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All Journal PROSIDING SEMINAR NASIONAL Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang

Moch. Abdul Mukid

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Author-ID : 475550

Decision Sciences, Operations Research & Management Education Other

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PEMODELAN DATA INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN REGRESI PENALIZED SPLINE Novia Agustina; Suparti Suparti; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesia Composite Index (IHSG) is an indicator of stock price changes in Indonesia Stock Exchange. IHSG is time series data that can be modeled with parametric models. But there are some assumptions for parametric model, while the fluctuated IHSG data usually doesn’t occupy these assumptions. Another alternative for this study is nonparametric regression. Penalized spline regression is one of nonparametric regression method that can be used. The optimal penalized spline models depends on the determination of the optimal smoothing parameter λ and the optimal number of knots, that has a minimum value of Generalized Cross Validation (GCV). The best model in this study is penalized spline degree 1 (linear) with 1 knot, that is 5120,625, smoothing parameter λ value is 41590, and GCV value is 1567,203. R2 value for in sample data is 83,2694% and R2 value for out sample data is 96,4976% show that the model have a very good performance. MAPE values for in sample data is 0,5983% and MAPE values for out sample data is 0,4974%. Because the value of MAPE in sample and out sample is less than 10%, it means that the performance of the model and forecasting are very accurate. Keywords: Indonesia Composite Index, Nonparametric Regression, Penalized Spline Regression, GCV, MAPE

PERBANDINGAN METODE K–MEANS DAN SELF ORGANIZING MAP (STUDI KASUS: PENGELOMPOKAN KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA 2015) Rachmah Dewi Kusumah; Budi Warsito; Moch. Abdul Mukid
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Cluster analysis is a process of separating the objects into groups, so that the objects that belong to the same group are similar to each other and different from the other objects in another group. In this study used two method to classify data of district / city in Central Java based on indicators of Human Development Index (HDI) 2015 are K-Means and Self Organizing Map (SOM) with the number of groups as much as two to seven. Furthermore, the results of both methods were compared using the Davies-Bouldin Index (DBI) values to determine which method is better. Based on the research that has been conducted found that the K-Means (K=4) method works better than SOM (K=2) to classify district / city in Central Java based on indicators of Human Development Index (HDI) as evidenced by the value of the Davies-Bouldin Index (DBI) on K-Means (K=4) of 0.786 is smaller than the value at SOM (K=2) Davies-Bouldin Index (DBI) which is equal to 0.893. Keywords: clustering, HDI, K-Means, SOM, DBI

Pemodelan Regresi 2-Level Dengan Metode Iterative Generalized Least Square (IGLS) (Studi Kasus: Tingkat Pendidikan Anak di Kabupaten Semarang) Dyan Anggun Krismala; Dwi Ispriyanti; Moch. Abdul Mukid
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In a research, data was used often hierarchical structure. Hierarchical data is data obtained through multistage sampling from a population with independent variables can be defined within each level and dependent variable can be defined at the lowest level. One analysis that can be used for data with a hierarchical structure is a multilevel regression analysis. Multilevel regression analysis is the most simple regression analysis 2-levels. 2-level regression analysis will be used to construct a regression model the education level of children in Semarang where children (level-1) nested on the distrits (level-2) with the factors that influence. Estimation of parameter in 2-level regression model can use some methods, one of them is Iterative Generalized Least Square (IGLS). From the results of the discussion indicates that the factors which affect the level of education of children in Semarang is the mother’s education, father’ education, and percentage of farm families. The diversity level of the education of children in Semarang caused more variation among children than the variation between districts.

PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI PROVINSI JAWA TENGAN TAHUN 2008-2013 DENGAN MENGGUNAKAN REGRESI DATA PANEL Muhammad Rizki; Agus Rusgiyono; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Human Development Index (HDI) is a way to measure the success of human development based on a number of basic components quality of life. HDI is formed by three basic variables namely health, education and decent living standards. This study aims to identify factors that influence the Human Development Index in Central Java Province and get a model Human Development Index in Central Java province in 2008-2013. The data used in this study is a combination of cross section data and time series data are commonly called panel data, then this HDI modeling using panel data regression. There are three estimation of panel data regression model namely Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM). Estimation of panel data regression model used is the Fixed Effects Model (FEM). FEM estimation results show the number of health facilities, school participation rate and Labor Force Participation Rate significantly affect the HDI by generating for 93.58%.Keywords : Fixed Effect Model, panel data regression, HDI in Central Java Province

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI LAJU PERTUMBUHAN PENDUDUK KOTA SEMARANG TAHUN 2011 MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION Catra Aditya Wisnu Aji; Moch. Abdul Mukid; Hasbi Yasin
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Geographically Weighted Logistic Regression (GWLR) is a local form of logistic regression where geographical factors considered and it is assumed that the Bernoulli distribution of data used to analyze spatial data from non-stationary processes. This research will determine the factors that affect the Population Growth Rate (PGR) in the Semarang city using logistic regression and GWLR with a weighting function of bisquare kernel and gaussian kernel. The result showed that GWLR model with a weighting function of bisquare kernel better than logistic regression model and GWLR model with a weighting function of gaussian kernel because it has the smallest AIC value and classification accuracy is 87,5%. Factor that have significant effect is the number of couples of childbearing age in the Semarang city.

IDENTIFIKASI VARIABEL YANG MEMPENGARUHI BESAR PINJAMAN DENGAN METODE POHON REGRESI (Studi Kasus di Unit Pengelola Kegiatan PNPM Mandiri) Shaumal Luqman; Moch. Abdul Mukid; Abdul Hoyyi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Most people need a loan to fullfil their daily needs, such as a loan of goods or money. Loan can be obtained from financial institutions or individuals. In order to the loan granted by a financial institutions is not wrong target, financial institutions usually apply precaution principle. In making decisions related to how much a decent loan granted to a customer, the financial institutions often use the help of statistical methods. One methods often used is the Classification and Regression Trees (CART). Classification and Regression Trees (CART) is a nonparametric method that can be used to identify the variable that affect the amount of the loan at a financial institutions and estimate how much worth of loans granted. Because of the loan is a continous variable so the form of the tree is a Regression Tree. In this thesis, the financial institutions is UPK PNPM Mandiri Mekar Sejati in Kecamatan Bawang Kabupaten Batang. Variables that may be affected for large loans are age, occupation, type of warranty, the number family members, and the average income per month. The analysis showed that the variables that most influence on the income of the loans. Mean Absolute Percentage Error (MAPE) value from this method is 36%.Keyword : Regression tree, CART, Large loans.

ANALISA FAKTOR-FAKTOR YANG MEMPENGARUHI KINERJA PERUSAHAAN MENGGUNAKAN PENDEKATAN PARTIAL LEAST SQUARE (Studi Kasus pada PT. Telkom Indonesia Divisi Regional Jawa Tengah-DIY dan Wilayah Telekomunikasi Semarang) Endah Cahyaningrum; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Persaingan dalam pasar global membawa banyak perubahan yang cukup dinamis pada semua aspek di suatu perusahaan. Hal ini menimbulkan trend baru dimana perusahaan yang berkelanjutan bergantung pada kemampuan perusahaan dalam merespon perubahan-perubahan yang ada secara efektif. Adanya sejumlah keunikan yang menjadi karakteristik sebuah perusahaan dan tidak dimiliki perusahaan lain dapat menciptakan faktor-faktor yang dapat meningkatkan suatu kinerja perusahaan. Faktor-faktor yang mempengaruhi kinerja perusahaan pada PT. Telkom Indonesia diungkapkan secara komprehensif dengan persamaan struktural berbasis komponen, Partial Least Square (PLS). PLS merupakan metode analisis yang tidak didasarkan pada banyak asumsi. Pada PLS tidak diperlukan asumsi normal multivariat, dapat menggunakan skala pengukuran nominal, ordinal, interval dan rasio serta ukuran sampel tidak harus besar. PLS mengestimasi model hubungan antar variabel laten dan variabel laten dengan indikatornya. Berdasarkan hasil analisis diperoleh kesimpulan bahwa kinerja perusahaan dipengaruhi oleh kinerja manajerial, keunggulan bersaing, Total Quality Management, kompensasi, sistem pengukuran kinerja dan budaya kualitas namun angkanya relatif kecil. Kata kunci : Partial Least Square, kinerja perusahaan

PERBANDINGAN DISKRIMINAN KUADRATIK KLASIK DAN DISKRIMINAN KUADRATIK ROBUST PADA KASUS PENGKLASIFIKASIAN PEMINATAN PESERTA DIDIK (Studi Kasus di SMA Negeri 1 Kendal Tahun Ajaran 2014/2015) Laili Isna Nur Khiqmah; Moch. Abdul Mukid; Alan Prahutama
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Discriminant is a multivariate statistical technique that can be used to perform the classification new observation into a particular group. Quadratic discriminant analysis tied to an assumption of normal multivariate distributed observations and variance covariance matrix inequality. Robust quadratic discriminant analysis can be used if the observations contain outliers. Classification using robust quadratic discriminant analysis with the Minimum Covariance Determinant (MCD) estimator in the data specialization students of SMA Negeri 1 Kendal that containing outliers gives the results of the classification accuracy of 95,06% with a percentage of 4,94% classification error while generating the classical quadratic discriminant analysis classification accuracy of 92,59% with a percentage of 7,41% classification error. Thus a robust quadratic discriminant analysis with the MCD estimator is more appropriate in the case of the data which contains outliers. Keywords : discriminant, outliers, robust, MCD estimators, classification

SEGMENTASI PASAR PADA PUSAT PERBELANJAAN MENGGUNAKAN FUZZY C-MEANS (STUDI KASUS: RITA PASARAYA CILACAP) Nurhikmah Megawati; Moch. Abdul Mukid; Rita Rahmawati
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

RITA Pasaraya Cilacap is the first supermarket in Cilacap. Previously, RITA Pasaraya become a shopping center for all people in Cilacap. Now, more and more supermarket is standing. To find this target, RITA Pasaraya needs grouping or market segmentation. Grouping method used fuzzy cluster means (FCM). For an optimal cluster number using the accuracy of the measurement criteria is Xie Beni Index. Research data obtained by questionnaire on RITA Pasaraya Cilacap with 10 psikografik variables. Results of the research, consumer segmentation more accurate if grouped into 2 clusters. The final result is respondents in cluster 1 more attention to low price levels, complete goods, big discounts, satisfactory service, strategic location, roomy parking, comfortable for shopping, adequate public facilities, complete payment facilities, and cleaner room than to respondents in cluster 2. Basically, similar profiles cluster in cluster 1 and cluster 2. Mainly RITA Cilacap Supermarkets are women, with the range age of 16-29 years, with a frequency of shopping 2-4 times per month. Only last education and income are different. In cluster 1, dominated b senior high school with income of 2-5 million every month, and in cluster 2 dominated by bachelor with income <2 million every month.

ANALISIS PENGARUH INFLASI, KURS, DAN SUKU BUNGA SERTIFIKAT BANK INDONESIA TERHADAP INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN REGRESI LINIER BERGANDA BAYES Marta Widyastuti; Moch. Abdul Mukid; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Jakarta Composite Index (JCI) is one of the stock price index emitted by Indonesia Stock Exchange (IDX). JCI is influenced by macro factors (external factors of a company) and micro factors (factors that come within the company). Some of the macro factors include inflation, exchange rate, and interest rate of Bank Indonesia Certificate. To obtain model of inflation, exchange rate, and interest rate of Bank Indonesia Certificate on JCI, Bayesian multiple linier regression can be used so that researcher is able to take into account prior information and apply it together with current data to obtain posterior estimation. From the data processing, it is known that interest rate of Bank Indonesia Certificate is not significantly influencing the model. Meanwhile, inflation and exchange rate are significantly influencing the model and both of them result 72,72% of R-Squared. Furthermore, the final model of Bayesian multiple linier regression proven to be very accurate because it has 4,951% of MAPE. Keywords: JCI, inflation, exchange rate, interest rate of Bank Indonesia Certificate, Bayesian multiple linier regression, prior, posterior, MAPE

Co-Authors Abdul Hoyyi Agus Rusgiyono Alan Prahutama Alvita Rachma Devi Anifa Anifa Anissa Pangastuti Arief Rachman Hakim Bella Cynthia Devi Bramaditya Swarasmaradhana Budi Warsito Bunga Maharani Catra Aditya Wisnu Aji Chainur Arrasyid Hasibuan Cyntia Surya Utami Dedy Douglas Harianja Desy Trishardiyanti Adiningtyas Diah Safitri Dwi Ispriyanti Dyan Anggun Krismala Elyas Darmawan Endah Cahyaningrum Erna Fransisca Angela Sihotang Erna Puspitasari Etik Setyowati Fatma Septy Deviana Fitra Ramdhani Hasbi Yasin Herwindhito Dwi Putranto Iantazar Rezqitullah Maharsi Kiki Febri Azriati Laili Isna Nur Khiqmah Landong Panahatan Hutahaean Mardison Purba Marta Widyastuti Muhammad Rizki Naomi Rahma Budhianti Niken Anggraini Dewi Novia Agustina Nunung Hanurowati Nurhikmah Megawati Panji Wisnu Wirawan Rachmah Dewi Kusumah Restu Sri Rahayu Rita Rahmawati Riza Adi Priantoro Shaumal Luqman Solly Aryza Sudarno Sudarno Sugito Sugito Suparti Suparti Syaifudin Karyadi Syarah Widyaningtyas Tatik Widiharih Trianita Resmawati Triastuti Wuryandari Ungu Siwi Maharunti Yosi Dhyas Monica Yuciana Wilandari

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