Penelitian ini bertujuan untuk mengetahui pengaruh kualitas pelayanan, nilai pelanggan, dan kepercayaan terhadap kepuasan pelanggan pada Fixpay. Fixpay adalah sebuah platform Mobile Payment yang dapat melakukan beragam jenis pembayaran dan pembelian secara online dari smartphone..Penelitian ini menggunakan pendekatan kuantitatif dengan metode asosiatif. Data yang digunakan menggunakan data primer berupa kuesioner yang diperoleh melalui google form. Pengambilan sampel menggunakan teknik non random sampling sehingga diperoleh sampel penelitian sebanyak 100 responden. Hasil penelitian menunjukkan bahwa kualitas pelayanan, nilai pelanggan, dan kepercayaan berpengaruh signifikan terhadap kepuasan pelanggan Fixpay baik secara parsial maupun simultan. Disarankan kepada pihak perusahaan untuk terus meningkatkan kepuasan pelanggan, seperti dengan membuat mudah aplikasi Fixpay untuk dioperasionalisasikan, mudah mengakses aplikasi, dan meningkatkan nilai kegunaan dari aplikasi Fixpay. Pengolahan data dalam penelitian ini menggunakan Structural Equation Modeling (SEM) dengan Partial Least Square (PLS).
Palembang Songket is one type of songket characteristic of Indonesian culture which has various types of motifs. Various types of motifs make it difficult for ordinary people to recognize songket that has a similar motif. This study aims to identify 2 types of Palembang songket motifs, namely bintang berante and nampan perak. The classification process will go through 3 stages, namely preprocessing, feature extraction and classification. The preprocess changes the color image of songket into grayscale image. In the feature extraction stage, the grayscale image is increased in contrast with histogram equalization and then uses Canny edge detection to obtain patterns from the songket motif. The extraction results are then grouped and labeled according to their motives for further classification using Principal Component Analysis (PCA) and k-Nearest Neighbor (KNN). Previous studies have resulted in an accuracy of 90.5% for 5 types of motifs using the back propagation algorithm. From the trial results obtained an accuracy value reached 91.67%.
This study aims to classify the disease image on grape leaves using image processing. The segmentation uses the k-means clustering algorithm, the feature extraction process uses the VGG16 transfer learning technique, and the classification uses CNN. The dataset is from Kaggle of 4000 grape leaf images for four classes: leaves with black measles, leaf spot, healthy leaf, and blight. Google images of 100 pieces were also used as test data outside the dataset. The accuracy of the CNN model training is 99.50 %. The classification yields an accuracy of 97.25 % using the test data, while using test image data outside the dataset obtains an accuracy of 95 %. The designed image processing method can be applied to identify and classify disease images on grape leaves.
The presence of leaf diseases in grapes can reduce the productivity of grapes and result in losses for farmers. Leaf diseases are mainly caused by bacteria, fungi, virus etc. A proper diagnosis of disease in plants is needed in order to take appropriate control measures. This paper aims to assist in the identification and classification of grape leaf diseases Convolutional Neural Network (CNN). CNN is basically an artificial neural network architecture that requires repeated training processes to get good accuracy. CNN consists of 3 stages, namely Data Input, Feature Learning, and Classification. The implementation of CNN in this study uses Keras libraries that use the python programming language. Keras is a framework created to facilitate learning of computers. The CNN training process using 0.0001 learning rate obtained results with an accuracy rate of 91,37%
Pears is a fruit that is widely available in tropical climates such as in western Europe, Asia, Africa and one of them is Indonesia. There are many types of pears in Indonesia. Types of pears can be distinguished from the color, size, and shape. But it is still difficult for ordinary people to get to know the types of pears. This is what gave rise to the idea to conduct research related to image processing to classify three types of pears namely abate, red and william pears in order to help determine the type of pears. The pear type classification process is done by verify the image of pears based on existing training data. The research method used consisted of preprocessing image segmentation with morphological operations and feature extraction into Principal Component Analysis (PCA). The classification algorithm used is K-Nearest Neighbor (KNN). The use of adequate training data will further improve the classification of types of pears. The final results of this study amounted to 87.5%.
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