The development of predictive models for infectious diseases, specifically COVID-19, is an important step in early control efforts to reduce the mortality rate. However, traditional time series prediction models used to analyze the disease spread trends often encounter challenges related to accuracy, necessitating the need to develop prediction models with enhanced accuracy. Therefore, this research aimed to develop a prediction model based on the Long Short-Term Memory (LSTM) networks to better predict the number of confirmed COVID-19 cases. The proposed optimized LSTM (popLSTM) model was compared with Basic LSTM and improved MinMaxScaler developed earlier using COVID-19 dataset taken from previous research. The dataset was collected from four countries with a high daily increase in confirmed cases, including Hong Kong, South Korea, Italy, and Indonesia. The results showed significantly improved accuracy in the optimized model compared to the previous research methods. The contributions of popLSTM included 1) Incorporating the output results on the output gate to effectively filter more detailed information compared to the previous model, and 2) Reducing the error value by considering the hidden state on the output gate to improve accuracy. popLSTM in this experiment exhibited a significant 4% increase in accuracy.

Rinjani Mountain in West Nusa Tenggara is famous destination for travelling which has Instagram able for tourism photos. Its popularity because many social media tolls which easy to uploading photos. Instagram is a digital media for sharing photos and videos to others. This paper aimed are (a) develop a sentiment classifier required method based on Lexicon-Based method of conducting a dictionary-based sentiment analysis and (b) Identifying the sentiments of each opinion words contained in the response and hashtag data. The results are posting photo or picture in Instagram make positive comment from visitor and the impact is increasing visitor to Rinjani Mountain. The trial and evaluations system created be able to provide information about positive, negative and neutral opinion of the community on tourism in Mount Rinjani.

Pandemi COVID-19 terjadi terjadi diseluruh dunia, seluruh aktifitas dan kegiatan menjadi terganggu termasuk aktifitas pendidikan. Seluruh kegiatan pendidikan yang semula dilaksanakan secara offline, harus dilaksanakan secara online. Salah satu cara untuk mempermudah kegiatan belajar mengajar, adalah dengan mengunggah video yang berisi materi-materi pembelajaran ke media sosial seperti Youtube ataupun sejenisnya. Hal ini tentunya membawa perubahan yang sangat signifikan terhadap kegiatan belajar mengajar termasuk kegiatan di SD Muhammadiyah Rabbani Kabupaten Klaten Jawa Tengah. Open Broadcast Software atau OBS merupakan salah satu tools open source yang bisa dimanfaatkan untuk membuat video pembelajaran dengan mudah dan tidak terkoneksi internet. Tujuan dari pelatihan ini adalah tentang tatacara pembuatan OBS. Evaluasi menggunakan kuesioner online dan diolah menggunakan SPSS versi 25. Hasil evaluasi menunjukkan bahwa 60% peserta menjawab sangat setuju bahwa pelatihan mudah dimengerti, pemateri sangat handal dan OBS mudah di implementasikan, sedangkan 30% lainnya menjawab setuju.

The outbreak of the COVID-19 pandemic occurred some time ago, making the world a pandemic. Based on this condition is important to predict early to prevent the COVID-19 disease if someday pandemic occurs. The aim of the study is to compare the analysis result of cumulative cases of COVID-19 using multiple linear regression (MLR), ridge regression (RR), and long short term memory (LSTM) models for cases study Java and Bali islands. We chose both islands as a case study because they have very dense populations. These three models are the most widely used time series-based prediction models and have relatively high accuracy values. The predictive variables used are the number of cumulative cases, the daily cases, and population density. The research data was taken from Kaggle and processed using google collabs. Data was taken from January 20, 2020, to August 8, 2020, and data training was carried out for 12 days. The results show the accuracy of LSTM is better than other models. it can be seen in the accuracy value (99.8 %) of the model test result. The testing model uses R2, mean square error (MSE), and root mean square error (RMSE).

This study discusses the implementation of the proposed optimizedlong short-term memory (LSTM) to predict the number of infectious disease cases that spread in Central Java, Indonesia. The proposed model is developed by optimizing the output layer, which affects the output value of the cell state. This study used cases of four infectious diseases in Indonesia's Central Java Province, namely COVID-19, dengue, diarrhea, and hepatitis A. This model was compared to basic LSTM and MinMax schaler LSTM improvement to see the difference in the accuracy of each disease. The results showed a significant difference in the average prediction results with real cases between the three models. The main objectives of this study were: modifying the LSTM algorithm to predict the number of infectious disease cases to get a smaller residual value, comparing the results of the optimization accuracy of the LSTM algorithm with the LSTM algorithm in previous studies, and evaluating the use of spatial variables in applying infectious disease prediction models using the LSTM algorithm. The results found that the performance difference between the proposed optimization algorithm and the model in the previous study was obtained. The proposed LSTM optimization algorithm had an accuracy improvement of about 2% over the previous model.

AMIKOM-RECSYS: Enhancing Movie Recommender System using Large Language Model (ChatGpt), Deep Learning and Probabilistic Matrix Factorization