Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%. Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%.

Diabetes is a chronic disease that can cause serious illness. Women are four times more likely to develop heart problems caused by diabetes. Women are also more prone to experience complications due to diabetes, such as kidney problems, depression, and decreased vision quality. Nearly 200 million women worldwide are affected by diabetes, with two out of five affected by the disease being women of reproductive age. This paper aims to predict women with at least 21 years of age having diabetes based on eight diagnostic measurements using the statistical learning methods; Multinomial Naive Bayes, Fisher Discriminant Analysis, and Logistic Regression. Model validation is built based on dividing the data into training data and test data based on 5-fold cross-validation. The model validation performance shows that the Gaussian Naïve Bayes is the best method in predicting diabetes diagnosis. This paper’s contribution is that all performance measures of the Multinomial Naïve Bayes method have a value greater than 93 %. These results are beneficial in predicting diabetes status with the same explanatory variables.

Segmentasi pembuluh darah pada retina diperlukan pada deteksi dini penyakit Diabetic Retinopathy pada citra retina. Penelitian ini menggunakan tiga tahapan yaitu pre-processing, segmentasi dan post-processing yang akan membandingkan hasil dari 3 metode segmentasi yang menggunakan nilai Thresholding yaitu Adaptive Thresholding, Binary Thresholding, dan Otsu Thresholding. Hasil pengujian terhadap tiga metode yang digunakan menunjukan bahwa metode Binary Thresholding mendapat rata-rata akurasi, sensitivitas dan spesifisitas tertinggi yaitu 95%, 58%, 98%. Untuk Adaptive Thresholding mendapat rata-rata akurasi sebesar 91%, sensitivitas 36%, spesititiftas 97%. Dan metode Otsu Thresholding mendapatkan rata-rata akurasi 86%, sensitivitas 22%, dan spesifisitas 90%.  Dari hasil ketiga metode ini dapat dilihat akurasi yang dihasilkan oleh metode Thresholding sudah sangat baik dalam melakukan segmentasi citra, tetapi nilai sensitivitas dari masing-masing metode Thresholding masih rendah. Hal ini dapat disimpulkan metode Thresholding masih sulit mendapatkan lebih banyak fitur pembuluh darah pada citra retina.

This paper discussed about Calculation of joint life insurance premiums based on Gompertz mortality assumptions that affect the probability of joint insurance participants. One important variable in calculating premiums and reserves is interest rates. The foreign exchange rate is the price of a currency if exchanged with another countrys currency. Depreciation in the foreign exchange rate causes fluctuations in interest rates which ultimately affect the calculation of premiums for joint life insurance. Based on the calculation results, the greater the depreciation value, the greater the interest rate. This will increase risk for insurance companies. Calculation of insurance premiums that are affected by dollar fluctuations, is expected to be able to obtain calculations that are in accordance with the world economic trends.

Some of the obstacles in the cultivation of maize that cause low productivity of maize yields are diseases and pests. Early detection of maize diseases and pests is expected to reduce farmer losses. A system for the early detection of diseases and pests can be created by classifying them based on digital images. This study aimed to classify maize diseases and pests using multinomial logistic regression. The model and testing resampling were based on resampling technique of k-fold cross-validation. The research data was obtained from the RGB color feature extraction process for each object in each class of diseases and pests of corn. The results showed that the classification into seven classes using five folds had an accuracy rate of 99.85%, macro precision of 98.59%, and macro recall of 98.15%.