Cabai merah adalah salah satu tanaman utama dari salah satu tanaman petani di Indonesia. Permasalahan yang timbul adalah sulit untuk menentukan biji cabai mana yang baik dan tahan terhadap virus. Selain itu, petani juga kesulitan menentukan benih yang baik dengan harga terjangkau. Sulitnya menentukan benih putih yang baik membuat sebagian petani gagal panen dan menderita kerugian yang cukup besar. Penelitian ini dilakukan di Desa Bandar Siantar, Kabupaten Gunung Malela. Data diperoleh dengan mewawancarai dan mengamati langsung ke petani cabai. Penelitian ini menggunakan teknik sistem pendukung keputusan dengan metode Multi-Objective Optimization berdasarkan Analisis Rasio (MOORA) yang dapat membantu petani untuk merekomendasikan benih cabai yang baik. Kriteria penilaian yang digunakan sebanyak 6 yakni: Harga bibit (C1), Masa Panen (C2), Panjang Buah (C3), Berat Buah (C4), Penyakit Cabai (C5), Banyaknya Cabang (C6) dan 8 alternatif, yaitu: Lado (A1), Taro (A2) ), Belinda (A3), TM (A4), Kripsy (A5), Tebing (A6), Indra Pura (A7) dan Keling (A8). Hasil penelitian menunjukkan jenis Lado (A1) dengan nilai (Yi (maks) = 0,2080) menjadi rekomendasi pertama, TM (A4) dengan nilai (Yi (maks) = 0,2071) berada di peringkat kedua dan Indra Pura (A7) dengan nilai (Yi (maks) = 0,1974) menjadi tempat ketiga. Penelitian ini diharapkan dapat membantu para petani untuk menghindari kegagalan panen, mereka dapat membantu para petani untuk menghindari kegagalan panen, terutama di desa Bandar Siantar, Kabupaten Gunung Malela.
The purpose of this research is to see how much open unemployment rate according to the highest education completed in the country of Indonesia for subsequent years through predictions used on the basis of existing data, which later as input for the government so that the government can make better policies to suppress the unemployment rate. This research uses artificial neural network application using a combination of Levenberg-Marquardt Algorithm with bipolar sigmoid function. Open unemployment data according to the highest education is sourced from the National Labor Force Survey of the Republic of Indonesia, 2013-2017 in each semester. The data processing consists of two stages where the first phase of pattern recognition and the second stage is predicted. Pattern recognition and prediction use different data from the same process that uses data training and data testing. Data Training year 2013-2015 with a target of 2016, while data testing year 2014-2016 with the target year 2017. Architectural model used there are five, among others 6-2-5-2, 6-5-6-2, 6- 5-8-2, 6-5-10-2 and 6-8-12-2. From the 5 models, it can be concluded that the best model is 6-5-10-2 with the epoch of 13 iterations, MSE in February 0.0109696004, MSE in August 0.0233797200. While the accuracy rate in February and August is the same, that is equal to 88%.
The importance of efficiency in the space of search rules C4.5 decision tree algorithm has been the focus of a lot of researchers. Therefore, the development needs to be conducted to form a new, more efficient method but it can not be separated from the accuracy of the analysis as the results of the algorithm itself. For that purpose, by using a genetic algorithm (GA), it is expected to optimize and simplify the search rules of more complex combinations. The use of C4.5 with Hybrid genetic algorithm in search of a more effective rules requires a better understanding and a long time. But the use of the two algorithms will be mostly effective if the cases faced are very complex, having more branching condition and highly accurate.
Based on data on the results of oil palm production in PTPN IV Marihat displays several locations with fruit yields that vary in number. For this reason, grouping of potential fruit-producing locations is needed to know which locations produce large or small numbers of palm fruit. The production sharing is usually done based on the location or block of harvesting oil palm fruit. Therefore, a method is needed to facilitate the grouping of fruit producing locations. With the K-Means clustering approach, the division of location groups can be done based on harvested area (Ha), production realization (kg) and harvest year. In this research, clustering of potential fruit-producing areas was carried out using the K-Means algorithm. By using K-Means aims to facilitate the grouping of a block with a lot of fruit production, and low. The result of this research is that C1 (highest) is 14 Harvest Block data, and C2 (lowest) is 11 Harvest Block data.
Measles is a contagious infections disease that attacks children caused by a virus. Transmission of measles from people through coughing and sneezing. Measles causes disability and death, so further threatment is needed. Measles immunization program that can inhibit the development of measles is one of the efforts in eradicating the disease. In this study the data used were sourced from the Central Statistics Agency National in 2013-2017. This study uses datamining techniques in data processing with K-Medoids algorithm. The K-Medoids method is a clustering method that functions to break datasets into groups. The advantages of this method are the ability to overcome the weaknesses of the K-Means method which is sensitive to outliers. Another advantage of this algorithm is that the results of the clustering process do not depend on the entry sequence of the dataset. The k-medoids clustering method can be applied to the data on the percentage of measles immunization can be identified based on province, so that the grouping of provinces based on these data. From the data grouping three clusters are obtained: low cluster (2 provinces), medium cluster (30 provinces) and high cluster (2 provinces) with the percentage of measles immunization in each of these provinces from data grouping in percentage. It is expected this research can provide information to the govermant about the data on grouping measles immunization for toddlers in Indonesia which has an impact on the distribution of immunization against measles toddlers in Indonesia.
One important point in carrying out the functions of the Tridharma of Higher Education by lecturers is to carry out research and publish the results of their thoughts and analyzes. The demands of publication by the academic community of Higher Education have a considerable impact on the awareness of the lecturers of the importance of conducting studies, research and writing scientific works. The development of scientific work in Indonesia has been relatively better, especially since the enactment of government regulations, which required S1, S2 and S3 students to write articles in scientific journals as one of the prerequisites for graduation. Lecturers certainly have greater demands to actively write in scientific journals both at accredited national level and reputable international journals. So the authors conducted this study aimed at analyzing the correlation of the level of lecturer workload to the increase in the number of publications. STIKOM Tunas Bangsa does not yet have a system to analyze the level of lecturer workload with an increase in the number of studies. For this reason, it is necessary to apply the Backpropagation algorithm. ANN combined with the Backpropagation algorithm can measure the level of correlation. The variables used are structural positions, number of even and odd semester credits, number of services. The target used is the amount of research. So the pattern of correlation between the two variables is formed. The output of the lecturer workload is reduced by the target which is the number of publications. So the results obtained are correlations between lecturers' workloads to the increase in the number of publications.
This study aims to analyze the level of community satisfaction with services in the District Court Simalungun using the C4.5 Algorithm method. The data source used in this study was taken with an instrument in the form of a questionnaire with closed and open answers, where the data is in the form of numbers and analyzed static descriptive. The study population was all service users in the District Court Simalungun, including Procedures, Service Time, Costs/ Tariffs, Implementing Behavior, etc. After doing the calculations manually, the verification is also done using the application, namely RapidMiner. From the analysis process, it can be seen that the responsive aspect is the most dominant aspect in determining the level of community satisfaction at the District Court Simalungun
Illiteracy is the state of being unable to read and to write for communication. A large number of people still experiencing illiteracy in a country is one indicator showing that the country is still not developed. As many as 3.4 million people or around 2.07% of the population in Indonesia are still illiterate. This study aims to create a grouping model using the k-medoids algorithm. The k-medoids method is a clustering method that serves to break down datasets into groups. The data used is sourced from the Central Statistics Agency. Entered data are percentage of illiterate population in 2009-2017. The number of records used is 34 provinces which are divided into 3 clusters namely high cluser, medium cluster and low cluster. From the results of k-medoids calculation, one (1) province was categorited as a high cluster, twelve (12) provinces as a medium cluster and twenty-one (21) provinces as a low cluster. The implementation process using the RapidMiner 5.3 application is used to help find accurate values. It is hoped that this research can be used as one of the bases for decision making for the government in an effort to equalize the level of illiteracy according to the province which has an impact on reducing of illiteracy rates in Indonesia.
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