In recent years, Bitcoin has attracted a lot of attention because of its nature that supports encryption technology and monetary units. For traders, Bitcoin becomes a promising investment since its fluctuating prices potentially draw high profit (the higher the risk the higher the return). Unlike conventional stock, Bitcoin trades for 24 hours a day without a closing period, so that it escalates the risk. Predicting the value of Bitcoin is expected to minimize the risk by considering some information such as blockchain information, macroeconomic factors, and global currency ratios. However, the multicollinearity among these independent variables causes regression method cannot be used. This research employs Bayesian Regularization Neural Network (BRNN) which is a free assumption. This method is Single Hidden Layer Feed Forward Neural Network (SLNN) that utilize Bayesian concept to optimize weights, biases, and connection strengths. The data is time series data from January 23, 2017, to January 23, 2019. Regression with subset selection is employed to reduce independent variables, from a total of 25 variables to 14 variables. As a result, the predicted value is not much different from the actual data, with an accuracy of 91.1% based on the MAPE value.

The year 2020 has marked the beginning of a new life in which humans must struggle and adapt to coexist with a new coronavirus, known as COVID-19. Population density is one of the most significant factors affecting the speed of COVID-19’s spread, and it is closely related to human activity and movement. Therefore, many countries have implemented policies that restrict human movement to reduce the risk of transmission. This study aims to identify the temporal dependence between human mobility and virus transmission, indicated by the number of active cases, in the context of large-scale social restriction policies implemented by the Indonesian government. This analysis helps identify which government policies can significantly reduce the number of active COVID-19 cases in Indonesia. We conducted a temporal interdependency analysis using a time-varying Gaussian copula, where the parameter fluctuates throughout the observation. We use the percentage change in human mobility data and the number of active COVID-19 cases in Indonesia from March 28, 2020, to July 9, 2021. The results show that human mobility in public areas significantly influenced the number of active COVID-19 cases. Moreover, the temporal interdependencies between the two variables behaved differently according to the implementation period of large-scale social distancing policies. Among the five types of policies implemented in Indonesia, the policy that had the most significant influence on the number of active COVID-19 cases was several restrictions during the Implementation of Restrictions on Community Activities (Pelaksanaan Pembatasan Kegiatan Masyarakat/PPKM) period. We conclude that the strictness of rules restricting social activities generally affected the number of active COVID-19 cases, especially in the early days of the pandemic. Finally, the government can implement policies that are at least equivalent to the rules in PPKM if, in the future, cases of COVID-19 spike again.

Located in the capital city of Indonesia, Soekarno-Hatta Airport is considered as the main airport. Since there are some aviation companies providing low cost flight, the number people coming and leaving trough this airport has increased. The passenger volume can be considered as seasonal data since it shows increment in particular months, such as long holiday. Knowing in advance the volume of passenger will help the government to improve its service effectively. There is a simple and accurate method for forecasting seasonal data that is called Holt-Winter Exponential Smoothing (HWE). However, HWE always encounters over forecasting problem when it is employed to forecast in some future periods (m>1). In order to solve this problem, we add the damped parameter that will be damping the exponentially growth on HWE. This method called HWE damped trend. We employed the domestic passenger volume data of Soekarno-Hatta

memiliki bobot sks cukup tinggi yaitu 6 sks. Pada umumnya, mahasiswa dengan latar belakang non eksakta cenderung mengalami keengganan dan kejenuhan ketika belajar material eksak. Namun maraknya inovasi bidang digital dalam pendidikan membuka peluang untuk mengatasi permasalahan tersebut. Strategi blended learning (separuh tatap muka dan separuh daring) diharapkan menjadi variasi dalam proses pembelajaran. Oleh karena pembelajaran dilakukan secara jarak jauh akibat kebijakan terkait pandemi, blended learning dilaksanakan dengan daring sinkron dan daring asinkron. Untuk membantu mahasiswa dalam memahami teori statistika, digunakan metode PBL (Problem Based Learning). Problem ekonomi disampaikan di awal pembelajaran, kemudian diikuti dengan penyampaian teori statistika yang mendukung penyelesaian problem. Berdasarkan kuisioner dan wawancara, sebanyak 61.7% mahasiswa memilih preferensi pembelajaran secara blended learning. Delapan dari sepuluh mahasiswa setuju bahwa pemberian problem ekonomi membantu mereka untuk memahami kegunaan teori statistika. Sebesar 67% mahasiswa memperoleh nilai A. Keterbatasan strategi dan metode dalam penelitian ini adalah ketersediaan ruang bagi mahasiswa untuk berdiskusi menjadi minim. Diskusi yang sebelumnya direnccanakan dalam pertemuan taatp muka menjadi tidak terakomodasi dengan pertemuan daring sinkron

Aircraft has  became the best choice for long distance traveling because it has shortest travel time than any other transportations. Moreover, in recent years, aviation industries have competed for providing low cost flight so that it can also be enjoyed by middle class society. Thus escalate the popularity of aircraft as economical carrier. Knowing the volume of passengers in advance will help government and related institutions to effectively providing facilities. The volume of passengers can be predicted using classic model such as double exponential smoothing model which is simpler and has high accuracy. However, the randomness of Indonesian passenger volume data cause double exponential smoothing (DES) cannot follow both data pattern and data trend. Moreover, classic model often encounters overfitting where the prediction is bigger than the actual data. Therefore, we employed Grey Method applied on DES (GDES) to overcome this problem. GDES enabled the researcher to perform better data fitting because it would generate smoothing curve which showed clearer trend. As the result, although GDES fitting curve had higher error measurement (MSE) than DES, the forecasting result of GDES was more precise than DES. Keyword: Double Exponential Smoothing, Grey Method, Levenberg-Marquardt