Penelitian ini menganalisis faktor-faktor apa saja yang dipertimbangkan bagian kepegawaian perusahaan langganan dalam membeli Layanan Medical Check Up Rumah Sakit Pusat Pertamina Tahun 2012. Penelitian ini juga menentukan faktorfaktor apa saja yang berperan paling besar sebagai pertimbangan bagian kepegawaian perusahaan langganan dalam membeli layanan Medical Check Up RSPP Tahun 2012. Dalam penelitian ini digunakan 31 (tiga puluh satu) variabel dengan populasi penelitian perusahaan pelanggan MCU RSPP yang telah menjalin kerjasama dengan MCU RSPP selama 2 (dua) tahun berturut- turut yaitu tahun 2011- 2012 sebanyak 88 (delapan puluh delapan) perusahaan. Sebanyak 45 (empat puluh lima) perusahaan sebagai responden yang diambil dengan teknik purposive sampling. Metode Principal Component Analysis (PCA), menghasilkan 6 (enam) faktor yang dipertimbangkan bagian kepegawaian perusahaan langganan dalam membeli Layanan Medical Check Up Rumah Sakit Pusat Pertamina Tahun 2012 dan mampu menjelaskan 25 variabel dalam data, yaitu sebesar 79,3 persen. Keenam faktor tersebut adalah Faktor 1 (Alat Promosi yang Menarik dan Informatif), memiliki eigen value sebesar 10,077 dan variansi sebesar 40,3 persen. Faktor 2 (Kecukupan Tersedianya Tempat Parkir) memiliki eigen value sebesar 2,719 dan variansi sebesar 10,8 persen. Faktor 3 (Mutu Produk) memiliki eigen value sebesar 2,062 dan variansi sebesar 8,2. Faktor 4 (Kesesuaian Harga) memiliki eigen value sebesar 1.487 dan variansi sebesar 5,9 persen. Faktor 5 (Ketepatan penerimaan hasil Pemeriksaan) memiliki eigen value sebesar 1,305 variansi sebesar 5,2 persen. Faktor 6 (Transportasi menuju Rumah Sakit) memiliki eigen value sebesar 1,026 dan variansi sebesar 4,1 persen. Ketepatan model yang dihasilkan berdasarkan hasil estimasi matriks faktor adalah sebesar 38 persen atau sebanyak 115 residual dengan nilai absolut di atas 0,05. Hal ini menunjukkan bahwa model memiliki ketepatan sebesar 62 persen pada tingkat penyimpangan 5 persen.

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The problems derived from the study - be that as it may, were the factors considered by the clientele of Human Resources Departments In Subscribing to Medical Check-Up Service of Pertamina Central Hospital in 2012. The purpose of this study was to analyze and determine the most affecting factors as far as clients are concerned due to their commitment and preferences in proceeding with the Medical Check-Up of Pertamina Central Hospital in 2012. In order to decide the most dominant variables representing each factor established and in conjunction to assess the other remaining variables included in the fully-formed dominant factors, this study picked out 31 (thirty-one) variables and the population, namely the existing customers of RSPP’s MCU service who have worked closely over the past 2 (two) consecutive years, 2011-2012. The mass of the addressed population comprised of 88 (eighty-eight) companies and the quantity of the respondents taken as study sample were narrowed down to 45 (forty-five) companies altogether and conducted by purposive sampling techniques. The Principal Component Analysis (PCA) method, resulting 6 (six) factors that were taken into consideration by the Human Resources Departments of existing customers in trying out the Medical Check-Up services provided by Pertamina Central Hospital in 2012 and were able to explain 25 (twenty-five) variables in data, which amounted to 79,3 percent. The 6 (six) underlying factors as previously mentioned were Factor 1 (Informative and Attractive Promotional Tools) with 10.077 eigen value and 40,3 percent variance. Factor 2 (The Parking Space Availability) with 2,719 eigen value and 10,8 percent variance. Factor 3 (Product Quality), with 2,062 eigen value and 8,2 percent variance. Factor 4 (Price Reasonability/Affordability), with 1,487 eigen value and 5,9 percent variance. Factor 5 (Punctuality of Examination Results), with 1,026 eigen value and 4.1 percent variance. Last but not least, Factor 6 (Transportation Means to the Hospital), with 1,026 percent of eigen value and a 4,1 percent variance. All the model precision generated were based on the size of residuals, which is the difference from the produced correlations; in accordance to the matrix estimation outcome, the factor was as large as 38 percent or as many as 115 residuals, with absolute significance value above 0,05. These numbers statistically showed that the model had performed accuracy of 62 percent by 0,05 deviation, or 5 percent so to speak/per se.