Judul: Pengembangan Sistem Identifikasi Spesies Burung yang Dilindungi Berbasis Citra menggunakan Convolutional Neural Network
Abstrak: Satwa yang dilindungi adalah satwa yang berpopulasi yang sedikit, jumlah individunya di alam menurun tajam, atau endemik. Demi menjaga kelestariannya, terdapat larangan atas tindakan memiliki, memelihara, atau memperniagakannya. Upaya pelestarian diawali oleh identifikasi. Pemerintah Republik Indonesia telah menetapkan 562 spesies burung yang dilindungi. Hal ini menjadi tantangan dalam identifikasi spesies burung. Penelitian ini bertujuan untuk mengembangkan aplikasi web yang menerapkan model convolutional neural network (CNN) untuk mengidentifikasi spesies burung yang dilindungi berbasis citra. Penelitian ini menggunakan citra dari sepuluh spesies burung yang dilindungi di Indonesia. Tahapan penelitian ini terdiri atas pengumpulan, praproses, dan pembagian data, pengembangan model CNN, evaluasi model, dan pengembangan web dengan metode Prototyping. Penelitian ini menghasilkan model dengan taraf akurasi 97%, precision 98%, dan recall 97% pada data uji. Aplikasi web dibangun dengan HTML, CSS, Javascript, dan library Tensorflow,js. Prototipe yang dikembangkan sudah dapat diterima berdasarkan hasil pengujian black-box., The protected animals are the animals that have small populations, a sharp decline in the number of individuals in the wild or endemic. The government has banned owning, keeping, or trading these animals. The first step of preserving these animals is identification. The government of the Republic of Indonesia has defined 562 bird species as the protected birds. This issue becomes a challenge on bird species identification. This study aims to develop a web application that implements a convolutional neural network (CNN) model for image-based protected bird species identification. This study uses the images of ten protected bird species in Indonesia as the research subject. This study consists of some stages: data collection, data preprocessing, data splitting, CNN model development, model evaluation, and web development using the Prototyping method. This study has successfully developed a model that gained 97% accuracy, 98% precision, and 97% recall on testing data. Web development uses HTML, CSS, Javascript, and Tensorflow.js. The black-box testing result shows that the prototype is acceptable.
Keyword: burung yang dilindungi, convolutional neural network, identifikasi spesies burung, Prototyping

Judul: Rice Variety Identification System Based on Drone Image  to Support Seed Certification Process
Abstrak: Proses sertifikasi benih di Indonesia dihadapkan dengan tantangan semakin berkurangnya petugas Pengawas Benih Tanaman (PBT) dan beban kerja yang meningkat dalam hal pengawasan benih bersertifikat. Pemanfaatan teknologi menjadi suatu hal yang penting dalam meningkatkan efisiensi pada pemeriksaan lapangan, salah satu pemeriksaan tersebut adalah pemeriksaan kebenaran varietas tanaman padi yang disertifikasi. Penelitian ini dilakukan untuk mengembangkan sistem identifikasi varietas tanaman padi pada pemeriksaan lapangan sertifikasi benih berbasis citra drone dengan menerapkan algoritme Convolutional Neural Network (CNN). Penelitian ini dilakukan dengan mengidentifikasi dua varietas padi yang berbeda berdasarkan karakter agronomisnya, kemudian membangun model CNN untuk mengotomatisasi identifikasi varietas tersebut berbasis citra drone. Hasil analisis data pada karakter agronomis yang diamati menjustifikasi bahwa kedua varietas yang digunakan berbeda, baik pada karakter tinggi tanaman, jumlah anakan, warna daun, jumlah anakan produktif, panjang malai, dan jumlah gabah per malai. Penelitian ini menghasilkan tiga jenis model CNN yang mampu mengidentifikasi secara akurat varietas IPB 3S dan Inpari 32 dengan tingkat akurasi antara 99,52% sampai 100%., The seed certification process in Indonesia is faced with the challenge of a decreasing number of seed inspectors and an increasing workload in supervising certified seeds. The utilization of technology becomes an important thing in increasing the efficiency of field inspections, one of these inspections is checking the correctness of certified rice varieties. This research was conducted to develop a rice varietal identification system for field inspection of drone image-based seed certification by applying the Convolutional Neural Network (CNN) algorithm. This research was conducted by identifying two varieties different rice varieties based on their agronomic characters, then building a CNN model to automate the identification of these varieties based on drone imagery. The results of data analysis on the observed agronomic characters justified that the two varieties used were different, in terms of plant height, number of tillers, leaf color, number of productive tillers, panicle length, and number of grains per panicle. This study produced three types of CNN models that could accurately identify the varieties of IPB 3S and Inpari 32 with an accuracy rate of 99.52% to 100%.
Keyword: CNN, deep learning, technology, unmanned aerial vehicle

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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