Judul: Penggunaan Elman Recurrent Neural Network dalam Peramalan Suhu Udara sebagai Faktor yang Mempengaruhi Kebakaran Hutan
Abstrak: Recent forest and land wildfires in Indonesia not only cause to the ecosystem lost but also economical lost as well as health and polution effect. On the other hand, fire weather is concerned to be an important aspects for fire occurrences. Fire weather directly affects fuel temperature, which accelerating its easiness to be burnt. Thus, it is important to clarify the effects of air temperature to forest fire incidence. A statistical analysis and forecasting for air temperature is used to predict the future air temperature condition. In this research we use Elman Recurrent Neural Network (ERNN) to predict the temperature for a few days ahead, then the result of this prediction will be compared by using ARIMA (Ramdani, 2011). In this research we use the air temperature data of 2001- 2004 which implemented using MATLAB. The best result in this research is one day ahead prediction with RMSE 0.51 and MAPE 1.55%. ERNN gave the better performance with MAPE (ERNN) 1.55% and MAPE (ARIMA) 3.11% (Ramdani, 2011).
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Judul: Prediksi Temporal untuk Kemunculan Titik Panas di Provinsi Riau menggunakan Elman Recurrent Neural Network
Abstrak: Kebakaran hutan adalah salah satu faktor menurunnya jumlah hutan yang dapat berasal dari manusia ataupun keadaan alamnya. Berdasarkan data Kementrian Lingkungan Hidup, luas kebakaran hutan tahun 2014 di Provinsi Riau menduduki peringkat pertama dari keseluruhan provinsi di Indonesia. Dari fakta tersebut, diperlukan sebuah cara untuk mengantisipasi adanya kebakaran hutan terutama pada daerah yang sering terjadi kebakaran hutan, yaitu di Provinsi Riau. Salah satu cara mengantisipasinya adalah dengan memprediksi frekuensi kemunculan titik panas. Salah satu metode untuk melakukan prediksi dengan pola pembelajaran adalah menggunakan Elman recurrent neural network (ERNN). ERNN merupakan salah satu metode peramalan yang dapat digunakan untuk memprediksi kemunculan titik panas dengan mempelajari pola data latih yang berasal dari data yang berbentuk deret waktu. Hasil dari pembelajaran ERNN ini baik dalam memprediksi nilai dengan titik ekstrim data aktual. Hasil pembelajaran menggunakan ERNN dengan arsitektur berdasarkan plot autokorelasi parsial untuk menentukan node lapisan masukan mendapatkan nilai MAPE sebesar 67.54% dan RMSE sebesar 252.98.
Keyword: autokorelasi parsial, deret waktu, ERNN, titik panas

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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