No abstract

In previous studies, researchers have determined the classification of fruit ripeness using the feature descriptor using color features (RGB, GSL, HSV, and L * a * b *). However, the performance from the experimental results obtained still yields results that are less than the maximum, viz the maximal accuracy is only 76%. Today, transfer learning techniques have been applied successfully in many real-world applications. For this reason, researchers propose transfer learning techniques using the VGG16 model. The proposed architecture uses VGG16 without the top layer. The top layer of the VGG16 replaced by adding a Multilayer Perceptron (MLP) block. The MLP block contains Flatten layer, a Dense layer, and Regularizes. The output of the MLP block uses the softmax activation function. There are three Regularizes that considered in the MLP block namely Dropout, Batch Normalization, and Regularizes kernels. The Regularizes selected are intended to reduce overfitting. The proposed architecture conducted on a fruit ripeness dataset that was created by researchers. Based on the experimental results found that the performance of the proposed architecture has better performance. Determination of the type of Regularizes is very influential on system performance. The best performance obtained on the MLP block that has Dropout 0.5 with increased accuracy reaching 18.42%. The Batch Normalization and the Regularizes kernels performance increased the accuracy amount of 10.52% and 2.63%, respectively. This study shows that the performance of deep learning using transfer learning always gets better performance than using machine learning with traditional feature extraction to determines fruit ripeness detection. This study gives also declaring that Dropout is the best technique to reduce overfitting in transfer learning.

Abstract. The recording of EEG signals often still contains many contaminants electrical signals that originate from non-cerebral origin such as ocular muscle activity called artefacts. The amplitude of artefacts can be quite large relative to the size of amplitude of the cortical signals of interest. In this paper, an application of wavelet denoising method for artefacts removal of EEG signals is proposed. The experiment result shows that contaminant artifact of EEG signals can significantly removed.

No abstract

No abstract

Vector space model (VSM) is an Information Retrieval (IR) system model that represents query and documents as n-dimension vector. GVSM is an expansion from VSM that represents the documents base on similarity value between query and minterm vector space of documents collection. Minterm vector is defined by the term in query. Therefore, in retrieving a document can be done base on word meaning inside the query. On the contrary, a document can consist the same information semantically. LSI is a method implemented in IR system to retrieve document base on overall meaning of users’ query input from a document, not based on each word translation. LSI uses a matrix algebra technique namely Singular Value Decomposition (SVD). This study discusses the performance of VSM, GVSM and LSI that are implemented on IR to retrieve Indonesian sentences document of .pdf, .doc and .docx extension type files, by using Nazief and Adriani stemming algorithm. Each method implemented either by thread or no-thread. Thread is implemented in preprocessing process in reading each document from document collection and stemming process either for query or documents. The quality of information retrieval performance is evaluated based-on time response, values of recall, precision, and F-measure were measured. The results show that for each method, the fastest execution time is .docx extension type file followed by .doc and .pdf. For the same document collection, the results show that time response for LSI is more faster, followed by GVSM then VSM. The average of recall value for VSM, GVSM and LSI are 82.86 %, 89.68 % and 84.93 % respectively. The average of precision value for VSM, GVSM and LSI are 64.08 %, 67.51 % and 62.08 % respectively. The average of F-measure value for VSM, GVSM and LSI are 71.95 %, 76.63 % and 71.02 % respectively. Implementation of multithread for preprocessing for VSM, GVSM, and LSI can increase average time response required is about 30.422%, 26.282%, and 31.821% respectively.

Dalam sebuah search engine terdapat beberapa komponen penting yang salah satunya adalah crawler / web crawler. Crawler adalah sebuah komponen dalam search engine yang berfungsi untuk mencari semua link pada setiap halaman dimana hasil pengumpulan alamat web selanjutnya akan diindeks. Crawler bekerja dengan menggunakan algoritma pencarian yang beragam, diantaranya adalah Breadth First Search dan Backlink. Breadth first search merupakan algoritma untuk melakukan pencarian secara berurutan dengan mengunjungi setiap simpul secara preorder. Backlink memanfaatkan tautan yang berada disitus lain dan mengarah ke situs tertentu. Adapun hasil dari uji aplikasi yaitu dengan membandingkan kedua metode tersebut dengan cara melihat performa pengambilan URL terbanyak pada Detik.com dan Kompas.com. Metode breadth first search secara performa lebih baik dibandingkan dengan metode backlink, dalam pengujian crawling, perbedaan jumlah url mencapai 25,17 pada website detik.com dan 28,94% pada website Kompas.com.

No abstract