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: 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: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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