Hari Wijayanto

Pemodelan Loyalitas Konsumen Susu Pertumbuhan dalam Mengikuti Program Rewards Menggunakan Metode Random Forest dan Neural Network

Business competition in Indonesia has been becoming more competitive. This is the reason for companies to create a strategy in maintaining their customers through of loyalty program. However, on one of the rewards programs in nutrition companies, 37.62% of registered members have left the program or commonly known as churn. The classification model of customer loyalty is built to anticipate this, based on their profiles and activities in the program during the prediction period. The classification model is built for the two largest brands with random forest and neural network methods. These two methods are evaluated and compared based on the ROC (Relative Operating Characteristics) curve by considering the area under curve (AUC) value. The performance of these two methods are not significantly different, but neural network method yields greater AUC value, both in modeling of brand A and brand B.

Analisis Peran Remunerasi dan Budaya Organisasi Terhadap Kompetensi Pegawai Guna Meningkatkan Kinerja Pegawai di Lembaga Penerbangan dan Antariksa Nasional

Ideally the performance of a Government employees (PNS) aretheir service to the community in living and meeting their daily needs. The final goal of the PNS none other than to improve the welfare of society. Government Remuneration is an integral part of bureaucratic reform policy. Base on the bureaucracy awareness and also the government commitment to create a clean and good governmance. The purpose of bureaucratic reformation is to build a public trust (public trust building) and eliminate the negative image of government bureaucracy. The bureaucratic reformation vision is the establishment of the State apparatus professional and good governance. The mission is to change the pattern of bureaucratic reform mindset, cultural set, and the system of governance. Implementation of remuneration applied at LAPAN on July 2013 granted in accordance with the competences of the employees, work performance of the employees, and work discipline. The objective of this analysis is to analyze the relationship with remuneration and organizational culture, and the influence of remuneration to the employee competencies, organizational culture on employee competencies, employee competence to employee performance, remuneration to employee performance and the influence of organizational culture on employee performance. Analysis of the data used in this research is Structural Equation Modeling (SEM). The results of Study in simultaneously and partially are known to constructs remuneration, organizational culture, and employee competence positive effect on the performance of employees at every level positions and there is a correlation relationship between Construct Remunerasi and Cultural Organization to all employees and the level of structural positions and Special and strong correlation relationships at the level of the General Functional

A Comparative Study of Imputation Methods for Estimation of Missing Values of Per Capita Expenditure in Central Java

Missing values in repeated measurements have attracted concerns from researchers in the last few years. For many years, the standard statistical methods for repeated measurements have been developed assuming that the data was complete. The standard statistical methods cannot produce good estimates if the data suffered substantially by missing values. To overcome this problem the imputation methods could be used. This paper discusses three imputation methods namely the Yates method, expectation-maximization (EM) algorithm, and Markov Chain Monte Carlo (MCMC) method. These methods were used to estimate the missing values of per-capita expenditure data at sub-districts level in Central Java. The performance of these imputation methods is evaluated by comparing the mean square error (MSE) and mean absolute error (MAE) of the resulting estimates using linear mixed models. It is showed that MSE and MAE produced by the Yates method are lower than the MSE and MAE resulted from both the EM algorithm and the MCMC method. Therefore, the Yates method is recommended to impute the missing values of per capita expenditure at sub-district level.

Synthetic Over Sampling Methods for Handling Class Imbalanced Problems : A Review

Class imbalanced commonly found in any real cases. Class imbalanced occur if one of the classes has smaller amount, called minority class, than other class (majority class). The problem of imbalanced data is usually associated with misclassification problem where the minority class tends to be misclassified as compared to the majority class. There are two approaches should be performed to solve imbalanced data problems, those are solution at data level and solution at algorithm level. Over sampling approach is used more frequently than the other data level solution methods. This study gives review of synthethic over sampling methods for handling imbalance data problem. The implementation of different methods will produce different characteristics of the generated synthetic data and the implementation of appropriate methods must be adapted to the problems faced such as the level and pattern of imbalanced data of data available. Results of the review show that there is no absolute methods that are more efficient in dealing with the class imbalance. However, the class imbalance problem depends on complexity of the data, level of class imbalance, size of data and classifier involved. Determination of over sampling strategy will affect the outcome of the over sampling. So it is still open better development oversampling methods for handling the class imbalance. The selection classifier and evaluation measures are important to get the best results. Statistical test approach is needed to assess the theoritical propertis of synthetic data and evaluate missclassification in addition to the evaluation methods that have been used.

Small area estimation models with time factor effects for repeated measurement data

There has been an increasing demand for small area statistics based on sample survey in last few years. Generally, sample survey is designed for a specific area with large sample size. Since the sample size of a specific area is small, a direct estimation for sub population fails to provide enough precision. On the other hand, the per capita expenditure as a measure of prosperity and well-being is becoming an important issue in developing countries. In Indonesia, the available data related to this issue is usually measured repeatedly in quarterly using the national social-economic survey (Susenas). There are two problems to analyze Susenas data in district level. Firstly, the sample size in each quarter is relatively small. Secondly, the auxiliary data usually is available yearly only. To overcome this problem, a proper estimation technique was exercised using modeling systems. An extended of small area estimation (SAE) technique, based on both Fay-Herriot and Rao-Yu models in repeated measurement using Susenas data was discussed. Especially, SAE models with time factor effects motivated by Rao and Yu [7] were proposed. The performances of these models were evaluated by comparing the root mean square error (RMSE) and coefficient of variation (CV) of the estimators. The results showed that Rao-Yu with time factor effect models produced smaller RMSE which led to a significant reduction in a CV relative to other models.

Satisfaction of women’s and men’s health conditions in DKI Jakarta, A multi-group structural equation model approach

Satisfaction of health condition is one of several indicators used to measure the happiness index of Indonesia. Satisfaction of health condition is a latent variable that is measured using indicators: illness, ownership of health insurance, physical limitations, effort to maintain health, health problems, and the ability to control emotions. One of the health indicators used in the human development index is life expectancy. Statistics shows that the life expectancy of women is higher than the life expectancy of men. This study will model the satisfaction of health conditions in DKI Jakarta on 2013 using Multi-group Structural Equation Models (SEM). Multi-group SEM chosen in order to see the difference in satisfaction model of health conditions between women and men in DKI Jakarta. The data used in this study is the result Level Measurement of Happiness Survey 2013 in DKI Jakarta. The results showed that satisfaction with health condition in men is influenced by indicators of illness and health problems. ...

Ensemble Decomposition Method for Predicting the Price of Rice in Jakarta

Time series analysis is one of statistical procedures in time series data which is applied to predict the conditions that will come in the context of decision making. Generally, the huge size of data not only non linear but also non stationary and it is difficult to be interpreted in concrete. This problem can be solved by performing the decomposition process, the process of changing into a simpler form. Decomposition method that is Ensemble Empirical Mode Decomposition (EEMD). Decomposed time series data can also be used for prediction of the initial data. The ensemble methods can be used such as Fourier analysis used because IMF patterned sinusoid and ARIMA is used because this method is very popular in time series data. The methodology is applied to forecast weekly rice prices in Jakarta province from January 2002 to August 2013. Keyword: ARIMA, EEMD, Ensemble, Fourier Analysis, Time Series data.