Judul: Identification of Land Surface Temperature Distribution of Rice Growth Phase Using Thermal Drones
Abstrak: Teknologi drone sudah banyak digunakan untuk pemantauan lahan pertanian. Pertumbuhan padi dipengaruhi oleh suhu permukaan yang berperan penting dalam keseimbangan suhu disekitar tanaman. Dengan demikian, pemantauan persebaran suhu dalam suatu lahan sawah penting untuk ditelaah. Penelitian ini bertujuan mengetahui karakteristik termal tanaman pangan padi sawah, persebaran suhu permukaan (Land Surface Temperature, LST) pada beberapa fase pertumbuhan padi, dan mengetahui ketelitian estimasi temperatur permukaan dari drone dengan pengukuran temperatur permukaan lapangan. Penelitian dilakukan di Desa Neglasari Kabupaten Cianjur dan Desa Situgede Kota Bogor. Penelitian mencakup tahap persiapan, pengambilan data lapang, pengolahan foto drone, dan analisis data. Pengambilan foto drone termal dilakukan bersamaan dengan pengambilan data karakteristik tanaman dan suhu permukaan di lapangan. Hasil foto drone termal dikorelasikan dengan data observasi menggunakan persamaan linier. Hasil penelitian menunjukkan bahwa temperatur permukaan hasil observasi dan foto drone termal memiliki pola yang hampir sama. Suhu permukaan dari fase vegetatif awal padi relatif tinggi dan menurun hingga fase generatif akhir dan meningkat kembali pada fase bera basah. Hasil analisis statistika LST antara foto drone termal dan data observasi menunjukan hubungan yang positif dan cukup baik (R2 > 0.6) pada kedua lokasi. Dengan demikian, persamaan dapat digunakan untuk koreksi suhu permukaan aktual dari rekaman drone termal., Drone has widely been used for monitoring agricultural lands, with varieties of applications and sensors. Rice growth is influenced by surface temperature, which regulates temperature balance around plants. Thus, monitoring distribution of surface temperature in a rice field is an important aspect in rice monitoring. This study aimed to determine thermal characteristics of paddy plants, the distribution of land surface temperatures (LST) in several phases during rice growth, and to assess the accuracy of LST estimation from drones with reference of field-based LST measurements. The research was conducted in Neglasari, Cianjur District and Situgede, Bogor City. Research steps included preparation stage, data collection, image processing, and data analysis. Thermal drone data were recorded simultaneously with in-situ data collection of plant characteristics and LST. Thermal photos were then correlated with observation data using linear equations. The results showed that observed LST data and thermal drone photos shared similar pattern. The LST of early vegetative phase of rice field was relatively high and depeleted until generative phase and increased again in wet fallow phase. Statistical analysis between the LST of thermal drone and of the observation data showed fair result (R2 > 0.6) from both locations. In that sense, regression analysis could be implemented to estimate actual LST from thermal drone.
Keyword: rice growth phase, land surface temperature, thermal drone

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: Pelabelan Super Vertex Magic
Abstrak: Super vertex magic labeling is labeling on graph with is a set of edges mapped into and is a set of vertices mapped into . It has a vertex magic labeling in which and represents the size and order of the graph respectively. This manuscript proves that paths, cyclics, and disjoint union of cyclics have a super vertex magic labeling. There are four theorems to be discussed. The first theorem proves that path is super vertex magic if and only if is odd and . The second theorem proves that cyclic is super vertex magic if and only if is odd. The third theorem proves that bijection function from an edge set onto of graph can be extended to a super vertex magic labeling if and only if the set amount of edge label that incidents with a vertex in the graph, consists of sequential integers as many as vertices in the graph. The fourth theorem proves that disjoint union of the cyclics consists of cyclics with each cyclic has vertices is super vertex magic if and only if and are odd.
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