Judul: Penerapan Model ARIMA untuk Prediksi Anomali Curah Hujan Berdasarkan Indeks Monsun dan El Niño di Beberapa Kawasan di Indonesia.
Abstrak: This study based on atmosphere-ocean interaction that is very important to be investigated because of its influences to the rainfall variability over Indonesia. This study aims to investigate Monsoon and Niño 3.4 interaction in influencing rainfall fluctuation over Indonesia. The study areas cover Lampung, Sumbawa Besar, Indramayu, Banjarbaru, and Pandeglang from Januari 1976 - Desember 2000. The results of Power Spectral Density (PSD) and Wavelet analysis on rainfall anomaly and monsoon index data show strong at 12 months. Dominant oscillation pattern of Niño 3.4 is about 60 months. The result of regression analyses shows more significant relation between rainfall anomaly and AUSMI (Australian Monsoon Index) and WNPMI (Western North Pacific Monsoon Index). Therefore, it is suggested that the Monsoon (AUSMI and WNPMI) and El-Niño(Niño 3.4) can be used for further analysis. This study was undertaken with assumption that Monsoon oscillation and El-Niño are interacted and the model is developed by using multivariate regression method: Y = a + bAUSMI + cWNPMI + dNiño3.4. By using Box-Jenkins method based on ARIMA (Autoregressive Integrated Moving Average), the prediction model that is close to observed time series data of rainfall anomaly is ARIMA (1,0,1)12 with model equation Zt = 0.9989Zt-12 - 0.9338at-12 + at (AUSMI), ARIMA (1,1,1)12 with model equation Zt = -0.0674Zt-12 + Zt-12 - Zt-24 - 0.9347at-12 + at,(WNPMI), dan ARIMA (2,0,2) with model equation Zt = 3.594Zt-1 -0.8362Zt-2 – 1.634at-1 - 0.1053at-2 + at (NINO3.4). Model equation shows that time series data forecast of rainfall anomaly depend on previous “t” months data and previous ”t” months error.
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Judul: Pengembangan model monsun indonesia berbasis hasil analisis data indeks monsun regional
Abstrak: Monsoon is one of regional phenomenon that affects weather and climate in Indonesia. This is caused by the territory of Indonesia which is located in equatorial zone and between Asia in the north and Australia in the south. Due to the location, there are two monsoons named Asia which causes wet season (December-January-February) and Australia which causes dry season (June-July-August). Until this time, Indonesia doesn’t have monsoon index which is needed by BMKG (Meteorology, Climatology and Geophysics Agency). Regional monsoon index (RMI) data must be calibrated with data from Indonesia, EAR (Equatorial Atmosphere Radar) to examine the relation of EAR with IMR data. Selected time-series of IMR data is used to develop monsoon model. The method that used is monsoon analysis and spectral analysis with Matlab and cross correlation analysis and the Box-Jenkins method, ARIMA with SPSS 16.0. The results of the analysis show that AUSMI (Australian Monsoon Index) at 200 mb layer which oscillate around 12 months has high correlation with EAR data 0.781. The appropriate model to show the monsoon model is ARIMA (1,1,1). AUSMI period t is decided by one and two months latest data (Zt-1) and (Zt-2) and a month error latest data (at-1) with equation Zt = 1.047 Zt-1-.047Zt-2 + 0.093at-1. The validation results obtained using model with actual data IMR February 2004-December 2008 give average error 2.3906 and correlation 0.7139.
Keyword: Monsoon, EAR, Regional monsoon index data, Box-Jenkins(ARIMA), Bogor Agricultural University, Institut Pertanian Bogor, IPB

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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