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"Area studies, including Asian and Southeast Asian studies, in the post-Cold War era have been facing an epochal challenge that is rooted in two conditions: on the one hand, the end of the Cold War and the fading geopolitical rationale, and on the other, the emergence of the technology-driven transformation of the global economy and society. The consequences thus far are paradoxical: 1) While the technology-led transformation needs a workforce with critical and innovative abilities, higher education becomes more hyper-utilitarian; 2) While the transformation instigates increasing diversity of identities in global cultures, many countries thrive for STEM education at the expense of learning languages and cultures, including area studies which are essential for diversity. Southeast Asian studies programs need to change in response to these new conditions. These changing conditions and paradoxes, nevertheless, take different forms and degrees in the American, European and Asian academies, thanks to their different histories of higher education. The prospects for Southeast Asian Studies in these various academies are likely to be different too. "

"Distribusi Poisson adalah distribusi yang sangat banyak dipakai dalam pemodelan data cacahan. Namun, distribusi Poisson memiliki keterbatasan yaitu kesamaan antara nilai mean dan variansi (equidispersi) dari data yang akan dimodelkan, sehingga distribusi Poisson tidak cocok digunakan untuk memodelkan data yang tidak memenuhi syarat tersebut. Kasus overdispersi (variansi lebih besar daripada nilai mean) dan underdispersi (variansi lebih kecil daripada nilai mean) sering kali ditemukan dalam kasus riil. Oleh karena itu, distribusi baru perlu dikembangkan dalam menangani data dengan kasus ini. Salah satu distribusi yang dapat menangani kasus ini adalah distribusi hyper-Poisson. Distribusi ini dapat diturunkan melalui hubungan rekursif dari keluarga distribusi Lagrangian Katz yang merupakan keluarga distribusi data cacahan. Distribusi ini juga dapat diklasifikasi berdasarkan parameternya, sehingga dapat digunakan untuk mengatasi kasus overdispersi dan underdispersi secara fleskibel. Pada skripsi ini dijelaskan mengenai pembentukan fungsi distribusi hyper-Poisson, karakterisitik dari distribusi hyper-Poisson, dan penggunaan distribusi hyper-Poisson dalam memodelkan data riil terkait kasus overdispersi dan underdispersi.

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The Poisson distribution is a distribution that is very widely used in count data modeling. However, the Poisson distribution has a limitation, namely the equality between the mean and variance values (equidispersion) of the data to be modeled, so the Poisson distribution is no longer suitable for modeling data that does not meet this condition. Cases of overdispersion (variance greater than the mean value) and underdispersion (variance smaller than the mean value) are often found in real cases. Therefore, new distributions need to be developed to handle data with these cases. One distribution that can handle this case is the hyper-Poisson. This distribution can be derived through the recursive relation of the Lagrangian Katz family of distribution, which is a family of distribution of count data. This distribution can also be classified based on its parameter, so it can be used to handle overdispersion and underdispersion cases flexibly. This thesis studies how to generate the distribution function of the hyper-Poisson distribution, the characteristics of the hyper-Poisson distribution, and the use of the hyper-Poisson distribution in modeling real data related to overdispersion and underdispersion cases."