Nama Lengkap : Prof. Dr. Budi Nurani Ruchjana, MS
E-mail : [email protected]
Bidang Keahlian : Statistika, Pemodelan Spatio Temporal, Etnomatematika
Prodi S2 Calon Mahasiswa : Magister Matematika
Prodi S3 Calon Mahasiswa: Doktor Matematika
Judul Penelitian yang Ditawarkan:
Pengembangan Model Spatio Temporal Data Mining dalam Era Big Data berbasis Media Sosial
Development of the Spatio Temporal Data Mining Model in the Big Data Era based on Social Media
Abstrak:
Penelitian berjudul Pengembangan Model Spatio Temporal Data Mining dalam Era Big Data berbasis Media diajukan dalam skema Beasiswa Unggulan Pascasarjana Universitas Padjadjaran 2022. Topik ini dipilih untuk mendukung aktivitas akademik yang dapat diikuti oleh calon mahasiswa program Fast-Track Magister Doktor Bidang Matematika pada Fakultas Matematika dan Ilmu pengetahuan Alam Universitas Padjadjaran periode 2022-2026. Kajian penelitian difokuskan untuk pengembangan model Spatio Temporal berbasis model time series Box-Jenkins (1976) yang dikembangkan menjadi model Generalized Space Time Autoregressive (GSTAR) oleh Ruchjana (2002) dan dalam rencana penelitian akan dikembangkan menjadi model Generalized Space Time Autoregressive Integrated Moving Average Exogeneous (GSTARIMAX) yang merupakan integrasi analisis data time series, analisis data spasial dan kombinasi keduanya berupa analisis spatio temporal atau analisis space time .
Di era revolusi industri 4.0 dan society 5.0, big data atau data science berupa data dalam ukuran besar dari sisi variable-velocity-variety- serta complexity menjadi bagian penting dalam berbagai aplikasi keilmuan didukung dengan data media sosial yang bergerak dengan cepat baik dari sisi waktu maupun lokasi. Metode penelitian digunakan Knowledge Discovery in Database (KDD) melalui pendekatan tiga tahap preprocessing-data mining- postprocessing. Untuk studi kasus akan dipilih topik budya dan pariwisata untuk mendukung peningkatan kegiatan pariwisata pasca pandemi Covid-19 dan merealisasikan pilar 11 SDGs berupa kota dan pemukiman berkelanjutan. Dalam penelitian ini dikaji model spatio temporal secara teoritis dan aplikasinya didukung komputasi dengan perangat lunak R atau phyton di bawah bimbingan Tim Promotor dalam bidang Matematika-Statistika, dan Ilmu Komputer bekerja sama dengan tim peneliti bidang Ilmu Komputer dari Albaha University, Saudi Arabia maupun University of Agder Norwegia.
Hasil penelitian berupa kajian pengembangan model secara teoretis dan aplikasinya didukung dengan komputasi menggunakan perangkat lunak open source R atau Phyton, diharapkan dapat memberikan kontribusi bagi pengembangan ilmu dan aplikasi dalam rumpun Ilmu Formal meliputi bidang Matematika-Statistika dan Ilmu Komputer. Penelitian tesis-disertasi ini diharapkan menghasilkan publikasi karya ilmiah mahasiswa program Magister-Doktor Matematika FMIPA Unpad pada jurnal internasional bereputasi terindeks Scopus atau Web of Science maupun jurnal nasional terakreditasi Sinta, sehingga dapat meningkatkan kualitas akademik mahasiswa dan lulusan Magister-Doktor Matematika di tingkat nasional maupun internasional.
Abstract :
The research entitled Development of a Spatio Temporal Data Mining Model in the Media-Based Big Data Era was proposed in the 2022 Padjadjaran University Postgraduate Excellence Scholarship scheme. This topic was chosen to support academic activities that can be followed by prospective students of the Fast-Track Masters Doctoral Program in Mathematics at the Faculty of Mathematics and Natural Sciences. Natural Sciences, Padjadjaran University for the period 2022-2026. The research study is focused on developing a Spatio Temporal model based on the Box-Jenkins (1976) time series model which was developed into a Generalized Space Time Autoregressive (GSTAR) model by Ruchjana (2002) and in the research plan it will be developed into a Generalized Space Time Autoregressive Integrated Moving Average Exogeneous model. (GSTARIMAX) which is an integration of time series data analysis, spatial data analysis, and a combination of both in the form of spatio temporal analysis or space-time analysis.
In the era of the industrial revolution 4.0 and society 5.0, big data or data science in the form of large data in terms of variables-velocity-variety- and complexity become an important part of various scientific applications supported by social media data that move quickly both in terms of time and location. The research method used is Knowledge Discovery in Database (KDD) through a three-stage preprocessing-data mining-postprocessing approach. For the case study, the topic of culture and tourism will be chosen to support increased tourism activities after the Covid-19 pandemic and realize the 11th pillar of the SDGs in the form of sustainable cities and settlements. In this study, the theoretical spatio temporal model is studied and its application is supported by computation with R or Python software under the guidance of the Promotor Team in the field of Mathematics-Statistics, and Computer Science in collaboration with a research team in the field of Computer Science from Albaha University, Saudi Arabia and the University of Agder Norway.
The results of the research in the form of a study of theoretical model development and its application supported by computing using open-source software R or Python, are expected to contribute to the development of science and applications in the Formal Science including the fields of Mathematics-Statistics and Computer Science. The research of theses and dissertations is expected to result in the publication of scientific works of students from the Masters-Doctoral Mathematics program at FMIPA Unpad in reputable international journals indexed by Scopus or Web of Science as well as Sinta-accredited national journals, so as to improve the academic quality of students and graduates of Masters-Doctoral Mathematics at the national and international levels.