Irrigated area has been decreasing since last decade in Indonesia. Surface irrigation scheduling performed predominantly due to water limitation and plant heterogeneity. Plant type and growth phase relate to the performance of water delivery. The research objective is to compare land use classification (LUC) from Landsat 8 and Unmanned Aerial Vehicle (UAV) with supervised algorithm. Supervised method (i.e. minimum distance algorithm) was applied. The result showed six LUC from UAV, i.e.: vegetative stage of dry crop (39%), ripening stage of dry crop (23%), vegetative stage of paddy (15%), tillage (15%), bare land (7%), and paddy nursery (6%). On the other hand, five LUC were performed by Landsat 8 image, i.e.: vegetative stage of dry crop (10%), ripening stage of dry crop (17%), vegetative stage of paddy (5%), tillage area (62%), bare land (6%). UAV’s image source performed more detail and accurate than satellite image. Thus, supervised method appropriate for UAV image for crop stage classification in small irrigation district.
A572Abstrak-Glaukoma adalah penyebab kebutaan kedua terbanyak setelah katarak. Berbeda dengan katarak, kebutaan yang disebabkan oleh glaukoma bersifat permanen. Hal ini karena glaukoma disebabkan oleh tekanan besar pada bola mata yang menyebabkan tersumbatnya pembuluh darah yang menuju ke syaraf mata sehingga syaraf mata tidak mendapatkan suplai darah yang cukup dan akhirnya akan mengalami kerusakan. Gejala glaukoma yang timbul biasanya tidak dapat dirasakan secara langsung. Sehingga perlu dilakukan pemeriksaan mata terlebih dahulu untuk mengetahui adanya glaukoma. Pada pengerjaan tugas akhir ini, dibangun sebuah perangkat lunak untuk mendeteksi penyakit glaukoma pada citra fundus retina mata. Tahap pertama dalam pengerjaan tugas akhir ini adalah proses preprocessing citra. Tahap preprocessing terbagi menjadi preprocessing optic cup, preprocessing optic disk, dan preprocessing pembuluh darah. Kemudian akan dilakukan proses segmentasi optic cup, optic disk, dan pembuluh darah dengan menggunakan metode adaptive thresholding. Setelah proses segmentasi selesai, maka fitur Cup to disk ratio (CDR), ISNT Neuro Retinal Rim (NRR), dan ISNT pembuluh darah akan diekstraksi dari masingmasing citra yang telah tersegmentasi. Ketiga fitur tersebut kemudian dijadikan masukan pada pengklasifikasi support vector machine dengan menggunakan metode pencarian hyperplane sequential minimal optimization dan fungsi kernel linear. Dengan menggunakan data yang diambil dari database RIM-ONE, didapatkan nilai akurasi rata-rata sebesar 80%.Kata Kunci-glaukoma, klasifikasi, optic cup, optic disk, segmentasi
Over the past few years, people have been able to get and share information through social media easily. Some of that information can be a false issue created by a buzzer account that intends to influence people into a specific opinion. Politicians often use social media to maintain a good image in society by utilizing buzzer accounts. The main characteristic of a buzzer account is that they upload the same content repeatedly within a certain period. Before analyzing data taken from social media such as Twitter, we need a buzzer detection system to filter data from buzzer users. This research attempts to build a buzzer detection system using text processing and classification method. We use the similarity of tweets as a feature for the buzzer detection system by applying Cosine Similarity to the Term Frequency - Inverse Document Frequency (TF-IDF) feature of the tweets. In addition, we will use other features such as the number of followers, number of followings, the intensity of tweets, the ratio of retweets, and the ratio of tweets that contain links as additional features in this study. This research uses these features as inputs to the Support Vector Machine model to determine whether an account is a buzzer or not. This system has promising results by having 89% accuracy, 86.67% precision, 70.91 % recall, and 78% F1-score.
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