This research was conducted in Department of Statistics Islamic University of Indonesia. The data used are primary data obtained by post @explorejogja Instagram account from January until December 2017. In the @explorejogja Instagram account found many tourist destinations that can be visited by tourists both in the country and abroad. Therefore, it is necessary to form a cluster of existing tourist destinations based on the number of likes from user Instagram assumed as the most popular. The purpose of this research is to know the most popular distribution of tourist spot, the cluster formation of tourist destinations, and central popularity of tourist destinations based on @explorejogja Instagram account in 2017. Statistical analysis used is descriptive statistics, Affinity propagation, and social network analysis.
The decline and increase in the price of shares of plantation companies is a problem for investors in making decisions to buy or sell shares. Factors influencing the movement of plantation stock prices include CPO commodity price fluctuations, world oil price fluctuations, Rupiah exchange rate fluctuations, government regulations and policies, demands from importing countries, and climate. Forecasting stock prices is expected to help investors to deal with uncertainty in the movement of plantation stock prices. This study applies the Long Short-Term Memory (LSTM) to predict the stock prices of plantation companies using SSMS, LSIP, and SIMP share price data from the period 1 July 2014 - 22 July 2019. Based on the results of the study it was found that the best LSTM model on SSMS shares by using the RMSProp optimizer and 70 hidden neurons produced an RMSE value of 21,328. Then the best LSTM model on LSIP stock by using Adam optimizer and 80 hidden neurons produces an RMSE value of 33,097. Whereas the best LSTM model on SIMP shares using Adamax optimizer and 100 hidden neurons produced an RMSE value of 8,3337.
The Qur’anic Self-Development (PDQ)-Ta'lim Program is one of the student activities that must be followed by diploma and bachelor program students in Universitas Islam Indonesia (UII). The implementation of PDQ is coordinated by each faculty which is carried out for 4 semesters with 12 meetings for each semester. After carrying out PDQ activities, it is necessary to know the student profiles that can be used as the basis for policy making in the implementation of PDQ activities in the next period. In order to find out the profile of students after participating in PDQ activities, it is necessary to group these students based on related variables. This study uses the ROCK method to group students participating in the PDQ Faculty of Mathematics and Natural Sciences (FMIPA) UII batch 2020. The ROCK method is a robust agglomerative hierarchical-clustering algorithm based on the notion of links. The ROCK method is a suitable clustering method for grouping data with categorical variables. Based on the results of the analysis of the ROCK method of student data for the batch 2020 FMIPA UII, obtained three optimum clusters (k=3) at a threshold value of θ of 0.20. Threshold 0.20 has the smallest SW/SB ratio value of 0.0514 or 5.14% and the largest R-squared value is 61.76% compared to other thresholds.
This community service aims to improve the management and governance of the home industry of Familo's Ginger Javanese Sugar through training and mentoring marketing management with e-commerce systems. The program of community service activities is carried out through several stages, namely the preparatory stage, the implementation stage, and the evaluation stage. The results achieved through this activity are participants be able to market their products by e-commerce system optimally.
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