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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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PERAMALAN HARGA SAHAM DENGAN METODE EXPONENTIAL SMOOTH TRANSITION AUTOREGRESSIVE (ESTAR) (Studi Kasus pada Harga Saham Mingguan PT United Tractors) Rahmayani, Dwi; Ispriyanti, Dwi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The stock price data series of PT United Tractors in the period of December 1th 2008 to December 29th 2014 is fluctuative. To model data nonlinear time series one method that can be used is Smooth Transition Autoregressive (STAR), if the function of an exponential transition then a method that can be used is Exponential Smooth Transition Autoregressive (ESTAR). In modelling ESTAR determined transition variable ( of transition function ). Of the research result obtained model ESTAR (1,1). With significance level of 5% obtainedthe value of the stock price data for pt united tractors in the next four to the original. It was also strengthened by Mean Absolute Percentage Error (MAPE) 0,768233 % are relatively small. Keywords : Autoregressive,time series, nonlinearity, ESTAR, MAPEThe stock price data series of PT United Tractors in the period of December 1th 2008 to December 29th 2014 is fluctuative. To model data nonlinear time series one method that can be used is Smooth Transition Autoregressive (STAR), if the function of an exponential transition then a method that can be used is Exponential Smooth Transition Autoregressive (ESTAR). In modelling ESTAR determined transition variable ( of transition function ). Of the research result obtained model ESTAR (1,1). With significance level of 5% obtainedthe value of the stock price data for pt united tractors in the next four to the original. It was also strengthened by Mean Absolute Percentage Error (MAPE) 0,768233 % are relatively small. Keywords : Autoregressive,time series, nonlinearity, ESTAR, MAPE

PEMILIHAN MEREK LIPSTIK TERFAVORIT DENGAN MADM BERBASIS GUI MATLAB Finisa, Husnul; Widiharih, Tatik; Mukid, Moch. Abdul
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Lipstick is a cosmetic usually worn by women to improve appearance with apply to the lips. The interest on lipstick among student at indonesia based on the various brands lipstick of national and international land of selling in indonesia. Based on this condition , it takes a method that can evaluate most favorite brand lipstick according to college student . The method applied to choose most favorite brand lipstick are Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both this method can do the decision to establish an alternative best of a number of alternatives based on a number of certain criteria in overcoming Multi Attribute Decision Making (MADM), The concept of SAW is looking for a sum of the weighted performance rating for each alternative in all criteria. While TOPSIS using the principle that alternative chosen should have the shortest distance of a solution ideal positive and farthest of a solution ideal negative. There are 10 alternative brand lipstick and 10 criteria, the criterias are the price, color, form, packaging, resilience, pigmentation, texture, scent, the availability of code expired lipstick. The result of the research indicated that to the SAW method most favorite brand lipstick is of NYX and to the TOPSIS method most favorite brand lipstick is Wardah. The research also produce an application programming GUI Matlab that can help users in process data uses the method saw and topsis for an election most favorite brand lipstick.Keywords : GUI, Lipstick, MADM, SAW, TOPSIS

PEMBENTUKAN POHON KLASIFIKASI BINER DENGAN ALGORITMA CART (CLASSIFICATION AND REGRESSION TREES) (Studi Kasus: Kredit Macet di PD. BPR-BKK Purwokerto Utara) Mardika, Zulfa Wahyu; Mukid, Moch. Abdul; Yasin, Hasbi
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Modernization and globalization of the world today has entered into various lines of Indonesian society. One consequence is people's lifestyles are more consumptive. This lifestyle causes people take out a loan at a bank or other financial institution to fulfill his wish. Some people pay the loan on credit. But in implementation, there is a variety of things causes the credit not running properly or called with problem loan. As a service provider of credit institutions, PD. BPR-BKK Purwokerto Utara is also not free from this problem. Therefore, it is necessary to classify customers based on demographic variables using Classification and Regression Trees (CART) to minimize the chances of problem loans. Based on analysis of customer credit status data PD. BPR-BKK Purwokerto Utara, optimal classification tree formed by the number of terminal nodes as much as 6 nodes. This means there are 6 characteristics of customers PD. BPR-BKK Purwokerto Utara. And level of accuracy of the classification tree in classifying credit status of customers is 81.0 % . Keywords: Modernization, Globalization, Credit, Problem Loan, Customer, CART, Classification Tree.

ANALISIS CREDIT SCORING MENGGUNAKAN METODE BAGGING K-NEAREST NEIGHBOR Fatimah, Fatimah; Mukid, Moch. Abdul; Rusgiyono, Agus
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

According to Melayu (2004) credit is all types of loans that have to be paid along with the interest by the borrower according to the agreed agreement. To keep the quality of loans and avoid financial failure of banks due to large credit risks, we need a method to identified any potentially customer’s with bad credit status, one of the methods is Credit Scoring. One of Statistical method that can predict the classification for Credit Scoring called Bagging k-Nearest Neighbor. This Method uses k-object nearest neighbor between data testing to B-bootstrap of the training dataset. This classification will use six independence variables to predict the class, these are Age, Work Year, Net Earning, Other Loan, Nominal Account and Debt Ratio. The result determine k =1 as the optimal k-value and show that Bagging k-Nearest Neighbor’s accuracy rate is 66,67%. Key word : Credit scoring, Classification, Bagging k-Nearest Neighbor

KOMPUTASI GUI UNTUK INFERENSI VEKTOR MEAN DAN INFERENSI MATRIKS KOVARIANSI DENGAN MENGGUNAKAN SOFTWARE R Subakti, Yudha; Mukid, Moch. Abdul; Yasin, Hasbi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multivariate statistics is a branch of statistical science that discuss the analysis for multivariable case. Some analysis in multivariate statistics are discussing about inferences, there are inferences about mean vector and inferences of covariance matrices. Along with the development of technology, to support statistical analysis from both of inferences is requiring a statistical software, R is one of it with open source based. R is often used in statistical computing with command line interface (CLI) as the interface. In implementation, CLI requires the R user to remember names of used syntax and function. It makes less effective when the inferences have many related statistical analysis, so graphical user interface (GUI) needed to giving an easy way to accessing all of it. Testing for mean vectors of two populations will be performed using S. Rockiki’s data about measures of oxygen consumption for 25 males and 25 females. Results about assumptions showing both populations are normal multivariate distributed and have different covariannce matrix. The conclusion from the testing for mean vectors of two populations has performed is both populations have different mean vectors. There are packages are used on construction of GUI in R, including gdata, tcltk2, and devtools with additional software like Rtools and ActivePerl. The GUI has four main menus such as File, Analysis, Plot, and Help. Based on GUI usage, the GUI has been able to processing the chosen analysis and showing valid output.. Keywords: Multivariate Statistics, Inferences about mean vector, Inferences of covariancematrices, R, GUI.

APLIKASI METODE GOLDEN SECTION UNTUK OPTIMASI PARAMETER PADA METODE EXPONENTIAL SMOOTHING Mahkya, Dani Al; Yasin, Hasbi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting is predicting the activities values that have been previously known. One of the methods that can be used to predict is Exponential Smoothing. In this study, exponential smoothing method used is Single Exponential Smoothing (SES), Holt Double Exponential Smoothing (DES) and Triple Exponential Smoothing Holt-Winter (TES) Additive and Multiplicative models. Data used is value of Central Java Export from the period January 2006 until December 2013. There is some weighting parameters were evaluated in this method in order to produce a minimum error. Trial error method is used to obtain the weighting parameters. For SES method parameters evaluated were the parameters α, in DES method there are α and γ. And TES method there are α, γ and β. The value that will be minimize is Persentage Mean Absolute Error (MAPE). This study used the Golden Section method to find the parameter values that minimize the weighting function of MAPE. And built a Graphical User Interface (GUI) MATLAB in order to facilitate the analysis process. The Golden Section analysis found the best model is the TES Holt Winters Additive because it has a minimum value of MAPE. With Use the TES Holt Winters Additive will continue to predict the value of exports of Central Java 12 periods ahead with weighting parameters that minimize MAPE. Keywords : Exponential Smoothing, Graphical User Interface (GUI), Export, Golden Section, Predict

PERBANDINGAN ANALISIS DISKRIMINAN LINIER KLASIK DAN ANALISIS DISKRIMINAN LINIER ROBUST UNTUK PENGKLASIFIKASIAN KESEJAHTERAAN MASYARAKAT KABUPATEN/KOTA DI JAWA TENGAH Kartikawati, Ana; Mukid, Moch. Abdul; Ispriyanti, Dwi
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Discriminant analysis is a statistics method which is used to classify an individual or object into certain group which has determined based on its independent variables. Discriminant analysis that commonly used is classical discriminant analysis which consist of classical linear discriminant analysis and classical quadratic discriminant analysis. In classical linear discriminant analysis there are two assumptions to be fulfilled i.e. independent variables have to be normal multivariate distributed and the covariance matrix from the two observed objects should be the same. Classical discriminant analysis cannot work properly if the data which being analyzed consists of many outliers. In order to make discriminant analysis works optimally within the classification though in the condition of data which contains of many outliers, robust estimator is needed. The robust discriminant analysis is used to get the high classification accuracy for data which contains of many outliers. Fast-MCD estimator is one of the robust estimators which is aimed to get the smallest determinant of covariance matrices. The robust linear discriminant analysis with fast-MCD method in this graduating paper is implemented to determine the prosperity status of the people in the regencies or towns in Central Java. The total proportion of classification accuracy using robust linear discriminant analysis method on the data of Central Java people prosperity is 77.14 percent. It is equal with the result from classic linear discriminant analysis which is also 77.14 percent. It is caused by the few amount of outlier on the data of Central Java people prosperity.

PENGELOMPOKAN PASIEN DEMAM BERDARAH RSUD dr. SOEHADI PRIJONEGORO DENGAN METODE ANALISIS KELAS LATEN Nurhayati, Noviana; Mukid, Moch. Abdul; Ispriyanti, Dwi
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The degree of disease dengue patients in early at the hospital is latent or unknown directly. Therefore it needs an indicator variables such as the examination of hematocrit, leukocytes and platelets to classify patients with dengue fever into classes according to the degree of disease. In this study, the method used to classify patients with dengue fever is a latent class analysis method. The purpose of this study is to establish a latent class model and describes profile of the class on cases of grouping dengue fever patients in dr. Soehadi Prijonegoro Sragen. The results from latent class analysis showed that the latent class model formed is two latent class model. There are two classes formed is class 0 for disease dengue infection with danger signs have criteria a normal hematocrit, abnormal leukocyte and platelet abnormal and class 1 for disease dengue infection without signs of danger have criteria a normal hematocrit, normal leukocytes and normal platelets.Keyword : dengue fever, latent class analysis, latent variables

ANALISIS PENGENDALIAN PERSEDIAAN PRODUK OLI MENGGUNAKAN METODE ECONOMIC ORDER QUANTITY PROBABILISTIK DENGAN MODEL (q,r) (Studi Kasus di bengkel Maju Jaya Tuban) Werti, Wetty Anggun; Sudarno, Sudarno; Mukid, Moch. Abdul
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Inventory has an important role for the continuity of the trading business. In the trading business, consumer demand for the product is usually random. Consumer demand opportunities are aspects that need to be considered in the process of inventory management. Economic Order Quantity (EOQ) probabilistic model (q,r) is the method used when consumer demand is random and the time between ordering until the product comes (lead time) is not equal to zero. This research aims to apply methods EOQ probabilistic model (q,r) in determining the total cost savings in the inventories of oil products in Maju Jaya Tuban workshop. The oil products analyzed were Top 1 and Yamalube oil products. These results indicate that the method EOQ probabilistic model (q,r) has a total inventory cost less than the policy Maju Jaya Tuban workshop. Total inventory cost savings when the ordering cost (10%) and holding cost (1%) is Rp 4.313,- for Top 1 oil products and Rp 3.086,- for Yamalube oil products.Keywords: Oil Demand, EOQ Probabilistic (q,r), Cost Savings

PEMBANGKITAN SAMPEL RANDOM MENGGUNAKAN ALGORITMA METROPOLIS-HASTINGS Irwanti, Lies Kurnia; Mukid, Moch. Abdul; Rahmawati, Rita
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Generating random samples can be done directly and indirectly using simulation techniques. This final project will discuss the process of generating random samples and estimate the parameters using an indirect simulation. Indirect simulation techniques used if the target distribution has a complicated shape and high dimension of density functions. Markov Chain Monte Carlo (MCMC) simulation is a solution to do it. One of the algorithms that is commonly used is Metropolis-Hastings. This algorithm uses the mechanism of acceptance and rejection to generate a sequence of random samples. In the example to be discussed, Metropolis-Hastings algorithm is applied to generate random samples of Beta distribution and also estimate the parameter value of the Poisson distribution using a proposal distribution random-walk Metropolis.

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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