Judul: Mapping of Benthic Habitat and Mangrove Using Drones in the west of Menjangan Island, West Bali National Park
Abstrak: Habitat bentik merupakan lingkungan hidup di dasar perairan yang meliputi berbagai macam substrat seperti pasir, lamun, pecahan karang, batu, dan terumbu karang. Serta ekosistem mangrove yang berupa kelompok tumbuhan yang terdiri dari beberapa jenis pohon mangrove yang tumbuh pada area yang dipengaruhi oleh pasang surut air laut. Penelitian ini bertujuan untuk mengklasifikasikan habitat bentik perairan dangkal dan sebaran mangrove menggunakan drone di Barat Pulau Menjangan, Taman Nasional Bali Barat. Metode yang digunakan yaitu klasifikasi berbasis objek atau object-based image analysis (OBIA) dengan algoritma support vector machine. Diperoleh 452 frame foto udara dengan resolusi spasial 2,5 cm/pixel untuk memetakan 5 kelas habitat bentik dengan didapatkan kelas pasir dengan memiliki luasan 7.32 ha, kelas batu memiliki luasan 5.73 ha, kelas pecahan karang memiliki luasan 2.59 ha, kelas lamun memiliki luasan 9.77 ha, kelas terumbu karang memiliki luasan 4.24 ha, dan sebaran vegetasi mangrove memiliki luasan 3.12 ha. Nilai akurasi yang didapatkan pada klasifikasi habitat bentik sebesar 84.34% dan pada klasifikasi darat sebesar 82.98%., Benthic habitat refers to a living environment at the bottom of the waters that includes various types of substrates such as sand, seagrass, coral, rubbles, stone, and coral reefs as well as the mangrove ecosystem which consists of a group of mangrove plants made up of several mangrove tree species that grow in areas affected by tidal currents. This study aims to classify the bottom water benthic habitat and mangrove distribution using drones in West Menjangan Island, Bali Barat National Park. Object-based image analysis (OBIA) is the method used to this study with a support vector machine algorithm. 452 aerial photo frames with a spatial resolution of 2.5 cm/pixel were obtained to map 5 classes of benthic habitat, with the sand class having an area of 7.32 ha, the stone class having an area of 5.73 ha, the coral rubbles class having an area of 2.59 ha, the mud class having an area of 9.77 ha, the coral reef class having an area of 4.24 ha, and the mangrove vegetation distribution having an area of 3.12 ha. The accuracy value obtained in the benthic habitat classification is 84.34% and at the land classification is 82.98%.
Keyword: drone, habitat bentik, klasifikasi, obia, mangrove, object based

Judul: Benthic Habitats Mapping in Pari Island, Kepulauan Seribu using Drone and Sentinel-2B Imagery with Object Based Image Analysis Method
Abstrak: Habitat bentik merupakan tempat hidup dan tempat berlindung berbagai jenis organisme perairan yang terdiri dari lamun, rumput laut, alga, karang mati, karang hidup dengan jenis-jenis substrat yang berbeda seperti pecahan karang, pasir dan lumpur. Habitat ini berpotensi mengalami perubahan sehingga perlu dilakukan pemantauan secara berkala, salah satunya dengan melakukan pemetaan habitat perairan laut dangkal menggunakan teknologi penginderaan jauh seperti citra satelit dan drone. Penelitian ini bertujuan mengklasifikasikan dan memetakan habitat bentik di perairan dangkal Pulau Pari menggunakan citra drone dan citra satelit dengan metode klasifikasi object based image analysis (OBIA). Sebanyak 1052 foto udara dengan resolusi 3.6 cm/piksel dan citra Sentinel-2B dengan resolusi spasial 10 m diolah menggunakan algoritma support vector machine (SVM). Hasil klasifikasi habitat bentik Pulau Pari didominasi oleh kelas lamun baik pada citra drone dan citra satelit dengan luas masing-masing 45.21 ha dan 76.06 ha dan kelas paling rendah untuk citra drone dan citra satelit adalah kelas makroalga dengan luas masing-masing 13.55 ha dan 10.01 ha. Hasil uji akurasi keseluruhan untuk citra drone dan citra satelit masing-masing 81.88% dan 71.01% dengan nilai koefisien kappa masing-masing 0.80 dan 0.70. Hasil uji akurasi dan koefisien kappa menyatakan bahwa hasil klasifikasi citra drone lebih baik daripada citra satelit serta resolusi spasial pada citra mempengaruhi nilai keakuratan., Benthic habitats are living places and shelters for various types of aquatic organisms consisting of seagrass, seaweed, algae, dead corals, living corals with different types of substrates such as rubble, sand and mud. This habitat has the potential to change so it is necessary to be monitored regularly, one of them is by mapping shallow-water habitats using remote sensing technology such as satellite imagery and drone. This study aims to classify and map benthic habitats in the shallow waters of Pari Island using drone images and satellite images using the object based image analysis (OBIA) classification method. Total of 1052 aerial photographs with a resolution of 3.6 cm / pixel and Sentinel-2B images with a spatial resolution of 10 m were processed using the support vector machine (SVM) algorithm. The results of the classification of benthic habitat on Pari Island are dominated by the seagrass class both in drone images and satellite images with an area of 45.21 ha and 76.06 ha respectively and the lowest class for drone images and satellite images is the macroalgae class with an area of 13.55 ha and 10.01 ha respectively. The overall accuracy test results for drone imagery and satellite imagery were 81.88% and 71.01%, respectively and kappa coefficient result were 0.80 and 0.70 respectively. The results of the accuracy test and the kappa coefficient stated that the results of the classification of drone images were better than satellite images and the spatial resolution of the images affected the accuracy value.
Keyword: Drone, OBIA, SVM, Benthic, satellite

Judul: Approximation of the Median of Poisson Distribution Using the Relation of Poisson-Gamma Distribution
Abstrak: A major drawback of median is that median cannot usually be defined in closed form, even in the cases where their distribution functions are known. This study gives an approximation of the closed form for the median of the Poisson distribution by using the relation of Poisson-Gamma distribution. The approximation uses elementary techniques based on the monotonicity of certain sequences involving tail probabilities of the Poisson distribution and the Central Limit Theorem. Furthermore, a closed form expression for the mean absolute deviation 􀜧|􀜰􀰒 􀵆 􀜯􀝁􀝀􁈺􀜰􀰒􁈻| is also studied. The median is relevant because of the fact that it minimizes the mean absolute deviation of a Poisson random variable (􀜰􀰒􁈻 by considering median as central tendency.
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