Judul: Skenario untuk Lahan Pertanian Terlantar di Kabupaten Karawang
Abstrak: The land plays a significant role in world life to continue its journey and fulfill living things’ needs. It becomes unarguable if the land use is then defined as the varying activities executed by humans to exploit the landscape. However, the dynamic of land use has recently caused irregularities in its original behavior. A noticeable increasing trend in farmland abandonment, directly affecting farmers’ livelihood and food security. However, there is lack study in Indonesia that describe, diagnostic, and floor the prescription to encounter the issue. Essentially, the Indonesian Central Statistics Agency has shown numbers to be able to describe farmland abandoned. However, the data does not have a spatial attribute, which in the future can result in errors in analyzing especially related to the distance. Therefore, the objectives of the study is to map farmland abandonment spatial distribution, followed by classifying the driving factors, and then the last objective is to endorse the scenario in Karawang as the second largest rice-producing district in Indonesia. Remote sensing technique by the maximum likelihood method worked to map the farmland abandoned. In addition, besides employing high resolution satellite imagery as an example data, study also manages a pattern of normalize different built-up index values to validate the quality of the sample data. Random forest analysis, as a part of machine learning, is used to sort out the importance degree of driving factors. Finally, the narrative scenario is employed to floor the prescriptive to prevent farmland abandonment issue in the future. As the result, all the districts and subdistricts in Karawang Regency have abandoned land with varying numbers and more distributed more intensely in the hub of the region. The study has discovered that the total number of farmland abandonment is 28 times of the Central Bureau Statistics has claimed. 75% producer accuracy, minimum unit data collected, terminology, land ownership, and the ability of remote sensing to read all earth reflections are the possible answer to the disparity. Machine learning informed that the top three essential variables are the irrigation service, the hillside, and the spatial plan that drives farmland abandonment. Thus, developing a new irrigation service to access the remote farmland and reviewing the spatial with more flavor to the agriculture is the scenario proposed by this study, while the hillside remains a constraint.
Keyword: Farmland Abandonment, geographic information system, land use, machine learning, regional planning, remote sensing

Judul: Development of Agriculture Land Evaluation Model Based on Environmental Risk
Abstrak: Karawang district is one of the largest rice granaries in Indonesia. The existence of agricultural areas is one of the important things that must be maintained to support the national food security program. However, industrial development and rapid population growth are factors in the conversion of agricultural land to non agricultural functions. The process of land conversion occurs massively on the productive area with a small scale and is difficult to identify. Based on the descriptions, it is necessary to evaluate agricultural land to support decision making, especially in the conservation and management of agricultural areas in Karawang district. The objectives of this study are to introduce the alternative method for improved image classification analysis based on hybrids time series normalized difference index for rapid mapping of agriculture area and developing the spatially explicit model to evaluate the potential of agricultural land to be converted based on environmental risks. The method introduced is a hybrid method by integrating the quantification of the Normalized Difference Tillage Index (NDTI) and the characterization of the Tillage Normalized Difference Vegetation Index (NDVI). The Normalized Difference Tillage Index (NDTI) has been widely used to identify biophysical parameters and agricultural practices. While the Normalized Difference Vegetation Index (NDVI) is optimized for the characterization and analysis of agricultural phenology. The hybrid method that integrates the quantification and characterization of the Normalized Difference Tillage Index (NDTI) and the Normalized Difference Vegetation Index (NDVI) promises a fast and precise method for image classification. From the results of the accuracy assessment using the Kappa coefficient, Producer accuracy is 95%, User accuracy is 71%, and Overall accuracy is 72% with a coefficient of 31.4%. The use of Sentinel-2 data with good spatial resolution can identify a unit area of less than 1 hectare, where that the classification results can support precision agriculture activities. The output of the Agricultural Land Evaluation model produces spatial information that can explicitly describe the effect of each environmental risk according to its weight. the agriculture area in Karawang district is dominated by areas with high risk (39.66%), very high (32.95%), and moderate (24.21%). Agricultural land objects with the highest and very high risk are adjacent to the road network. Based on validation using serial very high-resolution data from 2017 to 2020 in West Karawang and Purwasari sub-districts, it was found that agricultural areas with high and very high environmental risks have a high potential to be converted into non-agricultural areas. The advantageous information would be useful for decision-makers in determining priorities, management strategies, and alternative scenarios for agricultural land conservation.
Keyword: Agriculture land, remote sensing, SMCA, NDTI, NDVI

Judul: Uji sifat listrik dan struktur kristal fotodioda ba0,5sr0,5tio3 dengan variasi pendadahan fe2O3
Abstrak: Ba0,5Sr0,5TiO3 (BST) thin film was doped by Ferium (Fe2O3) has been done with doping variation 2.5%, 5%, 7.5% and 10% above the substrate Si (100) p-type using chemical solution deposition (CSD) methods by spin coating technique at 3000 rpm for ± 30 seconds. Ba0,5Sr0,5TiO3 thin film made with 1 M concentration and the annealing temperature by 850oC. The results of current-voltage (I-V curves) characterization shows that the BST thin film and BFST are photodiodes. Electrical conductivity values BST and BFST thin films are in the range of semiconductor materials and obtain the electrical conductivity increased when the higher intensity light is used. The result of the dielectric constant increases with the addition of Fe2O3 doping. SEM results showed that the BST and BFST material is a thin film with thickness of the order 10-6 m. Result of XRD showed that BST and BFST is cubic.
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