Tree. As a result, Naive Bayes was more accurate and the success rate reached 92%, compared to Decision tree which was only 88%. With this model, it is hoped that health workers will find it easier to find children at risk of stunting, so that preventive action can be taken earlier. This research aims to provide technology-based solutions to overcome the problem of stunting in the community.
Data Analysis of Student Monitoring Using the K-Means Clustering Method Sulistiani; Habibi , Ahmad Rizky Nusantara; Maulana , Adrian; Talirongan , Hidear; Abao , Anrom G.; Elmalky , Ahmed Mahmoud Zaki; Firdaus, Asno Azzawagama
Indonesian Journal of Modern Science and Technology Vol. 1 No. 2 (2025): May
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.2.50-57.2025

Abstract

This study aims to group student monitoring data by focusing on two main variables, namely anxiety level and mood score, using the K-Means Clustering method. The research data was obtained from the Kaggle platform, which contains 1000 rows of data with nine attributes, including Student ID, Date, Class Time, Attendance Status, Stress Level, Sleep Hours, Anxiety Level, Mood Score, and Risk Level. The research process involved several stages, from problem identification, data collection, data cleaning and preprocessing, to the application of the K-Means algorithm. The analysis results showed that the data could be divided into two main groups: Cluster 1 consists of students with low to moderate anxiety levels and high mood scores, while Cluster 2 includes students with high anxiety and low mood scores. These findings provide relevant information for schools or campuses to design more effective psychological support and emotional monitoring programs. Additionally, this clustering method can serve as a foundation for developing an early detection system for psychological issues among students.
Sentiment Analysis of User Reviews of TikTok App on Google Play Store Using Naïve Bayes Algorithm Hasanah, Rakyatol; Sani SR, Sahrul; Munzir, Misbahul; Firdaus, Asno Azzawagama; Sulton, Chaerus; Yunus, Muhajir
Indonesian Journal of Modern Science and Technology Vol. 1 No. 2 (2025): May
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.2.58-64.2025

Abstract

In recent years, user interaction through mobile applications has grown rapidly, making user reviews an important source of feedback for improving service quality. This study explores sentiment analysis on 5,000 user reviews of the TikTok application, collected from the Google Play Store using the google-play-scraper library. The data underwent several preprocessing steps, such as case folding, text cleaning, and selecting relevant columns like review content and rating score. Sentiment labeling was based on rating values: scores of 4 and 5 were treated as positive, while scores of 1 and 2 were considered negative. From the results, it was observed that negative reviews appeared more frequently, indicating an imbalance in the dataset. Despite this, the Naïve Bayes classification algorithm still achieved a reasonably good performance in categorizing the sentiments. These findings suggest that even with simple models, valuable insights can be gained from user-generated content. Moreover, the results provide meaningful input for TikTok developers to better understand user concerns and emphasize the potential need for applying balancing techniques in future analysis. Further studies are encouraged to explore other algorithms that may improve sentiment classification accuracy on more complex datasets.