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"Buku ini mengenai pengembangan baru dalam analisis sata kategorikal untuk ilmu sosial dan tingkah laku."

"Summary:This new edition continues to provide basic, comprehensive coverage of key methods in categorical data analysis with multiple variables. Maintaining the same nontechnical, user-friendly approach, coverage has been added to the Second Edition to take the topic of categorical data analysis into a more applied direction."

"Public organizations are now requested to continually increase its service quality to be able to compete with the private sector which is continually expanding to respond to the environment changes. One of the new paradigm in the management of the public sector is known as the New Public Management (NPM) , in which, according to some research conducted by experts, the Traditional Model of public Administration is no more suitable to the present organizational needs. A lot of work has been done towards the NPM approach , and the one , especially discussed in this writing , is the adoption of the Performance Management System (PMS) as realized by the private sector. This is because the performance measurement at the traditional model does not include the periodical evaluation towards the program as well as the individual. This measurement is also more leaning towards the economic perspective (input oriented) in which the connection between input cost and the goal has not yet been seen. For this purpose, some countries have tried to implement the PMS with some different conclusion. This writing tried to insvestigate whether the the PMS is successful or has failed based on some literature research as conducted by some scholars in this field. On the other hand, problems which are related to the implementation of the PMS and its effect towards the public sector have also been evaluated. At the end of this writing some recommendations have been presented. It is certain that each organization has different characteristics which means that the implementation of the program should be adjusted to the uniqueness of the public sector organization."

"Public organizations are now requested to continually increase its service quality to be able to compete with the private sector which is continually expanding to respond to the environmental changes. One of the new paradigm in the management of the public sector is known as the New Public Management (NPM), in which, according to some research conducted by experts, the Traditional Model of Public Administration is no more suitable to the present organizational needs. A lot of work has been done towards the NPM approach, and the one, especially discussed in this writing, is the adoption of the Performance Management System (PMS) as realized by the private sector. This is because the performance measurement at the traditional model does not include the periodical evaluation towards the program as well as the individual. This measurement is also more leaning towards the economic perspective (input oriented) in which the connection between input cost and the goal has not yet been seen. For this purpose, some countries have tried to implement the PMS with some different conclusion. This writing tried to investigate whether the PMS is successful or has failed based on some literature research as conducted by some scholars in this field. On the other hand, problems which are related to the implementation of the PMS and its effect towards the public sector have also been evaluated. At the end of this writing some recommendation have been presented. It is certain that each organization has different characteristic which means that the implementation of the program should be adjusted to the uniqueness of the public sector organization."

"Model deep learning adalah model dengan banyak lapisan jaringan saraf tiruan. Model Bidirectional Gated Recurrent Unit (BiGRU) adalah salah satu jenis model deep learning yang memproses urutan data dalam dua arah, yaitu arah maju dan arah mundur. Hal tersebut memungkinkan model BiGRU untuk mengakses informasi masa depan dan masa lalu dari setiap titik dalam urutan data untuk pemahaman konteks yang lebih baik. Model BiGRU dapat digunakan untuk analisis sentimen, yaitu proses mengategorikan sentimen opini dalam teks menjadi negatif, netral, atau positif. Representasi teks yang digunakan pada penelitian ini adalah Bidirectional Encoder Representations from Transformers (BERT) karena kemampuannya memahami kata secara kontekstual sehingga meningkatkan akurasi. Salah satu masalah umum pada analisis sentimen adalah ketidakseimbangan data Penggunaan data tidak seimbang mempengaruhi kinerja model dalam melakukan klasifikasi sentimen karena bias terhadap kelas mayoritas. Oleh karena itu, penggunaan Synthetic Minority Oversampling Technique (SMOTE) dalam mengatasi ketidakseimbangan kelas pada data dilakukan pada penelitian ini. SMOTE digunakan untuk melakukan oversampling pada data kelas minoritas dan dipasangkan dengan model BiGRU yang menggunakan fungsi kerugian categorical cross entropy menghasilkan kinerja dengan nilai akurasi sebesar 85,52% yang merupakan akurasi tertinggi dibandingkan dengan daripadamodel BiGRU dengan fungsi kerugian categorical cross entropy tanpa penanganan SMOTE (model standar dalam penelitian ini) dan model BiGRU dengan fungsi kerugian weighted cross entropy yang dibangun untuk memperkuat bukti bahwa model yang diajukan adalah model terbaik.

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Deep learning models are models with multiple layers of artificial neural networks. The Bidirectional Gated Recurrent Unit (BiGRU) model is one type of deep learning model that processes data sequences in two directions, the forward direction and the backward direction. This allows the BiGRU model to access future and past information from each point in the data sequence for better context understanding. The BiGRU model can be used for sentiment analysis, which is the process of categorizing the sentiment of opinions in text into negative, neutral, or positive. The text representation used in this research is Bidirectional Encoder Representations from Transformers (BERT) because of its ability to understand words contextually to increase accuracy. One of the common problems in sentiment analysis is data imbalance. The use of unbalanced data affects the performance of the model in performing sentiment classification due to bias towards the majority class. Therefore, the use of Synthetic Minority Oversampling Technique (SMOTE) in overcoming class imbalance in the data is done in this study. SMOTE is used to perform oversampling on minority class data and paired with the BiGRU model using the categorical cross entropy loss function results in performance with an accuracy value of 85.52% which is the highest accuracy compared to the BiGRU model with the categorical cross entropy loss function without SMOTE handling (the standard model in this study) and the BiGRU model with the weighted cross entropy loss function built to strengthen the evidence that the proposed model is the best model."