Judul: Forest Fire Hotspot Estimation for East Nusa Tenggara Districts/Cities using Poisson Regression with Machine Learning.
Abstrak: Kebakaran hutan dan lahan adalah bencana yang sering terjadi di Indonesia dengan penyebab alami dan/atau buatan. Kebakaran hutan dapat dilacak menggunakan sensor pada satelit buatan yang dapat mendeteksi perbedaan suhu antarlokasi. Daerah dengan suhu di atas ambang batas kemudian dinamakan hotspot. Kasus hotspot harian bersifat diskret dan langka, sehingga dibutuhkan metode khusus untuk mendapatkan dugaan tepat pada waktu yang ditentukan. Regresi Poisson adalah metode statistika yang cocok digunakan ketika satuan peubah respon diukur diskret. Extreme Gradient Boosting adalah metode pembelajaran mesin ensemble yang mampu menangani permasalahan regresi Poisson seperti overdispersi dan data zero-inflated. Kementrian Lingkungan Hidup dan Kehutanan melaporkan Provinsi Nusa Tenggara Timur mengalami jumlah kasus hotspot lebih banyak dibandingkan daerah lainnya dan waktu sebelumnya. Penelitian ini bertujuan menggunakan Extreme Gradient Boosting dengan dasar regresi Poisson untuk menduga banyak kasus hotspot suatu waktu pada kabupaten/kota di Provinsi Nusa Tenggara Timur. Hasil kajian menunjukkan bahwa model terbaik memiliki rata-rata Root Mean Square Percentage Error sebesar 7,585 dan peubah terpenting pendugaan hotspot adalah kelembapan, aliran air bawah tanah, dan total tutupan awan., Forest and land fires have become a common disaster in Indonesia with natural and/or human related causes. Fires can be tracked using sensors aboard satellites which detect differences in temperature between areas, with critically heated locations being dubbed a hotspot. Daily hotspot cases are discrete and rare in nature, which requires a sophisticated method to accurately predict the number of cases at a given time. Poisson regression is a statistical method suitable for when the response variable is expected to be in discrete values. Extreme Gradient Boosting is an ensemble machine learning method said to excel in handling Poisson regression issues such as overdispersion and zero-inflated data. The Ministry of Environment and Forestry reports an abnormally high count of hotspot cases in the province of East Nusa Tenggara relative to previous years and regions. This research aims to utilize Poisson regression-based Extreme Gradient Boosting to predict the number of hotspots at a given time within the regencies/cities of East Nusa Tenggara. Results show the best model has an average Root Mean Squared Percentage Error of 7.585 across all cities/regencies and hotspots are influenced most by humidity, runoff, and total cloud coverage.
Keyword: hotspot, Kebakaran hutan, data diskret, pembelajaran mesin, regresi poisson

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: 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.
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