Judul: Indication of Coffee Plantation Distribution in West Java using Google Earth Engine Machine Learning Classifiers
Abstrak: Penggunaan big data dan machine learning adalah bagian dari apa yang dikenal dengan Agriculture 4.0 yang mana akan dicapai melalui pengumpulan, pemrosesan, dan analisis data spatio-temporal secara real-time pada semua aspek industri pertanian. Kontribusi perkebunan kopi terhadap perekonomian nasional semakin meningkat pesat. Salah satu cara untuk mendukung terciptanya lingkungan investasi yang kondusif adalah dengan penyediaan data dan informasi yang berkualitas sebagai acuan kebijakan dan pengambilan keputusan. Kopi adalah tanaman yang tumbuh di bawah naungan pohon lain. Tantangan besar dalam pemetaan kebun kopi adalah pemisahan kelas hutan dari kopi. Kesamaan spektral antara sistem kopi dan hutan bahwa pemrosesan citra alternatif dan teknik klasifikasi diperlukan untuk meningkatkan akurasi secara keseluruhan. Google Earth Engine digunakan sebagai platform untuk mendapatkan kumpulan data indeks vegetasi dan non-vegetasi berbasis Sentinel-2A dan dilanjutkan dengan model klasifikasi. Algoritma Random Forests, CART, dan SVM, serta teknik ensemble Max, Med, dan Min digunakan untuk klasifikasi kebun kopi. CART memiliki akurasi validasi tertinggi dengan overall accuracy 0,88; user’s accuracy 0,81; producer’s accuracy 0,84; koefisien Kappa 0,73; TSS 0,74; overprediction rate 0,19; dan underprediction 0,15. Total luas perkebunan kopi yang diklasifikasi menggunakan algoritma CART di Provinsi Jawa Barat adalah sebesar 53.284 ha. Lima kabupaten/kota teratas yang memiliki wilayah perkebunan kopi terluas di Jawa Barat adalah Garut (10.537 ha), Cianjur (10.015 ha), Sukabumi (7090 ha), Bandung (6576 ha), dan Tasikmalaya (4512 ha)., The use of big data and machine learning is part of Agriculture 4.0 which will be achieved through the collection, processing and analysis of real-time spatio-temporal data on all aspects of the agricultural industry. The contribution of coffee plantations to the national economy is increasing rapidly. One way to support a conducive investment environment is the provision of quality data and information as references for policy and decision-making. Coffee is planted in the shade of a tree. The big challenge in mapping coffee plantations is the separation of forest classes from coffee. Spectral similarities between coffee and forest systems suggest that alternative image processing and classification techniques might be needed to improve the overall classification. Google Earth Engine is used as a platform to obtain a Sentinel-2A-based vegetation and non-vegetation indices dataset and continue with the classification model. Random Forest, CART, SVM algorithms and Max, Med, and Min ensemble techniques are used to classify coffee plantations. CART has the highest validation accuracy with overall accuracy 0,88; user's accuracy 0,81; producer's accuracy 0,84; Kappa coefficient 0,73; TSS 0,74; overprediction rate 0,19; and underprediction 0,15. As result, the indication of the total coffee plantation area in West Java Province is 53.284 ha. The top 5 districts have the largest coffee plantation area as follows Garut (10.537 ha), Cianjur (10.015 ha), Sukabumi (7090 ha), Bandung (6576 ha), and Tasikmalaya (4512 ha).
Keyword: ensemble, Google Earth Engine, Jawa Barat, machine learning, Perkebunan Kopi

Judul: Identifikasi Tanaman Kopi di Provinsi Jawa Barat Berbasis Machine Learning Random Forest Dengan Citra Satelit Sentinel-2 dan Landsat-8
Abstrak: Kopi merupakan komoditas yang penting baik bagi Indonesia maupun dunia. Namun demikian mengacu kepada data Badan Pusat Statistik (BPS) wilayah Provinsi Jawa Barat, masih terdapat perbedaan data luas tanaman kopi antara data BPS kabupaten/kota dengan provinsi. Oleh karenanya identifikasi dan pemetaan tanaman kopi perlu dilakukan dengan menggunakan metode lain yang lebih objektif seperti remote sensing dengan citra satelit menggunakan platform Google Earth Engine. Identifikasi tanaman kopi di wilayah Provinsi Jawa Barat pada penilitan ini dilakukan melalui proses machine learning menggunakan algoritma random forest dengan menambahkan olahan data citra satelit dan predictors-predictors yang dianggap mampu mengidentifikasi karakteristik tanaman kopi. Hasil penelitian menunjukkan pengolahan predictors NDVI untuk mengindikasikan kelas tutupan lahan vegetasi dan non-vegetasi sebelum proses identifikasi tanaman kopi penting dilakukan. Sedangkan predictors dominan yang dapat mengidentifikasi tanaman kopi adalah elevasi dan forest canopy density. Identifikasi tanaman kopi dengan machine learning menggunakan citra satelit Sentinel-2 dan Landsat-8 dapat dilakukan. Walaupun identifikasi tersebut menunjukkan hasil yang overestimated, masing-masing sebesar 61% dan 13,3% dari data acuan BPS provinsi. Akurasi total terbaik yang diperoleh dengan metode random forest adalah 92% untuk Sentinel-2 dan 81% untuk Landsat-8. Hasil tersebut sangat dipengaruhi oleh data training yang diberikan pada saat proses pembelajaran., Coffee is an important commodity for both Indonesia and the world. However, referring to the data from the Badan Pusat Statistik (BPS) for the West Java Province, there are still have differences data between district/city and provincial BPS data about coffee plant area. Therefore, identification and mapping of coffee plants needs to be done using other more objective methods such as remote sensing with satellite imagery using the Google Earth Engine platform. Identification of coffee plants in the province of West Java in this research is carried out through a machine learning process using a random forest algorithm by adding processed satellite image data and predictors that are considered capable of identifying the characteristics of coffee plants. The results showed the processing of NDVI predictors to indicate vegetative and non-vegetated land cover classes before the coffee plant identification process was carried out. While the dominant predictors that can identify coffee plants are the height and density of the forest canopy. Identification of coffee plants by machine learning using Sentinel-2 and Landsat-8 satellite imagery can be done. Although the identification shows overestimated results of 61% for Sentinel-2 and 13.3% for Landsat-8 from reference data (provincial BPS data), respectively. The best total accuracy obtained by the random forest method is 92% for Sentinel-2 and 81% for Landsat-8. These results are strongly influenced by the training data provided during the learning process.
Keyword: Google Earth Engine, Identification, Machine Learning, Remote  Sensing, Coffee Plants

Judul: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
Keyword: