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All Journal Bioma : Berkala Ilmiah Biologi Media Statistika SAINS DAN MATEMATIKA Jurnal Gaussian E-Dimas: Jurnal Pengabdian kepada Masyarakat

Moch. Abdul Mukid

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 324112

Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Education Mathematics Social Sciences Other

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ANALISIS KLASIFIKASI NASABAH KREDIT MENGGUNAKAN BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART) Desy Ratnaningrum; Moch. Abdul Mukid; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is one of the facilities provided by banks to lend money to someone or a business entity within the prescribed period. The smooth repayment of credit is essential for the bank because it influences the performance as well as its presence in daily life. Acceptance of prospective credit customers should be considered to minimize the occurrence of bad credit. Classification and Regression Trees (CART) is a statistical method that can be used to identify potency of credit customer status such as current credit and bad credit. The predictor variables used in this study are gender, age, marital status, number of children, occupation, income, tenor / period, and home ownership. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification and Regression Trees (Bagging CART) method. The classification of credit customers using Bagging CART gives the classification accuracy 81,44%. Key words : Credit, Bootstrap Aggregating Classification and Regression Trees (Bagging CART), Classification Accuracy

PENERAPAN DIAGRAM KONTROL MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING AVERAGE (MEWMA) PADA PENGENDALIAN KARAKTERISTIK KUALITAS AIR (Studi Kasus: Instalasi Pengolahan Air III PDAM Tirta Moedal Kota Semarang) Anastasia Arinda; Mustafid Mustafid; Moch. Abdul Mukid
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Water treatment is intended to change the original water quality that does not fulfill the health requirements become a water for human consumption and must comply with the levels of certain parameters. Quality control can be done by forming a Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. In the Multivariate Exponentially Weighted Moving Average (MEWMA) control charts with λ = 0.25 and UCL = 13.92658 seen that process controlled statistically. Once the process is under control, it can be done analysis of the ability of the process to determine whether the process fulfill the specifications or not. In the calculation process capability univariate each characteristics and multivariate process capability index values obtained more than 1 means that the process is going well. Keywords: water quality, Multivariate Exponentially Weighted Moving Average (MEWMA), process capability.

PEMODELAN DATA KEMISKINAN PROVINSI JAWA TENGAH MENGGUNAKAN FIXED EFFECT SPATIAL DURBIN MODEL Siska Alvitiani; Hasbi Yasin; Mochammad Abdul Mukid
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Based on data from the Central Statistics Agency, Central Java has 4,20 million people (12,23%) poor population in 2017 with Rp333.224,00 per capita per month poverty line. So, Central Java has got the second rank after East Java as the province which has the highest poor population in indonesia in 2017. In this research use the fixed effects spatial durbin model method for modeling poor population in each city in Central Java at 2014-2017. The spatial durbin model is a spatial regression model which contains a spatial dependence on dependent variable and independent variable. If the spatial dependence on dependent variable or independent variables is ignored, the resulting coefficient estimator will be biased and inconsistent. The fixed effect is one of the panel data regression models which assumes a different intercept value at each observation but fixed at each time, and slope coefficient is constant. The advantage of using fixed effects in spatial panel data regression is able to know the different characteristics in each region. The dependent variable used is poor population in each city in Central Java, and the independent variable is Minimum Wage, Life Expectancy, School Participation Rate 16-18 Years, Expected Years of Schooling, Total Population, and Per Capita Expenditure. The results of the analysis shows that the fixed effects spatial durbin model is significant and can be used. The variables that significantly affect the model are the Life Expectancy and Expected Years of Schooling, and the coefficient of determination (R2) is 99.95%. Keywords: Poverty, Spatial, Panel Data, Fixed Effects Spatial Durbin Model

PERBANDINGAN KINERJA MUTUAL K-NEAREST NEIGHBOR (MKNN) DAN K-NEAREST NEIGHBOR (KNN) DALAM ANALISIS KLASIFIKASI KELAYAKAN KREDIT Annisa Sugesti; Moch. Abdul Mukid; Tarno Tarno
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit feasibility analysis is important for lenders to avoid the risk among the increasement of credit applications. This analysis can be carried out by the classification technique. Classification technique used in this research is instance-based classification. These techniques tend to be simple, but are very dependent on the determination of K values. K is number of nearest neighbor considered for class classification of new data. A small value of K is very sensitive to outliers. This weakness can be overcome using an algorithm that is able to handle outliers, one of them is Mutual K-Nearest Neighbor (MKNN). MKNN removes outliers first, then predicts new observation classes based on the majority class of their mutual nearest neighbors. The algorithm will be compared with KNN without outliers. The model is evaluated by 10-fold cross validation and the classification performance is measured by Gemoetric-Mean of sensitivity and specificity. Based on the analysis the optimal value of K is 9 for MKNN and 3 for KNN, with the highest G-Mean produced by KNN is equal to 0.718, meanwhile G-Mean produced by MKNN is 0.702. The best alternative to classifying credit feasibility in this study is K-Nearest Neighbor (KNN) algorithm with K=3.Keywords: Classification, Credit, MKNN, KNN, G-Mean.

Co-Authors Abdul Hoyyi Agung Waluyo Agus Rusgiyono Agus Setyawan Agus Subagio Alan Prahutama Alwi Assegaf Ana Kartikawati Anastasia Arinda Annisa Sugesti Azzarah, Dian Fatimah Budi Warsito Dani Al Mahkya Desy Ratnaningrum Desy Ratnaningrum, Desy Di Asih I Maruddani Dini Anggreani Djalal Er Riyanto Dwi Ispriyanti Dwi Rahmayani, Dwi Erna Fransisca Angela Sihotang fatimah Fatimah Finisa, Husnul Fuad Muhammad Hasbi Yasin I Gusti Ngurah Antaryama Ilham Muhammad Inas Hasimah Jatmiko Endro Suseno Lies Kurnia Irwanti Masithoh Yessi Rochayani Maulida Azkiya, Maulida Mesra Nova Muhammad Mujahid Muhammad Nur Munifatul Izzati Mustafid Mustafid Noviana Nurhayati Nur Walidaini Nurhikmah Megawati Qorina Rara Sartika Redemtus Heru Tjahjana Restu Sri Rahayu, Restu Ririn Sulpiani Rita Rahmawati Saraswati, Mei Sita Satriyo Adhy Siska Alvitiani Sitomurang, Rosalina Aprilda Sri Wahyuningrum Sudarno Sudarno Sugito - Sugito Sugito Suparti Suparti Tarno Tarno Tatik Widiharih Tatik Widiharih Triastuti Wuryandari Udi Harmoko Wetty Anggun Werti, Wetty Anggun Yuciana Wilandari Yudha Subakti, Yudha Yulia Agnis Sutarno Yundari, Yundari Zulfa Wahyu Mardika, Zulfa Wahyu

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