Judul: Semantic Segmentation Based On Deep Learning for Identifying Weeds Using Drone Multispectral Imagery
Abstrak: Produksi karet di Indonesia memainkan peran penting dalam pendapatan devisa negara. Namun, pada tahun 2017-2021, industri karet di Indonesia menghadapi kesulitan akibat terjadi penurunan tingkat produksi sebesar 4,03% per tahun. Penyebab utama penurunan ini adalah serangan penyakit gugur daun karet yang disebabkan oleh patogen Pestalotiopsis sp. Pada perkebunan karet di Sembawa, terdapat beberapa bagian pohon karet yang telah mengalami defoliasi sehingga mengakibatkan pertumbuhan gulma di bawah pohon karet sakit. Pertumbuhan tersebut dapat mempengaruhi akurasi identifikasi kesehatan tanaman karet melalui remote sensing karena gulma memiliki warna visual yang serupa dengan tanaman karet sehat. Oleh karena itu, penelitian ini bertujuan untuk mengidentifikasi gulma guna membantu mengukur kesehatan tanaman karet secara tepat. Studi ini mengusulkan algoritma yang menggabungkan metode k-means++ clustering dan Segmentasi Semantik berbasis deep learning untuk mencapai segmentasi yang lebih akurat dan efisien dalam mengidentifikasi gulma. Algoritma yang diusulkan menunjukkan hasil yang menjanjikan dalam mengidentifikasi tanaman karet dan gulma dengan tingkat akurasi mencapai 95%., Rubber production in Indonesia plays an important role in the country's foreign exchange earnings. However, in 2017-2021, the rubber industry in Indonesia faced difficulties due to a decline in production levels of 4.03% per year. The main cause of this decline is the attack of rubber leaf fall disease caused by the pathogen Pestalotiopsis sp. In rubber plantations in Sembawa, there are several parts of rubber trees that have been defoliated, resulting in weed growth under diseased rubber trees. Such growth can affect the accuracy of rubber plant health identification through remote sensing because weeds have similar visual colors to healthy rubber plants. Therefore, this study aims to identify weeds to help measure the health of rubber trees precisely. This study proposes an algorithm that combines K-Means++ clustering and deep learning-based Semantic Segmentation methods to achieve more accurate and efficient segmentation in identifying weeds. The proposed algorithm shows promising results in identifying rubber plants and weeds with an accuracy rate of 95%.
Keyword: deep learning, K-Means, semantic segmentation, weed

Judul: Vegetation Index Analysis Using Multispectral Imagery on Drones for Measurement of Rubber Plant Health (Case Study: Sembawa Rubber Research Center)
Abstrak: Karet merupakan salah satu negara produsen karet terbesar di dunia. Pada tahun 2017 hingga 2019, nilai produksi karet di Indonesia mengalami penurunan dari 3,68 juta ton menjadi 3,32 juta ton. Salah satu penyebab menurunnya produksi karet dikarenakan adanya serangan penyakit gugur daun karet oleh Pestalotiopsis sp. Pada perkebunan karet di Sembawa, beberapa tajuk pohon sudah mengalami gugur daun sehingga menyebabkan gulma tumbuh di bawah pohon karet. Oleh karena itu, penelitian ini bertujuan untuk menganalisis indeks vegetasi yang terbaik dalam membedakan objek karet dan gulma, memverifikasi antara indeks vegetasi dan data ground truth, serta memprediksi tingkat kesehatan karet berdasarkan indeks vegetasi dengan ordinal logistic regression. Data penelitan yang digunakan adalah citra multispektral drone dan data ground truth dari pakar. Indeks vegetasi yang terbaik dalam membedakan objek karet dan gulma berdasarkan hasil segmentasi objek adalah NDRE dan LCI. Pada verifikasi hasil, NDRE dan LCI menunjukkan adanya hubungan dengan tingkat kesehatan karet dari data ground truth. Pada hasil prediksi menggunakan ordinal logistic regression, NDRE menunjukkan akurasi terbaik dalam memperkirakan tingkat kesehatan karet, yaitu sebesar 0,733 atau 73,3%., Rubber is one of the largest rubber-producing countries in the world. From 2017 to 2019, the rubber production value in Indonesia decreased from 3,68 million tons to 3,32 million tons. One of the causes of the decrease in rubber production was the attack of rubber leaf fall disease by Pestalotiopsis sp. In the rubber plantations in Sembawa, some of the tree crowns have leaf fall, causing weeds to grow under the rubber trees. Therefore, this study aims to analyze the best vegetation index in distinguishing rubber and weed objects, verify the vegetation index and ground truth data, and predict the rubber health level based on the vegetation index with ordinal logistic regression. The research data used are drone multispectral images and ground truth data from experts. The best vegetation index in distinguishing rubber and weed objects based on object segmentation results is NDRE and LCI. In the result verification, the NDRE and LCI showed a relationship with the rubber health level from the ground truth data. In the prediction results using ordinal logistic regression, NDRE showed the best accuracy in estimating the health level of rubber, which was 0.733 or 73.3%.
Keyword: drone, indeks vegetasi, karet, multispektral, multispectral, rubber, vegetation index

Judul: Synthesis of Biphasic Calcium Phosphate Made of Eggshells Through A Hydrothermal Method
Abstrak: Biphasic Calcium Phosphate mixed of hydroxyapatite and tricalcium phosphate. A hydrothermal method of synthesizing by mixing CaO as calcium source and (NH4)2HPO4 as phosphate source with distilled water. CaO got from calcination process made of eggshells. All samples were synthesized using hydrothermal reactor then followed by sintering process. The molar ratio of Ca and P for all samples was 0.5 M : 0.3 M. Hydrothermal temperature and sintering temperature were variated they are 250 ºC, 300 ºC for hydrothermal and 800 ºC, 900 ºC, 1000 ºC for sintering, higher treatment temperature, tricalcium phosphate appears. Final results of synthesis were characterized by using X-ray Diffraction and Fourier Transform Infrared. X-ray Diffraction patterns was ecompared with database Join Committee on Powder Diffraction Standards and found tricalcium phosphate and hydoxyapatite.
Keyword: biphasic calcium phosphate, hydrothermal, hydroxyapatite, tricalcium phosphate