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: Prediksi Kemunculan Titik Panas Di Provinsi Riau Menggunakan Seasonal Autoregressive Integrated Moving Average (Sarima).
Abstrak: Hutan Indonesia merupakan salah satu dari paru-paru dunia yang peranannya sangat penting bagi makhluk hidup. Sayangnya luas hutan di Indonesia semakin berkurang. Salah satu penyebabnya adalah kebakaran hutan. Kebakaran hutan dapat diprediksi melalui kemunculan titik panas yang dipengaruhi oleh cuaca sehingga bersifat musiman. Oleh karena itu, perlu metode yang dapat menangani unsur musiman dalam memprediksi titik panas. Penelitian ini bertujuan membuat model prediksi kemunculan titik panas menggunakan seasonal autoregressive integrated moving average (SARIMA). SARIMA merupakan metode prediksi yang dapat digunakan untuk pemodelan data deret waktu musiman. Pemodelan dilakukan pada data kemunculan titik panas perbulan pada periode 2001 sampai 2012 di wilayah Provinsi Riau. Hasil penelitian menunjukkan bahwa model prediksi terbaik adalah ARIMA(1,0,0)(0,1,2)12 dengan nilai mean absolute percentage error (MAPE) terbaik sebesar 0.579 untuk meramal satu bulan ke depan.
Keyword: model prediksi, SARIMA, titik panas

Judul: Karakteristik Carboxymethyl Chitosan Dengan Variasi Konsentrasi NaOH
Abstrak: Carboxymethyl chitosan is chitosan derivative product obtained from chemical modification. CMCh were made by alkalization process and carboxymethylation process. Factors that affect CMCh characteristics were excess sodium hydroxide in alkalization process, ratio between chitosan and monochloroacetic acid, and carboxymethylation temperature. The objectives of this research were to study the NaOH effect to CMCh characteristics, to learn process of making CMCh, to set the proper concentration of NaOH, and to analyze CMCh characteristics. CMCh yields were 110.72%-114.22%. CMCh pH value were 3.74-4.31. The optimum NaOH concentration to obtain the best CHCh was 10 M. Percentage of CMCh moisture content was 10.70%-11.42%. Ash content and nitrogen content of CMCh were 0.49%-0.50% and 4.52%-4.81%. CMCh viscousity was 8.25 cps. Percentage of CMCh solubility was 61.61%-91.50%. The different functional groups from chitosan which observed in CMCh were C=O group, CH group from alkene functional group, C−O group, and C−O−C group.
Keyword: solubility., functional group, CMCh characteristics, chitosan, carboxymethyl chitosan