– The development of technology is inversely proportional to cultural preservation in Indonesia. One of Indonesia's cultures which creates character through the advice and stories is a puppet. But this culture shows less because the devotees have decreased. This makes young people not knowing the names of puppet figures. The introduction of digital images of puppets through the system is very necessary to introduce to the generation of millennial children, bearing in mind that at this time people are familiar with the technology. This recognition is through the image classification of puppet figures with classification algorithms that have been trained previously with puppet images that have been labeled before. To recognize various puppet figures well, a good model is needed. The quality of the model can be measured by the accuracy, precision, and recall variables in the model testing. Several factors influence the formation of the model, including the rise of the dataset, number of iterations (epoch) in learning, and of course the treatment of data before it is used in the process of forming the model. This study used 400 datasets which are divided into 4 classes which will be trained using CNN (Convolutional Neural Network) algorithm to produce a model. Based on the results of experiments obtained the best accuracy of 97%, 93% precision, and 87% recall by applying a combination of augmentation, changing the image to grayscale in preprocessing stage, the use of 80:20 dataset ratio and 100 epoch is a very significant effect in increasing accuracy.Keywords – Classification, Punakawan Puppets, CNN, Image Processing. Abstract – Semakin berkembangnya teknologi berbanding terbalik dengan perkembangan pelestarian kebudayaan di Indonesia. Salah satu kebudayaan Indonesia yang bermanfaat membentuk karakter melalui nasihat dan cerita di dalamnya adalah wayang. Akan tetapi kebudayaan ini semakin jarang terlihat pertunjukkannya dikarenakan peminatnya telah berkurang. Hal tersebut mengakibatkan anak-anak muda tidak mengenal nama tokoh-tokoh pewayangan. Pengenalan citra digital tokoh pewayangan melalui sistem sangat diperlukan untuk mengenalkan kepada generasi anak milenial, mengingat saat ini masyarakat telah terbiasa dengan teknologi. Proses pengenalan ini melalui proses klasifikasi citra tokoh wayang dengan algoritma klasifikasi yang telah dilatih sebelumnya dengan data-data citra wayang yang telah diberi label sebelumnya. Untuk dapat mengenali berbagai tokoh wayang dengan baik dibutuhkan model yang baik. Kualitas model dapat diukur dengan variabel akurasi, presisi dan recall pada proses pengujian model. Terdapat beberapa faktor yang mempengaruhi pembentukan model, diantaranya adalah raiso pembagian dataset, jumlah perulangan (epoch) dalam pembelajaran dan tentunya perlakuan terhadap data sebelum digunakan dalam proses pembentukan model. Pada penelitian ini digunakan dataset sebanyak 400 data yang terbagi ke dalam 4 kelas yang akan dilatih menggunakan algoritma CNN (Convolutional Neural Network) untuk menghasilkan model. Berdasarkan hasil percobaan yang dilakukan didapatkan akurasi terbaik sebesar 97%, presisi 93% dan recall sebesar 87% dengan menerapkan kombinasi augmentation, mengubah citra menjadi grayscale pada tahap preproccessing, penggunaan rasio dataset 80:20 dan epoch sebesar 100 sangat berpengaruh signifikan dalam meningkatkan nilai akurasi.Kata kunci – Klasifikasi, Wayang Punakawan, CNN, Pengolahan Citra.
<p class="Body"><span>Nowadays, many algorithms are introduced, and some researchers focused their research on the utilization of convolutional neural network (CNN). CNN algorithm is equipped with various learning architectures, enabling researchers to choose the most effective architecture for classification. However, this research suggested that to increase the accuracy of the classification, preprocessing mechanism is another significant factor to be considered too. This study utilized Gaussian filter for preprocessing mechanism and VGG16 for learning architecture. The Gaussian filter was combined with different preprocessing mechanism applied on the selected dataset, and the measurement of the accuracy as the result of the utilization of the VGG16 learning architecture was acquired. The study found that the utilization of using contrast limited adaptive histogram equalization (CLAHE) + red green blue (RGB) + Gaussian filter and thresholding images showed the highest accuracy, 98.75%. Furthermore, another significant finding is that the Gaussian filter was able to increase the accuracy on RGB images, however the accuracy decreased for green channel images. Finally, the use of CLAHE for dataset preprocessing increased the accuracy dealing with the green channel images.</span></p>
Indonesia has been known as an agrarian country because of its fertile soil and is very suitable for agricultural land, including rice. Yogyakarta is one of the most significant granary regions in Indonesia, especially in the Sleman region. However, one of the main challenges in rice planting in recent years is the erratic rainfall patterns caused by climate anomalies due to the El Nino and La Nina phenomena. As a result of this phenomenon, farmers have difficulty determining planting time and harvest time and planting other plants. Therefore, we make rainfall predictions to recommend planting varieties with Moving Average and Naive Bayes Methods in Sleman District. The results showed that moving averages well use in predicting rainfall. From these results, we can estimate that in 2020 rice production will below. That can saw from the calculation of the probability of naive Bayes on rice plants being low at 0.999 and 0.923. So that the recommended intercrops planted in 2020 are corn and peanuts. We also find that rainfall prediction with Moving Average using data from several previous years in the same month is more accurate than using data from four past months or periods.
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