Judul: Application of Random Forest Algorithm on Sentinel-1A Imagery for Garlic Field Identification in Sembalun
Abstrak: Bawang putih (Allium sativum L.) merupakan tanaman hortikultura yang memiliki banyak manfaat dalam kehidupan manusia baik dalam bidang medis maupun non medis. Daerah Sembalun merupakan dataran tinggi (>700 meter dpl) yang memiliki luas lahan mencapai 4000 hektar sehingga cocok untuk pengembangan komoditas bawang putih. Penelitian ini bertujuan untuk menerapkan algoritme ensemble learning yang dapat meningkatkan akurasi dalam pemetaan lahan bawang putih menggunakan citra Sentinel 1-A. Data yang digunakan pada penelitian ini adalah Citra Sentinel-1A daerah Sembalun periode Juni dan November 2019. Pengklasifikasian citra Sentinel-1A menggunakan algoritme ensemble learning yaitu Random Forest. Penelitian ini menerapkan model dengan tiga skenario dengan akurasi terbaik sebesar 78,45% dan rata-rata akurasi pada semua skenario sebesar 76,55%. Rata-rata akurasi pada algoritme pembelajaran ensemble mendapatkan nilai yang lebih tinggi 0,1% dibandingkan penerapan algoritme decision tree C5.0 pada penelitian sebelumnya., Garlic (Allium sativum L.) is a horticultural plant that has many benefits in human life, both in the medical and non-medical fields. The Sembalun area is a highland (>700 meters above sea level) which has a land area of 4000 hectares so it is suitable for the development of garlic commodities. This study aims to apply an ensemble learning algorithm that can improve accuracy in mapping garlic fields using Sentinel 1-A imagery. The data used in this study is the Sentinel-1A image of the Sembalun area for the period June and November 2019. The classification of the Sentinel-1A image uses an ensemble learning algorithm, namely Random Forest. This study applies a model with three scenarios with the best accuracy of 78.45% and an average accuracy of 76.55% in all scenarios. The average accuracy of the ensemble learning algorithm is 0.1% higher than the application of the C5.0 decision tree algorithm in the previous study.
Keyword: garlic, Sembalun, ensemble learning, image classification, Sentinel-1A

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: Studi Konduktivitas Elektrolit Polimer Kitosan/PVA+KOH
Abstrak: The blend-based polymer electrolyte consist of chitosan and polyvinyl alcohol (PVA) as host polymers and potassium hydroxide (KOH) as the complexing salt was studied. Polymer electrolyte were obtained by the casting technique. An attempt was also made to investigate the effect of TiO2 concentration in the chitosan/PVA+KOH polymer electrolyte. The polymer electrolyte with good ionic conductivity properties were characterized by thermal analysis (DSC), FTIR, SEM, XRD and EDX. The best ionic conductivity values of 1,105 x 10-2 S cm-1 at room temperature were obtained for the sample containing 35%wt of KOH and 1,210 x 10- 2 S cm-1 for the sample containing 50% wt TiO2. The thermal analysis indicates that both glass transition temperature (75.30 °C) and crystallinity are low for chitosan/PVA+KOH 35%.
Keyword: