One important part of the eye that is critical for processing visual information before it is sent through the optic nerve to the visual cortex is the retina. The retina of each individual has its own uniqueness that can be used as a characteristic feature in identifying, verifying, and authenticating. The traditional authentication process has various weaknesses such as forgetting the PIN code or losing the ID card used for obtaining system authentication. The results of extracted retinal blood vessels can be used as a feature in the formation of an individual identification system. In the imaging using a fundus camera, the retina's blood vessel has distinguishing shape and number of candidates from one human retina to another. In this research, researchers will develop an algorithm for extracting the retinal fundus image's blood vessels. The feature extraction is done by taking the fundus image feature which is the blood vessel as one of the unique characteristics in forming an individual identification system. The number of blood vessel candidates will then be calculated from the extracted blood vessel result. This research uses wavelet function by looking at the very complex texture of blood vessels using the approximation coefficient. The direction detail coefficient on the wavelet is also used to perform the extraction of retinal blood vessels where the structure of the retinal blood vessels in the fundus image is in all directions. The results of these blood vessel candidates will be used in further research to formulate a biometric system that is formed by unique features in the retinal fundus image which will be used to identify individuals using body traits.
Authentication is needed to enhance and protect the system from vulnerabilities or weaknesses of the system. There are still many weaknesses in the use of traditional authentication methods such as PINs or passwords, such as being hacked. New methods such as system biometrics are used to deal with this problem. Biometric characteristics using retinal identification are unique and difficult to manipulate compared to other biometric characteristics such as iris or fingerprints because they are located behind the human eye thus they are difficult to reach by normal human vision. This study uses the characteristics of the retinal fundus image blood vessels that have been segmented for its features. The dataset used is sourced from the DRIVE dataset. The preprocessing stage is used to extract its features to produce an image of retinal blood vessel segmentation. The image resulting from the segmentation is carried out with a two-dimensional image transformation such as the process of rotation, enlargement, shifting, cutting, and reversing to increase the quantity of the sample of the retinal blood vessel segmentation image. The results of the image transformation resulted in 189 images divided with the details of the ratio of 80 % or 151 images as training data and 20 % or 38 images as validation data. The process of forming this research model uses the Convolutional Neural Network method. The model built during the training consists of 10 iterations and produces a model accuracy value of 98 %. The results of the model's accuracy value are used for the process of identifying individual retinas in the retinal biometric system.
Penyampaian keluhan konsumen lewat akun media sosial seperti Twitter dimaksudkan agar masalah yang dihadapi konsumen dapat diselesaikan dengan cepat. Pada penelitian ini, akan dilakukan analisis sentimen terhadap konsumen pengguna provider Indosat, menggunakan data tweet sejumlah 300 data acak yang di kumpulkan dari bulan desember 2018 hingga bulan april 2019. Data yang dianalisis adalah kalimat berbahasa Indonesia. Preprocessing pada penelitian ini terdiri dari beberapa tahapan proses antara lain tokenizing, filtering, stop word, dan stemming. Analisis dilakukan menggunakan metode K-Means Clustering. Penelitian ini berhasil menampilkan kelompok dari anggota masing-masing cluster yang berbentuk wordcloud ke dalam 3 buah wordcloud berbeda, pada wordcloud cluster 0 anggotanya berbicara tentang jaringan Indosat yang parah, pada wordcloud cluster 1 anggotanya berbicara tentang permintaan perbaikan jaringan sinyal Indosat, dan pada wordcloud cluster 2 anggotanya berbicara tentang jaringan sinyal parah Indosat pada daerah Bogor. Hasil penelitian ini diharapkan dapat menjadi masukan untuk provider dalam melihat keluhan yang masuk dari para konsumen mereka sehingga pihak provider dapat meningkatkan pelayanannya.
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