Judul: Klasterisasi Sekolah pada Penerimaan Mahasiswa Baru IPB University Jalur Rapor Menggunakan Algoritma K-Prototypes
Abstrak: Institut Pertanian Bogor atau IPB University merupakan perguruan tinggi negeri yang meraih peringkat pertama sebagai perguruan tinggi terbaik di Indonesia menurut Kemendikubristek pada tahun 2020 yang memiliki tiga jalur utama dalam sistem seleksi penerimaan mahasiswa baru. SNBP merupakan salah satu jalur penerimaan mahasiswa baru di IPB University berdasarkan nilai rapor tanpa tes. Seleksi penerimaan mahasiswa baru berdasarkan nilai rapor perlu dibuat indeks sekolah untuk melihat kualitas dan komitmen masing-masing sekolah dengan melakukan pengelompokan sekolah-sekolah pada pendaftar SNBP. Salah satu metode yang dapat digunakan adalah algoritma K-Prototypes. K-Prototypes dapat digunakan untuk mengelompokkan data berukuran besar dan bertipe campuran (numerik dan kategorik) dengan mengkombinasikan ukuran jarak dari dua metode non-hirarki, yaitu algoritma K-Means dan K-Modes. Berdasarkan hasil analisis, algoritma K-Prototypes menghasilkan tiga gerombol optimal dengan masingmasing gerombol memiliki karakteristik yang berbeda. Gerombol 1 merupakan gerombol terendah karena terdiri dari sekolah-sekolah yang memiliki kualitas dan komitmen paling rendah pada penerimaan mahasiswa baru di IPB University jalur rapor. Gerombol 2 memiliki kualitas yang tidak lebih baik dari gerombol 3 namun berada di atas gerombol 1. Gerombol 3 merupakan gerombol terbaik karena terdiri dari sekolah sekolah yang memiliki kualitas dan komitmen tinggi pada penerimaan mahasiswa baru di IPB University jalur rapor. Mayoritas sekolah pada gerombol ini merupakan sekolah unggul yang terlibat hampir pada seluruh jalur penerimaan dan sebagian besar sekolahnya melakukan daftar ulang ke IPB University saat dinyatakan lolos jalur rapor., Institut Pertanian Bogor or IPB University is a state university that was ranked first as the best university in Indonesia according to the Ministry of Research and Technology in 2020 which has three main channels in the new student admission selection system. SNBP is one of the new student admission pathways at IPB University based on report cards without a test. Selection of new student admissions based on report cards needs to create a school index to see the quality and commitment of each school by grouping schools in SNBP applicants. One method that can be used is the K-Prototypes algorithm. K-Prototypes can be used to cluster large and mixed type data (numeric and categorical) by combining distance measures from two non-hierarchical methods, namely the K-Means and K-Modes algorithms. Based on the analysis, the K-Prototypes algorithm produces three optimal clusters with each cluster having different characteristics. Cluster 1 is the lowest cluster because it consists of schools that have the lowest quality and commitment to new student admissions at IPB University through the report card route. Cluster 2 has a quality that is not better than cluster 3 but is above cluster 1. Cluster 3 is the best cluster because it consists of schools that have high quality and commitment to new student admissions at IPB University through the report card route.
Keyword: cluster, k-prototypes, new students, school, SNBP

Judul: Penggerombolan Sekolah Dalam Penerimaan Mahasiswa Baru Sekolah Vokasi IPB Jalur USMI Menggunakan Algoritme K-Prototypes
Abstrak: Undangan Seleksi Masuk IPB (USMI) merupakan jalur undangan dengan seleksi melalui nilai rapor tanpa tes tulis. Salah satu evaluasi seleksi USMI adalah melakukan pemeringkatan mutu kualitas sekolah melalui pengelompokan sekolah di masing-masing tahun akademik. Dalam penelitian ini digunakan tiga tahun akademik sebagai bahan evaluasi. Metode yang digunakan yaitu analisis gerombol menggunakan algoritme K-Prototypes pada masing-masing tahun akademik. Berdasarkan hasil penelitian, algoritme K-Prototypes menghasilkan tiga gerombol sekolah di masing masing tahun akademik. Secara umum dari ketiga gerombol yang dihasilkan pada masing-masing tahun diperoleh gerombol 1 merupakan kategori sekolah yang memiliki komitmen dan kualitas yang tinggi di setiap tahun akademik. Gerombol 2 merupakan kategori sekolah yang memiliki komitmen dan kualitas tidak lebih besar dari gerombol 1 tetapi di atas gerombol 3 di setiap tahun akademik. Gerombol 3 merupakan kategori sekolah yang memiliki komitmen dan kualitas yang lebih rendah dibandingkan dengan gerombol lain. Hal ini juga dikarenakan sekolah-sekolah yang berada di gerombol 3 sebagian besar hanya berpartisipasi di satu tahun akademik saja. Hasil penggerombolan sekolah menunjukkan bahwa persentase sekolah yang konsisten berada di gerombol yang sama di setiap tahun akademik sebesar 25% dengan persentase distribusi sekolah untuk gerombol 1, sebesar 18% gerombol 2 sebesar 1%, dan gerombol 3 sebesar 6%. Persentase sekolah yang tidak konsisten berada di gerombol yang sama sebesar 75% artinya sekolah yang tidak konsisten tersebut mengalami perpindahan gerombol di setiap tahun akademik., The IPB Entrance Selection Invitation (USMI) is an invitation route with selection through report cards without a written test. One of the evaluations of the USMI selection is to rank the quality of schools through grouping schools in each academic year. In this study, three academic years were used as evaluation material. The method used is cluster analysis using the K-Prototypes algorithm in each academic year. Based on the research results, the K-Prototypes algorithm produces three school clusters in each academic year. In general, from the three clusters produced in each year, cluster 1 is the category of schools that have high commitment and quality in each academic year. Cluster 2 is a school category that has a commitment and quality not greater than cluster 1 but above cluster 3 in each academic year. Cluster 3 is a school category that has lower commitment and quality compared to other clusters. This is also because the schools in cluster 3 mostly only participate in one academic year. The results of school clustering show that the percentage of schools that are consistently in the same cluster in each academic year is 25% with the percentage of school distribution for cluster 1, 18% for cluster 2, and 1% for cluster 3. The percentage of inconsistent schools being in the same cluster is 75%, meaning that the inconsistent schools experience cluster shifts in each academic year
Keyword: clustering, mixed-type data, k-prototypes, USMI

Judul: Thermal Characteristics and Dynamics of Tropical Cyclone over Western North Pacific Ocean: A Case Study of Typhoon Songda (Chedeng) 2011
Abstrak: The frequent occasion of Tropical Cyclone (TC) over Western North Pacific (WNP) Ocean lies from 10˚N to 26˚N and 121˚E to 170˚E. Sources of TC development come from warm ocean temperature where latent heat is released during condensation processes and subsequently concentrated in the boundary layer. Relative humidity at 850-700 hPa is responsible to produce the most intense storm. Meanwhile, weak vertical wind shear between 850-200 hPa is one of the dynamical factors that lead to the development of TC. Pressure level of 850-200 hPa is chosen to minimize the thermodynamic effect in the lower troposphere. Typhoon Songda (Chedeng in the Philippines) or TSC lasted from May 18th to 30th, 2011 and strengthened into powerful Super Typhoon stage between May 24th and 27th. Long curving track and the exhibition of all sequences of TC life cycle, TSC is then considered as an ideal case of TC. The objective of the study with case of TSC is to investigate the role of thermal characteristics and dynamics on TC intensity. The research is carried out by using Advanced Research of Weather Research and Forecasting model (WRF ARW model) version 3.3. Two domains are employed in the model. The first domain situate from 02˚N to 40˚N and 118˚E to 149˚E and cover spatial resolution () of 33.1 km. The second domain situate from 09˚N to 27˚N and 119˚E to 132˚E with of 11 km. Timestep of the model () is 120 seconds. The model is supported by the following physical parameterization, i.e., Yonsei University Scheme (YSU) in the boundary layer, Kain-Fritsch scheme (KF) in the cumulus, and WRF Single-Moment 3-class scheme in the microphysics. Initial meteorological condition prior to the formation of the cyclone is generated using WRF ARW standard-release model. Sensitivity of the model output is obtained from a selected single-parameter change scenarios, namely, 10 % to 20 % decrease and increase of wind shear initial data at 850-200 hPa, and by 10 % to 20 % decrease of relative humidity initial data at 850-700 hPa. The result showed that the negative correlations between wind shear and intensity are found in wind shear scenarios. Wind shears of 10 ms-1 which lead to strengthening of the wind intensity in the early period of Typhoon development are found in wind shear scenarios. The effect of wind shear changes on wind intensity is statistically significant in the 20 % wind shear scenarios. During mature stage, weak wind intensity occurred when wind shear increase as given in the WRF standard-release and the 20 % decreasing scenario. Different situation is found in the 20 % increasing scenario where wind shear tend to weak and lead to strong wind intensity at this stage. Meanwhile, the reduction of moisture supply at 850-700 hPa resulting from 20 % decreasing relative humidity play a major role in the weakening of wind intensity and reduces mechanical energy of the cycle process as much as 300 J kg-1.
Keyword: