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Implementation of Mathematics Learning Through Hydroponic Farming to Improve Mathematics Ability in Early Childhood Putri, Ni Wayan Suardiati; Wardika, I Wayan Gede; Kencana, Agung Pasek Surya
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 1 (2022): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i1.5610

Abstract

This study aims to produce and determine the feasibility of early mathematics learning media through hydroponic farming for Early Childhood. The type of research carried out is development research, because in this study an initial mathematics learning media was developed through hydroponic farming for Early Childhood. The research instruments used were media validation sheets and teacher response questionnaires. The data analysis used is descriptive quantitative. The product produced in this study is a medium for early mathematics learning through hydroponic farming for Early Childhood that meets valid and practical criteria. This study took subjects in Early Childhood at Kartika Kindergarten, Peguyangan Kaja Village. The results of this study regarding media validation showed that the initial mathematics learning media through hydroponic farming for Early Childhood had met the valid criteria. Judging from the results of the analysis of the teacher's response to learning media, namely the teacher's response to the use of media, it shows 75.8% in the good category. This shows that the media can be implemented practically by the teacher. 
The Performance of Support Vector Machine in Classifying Public Sentiment toward Student Suicide Cases I Gusi Gede Bagus Ngurah Sarjana; Made Leo Radhitya; Ni Wayan Suardiati Putri
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.431

Abstract

Introduction: The rapid growth of social media has generated large volumes of user-generated content that can be analyzed to understand public responses to sensitive social issues. This study evaluates the performance of Support Vector Machine (SVM) in classifying public sentiment toward a widely discussed student suicide case based on YouTube comments. Method: A total of 5,000 comments were collected from a video on the Denny Sumargo YouTube channel using the YouTube Data API and categorized into positive and negative sentiments. Text preprocessing included cleaning, normalization, tokenization, stop-word removal, and stemming. Term Frequency-Inverse Document Frequency (TF-IDF) was used for feature extraction, while Synthetic Minority Over-sampling Technique (SMOTE) addressed class imbalance. The dataset was divided into 80% training and 20% testing data, and SVM was applied for binary sentiment classification. Results and Discussion: The SVM model achieved 99.96% training accuracy and 89.25% test accuracy, with precision, recall, and F1-score consistently around 89%. These results indicate that the TF-IDF, SMOTE, and SVM pipeline effectively classified Indonesian social media comments despite the linguistic complexity of discussions surrounding sensitive issues. Conclusion: SVM demonstrates effective and robust performance for classifying public sentiment in Indonesian YouTube comments and provides a useful approach for analyzing public responses to sensitive social phenomena.