The purpose of the study was to classify the factors causing the decline in student achievement during the pandemic using the C4.5 datamining method. Sources of research data were obtained by conducting interviews and distributing questionnaires to 7th semester students of the 2020-2021 school year information system study program. Attributes that used in the classification of the factors causing the decline in student achievement include: Learning Method (C1), Study Time (C2), Material Understanding (C3), Giving Assignments (C4) and Environment (C5). The results of the calculation show that the Material Understanding (C3) attribute is the attribute that most influences the decline in student learning achievement. Testing was also carried out using the help of Rapidminer software and obtained an accuracy of 97.5%.
Backpropagation is a method of Artificial Neural Networks that is quite reliable in solving prediction problems (forecasting). However, in its application, this algorithm still has weaknesses such as optimizing the artificial neural network weights to avoid local minimums, the problem of long training times to achieve convergence and the process of determining the right parameters (learning rate and momentum) in the training process. The purpose of this research is to solve this problem by using Particle Swarm Optimization (PSO) which is a simple and reliable optimization algorithm to solve optimization problems. The data source is obtained from the site sumut.bps.go.id. There are 5 network architecture models used in this study, including 2-5-1, 2-7-1, 2-9-1, 2-11-1 and 2-13-1. The results of trials conducted with Rapid Miner software, the best architectural model is the 2-9-1 model with a total RMSE of 0.056 +/- 0.000 in the implementation of Backpropagation, while in the implementation of Backpropagation + particle swarm optimization the amount of RMSE is 0.055 +/- 0.000. The smaller the RMSE (Root Mean Squared Error), the better the model
Internet media has become one of the means of product promotion that has very good prospects today. The research aims to recommend the right social media for online businesses. The data collection method was conducted by interview and questionnaire to 300 respondents who conducted online business in Pematangsiantar city by random sampling. Based on the results of interviews and questionnaires obtained assessment criteria namely security (C1), application features (C2), community (C3), ease of access (C4) and response speed (C5). The alternatives used in this research are Facebook (A1), Instagram (A2), Line (A3) and WhatsApp (A4). The settlement method applied is a decision support system with the PROMETHEE II algorithm. The results of the algorithm show that the right alternative for doing online business is Facebook (A1) with a net flow of 0.25 and followed by WhatsApp (A4) with a net flow of ¬0.1. The results of the study are expected to provide recommendations in conducting online business.
The selection of Calcium Dairy Products that are suitable for old age uses the PROMETHEE II Algorithm. This study aims to recommend calcium milk products that are appropriate for elderly people based on consumer selection. This research was conducted in Simalungun district using interview techniques, observation and questionnaires to 350 elderly respondents randomly. This study also uses a quantitative data approach, which is testing four calcium milk products (alternatives) that are considered appropriate for use in old age. The four dairy products are widely used for the elderly, namely: Anlene Gold (A1), Entrasol (A2), Prolansia (A3) and Appeton 60+ (A4). The assessment criteria used in the selection of calcium milk recommendations appropriate for old age are: price (C1), content (C2), side effects (C3) and taste variants (A4). The solution used is to use a decision support system with PROMETHEE II algorithm. The results of PROMETHEE II calculation show that Anlene (A1) is obtained as the first recommendation of the right calcium milk with a value (net flow 0.5) and Appeton 60+ (A4) as the second recommendation with a value (net flow 0.484).
The objective of the study is to classify informal employment in non-agricultural sectors. Data sources are obtained from the Central Statistics Agency (BPS). The data used is the proportion of employment for informal non-agricultural sectors (2015-2018), consisting of 34 Provinces in Indonesia. The Method used to solve the problem is datamining technique K-Medoid. The results of the research showed that the percentage of informal employment of non-agricultural sectors based on the lowest region became a record for the government to further increase human resources and more open the field jobs in non-agricultural sectors, among others.Keywords: Informal sector, Datamining, K-Medoid, Clustering, Non-Agricultural
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