Classification Of Perceptions Of The Covid-19 Vaccine Using Multivariate Adaptive Regression Spline

Indonesia is one of the countries infected with the covid-19 virus. One of the government's efforts is the covid-19 vaccination. However, the covid-19 vaccination caused controversy for some people because many people refused to be vaccinated.  Public perception of the covid-19 vaccine can be categorized into two, namely positive and negative, based on survey from Indonesia ministry of health about acceptance of covid-19 vaccine state that this can be influenced by many factors. These factors are important to know as an effort to increase acceptance of covid-19. Multivariate Adaptive Regression Splines (MARS). The purpose of this study is to determine the classification model of public perception of the covid-19 vaccine and the factors that influence it. The method used in this study is Multivariate Adaptive Regression Splines (MARS). This method is appropriate classification method to be applied to categorical response variable data,  The outcomes demonstrate that the optimum mars model is produced by combining BF= 24, MI =3, MO= 1, and GCV=0.07340546. The resulting classification level is 91.5% with influencing factors yaitu gender (x_1), age (x_2), last education (x_4), willingness to vaccinate (x_6), education (x_8).  Based on the results obtained, the government can consider these factors for socializatio

DATA MINING USING RANDOM FOREST, NAÏVE BAYES, AND ADABOOST MODELS FOR PREDICTION AND CLASSIFICATION OF BENIGN AND MALIGNANT BREAST CANCER

This study predicts and classifies benign and malignant breast cancer using 3 classification models. The method used in this research is Random Forest, Naïve Bayes and AdaBoost. The prediction results get Random Forest = 100%, Naïve Bayes = 80% and AdaBoost = 80%. Results using Test and Score with Number of Folds 2, 5 and 10. Number of Folds 2 Random Forest model Accuracy = 95%, Precision = 95% and Recall = 95%, Naïve Bayes Accuracy = 93%, Precision = 93% and Recall 93%, AdaBoost Accuracy = 90%, Precision = 90% and Recall = 90%. With Number of Folds 5 with Random Forest = 96%, Precision = 96% and Recall 96%. Naïve Bayes Accuracy value = 94%, Precision = 94% and Recall = 94%, AdaBoost Accuracy value = 93%, Precision = 93% and Recall = 93%. With Number of Folds 10 Random Forest model = 96%, Precision = 96% and Recall 96%. Naïve Bayes Accuracy value = 94%, Precision = 94% and Recall = 94%, AdaBoost Accuracy value = 92%, Precision = 92% and Recall = 92%. Of the 3 models used, Random Forest got the best classification results compared to the others

MODEL EPIDEMIK SIR DAN MODEL JARINGAN SYARAF TIRUAN DEMAM BERDARAH DENGUE DI NTB

Salah satu masalah kesehatan untuk negara beriklim tropis dan subtropis adalah Demam Berdarah Dengue (DBD). Sepanjang 2021, ada 73.518 kasus DBD di Indonesia dengan 2.697 kasus terjadi di NTB dengan kasus kematian yang disebabkan oleh DBD sebanyak 21 kasus. Penyakit DBD dengan prediksi perkembangan jumlah penderita yang terinfeksi dapat dimodelkan dengan menggunakan model matematis epidemiologi yaitu model persamaan diferensial nonlinear SIR yang diklasifikasi ke dalam tiga kompartemen yaitu S (Susceptible), I (Infectious), dan R (Recovered) serta dapat disimulasikan dengan neural network. Dalam neural network, terdapat ide untuk melakukan pemetaan suatu fungsi pada konsep classifier yaitu pengklasifikasian objek dengan memanfaatkan persamaan linear, = () untuk meregresi data. Data yang digunakan adalah data sekunder yang bersumber dari Dinas Kesahatan Provinsi NTB. Data yang diperoleh kemudian dianalisis sehingga memperoleh titik keseimbangan. Dari hasil analisis model SIR penyebaran penyakit deman berdarah di NTB diperoleh dua titik keseimbangan namun hanya satu titik yang stabil asimtotik karena semua nilai eigen yang diperoleh bernilai negatif. Nilai dari , artinya titik keseimbangan bebas penyakit demam berdarah di NTB berangsur-angsur akan menghilang

Optimization of production process scheduling at Mataram Convection using the Campbell-Dudek and Smith method and the Ho and Chang method

Konveksi Mataram (Djagoan Kaos dan Seragam) is one of the industries engaged in the manufacture of various types of clothing models with fabric as the basic material. So far, the scheduling method used by the company is the First Come First Serve method, in which the completion of production is based on order-to-order data. In this case, with high order intensity, companies often experience difficulties in completing orders according to a predetermined pick-up time. The problems experienced by the company were caused by the production process scheduling that was not optimal. Based on the problems encountered, the purpose of this research is to obtain the optimal scheduling sequence by determining the smallest makespan (minimum total completion time) of the application of the method to the production process. The methods used in this study are the Campbell-Dudek and Smith method and the Ho and Chang method and from these two methods, it is known that the smallest production process is optimal. Based on the results of calculations using the Campbell-Dudek and Smith method, the optimal scheduling sequence with the smallest makespan is 39163 minutes or the production process will be completed in 73 working days. While the results of calculations using the Ho and Chang method obtained the optimal scheduling sequence with the smallest makespan of 38660.50 minutes or the production process will be completed in 72 working days. From the makespans of the two methods, the Ho and Chang method is superior to the Campbell-Dudek and Smith method with a difference of 502.50 minutes or about 1 working day, whereas when compared to the company's initial method, namely First Serve First Come with a makespan of 43025.50 minutes, the HC method can make completion time efficient with a difference of 4365 minutes or about 8 working days. Keywords: Campbell-Dudek and Smith methods, first come first serve, Ho and Chang, makespan, production scheduling MSC2020: 90B3

Prime submodul of an integer over itself

One of the sciences used in digital security systems is cryptography. Cryptography is closely related to the integer system, especially prime numbers. Prime numbers themselves have been abstracted a lot. One form of abstraction of prime numbers is the prime ideal. Previous studies have proven that an Ideal  is said to be a prime ideal on  if and only if I is constructed by a prime element. Other studies have also shown how the prime ideal develops. One of them is the research result of Dauns, where the prime ideal form is developed in the form of a prime submodule. A prime submodule is one of the objects in the module, which is an abstraction of prime numbers. Based on these things, it is exciting if the properties of the prime submodule are applied to other module forms, one of which is the integer module.One of the sciences used in digital security systems is cryptography. Cryptography is closely related to the integer system, especially prime numbers. Prime numbers themselves have been abstracted a lot. One form of abstraction of prime numbers is the prime ideal. Previous studies have proven that an Ideal is said to be a prime ideal on  if and only if I is constructed by a prime element. Other studies have also shown how the prime ideal develops. One of them is the research result of Dauns, where the prime ideal form is developed in the form of a prime submodule. A prime submodule is one of the objects in the module, which is an abstraction of prime numbers. Based on these things, it is exciting if the properties of the prime submodule are applied to other module forms, one of which is the integer module