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Abstrak :
Negara Indonesia merupakan salah satu negara di dunia, khususnya di benua Asia yang menjadikan beras sebagai bahan pangan pokok. Hal tersebut membuat permintaan akan bahan pangan tersebut menjadi tinggi, dan membuat banyak orang menanam padi di berbagai wilayah di Indonesia. Namun hal tersebut tidak membuat semua beras hasil panen dari berbagai wilayah menjadi bernilai sama di pasaran. Sehingga beras-beras yang ada tersebut kemudian dibedakan berdasarkan wilayah tanamnya. Mengidentifikasi jenis beras membutuhkan analisis DNA yang menggunakan PCR yang tentunya menghabiskan banyak waktu. Penelitian ini dibuat dengan tujuan membuat suatu sistem identifikasi serta menganalisis pengaruh wilayah tanam terhadap harga beras yang beredar di pasaran. Memanfaatkan pencitraan hiperspektral serta melakukan pemodelan klasifikasi dalam lima jenis beras yang berasal dari wilayah tanam berbeda yaitu Bandung, Indramayu, Subang, Karawang, dan Palembang. Kemudian dua skema variasi pada pemodelan klasifikasi, yaitu PCA – SVM dan CNN. Membandingkan kedua skema tersebut didapatkan akurasi rata – rata untuk pemodelan klasifikasi PCA-SVM sebesar 86.45% dan 97% untuk pemodelan klasifikasi CNN. ......Indonesia as one of nations in the world specifically in Asian continent that consumed rice as their main diet. The phenomena led rice as a high demanding food in the country and made many people in the country did paddy harvesting in many regions.   However, this did not make all the rice harvested from various regions had the same value in the market.  Then people differentiated rice from where it harvested. Identifying types of rice requires DNA analysis using PCR which is time consuming. This research was made with the aim of creating an identification system and analyzing the influence of the planting area on the price of rice on the market. Utilizing hyperspectral imaging and classification algorithm in five types of rice originating from different planting areas namely Bandung, Indramayu, Subang, Karawang, and Palembang. Then the two variation schemes in classification modeling, namely PCA - SVM and CNN. Along with comparing the two schemes of classification models, the average accuracy obtained for PCA-SVM classification model is 86.45% and 97% for CNN classification model.

Abstrak :
Padi merupakan bagian terpenting dalam sektor pertanian di Indonesia. Tanaman padi merupakan tanaman yang penting dan bermanfaat bagi kehidupan karena, beras yang dihasilkan merupakan makanan pokok bagi masyarakat Indonesia. Kelurahan Margajaya merupakan salah satu kelurahan di Kecamatan Bogor Barat yang memiliki luas lahan sawah terbesar setelah Kelurahan Situ Gede. Menurut data BPS (Badan Pusat Statistik) tahun 2020, Kecamatan Bogor Barat memiliki luas lahan sawah 158 ha dan memproduksi sebanyak 2.711 ton pada tahun 2019. Data citra UAV (Unmanned Aerial Vehicle) dengan sensor RGB (Red, Green, Blue) dapat dimanfaatkan sebagai pemantauan fase pertumbuhan padi dengan mengekstraksi nilai indeks vegetasi dan estimasi produktivitas padi. Penelitian ini menggunakan algoritma indeks vegetasi NGRVI (New Green-Red Vegetation Index), NGDBI (Normalized Green-Red Difference Index), dan ExG (Excess Green). Hasil penelitian menunjukan bahwa variasi spektral indeks NGRVI memiliki nilai R2 = 0,89 terhadap fase pertumbuhan padi. Variasi spektral menunjukan pola meningkat pada fase vegetatif menuju fase generatif dan kemudian menurun pada fase pematangan. Indeks vegetasi NGRVI menandakan hubungan yang positif terhadap produktivitas padi sehingga estimasi produktivitas padi di Kelurahan Margajaya memiliki rata-rata 3,20 ton/ha dengan nilai R2 = 0,82. ......Rice is the most important part of the agricultural sector in Indonesia. The rice plant is an important and beneficial plant for life because the rice produced is a staple food for Indonesian people. Margajaya Village is one of the sub-districts in West Bogor District, a large rice field after Situ Gede Village. According to data from the Central Statistics Agency for 2020, West Bogor District has 158 hectares of rice fields and produced 2.711 tons in 2019. UAV (Unmanned Aerial Vehicle) image data with RGB (Red, Green, Blue) sensors can monitor the growth phase rice by extracting the vegetation index value and rice productivity. This study uses the NGRVI (New Green-Red Vegetation Index), NGDBI (Normalized Green-Red Difference Index), and ExG (Excess Green) vegetation index algorithm. The results showed that the NGRVI index spectral variation had a value of R2 = 0.89 on the rice phase's growth. Spectral variations show an increasing pattern in the vegetative phase towards the generative phase and then decreasing in the maturation phase. The NGRVI vegetation index indicates a positive relationship with rice productivity so that rice productivity in Margajaya Village has an average of 3.20 tonnes/ha with a value of R2 = 0.82.

Abstrak :
Saline soil is a common problem in coastal paddy field, especially in Indonesia. Salinity affects rice growth and the activities of soil functional microbes, including functional bacteria, which play roles in plant growth. Some of these microbes are associated with rice plants and are able to survive under saline condition. The presence of functional microbes is also important to improve soil quality. Nitrogen and phosphate are essential soil nutrients and is available in soil due to the activities of nitrogen-fixing bacteria and free-living plant-associated bacteria. The objective of the present study was to obtain nitrogen-fixing, phosphate solubilizing and Indole Acetic Acid  (IAA)-producing bacteria that are able to survive and promote the growth of rice under saline conditions. From rice and peanut rhizosphere, Ca-phosphate (Ca-P) solubilizing and nitrogen-fixing bacteria were isolated separately using specific media. Then, the Ca-P solubilizing ability, phosphomonoesterase activity and IAA-producing ability were quantitatively examined. Based on the abilities, 20 strains were selected and identified as Burkholderia cepacia-complex, Burkholderia anthina, Burkholderia cenocepacia, Bacillus cereus-complex (three strains), Achromobacter spanius, Azospirillum sp. (four strains), Azotobacter sp. (three strains), Rhizobium leguminosarum, Rhizobium sp. (two strains), and Pseudomonas sp. (three strains). The inoculation of several single strains or the mixture of the selected strains promoted the growth of rice under saline conditions. These inoculants could be potential as biofertilizer in saline paddy fields.

Abstrak :

Beras merupakan komoditas penting dan strategis bagi masyarakat Indonesia dalam mempertimbangkan makanan, dalam hal ini beras merupakan kebutuhan pokok. Penelitian ini bertujuan untuk mengetahui fase pertumbuhan padi sawah dan perkiraan produktivitas padi di Kabupaten Jatisari, Kabupaten Karawang. Penelitian ini menggunakan dua algoritma untuk menentukan fase pertumbuhan tanaman padi, yaitu Normalized Difference Vegetation Index (NDVI) dan Atmosphericically Resistant Vegetation Index (ARVI). Algoritma NDVI umumnya digunakan dalam beberapa penelitian yang berkaitan dengan fase pertumbuhan tanaman padi dan produktivitasnya, penggunaan algoritma ARVI dalam penelitian ini disesuaikan dengan area penelitian karena nilai ARVI menurut EOS (Earth Observing System) digunakan untuk daerah dengan kandungan aerosol atmosfer tinggi (hujan, kabut, debu, asap, dan polusi udara). Sehingga penggunaan algoritma ARVI lebih efektif daripada algoritma NDVI di daerah penelitian ini. Dalam memproses data, kami menggunakan Google Earth Engine (GEE) sebagai alat. Dan untuk uji validasi dalam penelitian ini digunakan Confussion Matrix yang mencakup akurasi keseluruhan, akurasi produsen, dan akurasi pengguna. Berdasarkan nilai NDVI dan ARVI, Kecamatan Jatisari memiliki dua fase tanam yaitu dengan satu kali panen dan dua kali panen. Dan hasil penelitian ini adalah persamaan regresi linier dengan rumus, Produktivitas (ton / ha) = 6.9513 (NDVI) + 3.3384, dengan variasi nilai koefisien (R²) = 0,898 dan Produktivitas (ton / ha) ) = 3,9849 (ARVI) + 7,3992, dengan variasi nilai koefisien (R²) = 0,6505. Dan untuk estimasi produktivitas padi di Kabupaten Jatisari memiliki rata-rata, 7,55 ton / ha dengan akurasi 93,29% untuk NDVI dan 90,43% untuk ARVI. Ditemukan bahwa algoritma NDVI lebih efektif untuk menentukan fase pertumbuhan tanaman padi dibandingkan dengan algoritma ARVI. Dan penelitian ini membuktikan bahwa faktor atmosfer tidak terlalu berpengaruh di Kabupaten Jatisari.

 

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Rice is an important and strategic commodity for the Indonesian peoples staple food, in this case rice is a basic need. Technology-based monitoring is needed such as remote sensing for rice plants in Indonesia. This study aimed to determine the growth phase of wetland rice and estimated rice productivity in Jatisari District, Karawang Regency. This research used two algorithms to determine the growth phase of rice plants, they were Normalized Difference Vegetation Index (NDVI) and Atmospherically Resistant Vegetation Index (ARVI). NDVI algorithm was commonly used in several studies related to the growth phase of rice plants and their productivity, the use of the ARVI algorithm in this study was adjusted to the study area because the ARVI value according to EOS (Earth Observing System) is used for areas with high atmospheric aerosol content (rain, fog, dust, smoke and air pollution). So that the use of the ARVI algorithm is more effective than the NDVI algorithm in this research area. In processing data we use Google Earth Engine (GEE) as tool. And for the validation test in this study used Confussion Matrix which includes overall accuracy, producer accuracy, and user accuracy. This accuracy test is considered the most suitable because the data used are pixel and object based. Based on NDVI and ARVI values, Jatisari District has two planting phases, namely one harvest and two harvests. And the results of this research are a linear regression equation with the formula, Productivity (ton / ha) = 6,9513(NDVI ) + 3,3384, with the variation of  the coefficient value (R²) = 0,898 and  Productivity (ton/ha)  = 3,9849(ARVI) + 7,3992, with the variation of  the coefficient value (R²) = 0,6505. And for the estimation of rice productivity in Jatisari District had an average, 7,55 ton/ha with an accuracy of 83,29% for NDVI and 90,43% for ARVI. Found that the NDVI algorithm is more effective to determine the growth phase of rice plant compared to the ARVI algorithm. And this research proves that atmospheric factors are not very influential in Jatisari District.