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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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KLASIFIKASI CALON DEBITUR KREDIT PEMILIKAN RUMAH (KPR) MULTIGUNA TAKE OVER MENGGUNAKAN METODE k NEAREST NEIGHBOR DENGAN PEMBOBOTAN GLOBAL GINI DIVERSITY INDEX Inas Hasimah; Moch. Abdul Mukid; Hasbi Yasin
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

House credit (KPR) is a credit facilities for buying or other comsumptive needs with house warranty. The warranty for KPR is the house that will be purchased. The warranty for KPR multiguna take over is the house that will be owned by debtor, and then debtor is taking over KPR to another financial institution. For fulfilled the credit to prospective debtor is done by passing through the process of credit application and credit analysis. With the credit analysis, will acknowledge the ability of debtor for repay a credit. Final decision of credit application is classified into approved and refused. k Nearest Neighbor by attributes weighting using Global Gini Diversity Index is a statistical method that can be used to classify the credit decision of prospective debtor. This research use 2443 data of KPR multiguna take over’s prospective debtor in 2018 with credit decision of prospective debtor as dependent variable and four selected independent variable such as home ownership status, job, loans amount, and income. The best classification result of k-NN by Global Gini Diversity Index weighting is when using 80% training data set and 20% testing data set with k=7 obtained APER value 0,0798 and accuracy 92,02%. Keywords: KPR Multiguna Take Over, Classification, KNN by Global Gini Diversity Index weighting, Evaluation of Classification

PERBANDINGAN METODE REGRESI LOGISTIK BINER DAN METODE BACKPROPAGATION DALAM MENENTUKAN MODEL TERBAIK UNTUK KLASIFIKASI PENGGUNA PROGRAM KELUARGA BERENCANA Muhammad Mujahid; Hasbi Yasin; Moch. Abdul Mukid
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesia is one of the highest population density in the world has high birth level. One of the regulation to get the population density lower than before that is used by Government is Family Planning Program. On the reality, not all of the productive age join this program. The method is Binary Logistic Regression and Backpropagation. The predictor variables that is researched are husband’s age, wife’s age, age of the last child, count of children, husband’s education, wife’s education, husband’s job, wife’s job and the level of family prosperity. The aim of the research is to compare the classification accuracy between Binary Logistic Regression and Backpropagation. The result of the research by binary logistic regression method, shows the variables that affect the status of KB user is age of the last child and wife’s education with the classification accuracy are 66.98%, and the classification accuracy of Backpropagation are 67,30%. The conclution based on the research that is the Backpropagation is better than Binary Logistic Regression when classification the status of KB user in Semarang on March 2013 until Januari 2014. Keywords : Binary Logistic Regression, Backpropagation, Keluarga Berencana, Classification

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.

Aplikasi Teknologi Ulir Filter (TUF) dengan Media Geomembrane sebagai Upaya Peningkatan Kualitas dan Kuantitas Produksi Garam di Kabupaten Pati Jawa Tengah Hasbi Yasin; Sugito Sugito; Moch. Abdul Mukid; Alan Prahutama
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 10, No 2 (2019): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v10i2.3015

Kebutuhan akan garam semakin meningkat, baik kebutuhan garam rumah tangga apalagi kebutuhan terhadap garam industri. Kabupaten Pati sebagai salah satu pusat produksi garam di Jawa Tengah diharapkan mampu untuk memenuhi permintaan garam yang semakin meningkat. Upaya yang dapat dilakukan adalah dengan peningkatan kualitas dan kuantitas garam melalui teknologi yang tepat, murah, dan mudah diaplikasikan oleh para petani garam. Salah satu metode yang dapat digunakan adalah produksi garam dengan sistem Teknolgi Ulir Filter (TUF) dengan media Geomembrane. Metode ini mampu meningkatkan efisiensi waktu produksi dan juga mampu meningkatkan kualitas garam yang dihasilkan. Oleh karena itu, Tim Pengabdian PKUM Undip bekerja sama dengan Kelompok Usaha Garam Rakyat (KUGAR) Karya Makmur dan KUGAR Garam Mulya dalam upaya meningkatkan produksi kuantitas dan kualitas garam di Kabupaten Pati, Jawa Tengah. Kegiatan ini dilakukan dalam bentuk perbaikan Standar Operasional Prosedur (SOP) tentang teknik produksi garam dan pemberian bantuan alat produksi untuk meningkatkan kapsitas produksi dan kualitas garam yang dihasilkan. Hasil kegiatan ini mampu meningkatkan produksi garam mencapai 30-40% dengan kualitas garam yang lebih baik (Kualitas K1/Garam Super).

VALUE AT RISK IN STOCK PORTFOLIO USING T-COPULA: Case Study of PT. Indofood Sukses Makmur, Tbk. and Bank Mandiri (Persero), Tbk. Qorina Rara Sartika; Tatik Widiharih; Moch Abdul Mukid
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Value at Risk (VaR) is a measuring tool that can calculate the amount of the worst losses that occur in the stock portfolio with a certain level of confidence and in certain period of time. In general, financial data has a high volatility value, which is caused the variance of residual model is not constant and nonnormally distributed. In this case, Copula-GARCH can be used to calculate the VaR. The Generalized Autoregressive Conditional Heterocedasticity (GARCH) model can resolve the time series models that have non-constant residual variance. This research use the t-Copula to model the dependency structure in the combined distribution of stock returns. The t-copula function is good in terms of reaching the extreme value state that often occurs in the financial data of stock returns and has heavytails. The empirical data uses the stock return data of PT. Indofood Sukses Makmur, Tbk (INDF) and Bank Mandiri (Persero) Tbk (BMRI) in the period of October 8, 2012 - October 8, 2017. In this research, Value at Risk is calculated using the period 1 day ahead at 90% confidence level that is 0.042, at 95% confidence level that is 0.025 and at 99% confidence level that is 0.017 with weight of each stock is 50%.

ANALYSIS OF THE NUMBER INFANT AND MATERNAL MORTALITY IN CENTRAL JAVA INDONESIA USING SPATIAL-POISSON REGRESSION Alan Prahutama; Budi Warsito; Moch. Abdul Mukid
MEDIA STATISTIKA Vol 11, No 2 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Maternal and infant mortality are one of the most dangerous problems of the community since it can profoundly affect the number and composition of the population. Currently, the government has been taking heed on the attempt of reducing the number of maternal and newborn mortality in Central Java which requires data and information entirely. Poisson regression is a nonlinear regression that is often used to model the relationship between response variables in the form of discrete data with predictor variables in the form of discrete or continuous data. In space analysis, GWPR is one of method in space modeling which can model regional-based regression. It is based on some factors including the number of health facilities, the number of medical personnel, the percentage of deliveries performed with non-medical assistance; the average age of a woman's first marriage; the average education level of married women; average amount of per capita household expenditure; percentage of village status; the average rate of exclusive breastfeeding; percentage of households that have clean water and the percentage of poor people. Based on the analysis, it is revealed that the determinants of maternal and infant mortality in Central Java using Poisson and GWPR models, among others are the number of health facilities, the number of medical personnel, the average number of per capita household expenditure and the percentage of the poor. In the maternal and infant mortality model, the AIC value of GWPR model produces better modeling than Poisson regression. Keywords: Maternal and Infant mortality, Poisson, GWPR

CREDIT SCORING MENGGUNAKAN METODE LOCAL MEANS BASED K HARMONIC NEAREST NEIGHBOR (MLMKHNN) Tatik Widiharih; Moch Abdul Mukid
MEDIA STATISTIKA Vol 11, No 2 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit Scoring is designed so that lenders can easily make decisions regarding whether a loan proposal from a prospective customer is worthy of approval or not. This study examines the application of the Multi Local Means Based K Harmonic Nearest Neighbor (MLMKHNN) method in the case of motorcycle credit in a financial institution. The classification capability of this method in detecting potential borrowers into the credit category is either good or bad compared to its previous method, Local Means Based K Harmonic Nearest Neighbor (LMKNN). In this case the MLMKHNN method has not shown better performance than the LMKNN method. At the same level of total accuracy, MLMKHNN requires more numbers of neighbors than the number of neighbors required by the LMKNN method. Keywords: sampling design, all possible samples, statistical efficiency, cost efficiency

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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