Judul: Benthic Habitats Mapping in Jukung Island, Kepulauan Seribu using Drone with Object Based Image Analysis (OBIA) Method
Abstrak: Habitat bentik merupakan salah satu potensi sumberdaya perairan yang ada di Indonesia. Terdapat banyak upaya untuk menjaga, dan melestarikan habitat bentik, salah satunya adalah dengan membuat peta klasifikasi sebaran habitat bentik pada suatu daerah. Penggunaan drone dalam pemetaan memiliki keunggulan tersendiri, yaitu mampu untuk terbang rendah sehingga menghasilkan citra foto udara dengan resolusi yang lebih tinggi. Penelitian ini bertujuan untuk memetakan Habitat bentik di perairan dangkal Pulau Jukung menggunakan drone dengan metode klasifikasi object based image analysis (OBIA) dengan nilai akurasinya. Penelitian ini dilaksanakan di Pulau Jukung, Kepulauan Seribu, DKI Jakarta dengan menggunakan metode berbasis objek (OBIA) yang kemudian diklasifikasikan menggunakan algoritma nearest neighbor classification (NNC). Sebanyak 684 foto udara diproses untuk menghasilkan citra Pulau Jukung dengan nilai resolusi spasial 3.09 cm/piksel. Hasil klasifikasi sebaran habitat bentik Pulau Jukung menunjukan untuk kelas dengan tutupan tertinggi yaitu kelas karang hidup dengan luas 6.03 hektar, sedangkan kelas dengan tutupan terendah yaitu kelas pasir lamun dengan luas 0.88 hektar, dan untuk nilai uji akurasinya secara keseluruhan memiliki nilai 80% pada delapan kelas habitat bentik., Benthic habitats are one of the potential water resources in Indonesia. There are many efforts to maintain and preserve benthic habitats ecosystems, one of which is by making a benthic habitats classification scatter map. The use of drones in mapping has its advantages, namely flying low to produce aerial photo images with higher resolution. This study aims to map benthic habitats in the shallow waters of Jukung Island using a drone with object-based image analysis (OBIA) classification method with an accuracy value. This research was conducted on Jukung Island, DKI Jakarta, using the object-based method (OBIA), then classified using the nearest neighbor classification (NNC) algorithm. A total of 684 aerial photographs were processed to produce an image of Jukung Island with a spatial resolution value 3.09 cm/pixel. Jukung Island benthic habitats classification results show that the highest benthic cover is the coral class with an area of 6.03 hectares, while the class with the lowest cover is the seagrass sand class with an area of 0.88 hectares, and the overall accuracy test value has a value of 80% on eight classes benthic habitat
Keyword: Drone, habitat bentik, NNC, OBIA, Pemetaan, benthic habitat, mapping

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: The Exploration of Discrete Logarithm over Finite Field GF(3m)
Abstrak: The security of many public~key algorithms is based on the problem of finding discrete logarithms. The generalized discrete logarithm problem is the following: given a finite cyclic group G of order n, a generator a of G, and an element fl E G, find the integer x, 0 ~ x ~ n - 1, such that 0: = fl. Algorithm for discrete logarithm problem focused on Menezes et al. (1997) that consist of exhaustive search algorithm, the baby-step giant-step algorithm, Pollard's rho algorithm, Pohlig-Hellman algorithm, and index-calculus algorithm. These algorithms are explorated to be used in discrete logarithm problem over finite field GF(3m ). The exploration also produces some algorithms, i.e. naif negative algorithm, baby-step mother-step algorithm, baby-step mother free-step algorithm, and baby-step free-step algorithm. All algorithms implemented using Maple 11. The Pohlig-Hellman and baby-step giant-step algorithms are efficient enough to be used in discrete logarithm problem over finite field GF(3m ) for m < 20.
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