Emotions, being transient and variable, differ across locations, times, and individuals. Automatic emotion identification holds significant importance across various domains, such as education and business. In education, emotional analysis contributes to intelligent electronic learning environments, while in business, it aids in assessing customer satisfaction with products. This study advocates the application of Principal Component Analysis (PCA) to enhance the performance of text emotion classification using the Artificial Neural Network (ANN) method. PCA, a pattern identification method, reduces text dimensions, improving the classification process by determining word similarities. PCA offers the advantage of dimension reduction without compromising information integrity. The classification approach involves two stages: one after PCA dimension reduction and the other without PCA post TF-IDF stage. The study's conclusive findings, incorporating PCA in ANN classification, demonstrated a notable increase in recall for the happy class, reaching 0.92 compared to the pre-PCA score of 0.91. Furthermore, precision in the sadness class improved to 0.90, surpassing the pre-PCA precision of 0.80. This affirms the efficacy of integrating PCA in enhancing the accuracy and performance of emotion classification in text analysis.

<p><em>Efficiency is one of the performance parameters underlying an organization. Efficiency in the world of banking is one of the performance parameters that is quite popular and widely used because it is the answer to the difficulties in calculating banking performance measurements. The measurement of banking efficiency can be done with Multivariate Analysis. Multiveriate is a methodology for determining relative efficiency and managerial performance based on empirical data. In this method, profit is modeled to be deviated from its profit efficient frontier which is influenced by the input and output functions. The sample of this study was 40 Islamic Commercial Banks and Islamic Business Units for 4 years starting from 2014-2018 which were analyzed using the STATA 8 technique with panel data. The results of this research show that Islamic banks are technically efficient based on intermediation (100%) and the efficient scale based on intermediation and production. Efficiency can be increasing liquid assets, while based on production approach.</em></p>