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: Pemodelan Keparahan Penyakit Tanaman Karet Berbasis Citra Multispektral Drone Menggunakan Algoritme Random Forest
Abstrak: Karet menjadi salah satu komoditas ekspor yang menyumbang devisa nonmigas bagi negara. Indonesia merupakan negara dengan perkebunan karet terbesar di dunia. Pada tahun 2018 hingga 2020, produksi karet mengalami penurunan setiap tahunnya dari 3,63 juta ton menjadi 3,04 juta ton. Salah satu penyebab, yaitu penyakit Gugur Daun Karet (GDK) yang menyerang perkebunan karet pada berbagai wilayah di Indonesia. Penyakit GDK disebabkan oleh cendawan Pestalotiopsis sp. yang menyebar secara cepat melalui udara dan menyebabkan rontoknya daun karet hingga turunnya produksi getah. Oleh karena itu, penelitian ini bertujuan memodelkan keparahan penyakit tanaman karet menggunakan metode random forest melibatkan indeks NDRE dan LCI. Data penelitian yang digunakan terdiri dari citra multispektral drone dan data ground truth yang dilabeli berdasarkan citra hemispherical. Pada data ground truth, terdapat data yang overlap dengan jumlah relatif tinggi sehingga jumlahnya perlu dikurangi menggunakan prinsip densitas. Pada hasil pemodelan menggunakan random forest dengan optimasi, diperoleh rataan skor training sebesar 0.8225 atau 82.25% dengan akurasi skor testing sebesar 0.8246 atau 82.46% dan f1-score sebesar 0.8176 atau 81.76%. Kata kunci: drone, indeks vegetasi, multispektral, karet, severity, Rubber is one of the export commodities that contributes non-oil and gas foreign exchange to Indonesia. Indonesia is the country with the largest rubber plantation in the world. From 2018 to 2020, rubber production has decreased every year from 3.63 million tons to 3.04 million tons. One of the factors is the Gugur Daun Karet (GDK) disease that attacks rubber plantations in various regions in Indonesia. GDK disease is caused by the fungus Pestalotiopsis sp. which spreads rapidly through the air and causes the fall of rubber leaves to decrease latex production. Therefore, this study aims for modeling the disease severity of rubber plants using the random forest method by involving the NDRE and LCI indices. The research data used consists of multispectral drone images and ground truth data labeled based on hemispherical images. There is a relatively high amount of overlapping data in ground truth data. Therefore, the amount needs to be reduced using the density principle. The modeling results using random forest with optimization, the average training score is 0.8225 or 82.25% with a testing score accuracy of 0.8246 or 82.46% and f1-score of 0.8176 or 81.76%.
Keyword: drone, indeks vegetasi, multispektral, karet, severity

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