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Several problems related to determining the quality of dragon fruit quality are: fruit disease, harvest time selection, sorting process and post-harvest grading. Determination sorting dragon fruit quality by observing the appearance of fruit, fruit smoothness, presence or absence of defects and fruit size. However, this quality determination has disadvantages such as longer sorting time and different perceptions of farmers about the quality of dragon fruit. To solve this problem, we need a sorting system that is able to determine the quality of dragon fruit effectively and efficiently without damaging the dragon fruit. In this study, determining the quality of white dragon fruit using digital image processing techniques and intelligent systems. The output of the digital image processing technique is five morphological features such as area, perimeter, length, diameter and metric. This feature is the input of the backpropagation method so that the quality of white dragon fruit is divided into 3 classes such as class A, class B and class C. The results showed the best network architecture model was 5,8,5,3 with the best testing accuracy rate of 86.67%.

The myeloproliferative neoplasms (MPNs) are clonal hematopoietic stem cell disorders characterized by dysregulated proliferation and expansion of one or more of the myeloid lineages. The initial symptoms of MPN is a bone marrow abnormalities when producing red blood cells, white blood cells and platelets in large numbers and uncontrolled. An automatic and accurate white blood cell abnormality classification system is needed. This research uses digital image processing techniques such as conversion to the modified CIELab color space, segmentation techniques based on threshold values and feature extraction processes that produce four morphological features consisting of area, perimeter, metric and compactness. then the four features become input to the K-Nearest Neighborr (KNN) method. The testing process is based on variations in the value of K to get the best accuracy percentage of 94.3% tested on 159 test data.

Platelet disease is usually caused by abnormalities of the number or form of platelets, for example in Essential thrombocythemia (one of the groups of myeloproliferative syndrome). The characteristic of ET disease is that if a lot of giant platelets are found as large as leukocytes and cannot be detected using FBC, microscopic examination must be done manually by a clinical pathologist. The classification process begins with image processing techniques on the peripheral blood smear image, then texture features are taken using the Gray Level Co-Occurrence Matrix ( GLCM) which consists of ASM, IDM and Entropy features.This feature is input into the classification system using Backpropagation. The test results, Backpropagation was able to accurately identify cells in BG images, namely leukocytes 91.84%, normal platelet cells 92.86% and giant platelet cells 84.69%. Whereas in the AL image, the accuracy of leukocyte cells is 90.82%, normal platelet cells are 96.94% and giant platelet cells are 87.76%. The average accuracy of the Backpropagation method at 84.69% BG images and AL images was 87.76%. So this classification system is able to be used as a tool for doctors or medical analysts to speed up the process of early detection, especially in myeloproliferative syndrome patients.

The demand for cayenne pepper in Indonesia tends to increase annually, but the productivity of cayenne pepper continues to decline and depends on the changing seasons. One of the factors that must be considered in the harvest of cayenne pepper is the level of maturity. This research aims to classify the maturity level of cayenne pepper using the extraction of color and texture features. The extraction of features based on the color is taken from the mean saturation value, while the extraction of feature-based textures uses the value of the Gray Level Co-Occurrence Matrix (GLCM) feature ASM (Angular Second Moment), contrast, IDM (Inverse Difference (Entropy) and correlation (Correlation) then using angles of 0 ° and 45 °. These features become input in the classification process using the Backpropagation method. The results of the system training are able to classify the level of maturity of cayenne pepper with an accuracy of 81.4% and an accuracy of the testing process of 74.2%. Permintaan cabai rawit di Indonesia cenderung meningkat setiap tahunnya, namun produktivitas cabai rawit terus menurun dan bergantung pada pergantian musim. Salah satu faktor yang harus diperhatikan dalam panen cabai rawit adalah tingkat kematangan. Penelitian ini bertujuan untuk melakukan klasifikasi tingkat kematangan cabai rawit menggunakan ekstraksi fitur warna dan tekstur. Ekstraksi fitur berdasarkan warna diambil dari nilai mean saturasi, sedangkan ekstraksi fitur berdasarkan tekstur menggunakan nilai fitur Gray Level Co-occurrence Matrix (GLCM) yaitu ASM (Angular Second Moment), Kontras (Contrast), IDM (Inverse Difference Momentum), Entropi (Entropy) dan Korelasi (Correlation) dan menggunakan sudut 0° dan 45°. Fitur-fitur tersebut menjadi masukan pada proses klasifikasi menggunakan metode Backpropagation. Hasil pelatihan sistem mampu mengklasifikasi tingkat kematangan cabai rawit dengan akurasi sebesar 81,4% dan akurasi proses pengujian cabai rawit sebesar 74,2%.

Acute Respiratory Infection (ARI) is an infectious disease. One of the performance indicators of infectious disease control and handling programs is disease discovery. However, the problem that often occurs is the limited number of medical analysts, the number of patients, and the experience of medical analysts in identifying bacterial processes so that the examination is relatively longer. Based on these problems, an automatic and accurate classification system of bacteria that causes Acute Respiratory Infection (ARI) was created. The research process is preprocessing images (color conversion and contrast stretching), segmentation, feature extraction, and KNN classification. The parameters used are bacterial count, area, perimeter, and shape factor. The best training data and test data comparison is 90%: 10% of 480 data. The KNN classification method is very good for classifying bacteria. The highest level of accuracy is 91.67%, precision is 92.4%, and recall is 91.7% with three variations of K values, namely K = 3, K = 5, and K = 7.

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