PEMODELAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS)

Regresi nonparametrik adalah salah satu metode statistika yang digunakan untuk mengetahui pola hubungan antara variabel respon dan variabel prediktor yang tidak diketahui bentuk fungsinya dan pola menyebar maka kurva regresi dapat diduga. Multivariate Adaptive Regression Spline (MARS) adalah salah satu metode analisis regresi nonparametrik yang digunakan untuk mengatasi permasalahan data yang berdimensi tinggi yaitu data yang memiliki jumlah variabel prediktor sebesar 3 ≤ p ≤ 20 dan data sampel yang berukuran 50 ≤ n ≤ 1000. Pada penelitian ini, penulis akan meneliti mengenai pemodelan Multivariate Adaptive Regression Spline (MARS) pada kasus diare di Provinsi Jawa Tengah- Jawa Barat tahun 2019. Untuk memilih model MARS terbaik, pada penelitian ini menggunakan nilai Generalized Cross Validation (GCV) terkecil atau minimum yang dihasilkan tiap model kombinasi dari minimum observasi (MO), basis function (BF) dan Interaksi (MI). Hasil analisis MARS pada kasus jumlah kasus diare pada balita di Jawa Tengah-Jawa Barat menghasilkan model terbaik dengan bentuk persamaan: 1 2 3 4 Yˆ 0,62369691,011132*BF 1,308728*BF 0,2991534*BF 0,5829459*B

MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) FOR MODELLING OF CHILD LABOR IN JAKARTA

A Child is defined as male or female under the age of 18 years unless under the law of maturity has been reached earlier. Based on data from the ILO, there were four million child labors in Indonesia in 2002. Jakarta as the capital city of Indonesia has a high economic growth in 2010 by reaching 6.51 percent (LKPJ 2010). Behind that growth, Jakarta has a problem of high number of child workers as well, which is about 93 571 children in 2010. Some researchers often use regression analysis to determine the description of the factors that contribute to a response variable. Regression analysis has several assumptions that must be met, while research in the social subjects often violates those assumptions. To overcome this limitation required nonparametric method that is not tied to the assumption. One method is non-parametric regression Multivariate Adaptive Regression Spline (MARS). MARS method is an approach for nonparametric regression model that can accommodate multicollinearity in the model. This study uses secondary data drawn from SUSENAS in 2013 in DKI Jakarta. Response variable used is the status of work in children aged 10-17 years, while the predictor variables are fifteen variables that represent the characteristics of children and household. Based on the results of processing with MARS, obtained models are affected by Status of Child’s Education, Child’s Education, Child Live with Parent, Education of Head of Household, and etc. Key words: child labor, MARS, SUSENAS, Jakart

Analysis of earthquake hazards prediction with multivariate adaptive regression splines

Earthquake research has not yielded promising results, either in the form of causes or revealing the timing of their future events. Many methods have been developed, one of which is related to data mining, such as the use of hybrid neural networks, support vector regressor, fuzzy modeling, clustering, and others. Earthquake research has uncertain parameters and to obtain optimal results an appropriate method is needed. In general, several predictive data mining methods are grouped into two categories, namely parametric and non-parametric. This study uses a non-parametric method with multivariate adaptive regression spline (MARS) and conic multivariate adaptive regression spline (CMARS) as the backward stage of the MARS algorithm. The results of this study after parameter testing and analysis obtained a mathematical model with 16 basis functions (BF) and 12 basis functions contributing to the model and 4 basis functions not contributing to the model. Based on the level of variable contribution, it can be written that the epicenter distance is 100 percent, the magnitude is 31.1 percent, the location temperature is 5.5 percent, and the depth is 3.5 percent. It can be concluded that the results of the prediction analysis of areas in Lombok with the highest earthquake hazard level are Malaka, Genggelang, Pemenang, Tanjung, Tegal Maja, Senggigi, Mangsit. Meninting, and Malimbu

APLIKASI MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) TERHADAP PEMODELAN RISIKO KESEHATAN BAYI DENGAN BERAT BADAN LAHIR RENDAH (BBLR)

ABSTRAK Irmawati, 2019. Aplikasi Multivariate Adaptive Regression Spline (MARS) terhadap Pemodelan Risiko Kesehatan Bayi dengan Berat Badan Lahir Rendah (BBLR). Skripsi. Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam. Universitas Negeri Makassar (dibimbing oleh M. Nadjib Bustan dan Suwardi Annas). Kematian yang tinggi adalah faktor utama dalam bidang kesehatan yang harus diatasi, termasuk jumlah kematian bayi yang baru lahir. Penyebab utama kematian bayi yang baru lahir khususnya masa perinatal adalah berat badan lahir rendah (BBLR). Berdasarkan data yang ada pada Badan Pusat Statistik Sulawesi Selatan, Kota Makassar menempati urutan pertama mengenai bayi BBLR. Tujuan dari penelitian ini adalah mendapatkan model terbaik MARS dan menganalisis interaksi variabel bebas. Metode yang digunakan adalah Multivariate Adaptive Regression Spline (MARS). Maksimum basis fungsi (BF) adalah 2-4 kali banyaknya variabel bebas, maksimum interaksi (MI) yang digunakan adalah 1, 2, dan 3 sedangkan minimum jarak antara knot atau minimum observasi (MO) yang digunakan sebesar 0, 1, 2, dan 3. Berdasarkan hasil analisis yang diperoleh maka model terbaik MARS adalah dengan kombinasi BF = 28, MI = 3, dan MO = 2 dengan nilai dari model tersebut yaitu 0,178571 + 0,72003*X4 + 0,905344*X5*X4 – 0,688811*X6*X5 – 0,625874*X2*X7*X4. Berdasarkan model yang telah diperoleh maka dapat disimpulkan variabel bebas yang mempengaruhi kejadian BBLR adalah anemia (X2), paritas (X4), riwayat pendidikan (X5), gizi ibu (X6), dan usia kehamilan (X7). Kata Kunci: Berat Badan Lahir Rendah (BBLR), Multivariate Adaptive Regression Spline (MARS), Klasifikasi MARS

Classification Of Perceptions Of The Covid-19 Vaccine Using Multivariate Adaptive Regression Spline

Indonesia is one of the countries infected with the covid-19 virus. One of the government's efforts is the covid-19 vaccination. However, the covid-19 vaccination caused controversy for some people because many people refused to be vaccinated.  Public perception of the covid-19 vaccine can be categorized into two, namely positive and negative, based on survey from Indonesia ministry of health about acceptance of covid-19 vaccine state that this can be influenced by many factors. These factors are important to know as an effort to increase acceptance of covid-19. Multivariate Adaptive Regression Splines (MARS). The purpose of this study is to determine the classification model of public perception of the covid-19 vaccine and the factors that influence it. The method used in this study is Multivariate Adaptive Regression Splines (MARS). This method is appropriate classification method to be applied to categorical response variable data,  The outcomes demonstrate that the optimum mars model is produced by combining BF= 24, MI =3, MO= 1, and GCV=0.07340546. The resulting classification level is 91.5% with influencing factors yaitu gender (x_1), age (x_2), last education (x_4), willingness to vaccinate (x_6), education (x_8).  Based on the results obtained, the government can consider these factors for socializatio

MULTIVARIATE ADAPTIVE REGRESSION SPLINE DAN REGRESI KUANTIL PADA INDEKS HARGA SAHAM GABUNGAN PERIODE 2013-2018

Indeks Harga Saham Gabungan yang disingkat dengan IHSG adalah indikator pergerakan harga saham. IHSG merupakan salah satu pedoman bagi investor untuk melakukan investasi di pasar modal. Data IHSG yang fluktuatif cendrung melanggar asumsi normalitas, homoskedastisitas, autokorelasi, dan multikolinearitas. Permasalahan tersebut dapat diatasi dengan memodelkan data IHSG menggunakan regresi nonparametrik diantaranya metode Multivariate Adaptive Regression Spline (MARS)dan metode Regresi Kuantil, dengan variabel prediktor suku bunga, inflasi, nilai tukar (kurs), gold, Indeks Down Jones dan Indeks Nikkei 225. Data IHSG yang digunakan adalah periode April 2013 sampai dengan April 2018. Model terbaik dipilih dengan membandingkan nilai R2 dan MSE metode MARS dan metode Regresi Kuantil. Dari analisis nilai R2 metode MARS lebih besar dari metode Regresi Kuantil. Sedangkan nilai MSE metode MARS lebih kecil dari metode Regresi Kuantil. Ini artinya regresiMARS lebih baik digunakan pada penelitian IHSG ini.  Kata kunci : Multivariate Adaptive Regression Spline (MARS), Regrsi Kuantil, IHSG