PENGARUH KEPUASAN KERJA DAN PELATIHAN KERJA TERHADAP PRODUKTIVITAS KERJA KARYAWAN BAGIAN BIRO ADMINISTRASI UMUM PADA UNIVERSITAS MUHAMMADIYAH SURAKARTA

Penelitian ini bertujuan untuk mengetahui : (1) Pengaruh kepuasan kerja terhadap produktivitas kerja karyawan bagian Biro Administrasi Umum pada Universitas Muhammadiyah Surakarta. (2) Pengaruh pelatihan kerja terhadap produktivitas kerja karyawan bagian Biro Administrasi Umum pada Universitas Muhammadiyah bagian Biro Administrasi Umum pada Universitas Muhammadiyah Surakarta. (3) Pengaruh kepuasan kerja dan pelatihan kerja secara bersama-sama terhadap produktivitas karyawan bagian Biro Administrasi Umum pada Universitas Muhammadiyah Surakarta. Populasi dalam penelitian ini adalah karyawan Universitas Muhammadiyah Surakarta bagian Biro Administrasi Umum dengan sampel 50 orang. Teknik pengumpulan data dalam penelitian ini menggunakan metode penyebaran angket kuesioner. Sedangkan uji instrumen penelitian melalui uji validitas dan uji reliabilitas, selanjutnya teknik analisa data, datanya menggunakan analisa regresi linier berganda, uji t, uji F, koefisien determinasi (R2). Hasil penelitian ditemukan bahwa : (1) ada pengaruh yang positif antara kepuasan kerja terhadap produktivitas kerja karyawan. (2) ada pengaruh yang positif antara pelatihan kerja terhadap produktivitas kerja karyawan. (3) ada pengaruh yang positif antara kepuasan kerja dan pelatihan kerja secara bersama- sama terhadap produktivitas kerja karyawan. Dan pelatihan kerja adalah bagian yang paling dominan terhadap peningkatan produktivitas kerja karyawan bagian Biro Administrasi Umum pada Universitas Muhammadiyah Surakarta. Berdasarkan regresi linier berganda diperoleh koefisien regresi kepuasan kerja 0,253 dan koefisien regresi pelatihan kerja 0,310. Berarti pelatihan kerja berpengaruh lebih besar terhadap produktivitas kerja karyawan dibandingkan kepuasan kerja. Hasil uji t di peroleh t hitung kepuasan kerja 2,060 dan t hitung pelatihan kerja 2,973. Berarti secara parsial kedua variabel independen berpengaruh signifikan terhadap variabel dependen. Uji F diperoleh Fhitung 15,193 sehingga ada pengaruh secara bersama-sama antara kepuasan kerja dan pelatihan kerja terhadap produktivitas kerja karyawan. Hasil koefisien determinasi (R2) sebesar 39,3% produktivitas kerja di pengaruhi oleh kepuasan kerja dan pelatihan kerja sedangkan sisanya 60,7% dipengaruhi oleh variabel lain

Principal component analysis-based data clustering for labeling of level damage sector in post-natural disasters

Post-disaster sector damage data is data that has features or criteria in each case the level of damage to the post-natural disaster sector data. These criteria data are building conditions, building structures, building physicals, building functions, and other supporting conditions. Data on the level of damage to the post-natural disaster sector used in this study amounted to 216 data, each of which has 5 criteria for damage to the post-natural disaster sector. Then the 216 post-disaster sector damage data were processed using Principal Component Analysis (PCA) to look for labels in each data. The results of these labels will be used to cluster data based on the value scale of the results of data normalization in the PCA process. In the data normalization process at PCA, the data is divided into 2 components, namely PC1 and PC2. Each component has a variance ratio and eigenvalue generated in the PCA process. For PC1 it has a variance ratio of 85.17% and an eigenvalue of 4.28%, while PC2 has a variance ratio of 9.36% and an eigenvalue of 0.47%. The results of the data normalization are then made into a 2-dimensional graph to see the visualization of the PCA results data. The result is that there is 3 data cluster using a value scale based on the PCA results chart. The coordinate value (n) of each cluster is cluster 1 (n<0), cluster 2 (0 ≤n <2), and cluster 3 (n≥2). To test these 3 groups of data, it is necessary to conduct trials by comparing the original target data, there are two experiments, namely testing the PC1 results with the original target data, and the PC2 results with the original target data. The result is that there are 2 updates, the first is that the distribution of PC1 data is very good in grouping the data when comparing the distribution of data with PC2, because the variance ratio and eigenvalue values of PC1 are greater than PC2. While second, the results of testing the PC1 data with the original target data produce good data grouping, because the original target data which has a value of 1 (slightly damaged) occupies the coordinates of cluster 1 (n<0), while the original target data which has a value of 2 (damaged moderately) occupies cluster 2 coordinates (0 ≤n <2), and for the original target data the value 3 (heavily damaged) occupies cluster 3 coordinates (n≥2). Therefore, it can be concluded that PCA, which so far has been used by many studies as feature reduction, this study uses PCA for labeling unsupervised data so that it has an appropriate data label for further processing