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Now-a-days, coronary heart disease is one of the deadliest diseases in the world. An unfavorable lifestyle, lack of physical activity, and consuming tobacco are the causes of coronary heart disease aside from genetic inheritance. Sometimes the patient does not know whether he has abnormalities in heart function or not. Therefore, this study proposes a system that can detect heart abnormalities through the iris, known as the Iridology method. The system is designed automatically in the iris detection to the classification results. Feature extraction using five characteristics is applied to the Gray Level Cooccurrence Matrix (GLCM) method. The classification process uses the Support Vector Machine (SVM) with linear kernel variation, Polynomial, and Gaussian to obtain the best accuracy in the system. From the system simulation results, the use of the Gaussian kernel can be relied on in the classification of iris conditions with an accuracy rate of 91%, then the Polynomial kernel accuracy reaches 89%, and the linear kernel accuracy reaches 87%. This study has succeeded in detecting heart conditions through the iris by dividing the iris into normal iris and abnormal iris.

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Pada masa pandemi saat ini, pembelajaran jarak jauh atau daring merupakan metode pembelajaran yang wajib dilakukan oleh penyelenggara pendidikan di Indonesia. Saat ini pembelajaran daring masih memiliki kekurangan dalam terbatasnya waktu tatap muka sehingga berdampak pada kurang optimalnya proses belajar mengajar. Hal ini berdampak bagi siswa yang perlu melakukan pembelajaran secara mandiri dengan bantuan aplikasi pembelajaran yang saat ini banyak tersedia. Sering kali siswa menjumpai aplikasi pembelajaran yang memiliki fitur kurang lengkap atau aplikasi yang mengharuskan untuk melakukan pembayaran terlebih dahulu sebelum digunakan. Dari masalah tersebut, perlu adanya media pembelajaran yang dapat mengakomodir seluruh keperluan siswa dalam peningkatan pemahaman materi pembelajaran. Penelitian ini bertujuan untuk menampilkan desain aplikasi pembelajaran berbasis android. Desain yang ditampilkan mulai dari desain setiap fitur hingga pada desain perancangan sistem. Fitur yang disediakan pada penelitian berkelanjutan antara lain, fitur informasi, fitur materi dengan penjelasan materi dari guru melalui rekaman suara, fitur evaluasi untuk mengetahui pemahaman siswa dan fitur video sebagai penunjang penyampain materi. Hasil dari penelitian ini menunjukkan bahwa siswa memiliki daya tarik terhadap fitur dan tampilan desain aplikasi yang dirancang dan memungkinkan siswa dapat meningkatkan kemampuan akademis dalam proses pembelajaran mandiri.

Communication is essential in conveying information from one individual to another. However, not all individuals in the world can communicate verbally. According to WHO, deafness is a hearing loss that affects 466 million people globally, and 34 million are children. So it is necessary to have a non-verbal language learning method for someone who has hearing problems. The purpose of this study is to build a system that can identify non-verbal language so that it can be easily understood in real-time. A high success rate in the system needs a proper method to be applied in the system, such as machine learning supported by wavelet feature extraction and different classification methods in image processing. Machine learning was applied in the system because of its ability to recognize and compare the classification results in four different methods. The four classifications used to compare the hand gesture recognition from American Sign Language are the Multi-Class SVM classification, Backpropagation Neural Network Backpropagation, K - Nearest Neighbor (K-NN), and Naïve Bayes. The simulation test of the four classification methods that have been carried out obtained success rates of 99.3%, 98.28%, 97.7%, and 95.98%, respectively. So it can be concluded that the classification method using the Multi-Class SVM has the highest success rate in the introduction of American Sign Language, which reaches 99.3%. The whole system is designed and tested using MATLAB as supporting software and data processing.