Judul: Identification of Rice Fields and Rice Growth Stages Using Drone RGB and Multispectral Imagery with an Object-Based Image Analysis Approach
Abstrak: Perkembangan teknologi penginderaan jauh secara signifikan mampu meningkatkan kemampuan dalam melakukan pemetaan atau monitoring lahan seperti sawah. Penggunaan satelit dalam pemetaan memiliki keterbatasan di antaranya akuisisi data yang memiliki resolusi spasial dan temporal yang rendah dan adanya gangguan atmosfer. Teknologi drone dapat digunakan sebagai alternatif untuk mengatasi kelemahan pada citra satelit dengan resolusi spasial yang tinggi. Pengolahan citra dengan pendekatan OBIA mampu mengatasi kelemahan analisis berbasis piksel yang ada, karena selain mempertimbangkan nilai piksel, juga mempertimbangkan ukuran dan bentuk objek di dalam citra. Kombinasi OBIA dengan algoritma machine learning seperti Random Forest (RF) dan Support Vector Machine (SVM) pada beberapa penelitian mampu menghasilkan akurasi klasifikasi yang baik. Penelitian ini bertujuan membandingkan hasil klasifikasi algoritma RF dan SVM menggunakan data citra drone RGB dan Multispektral dalam mengidentifikasi petakan dan fase sawah dengan pendekatan OBIA. Hasil analisis menunjukkan klasifikasi dengan pendekatan berbasis objek cukup efektif dalam memetakan petakan sawah. Klasifikasi SVM memiliki perfoma yang lebih unggul dalam melakukan identifikasi petakan dan fase sawah dibandingkan dengan RF. Adapun citra drone RGB dan multispektral, keduanya bekerja cukup baik pada kedua algoritma yang digunakan. Kemampuan drone multispektral lebih konsisten dan lebih baik pada petak 7B, sedangkan citra drone RGB kemampuannya lebih baik pada petak 4B., The development of remote sensing technology can significantly increase the ability to map or monitor land such as rice fields. The use of satellites in mapping has limitations including data acquisition which has low spatial and temporal resolution and the presence of atmospheric disturbances. Drone technology can be used as an alternative to overcome the weaknesses in satellite imagery with high spatial resolution. Image processing with the OBIA approach can overcome the weaknesses of the existing pixel-based analysis, because in addition to considering the pixel value, it also considers the size and shape of the objects in the image. The combination of OBIA with machine learning algorithms such as Random Forest (RF) and Support Vector Machine (SVM) has in several studies been able to produce good classification accuracy. This study aims to compare the classification results of the RF and SVM algorithms using RGB and Multispectral drone image data in identifying rice fields and rice growth stages using the OBIA approach. The results of the analysis show that classification with an object-based approach is quite effective in mapping rice fields. The SVM classification has superior performance in identifying rice fields and rice growth stages compared to RF. As for the RGB and multispectral drone images, both works quite well on the two algorithms used. The ability of multispectral drones is more consistent and better on plot 7B, while the ability of drone RGB imagery is better on plot 4B.
Keyword: multispectral, random forest, RGB, rice field, support vector machine

Judul: Principal Component Analysis (PCA) and Tasseled Cap Transformation (TCT) of Multispectral Drone Image for Identification of Rice Fields
Abstrak: Pemantauan lahan sawah dengan menggunakan drone menawarkan solusi baru, efisien, dan hemat biaya dalam identifikasi atau pengumpulan informasi kondisi tanaman padi di lahan sawah. Penelitian bertujuan mengidentifikasi kanal kehijauan, kecerahan, dan kekuningan dari foto drone multispektral dengan transformasi PCA dan TCT serta mengetahui perbedaan hasil kedua transformasi. Penelitian dilaksanakan pada petak sawah 7A dan 1B milik Balai Benih Padi dan Palawija (BBPP) Cianjur. Transformasi PCA pada foto drone multispektral dari padi sawah menghasilkan 3 komponen utama yaitu PCA 3 dari petak 1B dan 7A merepresentasikan kanal kehijauan, PCA 4 dari petak 1B dan PCA 2 dari petak 7A menggambarkan kanal kecerahan, dan PCA 2 dari petak 1B dan PCA 1 dari petak 7A merepresentasikan kanal kekuningan. Transfromasi TCT dinilai lebih baik dalam merepresentasikan kanal kehijauan, kecerahan, dan kekuningan serta lebih mampu dalam menjelaskan perbedaan-perbedaan yang nampak pada citra., Monitoring paddy fields using drones offers a new, efficient and cost effective solution in identifying or collecting information on the condition of rice plants in paddy fields. The research aims to identify greenness, brightness, and yellowness channels from multispectral drone images with PCA and TCT transformations and to determine the differences in the results of the two transformations. The research was carried out on rice fields 7A and 1B belonging to the Cianjur Rice and Vegetable Seed Center (BBPP). The PCA transformation on the multispectral drone of paddy fields produced 3 main components, namely PCA 3 of plots 1B and 7A representing greenish channels, PCA 4 of plots 1B and PCA 2 of plots 7A representing brightness channels, and PCA 2 of plots 1B and PCA 1 of plot 7A representing yellowish channels. The TCT transfromation is considered better in representing the channels of greenness, brightness, and yellowness and able to explain the differences that appear in the images.
Keyword: drone multispektral, principal component analysis, tasseled cap transformation

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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