ABSTRAKIndonesia merupakan negara yang melimpah akan energi surya dilihat dari letak yang potensial migrasi tahunan matahari. Pemanfaatan energi fosil sampai saat ini cukup mengkhwatirkan karena semakin menipisnya sumber energi disamping efek negatif yang ditimbulkannya akibat meningkatnya konsentrasi Gas Rumah Kaca (GRK). Peningkatan GRK akan memicu meningkatnya suhu permukaan dan menciptakan lingkungan tidak kondusif. Matahari sebagai sumber energi terbesar seharusnya dapat dimanfaatkan secara optimal untuk wilayah Indonesia. Diversifikasi energi merupakan langkah yang harus ditempuh untuk mendapatkan sumber lain sehingga mengurangi ketergantungan akan sumber fosil. Untuk mendapatkan gambaran riil akan energi surya tersebut perlu dilakukan analisis spasial distribusi potensi energi matahari untuk wilayah Indonesia keseluruhan dan Ambon secara khusus. Dengan melakukan kombinasi antara teknik Kriging dan metode iterasi Point Successive Over-Relaxation (PSOR) diharapkan memperlihatkan proyeksi pemetaan dengan resolusi yang lebih baik. Sebelumnya dilakukan optimalisasi data sekunder dengan model Curve Fitting. ABSTRACTIndonesia is a country which excessive energy resources of solar showed by potential position of solar annual migration. Until this time fossil energy consumption so apprehensively, its because decreasing of energy resources besides negative effect of increasing Greenhouse Gases concentrate, that is increasing of surface temperature and creates inconducive environment. Sun as the biggest energy resource should be use optimally for Indonesia area. Diversification of energy is a final step to get another resources so release us of dependently fossil resources. For real description of solar energy, it needs spatial analysis of potential distribution of solar energy for Indonesia area particularly Ambon. By using combination between Kriging technique and iteration methods, Point Successive Over-Relaxation (PSOR), hoped indicates mapping projection with better resolution. Early by optimalisize secondary data using Curve Fitting models.
AbstrakKota Ambon merupakan ibukota Provinsi Maluku yang berada di kawasan timur Indonesia. Kota Ambon memiliki intensitas curah hujan yang relatif tinggi dan cenderung berubah-ubah setiap tahun. Informasi tentang curah hujan sangat penting bagi masyarakat Kota Ambon untuk merencanakan kehidupan mereka dan deteksi dini terhadap bencana yang diakibatkan oleh curah hujan ekstrim. Tujuan penelitian ini adalah menentukan model terbaik untuk curah hujan bulanan di Kota Ambon dan meramalkan curah hujan untuk beberapa periode ke depan. Data yang digunakan adalah data curah hujan bulanan di kota Ambon pada periode Januari 2005 -Desember 2013 yang berasal dari hasil pengamatan Stasiun Geofisika -BMKG Ambon. Penelitian ini menggunakan analisis time series yakni metode Box-Jenkins untuk pemodelan SARIMA. Hasil yang diperoleh adalah model ( )( ) ( ) yang memiliki nilai SSR, AIC, SBC/BIC, dan terkecil. AbstractAmbon city, the capital of Maluku Province, is located in eastern Indonesia. Ambon city has relatively high rainfall intensity and it fluctuated every year. Information about rainfall is very important for the people in Ambon to plan their life and to detect disasters. The purpose of this study is to determine the best model of monthly rainfall in Ambon City and to forecast rainfall for several periods. The study used monthly rainfall data in Ambon city, from January 2005 until December 2013, which came from observation of Geophysics Station -BMKG Ambon. The method for this study is Box-Jenkins for SARIMA modeling. The result of this study is ( )( ) ( ) which had the smallest value of SSR, AIC, SBC/BIC, MAPE and RMSE.
Structural Equation Modeling (SEM) is a statistical modeling technique that is very cross-sectinal, linear, and complex. SEM is a combination of two multivariate techniques i.e. confirmatory factor analysis and path analysis. In this research will be applied Structural Equation Modeling to analyze factors influencing customer loyalty. Where researchers take two factors: bank image and customer satisfaction. The data used in this study is the primary data obtained by using questionnaires to customers of PT Bank Negara Indonesia (BNI) KCU Ambon with a total sample is 102. This study aims to determine the effect of bank image and customer satisfaction on customer loyalty. The results of this study note that the model has been suitable to be used to identify and meet the criteria of goodness of fit. From the analysis of the model, the tvalue for the latent variable of the bank image is 0.42 and the t-value for the latent variable of customer satisfaction is 2.84. With a critical value of 1.96 (to 5% real level) it can be concluded that the latent variable of the bank's image has an influence on customer loyalty while the latent variabel of customer satisfaction has no influence on customer loyalty.
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