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All Journal Mulia International Journal in Science and Technical REKADATA (Rekayasa Data dan Kecerdasan Artifisial)
Dyah Nur Rochmah
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Prediction of Behavioral Patterns of Number Students Using Artificial Neural Networks. Endah Nurjanah; Dyah Nur Rochmah; Bagus Wirawan
Mulia International Journal in Science and Technical Vol 1 No 2 (2018): December
Publisher : Universitas Mulia

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Abstract

This study aims to predict student behavior patterns so they can predict based on the number of students. To achieve optimal results, this study uses Artificial Neural Networks with the Backpropagation method. A case study was conducted at the Faculty of Computer Science, X University. The data used is data on the number of students in the academic year two years ago as training data and the school year data is two years after that as testing data. Furthermore, the data are analyzed with several network architecture patterns, and the best design will be chosen to be implemented into the Matlab program. The system results show a correlation between the number of students that occur.
SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA UTILIZING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING TECHNIQUES Dyah Nur Rochmah
REKADATA Vol. 1 No. 1 (2025): REKADATA (Rekayasa Data dan Kecerdasan Artifisial)
Publisher : CV Mazaya Cahaya Utama

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The swift expansion of social media has generated a vast quantity of unstructured textual data that mirrors public sentiment on diverse subjects. Examining this data yields significant insights for enterprises, governments, and scholars. This research seeks to create a sentiment analysis system utilizing Natural Language Processing (NLP) and machine learning techniques to categorize social media messages as positive, negative, or neutral sentiments. The proposed system comprises several essential stages: text preprocessing, feature extraction via Term Frequency–Inverse Document Frequency (TF-IDF), and classification employing machine learning methods including Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression. A dataset including 10,000 social media postings was meticulously collected and extensively annotated to guarantee precision in sentiment classification. Experimental results indicated that SVM attained superior performance, achieving an accuracy of 87.4% and an F1-score of 0.86, surpassing both Naïve Bayes and Logistic Regression. The results illustrate the efficacy of natural language processing integrated with machine learning in the analysis of extensive social media datasets, providing a reliable method for sentiment classification. The study underscores the efficacy of sentiment analysis in gauging public opinion, facilitating commercial decisions, and identifying nascent social trends.
Co-Authors Anak Agung Bagus Wirawan Endah Nurjanah