Suchergebnisse

School Participation Rate (APS) is known as one of the indicators of the success of the development of educational services in regions both Province, Regency or City in Indonesia. The higher the value of the School Participation Rate, then the area is considered successful in providing access to education services. The purpose of this study is to predict School Participation Rates based on Provinces in Indonesia from Aceh to Papua. The prediction algorithm used is the backpropagation algorithm using the gradient descent with momentum & adaptive LR (traingdx) training function. Traingdx is a network training function that updates weight values and biases based on gradient descent momentum and adaptive learning levels. Usually, the backpropagation algorithm uses the gradient descent backpropagation (traingd) function, but in this study, the training function used is using gradient descent with momentum & adaptive LR (traingdx). The data used in this study data on School Participation Figures for each province in Indonesia in 2011-2017 aged 19-24 years were taken from the Indonesian Central Bureau of Statistics (BPS). The reason for choosing this age range is because at this age is one of the factors that determine the success of education in a country, especially Indonesia. This study uses 3 network architecture models, namely: 5-5-1, 5-15-1 and 5-25-1. Of the 3 models, the best model is 5-5-1 with an iteration of 130, the accuracy of 94% and MSE 0,0008708473. This model is then used to predict School Participation Rates in each province in Indonesia over the next 3 years (2018-2020). These results are expected to help the Indonesian government to further increase scholarships and improve the quality of education in the future. Keywords: Prediction, APS, Backpropagation, Traingdx.

The Internet today has become a primary need for its users. According to market research company e-Marketer, there are 25 countries with the largest internet users in the world. Indonesia is in the sixth position with a total of 112.6 million internet users. With the increasing number of internet users are expected to help improve the economy and also education in a country. To be able to increase the number of internet users, especially in Indonesia, it is necessary to predict for the coming years so that the government can provide adequate facilities and pre-facilities in order to balance the growth of internet users and as a precautionary step when there is a decrease in the number of internet users. The data used in this study focus on data on the number of internet users in 25 countries in 2013-2017. The algorithm used is Artificial Neural Network Backpropagation. Data analysis was processed by Artificial Neural Network using Matlab R2011b (7.13). This study uses 5 architectural models. The best network architecture generated is 3-50-1 with an accuracy of 92% and the Mean Squared Error (MSE) is 0.00151674.

Analysis on a prediction (forecasting) is very important to do in a study, So with this data analysis will be obtained a clear picture of the issues discussed. As well as in predicting the number of guests in non-star hotels. This research is expected to be useful for both Government and private parties as one of the study materials in the development of hotel business, as well as for academics as study material / research especially related to tourism and hospitality field. The data used in this study is data on the number of guests in non-star hotels by province from the Central Bureau of Statistics Indonesia from 2011 to 2016. This study uses the method of artificial neural network Backpropagation using 5 architectural models, those are 4-19-1, 4-50-1, 4-17-1, 4-16-1, 4-22-. From architecture, the best architecture is 12-19-1 with an accuracy of 88.2%, MSE 0.10206089 with error rate used 0.001 - 0.05. Thus, this model is good enough to predict the number of guests indonesia in non-star hotels

Knowing the age of life of the population in a country is useful for evaluating the performance of the government, whether the government is able to prosper the population in general, and improve health status in particular. The purpose of this paper is to forecast the age of the population in several major countries in Asia, so that the government has a benchmark in determining policies to further improve the welfare and health of the population in their respective countries. The forecasting method in this paper will use Machine learning algorithms with Bayesian Regulation techniques. The research data used is data on population expectations in several Asian countries sourced from the United Nations: "World Population Prospect: The 2010 Revision Population Database". This research is a development of research that has been done before, using the Cyclical order technique. Previous research used 5 architectural models (3-5-1, 3-8-1, 3-10-1, 3-5-8-1 and 3-5-10-1), with the best model being 3-5-10 -1 which results in an accuracy of 97%, MSE 0.0008358919, training time of 27 seconds and an error rate of 0.03. Meanwhile, this research only uses 3 modified architectural models (2-5-1, 2-10-1 and 2-5-10-1), with the best model being 2-5-1. The result is that this study is better than previous studies. The benchmark is seen from a smaller error rate (0.02), better accuracy (100%), to a faster training time (5 seconds). So it can be concluded, Bayesian Regulation technique works better than Cyclical order and the 2-5-1 architectural model will be used to make predictions

Analysis of a prediction is very important to do in a study, so that research becomes more precise and directed. Just as in predicting the extent of biopharmaceutical harvests in Indonesia, it is necessary to study and use appropriate methods to obtain optimal results. This research is expected to be widely used for both local government and biopharmaca farmers as one of the study materials in the development of biopharmaca harvest production, as well as for academics as research material especially related to agriculture and health. The data used in this research is the data of Harvested Area of Biopharmaceutical in Indonesia from National Bureau of Statistics from 2012 until 2016. This research uses the method of artificial neural network Backpropagation using 5 architectural models, namely: 3-3-1 later it will generate predictions with an accuracy rate of 80%, 3-4-1 = 87%, 3-5-1 = 73%, 3-6-1 = 60%, and 3-8-1 = 73% ,. So obtained the best architectural model using 3-4-1 model that yields an accuracy of 87%, MSE 0.062235528 with error rate used 0.001 to 0.05. Thus, this model is good enough to predict the area of biopharmaca harvest in IndonesiaKeywords—Analysis, Prediction, ANN, Backpropagation, BiopharmacaDOI : 10.5281/zenodo.1402402

This study aims to describe the management of the allocation of village funds in physical development in the village of Siborna, Panei Subdistrict, Simalungun Regency and to identify inhibiting factors and supporting factors in managing village fund allocation. The focus of this research is the management of village fund allocations which include: planning, implementation. The determination of the allocation of village funds will later use the Analytical Hierarchy Process (AHP) method with 4 criteria including: villagers, rural poor, village area and Village Geographical Difficulty Index. Alternative Samples in this study were 4 villages, including the villages of Sosor Hamlet, Simpang Bahbirong Hamlet, Hutabagasan Hamlet and Kebun Sayur Hamlet. The results of this study using the AHP method obtained by the Village of Vegetable Farm Village is an alternative with the highest value that is eligible to get a village fund allocation from the government with a value of 3,0000

Unemployment is a big problem faced by the Indonesian people from year to year besides poverty. Therefore it is necessary to predict the level of open unemployment in Indonesia so that later the government and private parties have the right references and references to work together to overcome this problem. The prediction method used is Resilient Backpropagation which is one method of Artificial Neural Networks which is often used for data prediction. The research data used is open unemployment data according to the highest education completed in 2005-2018 based on the semester obtained from the website of the Indonesian Central Bureau of Statistics. Based on this data a network architecture model will be formed and determined, including 12-6-2, 12-12-2, 12-18-2, 12-24-2, 12-12-12-2, 12-12-18 -2, 12-18-18-2 and 12-18-24-2. From these 8 models after training and testing, the results show that the best architectural model is 12-18-2 (12 is the input layer, 18 is the number of hidden neurons and 2 is the output layer). The accuracy of the architectural model for semester 1 and semester 2 is 75% with an MSE value of 0.0022135087 and 0.0044974696

Illiteracy is one of the most serious issues in Indonesia. The government's ignorance of illiterate people makes the illiteracy rate quite high. It should be one of the government's targets for reducing illiteracy in order to reduce the number of illiterate people. Illiteracy rate in Indonesia itself has reached 34.55% in Papua province. One way to suppress illiteracy rate in Indonesia is by predicting illiterate figures for subsequent years. The data to be predicted is the data of illiterate figures of each province in Indonesia which is sourced from the Indonesian Central Bureau of Statistics from 2011 to 2017. The method used in the prediction is Backpropagation Neural Network. Data analysis was done with the help of matlab software R2011b (7.13). This study uses 5 architectures, 4-5-1, 4-6-1, 4-9-1, 4-14-1 and 4-18-1. From these 5 models the best network architecture is 4-14-1 with 91% accuracy and Mean Squared Error 0,00274166.

North Sumatra is one of the areas in Indonesia which has various tourist attractions with its natural beauty. The potential of North Sumatra makes the attraction of foreign tourists want to visit the existing tourist attractions in the area. Every year foreign tourists who come to North Sumatra, especially tourists who come from the ASEAN region the number is changing. Therefore an estimate is needed to determine the number of tourists who come and later this estimate is useful for the government to anticipate the surge of tourists who come. The data to be estimated is the data of the number of tourists who come to North Sumatra according to national origin sourced from Central Bureau of Statistics of North Sumatra (BPS Sumut) in 2011-2015. The algorithm used to make estimates is the artificial neural network Backpropagation. There are five architectural models used in this estimate that is, 3-7-1 has 100% accuracy rate, 3-10-1 = 62.5%, 3-11-1 = 75%, 3-12-1 = 88% , and 3-15-1 = 87.5%. The best architecture of the five models is 3-7-1 with 100% accuracy rate and MSE of 0,006513Keywords—Estimates, Foreign Tourists, North Sumatra, Neural NetworksDOI : 10.5281/zenodo.1402836

Measles is one of the causes of death in children around the world which always increases every year. Although measles immunization programs have been implemented, the incidence of measles in children is still quite high. This study discusses the Implementation of Rapidminer with the K-Means Method (Case Study: Measles Immunization in Toddlers by Province). The increase in cases of measles in toddlers in Indonesia is a case that has never been separated from the government's attention. Data sources and research were obtained from the Central Statistics Agency (BPS). The data used in this study are data from 2004-2017 which consists of 34 provinces. The cluster process is divided into 3 (three) clusters, namely high cluster level (C1), medium cluster level (C2) and low cluster level (C3). So that the assessment for cases of immunization against measles based on high cluster province (C1) is 21 provinces for medium cluster (C2) of 12 provinces and for low cluster (C3) of 1 province. The results of the cluster can be used as input for the government, especially the provinces, so that provinces that enter the high cluster receive more attention and increase the socialization of measles immunization against children under five. Keywords: Data Mining, Measles, Clustering, K-means

Natural disasters are natural events that have a large impact on the human population. Located on the Pacific Ring of Fire (an area with many tectonic activities), Indonesia must continue to face the risk of volcanic eruptions, earthquakes, floods, tsunamis. Application of Clustering Algorithm in Grouping the Number of Villages / Villages According to Anticipatory / Natural Disaster Mitigation Efforts by Province With K-Means. The source of this research data is collected based on documents that contain the number of villages / kelurahan according to natural disaster mitigation / mitigation efforts produced by the National Statistics Agency. The data used in this study is provincial data consisting of 34 provinces. There are 4 variables used, namely the Natural Disaster Early Warning System, Tsunami Early Warning System, Safety Equipment, Evacuation Line. The data will be processed by clustering in 3 clushter, namely clusther high level of anticipation / mitigation, clusters of moderate anticipation / mitigation levels and low anticipation / mitigation levels. The results obtained from the assessment process are based on the Village / Kelurahan index according to the Natural Disaster Anticipation / Mitigation Efforts with 3 provinces of high anticipation / mitigation levels, namely West Java, Central Java, East Java, 9 provinces of moderate anticipation / mitigation, and 22 other provinces including low anticipation / mitigation. This can be an input to the government, the provinces that are of greater concern to the Village / Village According to the Natural Health Disaster Mitigation / Mitigation Efforts based on the cluster that has been carried out.Keywords: Data Mining, Natural Disaster, Clustering, K-Means

North Sumatra Province is a province well known for its rice field production. But from all regencies and cities in North Sumatra, its rice production is uneven and unstable, sometimes its production goes up, sometimes down. Therefore, it is needed a research in the form of prediction for Production of Rice Field Crops, so that local government of North Sumatra can make policy as early as possible, so that production of paddy rice field can continue to rise in order to support the achievement of food self-sufficiency. In this study data that will be predicted sourced from the Central Bureau of Statistics of North Sumatra Province from 2012 until 2016. The algorithm used to make this prediction is the Backpropagation algorithm. This algorithm has the ability to remember and make generalizations of what has been there before. There are 5 architectural models used in this research, among others 3-5-1 which later will produce predictions with 78% accuracy rate, 3-7-1 = 70%, 3-10-1 = 82%, 3-15 -1 = 82% and 3-9-1 = 91%. The best architecture of the 5 models is 3-9-1 with 91% accuracy and error rate of 0.001-0.05. It is expected that the results of this study can contribute to the government in determining agricultural policy in the future.Keywords—Application, Backpropagation, Prediction, Production, RiceDOI : 10.5281/zenodo.1402832

Home improvement assistance is a government program for disadvantaged people. Home improvement assistance program is one of the government's efforts as a form of concern related to the condition of the homes of underprivileged communities. Therefore the purpose of this study is to determine the recipient of housing improvement assistance in the West Siantar Subdistrict as a reference material for the Office of Spatial Planning and Settlements (Tarukim) of Pematangsiantar city in providing housing repair assistance to be on target. The DSS method used in this study is the Elimination Et Choix Traduisant La Realita (ELECTRE) method. The alternative data samples used were 5, which were obtained from the Pematangsiantar Tarukim Office. This study also uses 5 criteria, including: roof material conditions, floor conditions, wall conditions, Work and Bathroom ownership. Based on calculations, Alternative 2 (A002) on behalf of Mr. Wariman was chosen as the best. Therefore, Mr. Wariman's family home is a community that is entitled to receive housing repair assistance in the West Siantar sub-district

The aim of the research is to create a prediction model on the best neural network architecture by combining the k-medoids and backpropagation methods in the case of the COVID-19 pandemic in Indonesia. Data obtained from the Ministry of Health is sampled and processed from covid19.go.id and bnpb.go.id. The case raised was the number of the spread of the COVID-19 pandemic in Indonesia as of July 7, 2020, with 34 records. The variables used in this study are the number of positive cases (x1), the number of cases cured (x2), and the number of deaths (x3) by province. The process of data analysis uses the help of RapidMiner software. The solution provided is to combine the k-medoids and backpropagation methods. Where the k-medoids method is mapping the specified cluster. The cluster labels used are high cluster (C1 = red zone), alert cluster (C2 = yellow zone), low cluster (C3 = green zone). The results of cluster mapping are continued to the backpropagation method to predict the accuracy of the existing cluster results. By using the best architectural model 3-2-1, the accuracy value is 94.17% with learning_rate = 0.696. Cluster mapping results obtained nine provinces are in the high cluster (C1 = red zone), three provinces are in the alert cluster (C2 = yellow zone), and 22 provinces are in the low cluster (C3 = green zone). It is expected that the results of the research can provide information to the government in the form of cluster mapping of regions in Indonesia.