Evaluating the learning process requires a platform for students to express feedback and suggestions openly through online reviews. Sentiment analysis is often used to analyze review texts but typically captures only overall sentiment without identifying specific aspects. This study develops an aspect-based sentiment analysis (ABSA) model using IndoBERT, a pre-trained model tailored for the Indonesian language. The research uses 10,000 student reviews from Indonesian universities, processed through data labeling, text preprocessing, and splitting, followed by model training and performance evaluation. The model demonstrated superior performance with an aspect extraction accuracy of 0.973, an F1-score of 0.952, a sentiment classification accuracy of 0.979, and an F1-score of 0.974. Experimental results indicate that the proposed ABSA model surpasses previous state-of-the-art models in analyzing sentiment related to specific aspects of educational evaluation. By leveraging IndoBERT, the model effectively handles linguistic complexities and provides detailed insights into student experiences. These findings highlight the potential of the ABSA model in enhancing learning evaluations by offering precise, aspect-focused feedback, contributing to strategies for improving the quality of higher education.

This study aims to gain a deeper understanding of online student reviews regarding the learning process at a private university in Indonesia and to compare the effectiveness of several algorithms: Naive Bayes, K-NN, Decision Tree, and Indo-Bert. Traditional Sentiment Analysis methods can only analyze sentences as a whole, prompting this research to develop an Aspect-Based Sentiment Analysis (ABSA) approach, which includes aspect extraction and sentiment classification. However, ABSA has inconsistencies in aspect detection and sentiment classification. To address this, we propose the BERT method using the pre-trained Indo-Bert model, currently the best NLP model for the Indonesian language. This study also fine-tunes hyperparameters to optimize results. The dataset comprises 10,000 student reviews obtained from online questionnaires. Experimental results show that the aspect extraction model has an accuracy of 0.890 and an F1-Score of 0.897, while the sentiment classification model has an accuracy of 0.879 and an F1-Score of 0.882. These results demonstrate the effectiveness of the proposed method in identifying aspects and sentiments in student reviews and provide a comparison between the four algorithms.

PENENTUAN JUMLAH PRODUKSI PAKAIAN DENGAN METODE FUZZY TSUKAMOTO STUDI KASUS KONVEKSI NISA

PREDIKSI HASIL PERTANIAN PADI DI KABUPATEN KUDUS DENGAN METODE BROWN’S DOUBLE EXPONENTIAL SMOOTHING

Social media platforms generate many opinions, emotions, and views on all public services. Sentiment analysis is used in various institutions, such as universities, the business industry, and politicians. The evaluation process requires some data, both quantitative and qualitative. Researchers only focus on quantitative data but ignore qualitative data. The evaluation process given by students in the form of a review is qualitative data that is not structured, so it cannot use conventional methods. Unstructured data requires analysis as well as labeling. The labeling process of large amounts of data is a waste of time and money. Data labeling requires very high accuracy to avoid errors. Accuracy in data labeling is used for the process of classifying, training, and testing data. This study aims to automate data labeling using the K-Nearest Neighbors algorithm method. This labeling process can improve the accuracy of sentiment analysis. The results of the classification method can classify responses from Twitter users and can be used by universities as material for evaluating and assessing higher education services. The results of using a confusion matrix with 1.409 data obtained an accuracy rate of 79.43% with a value of k=15

Game becomes very popular within all ages. The intensity of someone playing games can influence the behavior of that person. World Health Organization (WHO) is classifying gaming disorder as an addictive behavior disorder. According to psychologists, there are six types of game addiction behavior, such as, Salience, Euphoria, Conflict, Tolerance, Withdrawal, and the last is Relapse and Reinstatement. A person is said to be addicted to the game if it meets at least three of the six types of behavior that exist. The six types of game addiction behavior then modeled using backward chaining algorithm. After that, the model was implemented into the system. Thus this research re-sult a web application to identify game addiction. Now we can measure how many people are affected using this web application.