Rekrutmen Elit Birokrasi Di Lingkungan Sekretariat Daerah Provinsi Jawa Tengah Tahun 2013-2015

In implementation of Government in Indonesian, region given a chance and freedom for organize the extensive of region autonomy, which it is included in section 18 and verse 5 of UUD 1945. From awarding the authority, the low level of bureaucrat given a chance for take the initiative and explore them creativity. Therefore the main problem of realization autonomy region is upgrading the quality of human power and agency. We need human power which qualified and professional. Qualified and professional human power depends on process of recruitment bureaucracy elite. Therefore the formulation of problem in this research is “How is the process of recruitment bureaucracy elite in area of Central Java Provincial Secretariat and what is the factor affecting.” The direction of this research is to detect how is process of recruitment bureaucracy elite in area of Central Java Pronvicial Secretariat and what is the factor affecting of process recruitment. The research method is descriptive qualitative. Primary source data retrieved from interview and secondary data from document, archieve, and the other source which it is still connect with research. Analysis technique using qualitative data with analyst in form essay, depiction, and drawing the conclusion of indication research. From the result of research, showing that process of recruitment bureaucracy elite in area of Central Java Pronvicial Secretariat using auction of position system or open promotion system. It was first time perfomed in Central Java. The result of this system judged can fixing the situation of our bureaucracy because a few of good governance idea which it is include idea of transpiration, accountability, obey the law, and participation of the citizen. Besides the advantage of open promotion system is recruitment system that puts meritocracy people on front. This recruitment system have to done truly and full of concistency, so that the result not useless. Also required commitment, courage, and provide a leadership in Indonesian, in particular the leadership of leaders such as, Governor, Regent, or Mayor in order to apply the punishment for all of the agency which make a mistake in the process recruitment

Rancangan D-Optimal Model Gompertz dengan Maple

Gompertz model is used in many areas including biological growth studies, animal and husbandry, chemistry, and agricultural. Locally D-optimal designs for Gompertz models with three parameters is investigated. We used the Generalized Equivalence Theorem of Kiefer and Wolvowitz to determine D-optimality criteria. Tchebysheff system is used to decide that the D-optimal design is minimally supported design or nonminimally supported design. The result, D-optimal design for Gompertz model is minimally supported design with uniform weight on its support

Analisis Intervensi Dan Deteksi Outlier Pada Data Wisatawan Domestik (Studi Kasus Di Daerah Istimewa YOGYAKARTA)

The tourist data is very interesting to be studied because the Indonesian tourism sector is an activator of the national economic which is potential to push higher economic growth in the future. Therefore, the forecast about tourist data is very needed for tourism business. The tourist data tend to fluctuate caused by many factors that affect the number of tourists extremely in an area, such as disasters, government regulation, social stability, violence and terrorism. That the extreme data can be assessed using intervention analysis and outlier detection. Intervention model is a time series model that can be used to forecast data consist of intervention of internal and external factors. In the intervention model, there are two kinds of intervention function, i.e., step and pulse functions. Step function is a form of intervention occurred in period of time while the pulse function is a form of intervention occurred only in a certain time. For the outlier detection, there are four types, such as additive outlier (AO), innovational outlier (IO), level shift (LS) and temporary change (TC). As an empirical studies was conducted by the domestic tourists data in Yogyakarta from January 2006 until December 2010 who staying on five-star hotels and motel throughout Yogyakarta. Based on the result of this research, known that the intervention occurred on January 2010 using the pulse function with MSE value 1172. Meanwhile based on the outliers detection, known any five outliers but only four outliers that significant included to the intervention model with MSE value 523,7167. So, the intervention model and outlier detection are chosen as a the best model based on the smallest MSE criterion

Pelatihan Feed Forward Neural Network Menggunakan Algoritma Genetika Dengan Metode Seleksi Turnamen Untuk Data TIME Series

Pemodelan time series seringkali dikaitkan dengan proses peramalan suatu nilai karakteristik tertentu pada periode mendatang. Salah satu metode peramalan yang berkembang saat ini adalah menggunakan artificial neural network atau yang lebih dikenal dengan neural network.Penggunaan neural network dalam peramalan time series dapat menjadi solusi yang baik, namun yang menjadi masalah adalah arsitektur jaringan dan pemilihan metode pelatihan yang tepat. Salah satu pilihan yang mungkin adalah menggunakan algoritma genetika. Algoritma genetika adalah suatu algoritma pencarian stokastik berdasarkan cara kerja melalui mekanisme seleksi alam dan genetik yang bertujuan untuk mendapatkan solusi dari suatu masalah. Algoritma ini dapat digunakan sebagai metode pembelajaran dalam melatih model feed forward neural network. Penerapan algoritma genetika dan neural network untuk peramalan time series bertujuan untuk mendapatkan bobot-bobot yang optimum dengan meminimumkan error. Dari hasil pelatihan dan pengujian pada data kurs Dolar Australia terhadap Rupiah didapatkan nilai RMSE sebesar 117.3599 dan 82.4917. Model ini baik untuk digunakan karena memberikan hasil prediksi yang cukup akurat yang ditunjukkan oleh kedekatan target dengan output

Pemodelan Markov Switching Vector Autoregressive (Msvar)

Economic and financial variables are variables that are fluctuated because of regime switching as a result of political and economical conditions. Linear modeling can not capture the regime switching, so it is better to use Markov Switching Vector Autoregressive Models (MSVAR). MSVAR is a combination of vector autoregressive models and hidden markov models. Daily return of Rupiah buying rate against the USD and Euro are economic variables that are fluctuated and they can explain economic condition of a country. The best model of five order iteration is MS (2) - VAR (4) with the smallest AIC value, that is -1460.48. Maximum Likelihood Estimation is a method to get parameters estimation. With 73 data, the return rates has transition probability 0.08 from crisis to normal state, while the transisition probablity of the opposite condition is 0.6. Expected value being at normal state is 13.10 days and being at crisis state is 1,68 days