At the macroeconomic level, the movement of the stock market index, which is determined by the moves of other stock market indices around the world or in that region, is one of the primary factors in assessing the global economic and financial situation, making it a critical topic to monitor over time. As a result, the potential to reliably forecast the future value of stock market indices by taking trade relationships into account is critical. The aim of the research is to create a time-series data forecasting model that incorporates the best features of many time-series data analysis models. The hybrid ensemble model built in this study is made up of two main components, each with its own set of functions derived from the CNN and LSTM models. For multiple parallel financial time-series estimation, the proposed model is called multivariate CNN-LSTM. The effectiveness of the evolved ensemble model during the COVID-19 pandemic was tested using regular stock market indices from four Asian stock markets: Shanghai, Japan, Singapore, and Indonesia. In contrast to CNN and LSTM, the experimental results show that multivariate CNN-LSTM has the highest statistical accuracy and reliability (smallest RMSE value). This finding supports the use of multivariate CNN-LSTM to forecast the value of different stock market indices and that it is a viable choice for research involving the development of models for the study of financial time-series prediction.

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Perancangan  ini  bertujuan untuk menghasilkan rancangan user interface aplikasi mobile komik online dalam bentuk prototipe yang dirancang menggunakan figma. Perancangan antar muka ini menggunakan metode User-centered design, dimana dalam perancangan ini berfokus pada user atau pengguna dan harus mempertimbangkan kebutuhan, tujuan, dan masukan dari pengguna, serta menggunakan teori mengenai ui desain, prinsip perancangan ui, prinsip kerja desain, prinsip desain aplikasi, penerapan layout, serta pendalaman psikologi warna sehingga pesan dan kesan yang ingin disampaikan dapat tercapai. Berdasarkan analisis dari kebutuhan target audience, di tentukan konten-konten yang akan ditampilkan, dan mengelompokkan informasiinformasi yang dibutuhkan, sketsa  ide serta implementasi hasil dalam bentuk prototipe digital. Hasil perancangan ini terdiri yaitu berupa prototype desain user interface aplikasi komik online.

The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. This study proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Results of experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM.