Judul: Penggunaan model ARIMA dalam peramalan suhu udara di sekitar Palangkaraya
Abstrak: Recent forest and land wildfires in Indonesia not only result in ecosystem loss but also economic loss and health and pollution effect. The other hand, fire weather is believed to be an important aspects for fire occurrences. Fire weather directly affects fuel temperature, accelerating its easiness to burn. Thus, it is important to clarify the effects of air temperature to forest fire incidence. A statistical analysis and forecasting for air temperature is needed to predict the air temperature condition in the future. Thisstudyused AutoregressiveIntegratedMovingAverage(ARIMA) model for air temperature forecasting in Palangkaraya, Central Kalimantan. Palangkaraya has become the fire dense areas due to several high fire occurrences in recent years. The result showedthat theAR(5) model could be usedfor daily airtemperaturemodeling foreight forecasting days with aMAPEvalue of3.11%. ARIMA(0,1,1) could be usedfor weekly airtemperatureforecastingwith aMAPEvalue of2.1%. Therefore these models could be used for air temperatureforecasting, althoughthey produced relativelyconstantvalueof temperature forecastingon27oCand28oC. For the monthly temperature, model MA (1) could be used with a MAPE value of 2.08%. Forecasting for the next 6 months also tend to resulta constant temperature at 26.8 oC, but MA (1) has lowest MAPE value, indicating that MA(1) is the best model for predicting the air temperature of Palangkaraya in the future.
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Judul: Prediksi Temporal untuk Kemunculan Titik Panas di Provinsi Riau Menggunakan Autoregressive Integrated Moving Average (ARIMA)
Abstrak: Forest has many benefits for human life. Nowadays forest areas in Indonesia have decreased because of illegal logging, forest fires and forest conversion. Wildfires have resulted economic losses, health problems and pollution. Forest fires can be indicated through hotspot occurrences. In this work, data modeling was conducted to predict hotspot occurrences using Autoregressive Integrated Moving Average (ARIMA). ARIMA is one of prediction methods that can be used for modeling timeseries data such as hotspots that are daily recorded by satellite sensor. Modeling was performed on monthly hotspots occurrences data for the period of 2001 to 2012 in Riau Province. The experimental results showed the ARIMA(2,0,0) model was the best model to predict the number of monthly hotspot occurrences with a Mean Absolute Percentage Error (MAPE) of 40.974.
Keyword: time series, hotspot, ARIMA

Judul: Struktur dan Karakteristik Optik Lapisan Semikonduktos Cu2O (Cuprous oxide) Hasil Deposisi Elektrokimia
Abstrak: Ballan semikonduktor Cu2O (Ctiprotis oxide) dipilih karena lnemiliki banyak keuntungan antara lain ; relatif murah, bahan dasar (Cu) mudah didapat, dan tidak beracun. Banyak metode deposisi telah digunakan untuk menghasilkan lapisan semikonduktor Cu,O, dalam penelitian ini digunakan metode deposisi elektrokimia. Deposisi elektrokimia dilakukan scbanyak dua salnpel dengan variasi tegangan -500 mV (sampel I ) dan 4 0 0 mV (sampel 2) terhadap SCE. Lapisan selnikonduktor Cu20 diatas kaca TCO yang dihasilkan dikal-akterisasi kristalografi (XRD), morfologi (SEM), dan optiknya (Spektrofotometer UV-VIS). Melalui karakterisasi XRD diketahui semikonduktor Cu20 yang terbentuk adalah polikristal.
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