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ABSTRAK
Material dari sport flooring memiliki standar tertentu agar dapat memfasilitasi atlet dari segi kenyamanan dan keamanan. Sport flooring yang tidak sesuai standar dapat menyebabkan cidera seperti ankle, meniscus, tendon, ACL, PCL, dll. Standar SNI menyatakan bahwa material yang dikatergorikan sebagai bahan bangunan untuk sport flooring gedung olahraga bola basket adalah lantai kayu (parket) dan beton dengan finishing semen. Berdasarkan standar DIN 18032-2 terdapat beberapa kriteria yang menjadi parameter sport flooring, yaitu kekesatan, daya pantul, dan kerataan. Skripsi ini bertujuan untuk mengetahui standar sport flooring yang baik untuk olahraga bola basket di dalam gedung. Kemudian membandingkan kualitas dari dua gedung olahraga dalam Kampus UI Depok, yaitu Gymnasium UI dan lapangan FH UI. Hasil studi kasus menunjukan kedua sport flooring gedung olahraga tersebut belum memenuhi 3 kriteria standar yang telah ditentukan berdasarkan tinjauan pustaka. Akan tetapi hasil tersebut bertolak belakang dengan hasil survey kuisoner terhadap responden. Dimana 50,68% responden merasa bahwa tingkat kerataan lantai Gymnasium UI sudah sesuai standar, dan 30 dari 32 orang merasa lebih nyaman melakukan teknik gerakan bola basket di Gymnasium UI.hr> ABSTRACT
Sport flooring material has certain standards in order to facilitate athletes in terms of comfort and safety. Non-standard sports flooring can cause injuries such as ankles, meniscus, tendons, ACL, PCL, etc. The SNI standard states that the material which is classified as building material for basketball sport flooring is a wooden floor (parquet) and concrete with cement finishing. Based on DIN 18032-2 standards there are several criteria that become parameters of sport flooring, which is friction, ball rebound, and flatness level. This thesis aims to find out the standard of a decent sport flooring for indoor basketball activities. Then, the quality of two sports buildings in the UI Depok Campus, which is UI Gymnasium and the FH UI field. The results of the case study showed that both sport floorings of sports buildings did not meet the 3 predefined criteria based on the theory. However, the case study results contrasts with the results of the questionnaire survey of respondents. The respondent says that flatness level of Gymnasium UI is corresponding to the standards applied, and 30 people out of 32 says that they are more comfortable in Gymnasium UI to perform basketball technique and movements.

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This fully illustrated, up-to-date handbook explains the inclusive design criteria for internal floor finishes in non-domestic buildings, covering staircases, entrances, kitchens and sports halls.

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Ibu Kota memiliki peran penting dalam menggambarkan seberapa besar kekuatan politik, kultural, dan ekonomi suatu negara. Apabila Ibu Kota suatu negara memiliki banyak masalah yang tidak terselesaikan, permasalahan tersebut dapat menjadi faktor–faktor yang memengaruhi suatu negara memindahkan Ibu Kotanya. Setelah ditelusuri, terdapat banyak negara yang pernah memindahkan Ibu Kotanya termasuk Indonesia. Tujuan dari penelitian ini adalah untuk membentuk model dan menganalisis faktor–faktor yang memengaruhi negara–negara di dunia memindahkan Ibu Kota dengan data yang mengandung masalah: 1. Outlier, 2. Missing values, 3. Data tak seimbang, 4. Multikolinearitas. Jika data mengandung masalah, maka model yang terbentuk menjadi tidak representatif dan sulit untuk diinterpretasikan. Sehingga diperlukan metode yang dapat digunakan untuk menangani 4 (empat) masalah tersebut, yaitu berturut-turut: 1. Quantile–Based Flooring Capping, 2. K–Nearest Neighbor, 3. Adaptive Synthetic (ADASYN), dan 4. Menerapkan model Least Absolute Shrinkage and Selection Operator (LASSO) pada regresi logistik. Hasilnya menunjukkan bahwa faktor yang memengaruhi suatu negara memindahkan Ibu Kotanya adalah ukuran populasi di Ibu Kota, populasi negara, luas area (km2), Usia Negara, sistem pemerintahan, Income Category, dan Sedangkan faktor yang tidak masuk ke dalam model yaitu Gross Domestic Product (GDP), Logistic Performance Index (LPI) Score, Regulatory Quality Index, dan E–Government Development Index adalah prediktor yang mengalami multikolinearitas, sehingga model LASSO pada regresi logistik berhasil menyusutkan prediktor tersebut menjadi 0. Adapun model akhir dari Least Absolute Shrinkage and Selection Operator (LASSO) pada regresi logistik yang diperoleh adalah g(x) = 0,3399 – 0,8019 POP_CITY + 3,5925 POP_COUNTRY + 0,3406 AREA – 0,0156 AIRPOL + 0,0679 GEI + 0,8351 PS_AVT – 0,5682 GOV_EFFECT – 1,8643 AGE – 0,7043 SYSTEM_A – 1,4408 SYSTEM_B – 0,7036 INCOME_A – 0,5272 INCOME_B – 3,7404 INCOME_C – 0,9489 ARCHIPELAGO. ......The capital city plays an important role in portraying how much political, cultural and economic power a country has. If the capital city has many unresolved problems, these problems can become factors that influence the country to move its capital city. After being traced, there are many countries that have moved their capital cities, including Indonesia. The purpose of this study is to model and analyze the factors that influence countries in the world to move its capital city with data containing problems: 1. Outliers, 2. Missing values, 3. Imbalanced data, 4. Multicollinearity. If the data contains these problems, the model formed becomes unrepresentative and difficult to interpret. Therefore, the methods that can be used to handle these 4 (four) problems, respectively: 1. Quantile-Based Flooring Capping, 2. K-Nearest Neighbor, 3. Adaptive Synthetic (ADASYN), and 4. Applying the Least Absolute Shrinkage and Selection Operator (LASSO) model in logistic regression. The results showed that the factors that influence a country to move its capital city are population size in the capital city, country population, area (km2), air pollution level (mg/m3), Global Entrepreneurship Index (GEI), Political Stability and No Violence/Terrorism Index, Government Effectiveness Index, Country Age, government system, Income Category, and whether a country is an archipelago or not. While the factors that did not enter the model, namely the Gross Domestic Product (GDP), Logistic Performance Index (LPI) Score, Regulatory Quality Index, and E-Government Development Index were predictors that experienced multicollinearity, so the LASSO model in logistic regression successfully shrinks these predictors to 0. The final Least Absolute Shrinkage and Selection Operator (LASSO) model in logistic regression obtained is g(x) = 0,3399 – 0,8019 POP_CITY + 3,5925 POP_COUNTRY + 0,3406 AREA – 0,0156 AIRPOL + 0,0679 GEI + 0,8351 PS_AVT – 0,5682 GOV_EFFECT – 1,8643 AGE – 0,7043 SYSTEM_A – 1,4408 SYSTEM_B – 0,7036 INCOME_A – 0,5272 INCOME_B – 3,740