Petra Christian University

About: Petra Christian University is a education organization based out in Surabaya, Indonesia. It is known for research contribution in the topics: Population & Customer satisfaction. The organization has 2412 authors who have published 2893 publications receiving 10429 citations.

Komitmen dan kapabilitas untuk meningkatkan kinerja reverse logistics

Abstract: Artikel ini mengkaji pengaruh pengorganisasian dan pemanfaatan teknologi logistic dalam pengelolaan reverse logistics di perusahaan manufaktur penghasil barang/kemasan plastik terhadap kapabilitas inovasi dan komunikasi perusahaan dalam meningkatkan kinerja reverse logistics perusahaan. Analisis yang digunakan adalah Structural Equation Modeling dengan menggunakan program SmartPLS. Hasil penelitian menunjukkan bahwa pengelolaan reverse logistics melalui alokasi anggaran dan pembentukan unit pengelola tersendiri disertai pendayagunaan teknologi, terutama pertukaran data secara elektronik, mampu meningkatkan kapabilitas inovasi, khususnya kemampuan kustomisasi dan fleksibilitas perusahaan dalam meningkatkan kinerja reverse logistics, dalam hal ketepatan waktu dan biaya operasional yang rendah. Di sisi lain, kapabilitas komunikasi belum terbukti dapat memengaruhi kinerja reverse logistics dikarenakan kapabilitas yang dimiliki belum dimanfaatkan secara optimal.

The Analysis of the Effect of Academic Service Quality on Student Loyalty Through Student Satisfaction and Organizational Performance

Abstract: This research aims to analyze the significant influence of quality academic services to student loyalty with student satisfaction and organizational performance as intervening. The sample in this study as many as 40 people, data collection using questionnaires, and then analyzed with SEM PLS using smart software PLS 3.0. Based on the results of the analysis show that the quality of academic services has an effect on student loyalty with a statistical t-value of 2.532, the quality of academic services has no effect on organizational performance with a statistical value of 0.026, the quality of academic services has an effect on student satisfaction with a value of 1.988 statistics, student satisfaction has an effect on organizational performance with a t-value of 1.891, student satisfaction has no effect on student loyalty with a statistical t-value of 0.370, organizational performance influences student loyalty with a statistical T value of 8.380, student satisfaction does not mediate the effect of academic service quality with a statistical t-value of 1.201, student satisfaction does not mediate the effect of academic service quality on student loyalty with a statistical t-value of 0.319, organizational performance mediates the effect of student satisfaction on student loyalty self with a statistical t-value of 1.778, student satisfaction and organizational performance does not mediate the effect of academic service quality on student loyalty with a statistical t-value of 1.169, organizational performance does not mediate the effect of academic service quality on student loyalty with a statistical T value of 0.025.

Currency movement forecasting using time series analysis and long short-term memory

Abstract: Foreign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day.

Pendekatan Analisis Data Menggunakan NVivo-software untuk Penelitian Desain Logo Museum Nasional Jakarta

Abstract: Qualitative data analysis can be an exhausting, tough, and time-consuming work because the data obtained is so numerous, varied, and unstructured. However, this problem has been resolved by using computer-assisted qualitative data analysis software (CAQDAS). CAQDAS can help researchers to save, organize, explore data easily, and reduce the risk of damaging the raw data. In this article, the writer will explain about how to apply a quite popular CAQDAS application, NVivo, in the research of logo design of Museum Nasional Jakarta.

Parametric Beam Modeling to Predict the First Natural Bending Frequency of Thin Wall Box-Shaped Structures Verified Using Experimental Modal Analysis

Abstract: Structural stiffness loaded with bending force is influenced by its dimension, especially by height and cross section. As the natural frequency of the structure depends on the structural stiffness, therefore the changes in the structure dimension will also change its natural frequency. In machining process of thin wall box-shaped workpiece using vertical milling machine the workpiece can be treated as a structure subject to bending force due to the cutting force. Therefore the structural stiffness of the workpiece is a crucial aspect to be considered. The natural frequency of the workpiece, especially its first natural frequency serves as a preliminary information of the workpiece modal parameter to avoid the self excited vibration called chatter. A new model based on parametric beam modeling was developed and implemented on computer programming to predict the first natural frequency of thin wall box-shaped workpieces. The first natural frequency of thin wall box-shaped workpiece can be calculated using this model for varying wall thickness, height, and length to width ratio of the workpiece cross section. The result of the parametric beam modeling is verified though an experimental modal analysis and also compared to a finite element numerical analysis.