APPROXIMATE BAYESIAN COMPUTATION DALAM MENGESTIMASI NILAI PARAMATER

This paper illustrates the use of approximate Bayesian computation for estimating the parameter values of the mathematical model.  We showed that the method can be used to estimate parameter values of the model although an improvement is required to make the estimate better

DETERMINISTIC AND STOCHASTIC DENGUE EPIDEMIC MODEL: EXPLORING THE PROBABILITY OF EXTINCTION

Dengue, a vector-borne disease, threatens the life of humans in tropical and subtropical regions. Hence, the dengue transmission dynamics need to be studied. An important aspect to be investigated is the probability of extinction. In this paper, deterministic and stochastic dengue epidemic models with two-age classes have been developed and analyzed, and the probability of extinction has been determined.  For the stochastic approach, we use the Continuous-Time Markov Chain model. The results show that vaccination of adult individuals leads to a lower number of adult infected individuals. Furthermore, the results showed that a higher number of initial infections causes a low probability of dengue extinction. Furthermore, factors contributing to an increase in the infection-related parameters have to be minimized to increase the potential reduction of dengue cases

DATA MINING UNTUK KLASIFIKASI STATUS GIZI DESA DI KABUPATEN MALAKA MENGGUNAKAN METODE K-NEAREST NEIGHBOR

Classification of village status according to the number of malnourished patients is very important in anticipating malnutrition cases in a region, especially for the areas in the district of Malaka. Cases of malnutrition recorded quite a lot in the District of Malaka demanded the district government of Malaka to immediately anticipate the problem. To overcome this problem, we used k-Nearest Neighbor method to classify the status of villages in Malaka District based on the level of under-five children under the red line into three target classes: low, medium, and high. Prior to the classification process, clustering process is done using K-Means method so that all data can be divided into classes that have been determined. The data used in this study as many as 174 data taken from the year 2013-2015. The final result, after validation of clustering data obtained resemblance to the original data of 98.25%, and the results of system testing of 93.10%. Determination of the best value of k with the test data of 34 pieces and the training data of 140 pieces is at k = 7 with the average percentage of similarity of 95.53%

KLASIFIKASI JURUSAN MENGGUNAKAN METODE NAÏVE BAYES PADA SEKOLAH MENENGAH ATAS NEGERI (SMAN) 1 FATULEU TENGAH

Naïve bayes is the classification method which utilizes the both probabilities and statistics to predict the future opportunity by using the last experiance. The system of major in the senior high school is the means of students directing to be more based on their interest and academic competence. The major in East SMAN 1 Fatuleu consists of the Science and Social majors. This research is using the Method of Naïve bayesto classify the student major. The data of student that is used here is the grade XI for second semester in the years of 2011 to 2015 with the 470 for the total data. For the testing proces is used 420 data (89%) as trains data and 50 data (11%) as tests data. The result of this research shows the amount of 99.31% accuracy in the process of major classification

Multinomial Naive Bayes untuk Klasifikasi Status Kredit Mitra Binaan di PT. Angkasa Pura I Program Kemitraan

Status classification of partner acordiing to sector parimeter, loan disbursement, loan reimbursment, loan arrears, remaining loan and grace period is very important in anticipating the case in PT. Angkasa Pura I. Problematic credit is very unbeneficial for the PT. Angkasa Pura I because it will disturb the economy condition of a company and will affect the next small and medium enerprises (SME). To solve this, the reserch uses Multinominal Naive Bayes to method to classify the partners status in the PT. Angkasa Pura I according to the parimeter that is divided into 4 clases namely smooth class, less smooth class, doubted and jammed class. The process used was classification process where it calculated probability value and the atribute of the partner. The data used in this research is consisted of 148 that taken from 2012-2015. The final result, after the classification is done, the class probability value that was taken randomly is gained, with the resuld to system test with 5 times of testing data division that is taken randomly, it is gained the accuracy as big as 86,56%, precision is as big as 73%, recall is as big as 73% and F-1 Measure is as big as 73%

SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN PINJAMAN MENGGUNAKAN APLIKASI FUZZY SIMPLE ADDITIVE WEIGHTING

The loan service process is one of many routines applied to improve the welfare of either members or the community in cooperative. This process requires a high accuracy in selecting the eligible loans. Bad credits, that oftenly occurred in many cooperative membership, mainly caused by the lack of accuracy of the cooperative itself in selecting eligible loans based on the specific criterias. Implements and development for loan decision support system using Fuzzy Simple Additive Weighting (F-SAW) method. This method is able to accommodate the deficiancy of SAW in terms of providing linguistic assessments. The system is tested by comparing the system decision to the cooperative decision. According to 7 test data with loan amount below Rp 10,000,000 and 5 test data with loan amount between Rp 15,000,000 – Rp 20,000,000, it appears that 9 of them provide the same decision as what the committee decided (75%), while 3 of them do not (25%)

DATA MINING UNTUK KLASIFIKASI STATUS GIZI DESA DI KABUPATEN MALAKA MENGGUNAKAN METODE K-NEAREST NEIGHBOR

Classification of village status according to the number of malnourished patients is very important in anticipating malnutrition cases in a region, especially for the areas in the district of Malaka. Cases of malnutrition recorded quite a lot in the District of Malaka demanded the district government of Malaka to immediately anticipate the problem. To overcome this problem, we used k-Nearest Neighbor method to classify the status of villages in Malaka District based on the level of under-five children under the red line into three target classes: low, medium, and high. Prior to the classification process, clustering process is done using K-Means method so that all data can be divided into classes that have been determined. The data used in this study as many as 174 data taken from the year 2013-2015. The final result, after validation of clustering data obtained resemblance to the original data of 98.25%, and the results of system testing of 93.10%. Determination of the best value of k with the test data of 34 pieces and the training data of 140 pieces is at k = 7 with the average percentage of similarity of 95.53%

MULTINOMIAL NAIVE BAYES UNTUK KLASIFIKASI STATUS KREDIT MITRA BINAAN DI PT. ANGKASA PURA I PROGRAM KEMITRAAN

Status classification of partner acordiing to sector parimeter, loan disbursement, loan reimbursment, loan arrears, remaining loan and grace period is very important in anticipating the case in PT. Angkasa Pura I. Problematic credit is very unbeneficial for the PT. Angkasa Pura I because it will disturb the economy condition of a company and will affect the next small and medium enerprises (SME). To solve this, the reserch uses Multinominal Naive Bayes to method to classify the partners status in the PT. Angkasa Pura I according to the parimeter that is divided into 4 clases namely smooth class, less smooth class, doubted and jammed class. The process used was classification process where it calculated probability value and the atribute of the partner. The data used in this research is consisted of 148 that taken from 2012-2015. The final result, after the classification is done, the class probability value that was taken randomly is gained, with the resuld to system test with 5 times of testing data division that is taken randomly, it is gained the accuracy as big as 86,56%, precision is as big as 73%, recall is as big as 73% and F-1 Measure is as big as 73%