IMAGE BACKGROUND PROCESSING FOR COMPARING ACCURACY VALUES OF OCR PERFORMANCE

Optical Character Recognition (OCR) is an application used to process digital text images into text. Many documents that have a background in the form of images in the visual context of the background image increase the security of documents that state authenticity, but the background image causes difficulties with OCR performance because it makes it difficult for OCR to recognize characters overwritten by background images. By removing background images can maximize OCR performance compared to document images that are still background. Using the thresholding method to eliminate background images and look for recall values, precision, and character recognition rates to determine the performance value of OCR that is used as the object of research. From eliminating the background image with thresholding, an increase in performance on the three types of OCR is used as the object of research

KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG

Heart disease is one of the types of deadly diseases whose treatment must be dealt with as soon as possible because it can occur suddenly to the sufferer.  Factors of heart disease that are recognized based on the condition of the body of a sufferer need to be known from an early age so that the risk of possible instant attacks can be minimized or can be overcome in various ways such as a healthy lifestyle and regular exercise that can regulate heart health in the body.  By looking at the condition of a person's body based on sex, blood pressure, age, whether or not a smoker and some indicators that become a person's characteristics of heart disease are described in a study using the Neural Network and Naïve Bayes algorithm with the aim of comparing the level of accuracy to attributes influential to predict heart disease, so the results of this study can be used as a reference to predict whether a person has heart disease or not

ALGORITMA C4.5 UNTUK MEMPREDIKSI PENGAMBILAN KEPUTUSAN MEMILIH DEPOSITO BERJANGKA

Deposits are one form of investment offered by the Bank or other financial institutions with the nature of regulating and binding according to the rules set by the manager and the investor or commonly called investors. The advantage of being an investor is getting a fee or profit calculated based on the agreed time period at the beginning of the agreement. Whereas for investment fund managers can be used to advance and develop their business and business. Finding and determining potential customers is the first step to running a financial business in the form of this deposit, before the transaction decision is taken which is a favorable decision for both parties, investors or managers, one of the decision-making techniques can be done using Data Mining using the C4.5 Algorithm which is a structured decision-making technique based on input variables so that it can produce the most potential typical information for customers to participate in time deposits

Klasifikasi Mahasiswa HER Berbasis Algoritma SVM dan Decision Tree

Mahasiswa di setiap perguruan tinggi dituntut untuk memperoleh pengetahuan dan keterampilan yang memenuhi syarat dengan prestasi akademik. Hasil dari pembelajaran mahasiswa didapat dari ujian teori dan praktek, setiap mahasiswa wajib menuntaskan nilai sesuai kriteria kelulusan minimum dari masing-masing dosen pengajar, jika dibawah batas minimum maka mahasiswa mengikuti her. Her adalah salah satu cara untuk menuntaskan kriteria kelulusan minimum. Mahasiswa yang mengikuti her setiap semesternya hampir mencapai angka yang relatif tinggi dari jumlah seluruh mahasiswa. Untuk mengurangi jumlah mahasiswa yang mengikuti her maka dibutuhkan sebuah metode yang dapat mengurangi hal tersebut, dengan metode Support Vector Machine (SVM) dan Decision Tree (DT). SVM dan DT adalah salah satu metode klasifikasi supervised learning. Oleh karena itu, dalam penelitian ini menggunakan SVM dan DT. SVM dapat menghilangkan hambatan pada data, memprediksi, mengklasifikasikan dengan sampling kecil dan dapat meningkatkan akurasi dan mengurangi kesalahan. Klasifikasi data siswa yang melakukan her/peningkatan dengan mengimprovisasi model kernel untuk visualisasi termasuk bar, histogram, dan sebaran begitu juga Decision Tree mempunyai kelebihan tersendiri. Dari hasil penelitian ini telah didapatkan akruasi dan presisi model DT lebih besar dibandingkan dengan SVM, akan tetapi untuk recall DT lebih kecil dibandingkan SVM. AbstractStudents in each tertiary institution are required to obtain knowledge and skills that meet the requirements with academic achievement. The results of student learning are obtained from the theory and practice exams, each student is required to complete grades according to the minimum graduation criteria of each teaching lecturer, if below the minimum limit then students take remedial. Remedial is one way to complete the minimum passing criteria. Students who take remedial every semester almost reach a relatively high number of the total number of students. To reduce the number of students who take remedial, a method that can reduce this is needed, with the Support Vector Machine (SVM) and Decision Tree (DT) methods. SVM and DT are one of the supervised learning classification methods. Therefore, in this study using SVM and DT. SVM can eliminate barriers to data, predict, classify with small sampling and can improve accuracy and reduce errors. Data classification of students who do remedial/improvements by improving the kernel model for visualization including bars, histograms, and distributions as well as the Decision Tree has its own advantages. From the results of this study it has been obtained that the accuracy and precision of DT models is greater than that of SVM, but for recall DT is smaller than SVM

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Kebutuhan Microsoft excel sebagai penunjang pekerjaan masih menjadi pilihan untuk sebagian perusahaan dan organisasi dalam memanajemen catatan keuangan. Simple, Praktis dan mudah digunakan adalah nilai lebih yang ada pada Microsoft excel sebagai tools pengolah angka dan data. Salah satu kasus yang dijadikan referensi pada penulisan buku ini adalah catatan persediaan pada sebuah Toko kasusnya yang kompleks memudahkan pembelajaran pada buku ini untuk langsung dipraktikan oleh pembaca. Studi kasus dengan object toko memudahkan pembaca untuk pengaplikasian rumus-rumus pada Microsoft excel beserta kegunaannya selain itu laporan dari hasil studi kasus dapat dengan mudah untuk di tampilkan dalam bentuk grafik supaya laporan lebih mudah difahami. Selain mempelajari Microsoft excel buku ini juga membahas topik menarik dari paket yang tersedia pada Microsoft office yaitu Microsoft Powerpoint fungsinya untuk menyajikan data supaya terlihat menarik dan ringkas, pada buku ini dibahas langkah-langkah membuat tampilan Microsoft powerpoint yang menarik dan enak dipandang pemanfaatan tab menu yang tersedia pada Microsoft powepoint seperti design template, effect animasi, visualisasi Audio dan Video dibahas pada buku ini dengan detail lengkap dengan panduan penggunaannya

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Kebutuhan Microsoft excel sebagai penunjang pekerjaan masih menjadi pilihan untuk sebagian perusahaan dan organisasi dalam memanajemen catatan keuangan. Simple, Praktis dan mudah digunakan adalah nilai lebih yang ada pada Microsoft excel sebagai tools pengolah angka dan data. Salah satu kasus yang dijadikan referensi pada penulisan buku ini adalah catatan persediaan pada sebuah Toko kasusnya yang kompleks memudahkan pembelajaran pada buku ini untuk langsung dipraktikan oleh pembaca. Studi kasus dengan object toko memudahkan pembaca untuk pengaplikasian rumus-rumus pada Microsoft excel beserta kegunaannya selain itu laporan dari hasil studi kasus dapat dengan mudah untuk di tampilkan dalam bentuk grafik supaya laporan lebih mudah difahami. Selain mempelajari Microsoft excel buku ini juga membahas topik menarik dari paket yang tersedia pada Microsoft office yaitu Microsoft Powerpoint fungsinya untuk menyajikan data supaya terlihat menarik dan ringkas, pada buku ini dibahas langkah-langkah membuat tampilan Microsoft powerpoint yang menarik dan enak dipandang pemanfaatan tab menu yang tersedia pada Microsoft powepoint seperti design template, effect animasi, visualisasi Audio dan Video dibahas pada buku ini dengan detail lengkap dengan panduan penggunaannya.xiv, 102 hlm.: 24 c

Feature Extraction With Forest Classifer To Predicate Covid 19 Based On Thorax X-Ray Results

Coronavirus 19 (COVID-19) is a highly contagious infection caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a new virus for which no cure has been found, marked by the increasing death rate worldwide. Coronavirus disease which can cause pneumonia which attacks the air sacs of the lungs with symptoms of dry cough, sore throat to acute respiratory distress (ARDS) that occurs in COVID-19 patients. One of the ways to detect the virus is by detecting chest X-rays in the patient. Over the past decade's mechine learning technology has developed rapidly and is integrated into CAD systems to provide accurate accuracy. This research was conducted by detecting thoracic radiographs using feature extraction Hu-Moments, Harralic and Histogram and detecting the best accuracy with a classification algorithm to detect the results of COVID-19. The study was conducted by testing the dataset obtained from the Kaggle repository which has images, namely 1281 X-rays of COVID-19, 3270 X-rays Normal, 1656 X-rays of  pneumonia, and X-rays of bacteria-pneumonia 3001. In general, this research is included in the Good category because it produces the highest accuracy by the Random forest classification algorithm where the accuracy result is 84% and the standard deviation is 0.015847. In addition, the research also produced Kappa of 0.713. The results of this accuracy are carried out in several stages, namely by feature extraction in the form of hu-moments, Harralic and histogram. In this study, the best results were given by the Random forest algorithm with feature extraction Histogram and Hu-Moment

Chicken Disease Detection Based on Fases Image Using EfficientNetV2L Model

Livestock farming requires technological innovation to increase productivity and efficiency. Chickens are a livestock animal with good market prospects. However, not all farmers understand about chicken diseases and signs of sickness. Detection of chicken diseases can be done through various methods, one of which is by looking at the shape of the chicken's feces. Images in feces can be detected using machine learning. Convolutional Neural Networks (CNN) are used to speed up disease prediction. Transfer learning is used to leverage knowledge that has been learned by previous models. In this study, we propose our own CNN architecture model and present research by building a new model to detect and classify diseases in chickens through their feces. The model training process is carried out by inputting training data and validation data, the number of epochs, and the created checkpointer object. The hyperparameter tuning stage is carried out to increase the accuracy rate of the model. The research is conducted by testing datasets obtained from the Kaggle repository which has images of coccidiosis, salmonella, Newcastle, and healthy feces. The results of the study show that our proposed model only achieves an accuracy rate of 93%, while the best accuracy rate in the study is achieved by using the EfficientNerV2L model with the RMSProp optimizer, which is 97%