Judul: Application of Random Forest Algorithm on Sentinel-2A Imagery for Garlic Land Classification Based on Growing Phase in Sembalun
Abstrak: Produksi bawang putih di Indonesia belum mencukupi besarnya kebutuhan masyarakat sehingga mengakibatkan pemerintah mengambil kebijakan impor bawang putih. Produktivitas bawang putih perlu ditingkatkan untuk menekan laju impor dan mewujudkan swasembada bawang putih tahun 2030. Sembalun merupakan salah satu sentra produksi komoditas bawang putih di Indonesia. Penelitian ini bertujuan melakukan klasifikasi lahan bawang putih di Sembalun berdasarkan fase tumbuh. Data yang digunakan pada penelitian ini adalah citra Sentinel-2A Level-1C pada Juli 2021 dengan empat bands resolusi 10 m dan nilai NDVI, serta data citra drone sebagai ground truth. Algoritme yang digunakan untuk melakukan klasifikasi citra adalah Random Forest. Penelitian ini menggunakan dua skenario dataset dengan nilai akurasi terbaik model dalam melakukan prediksi data baru sebesar 65,90% pada skenario kedua dengan menggunakan fitur NDVI. Model klasifikasi tanpa menggunakan fitur NDVI memberikan nilai akurasi sebesar 58,40%. Berdasarkan nilai akurasi tersebut, model dengan fitur NDVI mampu memberikan prediksi yang lebih baik., Garlic production in Indonesia is not sufficient to meet the needs of the community, which has resulted in the government adopting a garlic import policy. Garlic productivity needs to be increased to reduce the rate of imports and achieve garlic self-sufficiency in 2030. Sembalun is one of the centers of garlic commodity production in Indonesia. This study aims to classify garlic fields in Sembalun based on the growing phase. The data used in this study are Sentinel-2A Level-1C images in July 2021 with four bands of 10 m resolution and NDVI value, as well as drone image data as ground truth. The algorithm used to perform image classification is Random Forest. This study uses two dataset scenarios with the best model accuracy in predicting new data being 65.90% in the second scenario using the NDVI feature. The classification model without using the NDVI feature gives an accuracy value of 58.40%. Based on the accuracy value, the model with the NDVI feature is able to provide better predictions.
Keyword: garlic, growth phase, image classification, Random Forest, Sentinel-2A

Judul: Penerapan Algoritme Support Vector Machine pada Citra Sentinel-2A untuk Klasifikasi Fase Pertumbuhan Bawang Putih
Abstrak: Peningkatan kebutuhan konsumsi bawang putih (Allium sativum L.) di Indonesia tidak diimbangi dengan peningkatan jumlah produksinya. Pemerintah melakukan program ekspansi lahan penanaman bawang putih di Sembalun, Lombok Timur untuk mencapai swasembada bawang putih 2030-2045. Penelitian ini bertujuan untuk membuat model klasifikasi lahan bawang putih berdasarkan fase pertumbuhan bawang putih di Sembalun. Data yang digunakan adalah citra Sentinel-2A yang diakuisisi tanggal 1 Juli 2021 dan periode 31 Juli 2021. Algoritme yang digunakan adalah support vector machine (SVM) untuk klasifikasi data banyak kelas. Penelitian ini menerapkan model dengan dua skenario data yang memiliki perbedaan atribut. Model klasifikasi terbaik terdapat pada skenario kedua. Model ini memiliki akurasi terbaik sebesar 72,9% dengan nilai precision dan recall masing-masing sebesar 70,8% dan 71,4%. Berdasarkan evaluasi hasil klasifikasi, model SVM ini mampu mengidentifikasi fase pertumbuhan bawang putih di Sembalun., The increasing of need for consumption of garlic (Allium sativum L.) in Indonesia is not matched by an increase in the amount of production. The government carried out a program to expand the garlic planting area in Sembalun, East Lombok to achieve garlic self-sufficiency in 2030-2045. This study aims to create a model to classify the growth phase of garlic in Sembalun. The data used are Sentinel-2A imagery which was acquired on July 1st, 2021 and July 31st, 2021. The algorithm used is support vector machine (SVM) for multiclass classification data. This study applies a model with two data scenarios that have different attributes. The best classification model is found in the second scenario. This model has the best accuracy of 72.9% with precision and recall values of 70.8% and 71.4%, respectively. Based on the evaluation of the classification results, this SVM model able to identify the growth phase of garlic in Sembalun.
Keyword: bawang putih/garlic, fase pertumbuhan/growing phase, klasifikasi citra/image classification, Sentinel-2A, support vector machine

Judul: Characterization of CoCrMo alloy with titanium nitride and hydroxyapatite-chitosan coating
Abstrak: Endoprosthetic is moving tool for implant in the body, made of metals or polymers. Stainless steel, Co-based alloys, and Ti materials are widely used for implant materials. Co alloy used is CoCrMo which has excellent properties such as good biocompatibility, corrosion resistant, good mechanical properties such as hardness, strength, and high wear resistance, supporting its utilization as an implant material. CoCrMo alloy was synthesized using tri arc furnace with heating temperature of 3000 °C. TiN coating using plasma sputtering and nitriding method, characterized by a layer of gold. The content of Ti in the layer was 3.85% (w/w), whereas the content N was difficult to determine. Hydroxyapatite (HAp) was synthesized using wet method with Ca source of chicken eggshell and P source of H3PO4 80%. HAp-chitosan composite coating aimed at improving the biocompatibility and corrosion resistance of CoCrMo. In vitro test showed that CoCrMo with TiN and HAp-chitosan coating had excellent corrosion resistance in liquid medium infusion, whereas samples HAp and CoCrMo with TiN coatings and HAp-chitosan had high level of toxicity in endothelial cell culture tests.
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