COMPARISON OF RESAMPLING EFFICIENCY LEVELS OF JACKKNIFE AND DOUBLE JACKKNIFE IN PATH ANALYSIS

The assumption of normality is often not fulfilled, this causes the estimation of the resulting parameters to be less efficient. The problem with assuming that normality is not satisfied can be overcome by resampling. The use of resampling allows data to be applied free of distributional assumptions. In this study, a research simulation was carried out by applying Jackknife resampling and Double Jackknife resampling in path analysis with the assumption that the normality of the residuals was not fulfilled and the number of resampling was set at 100 with the degree of closeness level of relationship between variables consisting of low closeness, medium closeness, and high closeness. Based on the simulation results, resampling with a power of 100 can overcome the problem of unfulfilled normality assumptions. In addition, the comparison of the relative efficiency level of the resampling jackknife and double jackknife in the path analysis obtained by the resampling double jackknife has more efficiency than the resampling jackknif

Principal Component Regression Modelling with Variational Bayesian Approach to Overcome Multicollinearity at Various Levels of Missing Data Proportion

This study aims to model Principal Component Regression (PCR) using Variational Bayesian Principal Component Analysis (VBPCA) with Ordinary Least Square (OLS) as a method of estimating regression parameters to overcome multicollinearity at various levels of the proportion of missing data. The data used in this study are secondary data and simulation data contaminated with collinearity in the predictor variables with various missing data proportions of 1%, 5%, and 10%. The secondary data used is the Human Depth Index in Java in 2021, complete data without missing values. The results indicate that the multicollinearity in secondary and original data can be optimally overcome as indicated by the smaller standard error value of the regression parameter for the PCR using VBPCA method which is smaller and has a relative efficiency value of less than 1. VBPCA can handle the proportion of missing data to less than 10%. The proportion of missing data causes information from the original variable to decrease, as evidenced by immense MAPE value and the parameter estimation bias that gets bigger. Then the cross validation (Q^2 ) value and the coefficient of determination (adjusted R^2 ) are get smaller as the proportion of missing data increases.

Bootstrap Resampling in Gompertz Growth Model with Levenberg–Marquardt Iteration

Soybean plants have limited growth with a planting period of 12 weeks, which causes the observed sample to be very small. A small sample of soybean plant growth observations can be bias causes in the conclusion of prediction results on soybean plant growth. The  purpose this study is to apply  the bootstrap resampling technique in Gompertz growth model which overcomes residual distribution with small samples, the research data was taken from soybean plant growth in four varieties with four spacing treatments, five replications and twelve weeks (long planting period).   Gompertz growth model uses nonlinear least squares method in estimating parameters with Levenberg–Marquardt iteration. The value of the Gompertz model after resampling bootstrap has no significant difference. The adjusted R2 value of 0.96 is close to 1. This means that the total diversity of plant heights can be explained by the Gompertz model of 96 percent. Judging from the graph of predictions of soybean plant growth before resampling and after resampling coincide with each other it can also be seen in the initial growth values before resampling 14, 05 and 14.18, the maximum growth values are 55.13 and 55.60. Bootsrap resampling technique can overcome residual normality in the Gompertz growth model, but does not change the information in the initial data

Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis

This research aims to obtain the main component score of the latent variable ability to pay, determine the strongest indicators forming the ability to pay on a mixed scale based on predetermined indicators, and model the ability to pay on time as mediated by fear of paying using path analysis. The data used is secondary data obtained through distributing questionnaires with a mixed data scale. The sampling technique used in the research was purposive sampling. The number of samples used in the research was 100 customers. The method used is nonlinear principal component analysis with path analysis modeling. The results of this research show that of the five indicators formed by the Principal Component, 74.8% of diversity or information is able to be stored, while 25.20% of diversity or other information is not stored (wasted). Credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgage has a significant effect on payments by mediating the fear of being late in paying with a coefficient of determination of 73.63%.

Development of Accuracy for the Weighted Fuzzy Time Series Forecasting Model Using Lagrange Quadratic Programming

Limitation within the WFTS model, which relies on midpoints within intervals and linguistic variable relationships for assigning weights. This reliance can result in reduced accuracy, especially when dealing with extreme values during trend to seasonality transformations. This study employs the Weighted Fuzzy Time Series (WFTS) method to adjust predictive values based on actual data. Using Lagrange Quadratic Programming (LQP), estimated weights enhance the WFTS model. MAPE assesses accuracy as the model analyzes monthly IHSG closing prices from January 2017 to January 2023.The MAPE value of 0.61% results from optimizing WFTS with LQP. It utilizes a deterministic approach based on set membership counts in class intervals, continuously adjusting weights during fuzzification, minimizing the deviation between forecasted and actual data values.The Weighted Fuzzy Time Series Forecasting Model with Lagrange Quadratic Programming is effective in forecasting, indicated by a low MAPE value. This method evaluates each data point and adjusts weights, offering reliable investment insights for IHSG strategies.

IDENTIFIKASI DATA RATA-RATA CURAH HUJAN PER-JAM DI BEBERAPA LOKASI

Tujuan penelitian ini adalah untuk mengidentifikasi data curah hujan per-jam berdasarkan informasi lokasi. Identifikasi untuk mengetahui apakah data curah hujan telah memenuhi asumsi isotropik, homogen dan stasioner. Apabila satu atau lebih asumsi ini tidak terpenuhi maka hasil analisis yang diterapkan kurang tepat. Pemeriksaan asumsi dilakukan melalui pendekatan korelasi jarak antar lokasi (semivariogram). Hasil penelitian menunjukkan bahwa terdapat korelasi curah hujan yang signifikan antar lokasi (Nilai P < 0.000). Kata kunci: data lokasi, isotropik, homogen, stasioner, semivariogra

Deteksi Wajah Manusia Pada Citra Menggunakan Dekomposisi Fourier

Pada dua dekade ini banyak dilakukan penelitian yang berhubungan dengan identifikasi dan pengenalan wajah. Wajah adalah bagian dari manusia dan merupakan bagian yang dapat dibedakan dengan manusia lainnya. Ada beberapa pendekatan dalam penelitian wajah yaitu secara geometri, template, dan karakteristik obyek wajah. Salah satu penelitian berbasis karakteristik obyek wajah adalah dengan menggunakan transformasi avelet. Terdapat kesamaan dan ketidak samaan antara transformasi wavelet dan transformasi Fourier. Dengan memperhatikan keunggulan masing-masing maka penelitian ini memfokuskan diri pada pengenalan obyek wajah menggunakan karakteristik obyek wajah yaitu dengan menggunakan dekomposisi Fourier

SEARCHING FOR A MORAL CHARACTER: THE GENESIS OF THE AUDITOR'S DUTY

Frequently, questions are asked to the accounting profession in the face of ethical dilemmassuch as how auditors should behave. Many studies have shown moral character is importantin ethical judgment, but there is very little explanation about the moral character of its own.This study aimed to test empirically the effect of individual personality factors, such as moralcharacter variables comprising the dimensions of spirituality, idealism, moral courage, andperspective taking in the ethical judgment. Research data was obtained by distributing questionnaires to the auditor in Surabaya and Jakarta. Auditors' ethical decision-making ismeasured by making a story of ethical scenarios. Furthermore, the data were analyzed usingsoftware WarpPLS. This study shows importance of moral character in an auditor's ethicaldecision. This study shows that being an accountant is a choice being a noble human beingand not a mere pursuit of economic benefits

THE PERCEPTION OF INDIVIDUAL AND ORGANIZATIONAL CAREERS IN INCREASING THE ORGANIZATIONAL COMMITMENT

Organization commitment is very important for individuals working in any organization.Therefore, considering individuals and the perception toward the careers is really important.This study determines the direction of influence of basic individual careers and career developmentprograms on job satisfaction and organizational commitment. This research conducteda survey on Private Higher Education teaching staff of Kopertis (private higher educationcoordinator) Borneo in Banjarmasin. The data from 60 respondents were analyzedusing the Partial Least Square (PLS) to examine the relationship among variables basic individualcareers and career development programs that have a significant and positive impacton job satisfaction and organizational commitment. The results showed that the basic individualcareers and career development programs affect organizational commitment and jobsatisfaction. In addition, it is also proved that job satisfaction mediate the increasing organizationalcommitment

Student Satisfaction as Mediation Relationship Between Total Quality Management Practices and Performance of Management Study Program

Indonesian students who learn in Malaysia and Singapore were more than the number of Malaysian students who learn Indonesia (Marhum, 2013). This happens because assumption that higher education quality in Indonesia has decreased due to lower college performance. Therefore, this study aimed to describe a theoretical relationship of satisfaction as mediation between the practices of Total Quality Management (TQM) and performance of management study program. It can be described that (1) Higher TQM practices will increase management study program performance. (2) Student satisfaction does not mediate relationship between TQM practices on performance. (3) Control of process and priority development or precedence was carried in implementation of TQM practices. (4) Indicators of learning process needs to become a priority or precedence to improve student satisfaction in management study program. (5) On time graduation was most important indicator in measuring performance variables of management courses. (6) TQM practice can be implemented in management study program