ANALISIS RESIKO KANKER PAYUDARA (BREAST CANCER) MENGGUNAKAN FUZZY INFERENCE SYSTEM (FIS) MODEL MAMDANI

Breast cancer is a type of malignant cancer, in which cells form in the breast tissue, and is the most common type of cancer - apart from skin cancer - and is ranked second (after lung cancer) the type of cancer that causes death. Every year thousands of people die from cancer due to limited medical resources and the inability of society to use existing information sources effectively. The most efficient way and one of the means of protection against breast cancer is early diagnosis. In this study, a system to analyze the risk of breast cancer was developed using the Mamdani model of Fuzzy Inference System (FIS). By using 6 input variables, the developed Mamdani FIS is able to produce an accuracy of 85% with 20 data used.  Keywords: cancer, breast cancer, fuzzy inference system,,fuzzy logic, Mamdani model

PARTICLE SWARM OPTIMIZATION (PSO) FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN)

Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO) in Convolutional Neural Networks (CNNs), which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN.  The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.

Klasifikasi Mutu Telur Burung Puyuh Berdasarkan Warna dan Tekstur Menggunakan Metode K-Nearest Neighbor (KNN) dan Fusi Informasi

Kualitas produk merupakan faktor utama untuk menjamin keberlangsungan satu usaha peternakan. Perusahaan telur puyuh yang memiliki ribuan burung Puyuh seperti CV. NS Quail Farm mampu memproduksi ribuan telur dalam sehari karena seekor burung Puyuh mampu menghasilkan 250-300 butir telur per tahun. Penyeleksian ribuan telur-telur tersebut dilakukan secara tradisional oleh para pekerja peternakan sehingga kualitas telur-telur hasil seleksi bergantung pada perspektif masing-masing pekerja. Guna memperoleh telur hasil seleksi dengan kualitas yang sama, maka dibangun sebuah sistem pencitraan digital untuk pemilihan telur burung Puyuh berdasarkan fitur warna dan tekstur kulit telur menggunakan metode klasifikasi K-Nearest Neighbor (KNN) yang dikombinasikan dengan fusi informasi. 300 data citra telur burung Puyuh diolah menggunakan normalisasi Red, Green, Blue (RGB) dan Otsu thresholding guna memperoleh fitur warna dan fitur tekstur yang kemudian difusikan menjadi fitur terfusi tunggal sebagai masukan pengklasifikasi KNN. Dari hasil-hasil penelitian, disimpulkan bahwa sistem berhasil mengklasifikasikan mutu telur Baik, Sedang, dan Buruk dengan akurasi rata-rata sebesar 77,78%. Disamping itu, klasifikasi dengan fusi informasi mampu mengungguli klasifikasi tanpa fusi informasi sebesar 11,11% pada nilai  yang sama yakni 7 dan fusi informasi juga mampu mempercepat proses klasifikasi sebesar 0,22 detik dibandingkan terhadap klasifikasi tanpa fusi informasi.AbstractThe quality of product us a primary factor to ensure the sustainability of a farm business. A company which has thousands of quail such as CV. NS Quail is capable of producing thousand quail eggs in a day because a quail is able to produce 250-300 eggs per year. The selection of the eggs is carried out traditionally by the farm workers so that the quality of the selected eggs are depended on the perspective of each worker. In order to obtain the same quality of the selected eggs, a digital imaging system for quail egg selection based on color feature and texture feature using K-Nearest Neighbor (KNN) combined with information fusion is developed. 300 image data of quail egg was processed using Red, Green, Blue (RGB) and Otsu thresholding to obtain color feature and texture feature which then were fused to become single fused feature as the input to KNN classifier. From the research results, it is concluded that the system was managed to classify egg quality as good, medium, and bad with an accuracy of 77,78%. In addition, the classification with information fusion was able to outperform the classification without information fusion by 11.11% at the same  value of 7 and information fusion is also able to accelerate classification process by 0.22 seconds compared to that of without information fusion

Smart System to Recapitulate Student Attendance on Virtual Meeting Platforms During Covid-19

Educators have problems conducting online learning, such as monitoring student attendance while presenting the material. This paper aims to predict student names who attend zoom video conferences with various lighting conditions and face angles by comparing two detection and two recognition methods. This paper proposes an intelligent system based on the use of a bot that will analyse a combination of face detection and recognition method for attendance systems using video conferencing applications to carry out online learning. The proposed system will use the best combination of two methods to recapitulate student attendance. The face detection system uses Haar Cascade and MTCNN, and the face recognition system uses ResNet and FaceNet. The tests were conducted on video zoom footage taken during online lectures. The results show that MTCNN and FaceNet get the highest accuracy, 93.23%

Ellipse detection on embryo imaging using Random Sample Consensus (RANSAC) method based on arc segment

In Vitro Fertilization (IVF) is a method which is used to help couples who have a fertility problem. One of the problems of IVF is the success rate, which is only about 30%. One cause of the problem is the embryo morphology observation done by embryologist manually. Morphologically normal embryo does not mean the embryos are genetically normal. The aforementioned phenomena can be tested by using time lapse recording in which unavailable in the manual observation. Therefore it is very important to establish method for time lapsed recording of the embryos. This can be done by automatic observation on the embryo image, where the first step is to create a system that can automatically detect the embryo. This paper proposed Random Sample Consensus (RANSAC) method based on Arc Segment to automatically detect embryo.From the experiment that have been conducted, the proposed method can detect single and multiple ellipse on embryo with a better accuracy than the previous method, EDCircles by 6% and 3% for single and double respectively