Judul: Utilizing Random Forest for Hotspot Estimation in Peatlands
Abstrak: Titik panas atau hotspot dapat menjadi salah satu indikator tahap awal terjadinya kebakaran hutan dan lahan gambut yang juga menjadi salah satu permasalahan yang cukup masif terjadi di Indonesia. Penentuan hotspot sebagai identifikasi awal kebakaran hutan dan lahan gambut dapat menggunakan teknologi machine learning, salah satunya adalah Random Forest. Penelitian ini bertujuan mengestimasi hotspot menggunakan model Random Forest, serta melihat keakuratan model dan partisi indeks spektral terhadap estimasi hotspot. Penggunaan Random Forest dalam penelitian ini memanfaatkan berbagai indeks spektral sebagai prediktor terhadap estimasi hotspot di wilayah KHG Sungai Kahayan-Sungai Sebangau, Kalimantan Tengah, meliputi NDVI, NDMI, NDWI, EVI, EVI2, NBR, dan NBR2 yang diekstrak dari data citra satelit Sentinel-2A. Model Random Forest memanfaatkan indeks spektral untuk mengestimasi kelas pada setiap lokasi hotspot, meliputi kelas low, nominal, dan high. Model menunjukkan performa yang cukup rendah pada kelas low yang berada di kisaran 30%, sebaliknya model menunjukkan performa yang cukup baik dalam mengestimasi hotspot pada kelas nominal dan high yang berada di kisaran 60-70%. Nilai overall accuracy (OA) juga menunjukkan hasil yang cukup tinggi pada data testing dan validasi, yaitu mencapai 65% dengan indikasi model tidak mengalami overfitting. Tingkat partisi indeks spektral tertinggi adalah NBR2 dan partisi indeks terendah adalah EVI1. Estimasi hotspot dengan random forest juga menunjukkan indikasi adanya bias akibat class imbalance. Penentuan hotspot menggunakan model Random Forest diharapkan dapat berkontribusi terhadap kajian mengenai pemantauan kebakaran hutan dan lahan gambut., Hotspots can serve as early indicators of forest and peat fires, which are significant issues frequently occurring in Indonesia. Identifying hotspots as an early warning of forest and peat fires can be effectively conducted using machine learning technology, particularly the Random Forest algorithm. This study aims to estimate hotspots using the Random Forest model and assess the accuracy of the model and the spectral index partitions in estimating hotspots. The application of Random Forest in this research leverages various spectral indices as predictors for hotspot estimation in the KHG Sungai Kahayan-Sungai Sebangau area, Central Kalimantan. The indices used include NDVI, NDMI, NDWI, EVI, EVI2, NBR, and NBR2, all extracted from Sentinel-2A satellite imagery data. Random Forest model classifies each hotspot location into low, nominal, and high categories based on spectral indices. The model demonstrates lower performance for the low category, with an accuracy of approximately 30%, but shows better performance for the nominal and high categories, with accuracies ranging from 60% to 70%. The overall accuracy (OA) of the model is relatively high for both testing and validation datasets, reaching 65%, indicating that the model does not suffer from overfitting. The highest spectral index partition was NBR2, while the lowest was EVI1. The estimation of hotspots using the Random Forest model also indicated potential bias due to class imbalance. Utilizing the Random Forest model for hotspot detection is expected to contribute to the study of monitoring and management of forest and peat fires.
Keyword: hotspot, forest and peat fires, peatland, random forest, spectral indices

Judul: Implementation of Random Forest  Regression and Gradient Boosting Regression on the Number of Hotspot Data in  Kalimantan
Abstrak: Hotspot merupakan suatu indikator yang dapat digunakan sebagai pendeteksi awal terjadinya kebakaran hutan dan lahan (karhutla). Karhutla merupakan bencana alam yang rutin terjadi di Indonesia salah satunya di pulau Kalimantan. Tujuan penelitian ini yaitu mengonstruksi model regresi machine learning dengan metode Random Forest (RF) dan Gradient Boosting (GB) menggunakan data jumlah hotspot di beberapa titik wilayah Kalimantan dengan melakukan tuning pada hyperparameter model yakni n-estimators (banyaknya pohon yang dibangun), max-depth (maksimum kedalaman di tiap pohon), fungsi criterion (mengukur kualitas pemisahan suatu atribut) untuk RF, dan fungsi kerugian (metrik yang mengukur selisih nilai prediksi dengan data aktual selama proses training) untuk GB. Tuning pada metode random forest menggunakan fungsi criterion poisson menghasilkan nilai n-estimators sebesar 13 dan max-depth 28. Sementara itu, tuning pada metode gradient boosting dengan fungsi kerugian huber menghasilkan nilai n-estimators sebesar 53 dan max-depth 6. Perbedaan jumlah hotspot dari hasil keluaran model dengan data aktual diukur dengan nilai measurement Root Mean Square Error (RMSE). Metode random forest menghasilkan nilai RMSE sebesar 691.18, sedangkan gradient boosting menghasilkan RMSE sebesar 719.91., Hotspot is an indicator that can be used as early detector of forest and land fires (karhutla). Karhutla is natural disaster that occur regularly in Indonesia, especially in Kalimantan. This study aims to construct regression models using machine learning algorithms with random forest (RF) and gradient boosting (GB) methods by tuning the hyperparameters of model namely n-estimators (number of the tree to be built), max-depth (maximum depth in each tree), criterion function (measure the quality of separation of an attribute) for RF, and loss function (metric that measures the difference between the predicted and the actual value during the training process) for GB, by applying the number of hotspot data at several points in Kalimantan. Tuning with RF using poisson criterion resulted in n-estimators value of 13 and max-depth of 28. Meanwhile, tuning with GB using huber loss function resulted in n-estimators value of 53 and max-depth of 6. The differences of the number of hotspots between the output of the model and the actual data are measured using the Root Mean Square Error (RMSE). The RMSE value of the RF method is 691.18, while the RMSE value of GB method is 719.91.
Keyword: forest and land fires, GB, hotspot, hyperparameter tuning, RF

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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