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"Tujuan penelitian ini adalah untuk Untuk mengidentifikasi dan mengeksplorasi bagaimana tipologi komunikasi deliberatif yang terdapat pada program sosialisasi Empat Pilar MPR RI melalui ruang digital. Penelitian ini menggunakan paradigma penelitian post positivistik dengan pendekatan campuran (mixed method), teknik pengumpulan data pada penelitian ini terdiri dari dua kategori, untuk kategori pendekatan kuantitatif, pengumpulan data menggunakan survei kepada peserta Sosialisasi Empat Pilar MPR RI secara daring, sedangkan secara kualitatif melalui wawancara mendalam kepada informan yang sesuai dengan kriteria dalam penelitian ini. berdasarkan hasil penelitian dan analisis data keseluruhan dapat disimpulkan bahwa komunikasi deliberatif sudah terlaksana pada kegiatan Sosialisasi Empat Pilar MPR RI melalui ruang digital dan teridentifikasi berada pada dua tipologi dari empat tipologi yang ditawarkan oleh Beauvais (2020). Tipologi tersebut adalah Deliberasi (deliberation) dan komunikasi politik non-deliberatif (non-delibrative political communication).

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The aim of this research is to identify and explore how deliberative communication typologies are present in the socialization program of the Four Pillars of MPR RI through digital spaces. This research employs a post-positivist research paradigm with a mixed-method approach. Data collection techniques in this study consist of two categories: for the quantitative approach, data is collected using surveys from participants of the online Socialization of the Four Pillars of MPR RI, while for the qualitative approach, in-depth interviews are conducted with informants who meet the criteria of this study. Based on the research results and overall data analysis, it can be concluded that deliberative communication has been implemented in the activities of the Socialization of the Four Pillars of MPR RI through digital spaces and has been identified in two of the four typologies proposed by Beauvais (2020). These typologies are Deliberation and Non-Deliberative Political Communication."

"Dalam tulisan ini, denah lantai ditekankan pada dua metrik (footprint dan program) yang berkaitan dengan tipologi dalam arsitektur. Tipologi adalah studi tentang jenis elemen yang hadir sebagai ide utama dari arsitektur. Arsitek awalnya memulai suatu karya dari tipe, dan konsep tipe itu sendiri menyiratkan ide transformasi. Konsep tipe ini sejalan dengan proses yang harus dilalui arsitek dalam perancangan denah lantai. Proses untuk mendapatkan suatu desain denah yang baik sangat berulang dan membutuhkan tenaga manusia yang ekstensif. Banyak program komputer yang dapat membantu proses tersebut tetapi tidak dapat sepenuhnya mengotomatisasi. Otomatisasi akan memungkinkan arsitek dengan cepat menghasilkan banyak kemungkinan untuk gambar arsitektur, termasuk denah lantai. Otomatisasi tersebut dapat diadopsi menggunakan Machine Learning, khususnya GAN (Generative Adversarial Network), yang telah berkembang pesat di berbagai bidang studi. Berdasarkan analisis denah yang dihasilkan secara otomatis, dapat disimpulkan bahwa Machine Learning berhasil menghasilkan variasi yang tinggi tanpa mengabaikan kriteria desain denah yang baik.

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In this paper, the floorplan is being emphasized on two metrics (footprint and program) which correlates to the discourse of typology in architecture. Typology is a study of types of elements which presents as the very idea of architecture. Architect initially starts his work from the type, and the very concept of type implies the idea of transformation. This concept resonates with a process that architect has to pass through. The process in creating a good floorplan design is highly iterative and requires extensive human labour. Many computer programs could help the architect in the process, but they cannot fully automate the process. Automation would allow architects to quickly generate many possibilities for architectural drawings, including floorplan solutions. Such automation could be adopted using Machine Learning, particularly GAN (Generative Adversarial Network), which has been developed enormously in many fields of study. Based on the analysis of the auto-generated floorplans, it could be concluded that Machine Learning successfully generates high variation without neglecting the criteria of a good floorplan design."

"The purpose of this paper is to examine the strategic orientation in terms of Miles and Snow typologiesof the firms in emerging country and investigate their implications on performance. Unlikeprevious research, this paper focuses the importance of strategy implementation to the performanceimplications of Miles and Snow typology. Data was collected from 111 key informants from firms inThailand?s chemical industry using probability sampling. The one-way Analysis of Variance (ANOVA)results showed that prospectors performed better than the other three strategic types, whereas reactorsexhibited the lowest performance scores. While the success in strategy implementation is foundto be a significant predictor of firm performance, the two-way Analysis of Variance (ANOVA) resultsrevealed that the success in strategy implementation did not alter the relationship between Miles andSnow strategic type and a firm?s performance. This finding suggests that the relationship betweenMiles and Snow strategic type and a firm?s performance may be universal, regardless of the locationwhere the study is conducted. However, additional studies in other contexts are required before theconclusion can be made whether the strategic type ? performance relationship will be contingent onstrategy implementation."