Peningkatan kebutuhan listrik harus diiringi dengan pengingkatan sumber daya listrik, namun pada kenyataannya sumber energi listrik yang ada ternyata semakin lama semakin menurun. Menurunnya tingkat sumber energi listrik disebabkan karena sumber energi yang digunakan selama ini berasal dari bahan bakar fosil yang sifatnya tidak dapat diperbaharui, sehingga jika digunakan terus-menerus akan sumber energi tersebut akan habis, maka dari itu dibutuhkan sumber energi alternatif yang sifatnya dapat diperbaharui dan tidak akan habis jika digunakan secara terus-menerus. Sumber energi alternatif yang dapat digunakan untuk sumber energi listrik salah satunya yaitu gelombang laut dengan memanfaatkan sistem PLTGL-OWC yang mampu merubah menghasilkan energi listrik dari gelombang laut. Pada penelitian dilakukan peramalan terhadap energi listrik yang dihasilkan untuk menghindari ketidsksesuaian saat memasok energi listrik ke konsumen. Metode yang digunakan pada penelitian ini adalah metode Extreme Learning Machine (ELM) dengan jumlah node pada hidden layer sebanyak 100 yang menghasilkan nilai MAPE sebesar 2.3367%.
Cervical cancer is a deadly disease attacking women. It represents 6.6% of all female cancers. The stadium of cervical cancer is determined based on the presence of carcinoma. The cervical cancer classification system can be used to help medical workers to analyze the stadium of cervical cancer. In this study, cervical cancer stages were divided into five classes, namely, normal cervix, stadium I, stadium II, stadium III, and stadium IV based on colposcopy images. The proposed method is one of deep learning methods, that is convolutional neural network (CNN) using deep residual network (ResidualNet) architecture. This study compared ResidualNet-18, ResidualNet-50, and ResidualNet-101 models and some conventional methods. The comparison results show that ResidualNet is more accurate than conventional methods. From the experiment, based on the accuracy value and elapsed time, ResidualNet-50 is worth using for cervical cancer classification. The result of this evaluation is higher than the maximum achievement of the ResidualNet-18 architecture. In addition, the elapsed time of the classification process using the ResidualNet-50 architecture with the accuracy, sensitivity, and specificity values reaching 100% is shorter than ResidualNet-101, which is 4397 s.
ABSTRACT
Leukaemia is very dangerous because it includes liquid tumour that it cannot be seen physically and is difficult to detect. Alternative detection of Leukaemia using microscopy can be processed using a computing system. Leukemia disease can be detected by microscopic examination. Microscopic test results can be processed using machine learning for classification systems. The classification system can be obtained using Feed-Forward Neural Network. Extreme Learning Machine (ELM) is a neural network that has a feedforward structure with a single hidden layer. ELM chooses the input weight and hidden neuron bias at random to minimize training time based on the Moore Penrose Pseudoinverse theory. The classification of Leukaemia is based on microscopic peripheral blood images using ELM. The classification stages consist of pre-processing, feature extraction using GLRLM, and classification using ELM. This system is used to classify Leukaemia into three classes, that is acute lymphoblastic Leukaemia, chronic lymphoblastic Leukaemia, and not Leukaemia. The best results were obtained in ten hidden nodes with an accuracy of 100%, a precision of 100%, a withdrawal of 100%.
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