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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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MODEL REGRESI COX PROPORTIONAL HAZARDS PADA DATA LAMA STUDI MAHASISWA (Studi Kasus Di Fakultas Sains dan Matematika Universitas Diponegoro Semarang Mahasiswa Angkatan 2009) Landong Panahatan Hutahaean; Moch. Abdul Mukid; Triastuti Wuryandari
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

High education has important role to increase the intellectual life of the nation and the development of natural sciences and technology by producing the quality graduates. The quality graduates just need 48 month to finish the study. There are many factors that will affect time of study students as Grade Point Average(GPA), Bustle student level, etc. Hence, need to know what factors affecting time of study students. One method that can be used is Survival analysis. Survival Analysis is analysis of survival data from the beginning of time research until certain events occurred. One of the methods of survival analysis is Cox Proportional Hazards Regression. Cox Proportional Hazards Regression is a regression which used data of intervals of time an event. The case which is discussed in this research is factors that affect time of study students of Faculty of Science and Mathematics started 2009 Diponegoro of University with the second type of censoring. From the research give conclusion that factors affecting time of study students is Department, GPA, and Organization

SIMULASI STOKASTIK MENGGUNAKAN ALGORITMA GIBBS SAMPLING Anifa Anifa; Moch. Abdul Mukid; Agus Rusgiyono
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One way to get a random sample is using simulation. Simulation can be done directly or indirectly. Markov Chain Monte Carlo (MCMC) is an indirectly simulation method. MCMC method has some algorithms. In this thesis only discussed about Gibbs Sampling algorithm. Gibbs Sampling is introduced by Geman and Geman at 1984. This algorithm can be used if the conditional distribution of the target distribution is known. It has applied on two casses, these are generation of bivariate normal random data and parameters estimation using Bayesian method. The data used in this research are the death of pulmonary tuberculosis in ASEAN in 2007. The results obtained are and with standard error for and .

IDENTIFIKASI LAMA STUDI BERDASARKAN KARAKTERISTIK MAHASISWA MENGGUNAKAN ALGORITMA C4.5 (Studi Kasus Lulusan Fakultas Sains dan Matematika Universitas Diponegoro Tahun 2013/2014) Bramaditya Swarasmaradhana; Moch. Abdul Mukid; Agus Rusgiyono
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Based on academics regulation No. 209/PER/UN7/2012, the study period of students in Diponegoro University has been scheduled for 4 years. In this study the graduation status of students that graduate under or equal to 4 years categorized as graduate on time, meanwhile students that graduate over 4 years categorized as graduate out of time. Hence, it is important to understand the profile of students who graduate on time and out of time based on gender, majors, GPA, organizational experience, part time experience, scholarship, students origin and pathways scholar. The purpose of this study is to identify those students profiles using Algorithm C4.5. Algorithm C4.5 contructs a decision tree that able to handle missing values, able to handle continues attribute and able to simplify the trees by pruning. The accuration of the Algorithm C4.5 is 84.475% and the number of the nodes are 20 nodes where 13 nodes are leaf nodes. The students profile that identified graduate on time are students of Physics who had received scholarship and a woman; students of Chemistry with GPA > 3.06; students of Statistics with GPA > 3.43 from SNMPTN also PSSB and students of Mathematics with GPA > 2.96. Keywords: Study Period, Algorithm C4.5, Decision Tree.

ANALISIS DISKRIMINAN FISHER POPULASI GANDA UNTUK KLASIFIKASI NASABAH KREDIT Ungu Siwi Maharunti; Moch. Abdul Mukid; Agus Rusgiyono
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is the biggest asset carried out by a bank and become the most dominant contributor to the bank income. However, the activity to distribute the credit takes a risk which can influence health and continuance of bank business. The credit risk which potentially occurs can be measured and controlled by analyzing directly whichever the credit client categorized to. The credit risk categorized to current credit, in specific concern credit, less current credit, doubtful credit and bad credit based on Bank Indonesia Regulation No.: 7/2/PBI/2005. The independent variables used in this research are nominal credit, principal balance, in time being bank client, time period, and bank interest. Fisher multiple discriminant analysis is a method whose assumption equality of covariance matrices. The result from using the Fisher multiple discriminant analysis in data of credit client from bank “X” in Pati shows that variable principal balance, in time being bank client, time period, and bank interest significant to measure credit risk. The classification using the Fisher multiple discriminant analysis in data of credit client from bank “X” in Pati gives the accurate 64,33%. Keywords: credit, classification, fisher multiple discriminant analysis

ANALISIS EKUITAS MEREK SEPEDA MOTOR HONDA TERHADAP KEPUTUSAN PEMBELIAN DAN PERILAKU PASCA BELI MENGGUNAKAN STRUCTURAL EQUATION MODELLING (SEM) Herwindhito Dwi Putranto; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Research on the implementation of Structural Equation Modelingto analyze the Honda brand equityon purchase decision and post-purchase behavior is based on the strength of the brand equityas a market leader Honda motorcycles in Indonesia for many years. The problem saddressed in this study is how the relationship between brand equity Honda motorcycle on purchase decision and post purchase behavior of consumers. In this study developed six variables consisting of 4 exogenous variables, namely brand awareness, brand response, the impression of quality and product loyalty, to measure brand equityas well as two endogenous variables, ie, purchase decision and post-purchase behavior. The study involved 200 students of the University of Diponegoro as respondents using purposive sampling technique.Structura lequation modeling research is Behavioral Post Buy=Purchasing Decisions + error. From the Goodness of Fittest results, structural equation modelin this study can be used with a value of 70,237 and the Chi-Square probability AGF I1000 and 0951. Brand awareness of 10.1% influence on purchasing decisions and 10% of the post-purchase behavior and is avariable that gives the effect of CR 1477-value ≤2.58. Responses highest brandin fluenceis equal to 32.7% against 32.4% purchase decision and post-purchase behavior. Thusit was concluded that brand awareness does not affect the purchase decision, while there sponse the brand, the impression of quality and product loyalty influence purchasing decisions. Purchasing decisions also provide a positive influence on post-purchase decisions.

PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN KARAKTERISTIK KESEJAHTERAAN RAKYAT MENGGUNAKAN METODE K-MEANS CLUSTER Fitra Ramdhani; 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

Welfare have a relative explanation, dynamic, and quantitative. Quantitative formulation of welfare is never final because it will continue to evolve along with the development needs of human life. In 2011, the National Team for the Acceleration of Poverty Reduction (NTAPR) made priority sector that can serve as a benchmark the welfare in a region. From the priority sector will be made cluster or group which contains all 33 provinces based on the level of public welfare in the region uses data in 2012 were sourced from the Central Statistics Agency (CSA). The method that can be used to group the 33 provinces is K-Means Cluster method with number cluster as many as two, three, four, and five clusters. K-Means Cluster method is one of cluster analysis method who can partition the data into one or more clusters, so that the data with the same characteristics are grouped into the same cluster and data with different characteristics grouped into other clusters. To know the most optimal of the number of clusters we use Davies-Bouldin Index (DBI). We concluded that the optimal number of cluster is three with details the province in the first clusters have superiority in four sectors like net enrollment rate of primary school, net enrollment rate of junior high school, IMR (Infant Mortality Rate), and access to electricity. The province in the second clusters have superiority in one sector, that is open unemployment rate. The province in the third clusters have superiority in all sectors. Keywords: Welfare, NTAPR Priority Sector, K-Means Cluster Method, Davies-.Bouldin Index (DBI)

KUALITAS PELAYANAN PADA BANK JAWA TENGAH (Studi Kasus : Bank Jateng Cabang Tembalang) Yosi Dhyas Monica; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Service attendance quality is superiority level which is be expected and control above its superiority level for satisfying consumen's desire. In this case, there are 5 service quality dimensions. Those are tangible, reliability, responsiveness, assurance, and emphaty. This research study was doing at Bank Jateng, where the respondents are the customer of Bank Jateng. Importance Performance Analysis consist of two components, there are quadrant analysis and discrepancy analysis (gap). Quadran analysis can find out the respond of cusumens against variable which has plotted based on interest and performance level from those variables. While gap analysis is being used for perceiving discrepancy between performance of a variable with the expectation from consumen against its variable. Customer Satisfaction Index (CSI) is used for discovering overall satisfaction level of customers. The T2 hotelling control chart is to know the qualiy controlof two or more related quality characteristics. Result of the research is showing that for quadran analysis, those variables which representing 5 service quality dimensions be located spread in different quadran. For gap analysis, the service perormance of a bank represented by 20 variables who representing 5 service quality dimensions, all of which is still under customers expectation. CSI value aa big as 72,22% which is mean customers satisfaction index is on the satisfaction criteria. On T Hotelling chart is said that the process is not restrained statistically yet because there are 4 points is on the top of control chart

PENENTUAN MODEL RETURN HARGA SAHAM DENGAN MULTI LAYER FEED FORWARD NEURAL NETWORK MENGGUNAKAN ALGORITMA RESILENT BACKPROPAGATION (Studi Kasus : Harga Penutupan Saham Unilever Indonesia Tbk. Periode September 2007 – Maret 2015) Riza Adi Priantoro; Dwi Ispriyanti; Moch. Abdul Mukid
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Determination of a return of stock price model is often associated with a process of forecasting for future periods. A method that can be used is neural network. The use of neural network in the field of forecasting can be a good solution, but the problem is how to determine the network architecture and the selection of appropriate training methods. One possible option is to use resilent back propagation algorithm. Resilent back propagation algorithm is a supervised learning algorithm to change the weights of the layers. This algorithm uses the error in the backward direction (back propagation), but previously performed advanced stage (feed forward) to get the error. This algorithm can be used as a learning method in training model of a multi-layer feed forward neural network. From the results of the training and testing on the share return of stock price PT. Unilever Indonesia Tbk. data obtained MSE value of 0.0329. This model is good to use because it provides a fairly accurate prediction of the results shown by the proximity of the target with the output.Keywords : return, neural network, back propagation, feed forward, back propagation algorithm, weight, forecasting.

VERIFIKASI MODEL ARIMA MUSIMAN MENGGUNAKAN PETA KENDALI MOVING RANGE (Studi Kasus : Kecepatan Rata-rata Angin di Badan Meteorologi Klimatologi dan Geofisika Stasiun Meteorologi Maritim Semarang) Kiki Febri Azriati; Abdul Hoyyi; Moch. Abdul Mukid
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Forecasting method Box-Jenkins ARIMA (Autoregressive Integrated Moving Average) is a forecasting method that can provide a more accurate forecasting results. To verify the model obtained using the one Moving Range Chart. The control charts are used to determine the change in the pattern of file seen from the residual value (the difference between the actual file and the file forecasting). File used in this study the average wind speed in the Tanjung Emas harbor during January 2008 to December 2013. The best of Seasonal ARIMA model is ARIMA (0,0,1) (0,0,1) 12. The results of the verification using the Moving Range Control Chart on the model showed that all residual values are within control limits to the length of the shortest interval, means of verification results show that the model is a good model used for forecasting future periods. Forecasting is generated during the period of the next 15 shows the seasonal pattern. This is shown in the figure forecast 2014 average wind speeds are highest in January, as well as forecasting the 2015 figures the average speed of the highest winds also occurred in January. Forecasting results reflect past file, because the actual file used also showed a seasonal pattern with the same seasonal period is annual, where the numbers mean wind speeds are highest in January. Keywords : Seasonal ARIMA, Moving Range Control Chart, Mean wind speeds.

PEMODELAN DAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG), JAKARTA ISLAMIC INDEX (JII), DAN HARGA MINYAK DUNIA BRENT CRUDE OIL MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE EXOGENOUS (VARX) Nunung Hanurowati; Moch. Abdul Mukid; Alan Prahutama
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Index of stocks listed on the Indonesia Stock Exchange (IDX) there are conventional that one of them is the Composite Stock Price Index (CSPI) and the index of stocks that are sharia is the Jakarta Islamic Index (JII). In its movement, the value of CSPI and JII often increases and decreases that are influenced by several factors, one of which is the world oil price of Brent Crude Oil. To see the value of CSPI and JII conditions during the period of the next few months it takes the model equations. Because the third such data included in the time series data, we used time series analysis with the appropriate method is the Vector Autoregressive Exogenous (VARX). VARX(p,q) is a model of multivariate time series that consists of several endogenous variable of the time series order p with q adding exogenous variables. The purpose of this study is to obtain an appropriate VARX models and forecasting for data CSPI and JII. The model to predict CSPI and JII with exogenous variables that influence the world oil prices of Brent Crude Oil is VARX(1,1). Test parameters for exogenous variables in the model VARX(1,1) not significant at significance level α = 5%, but this result could be ignored and continues to testing residual assumptions. Residual model VARX(1,1) satisfies the assumption of white noise and multivariate normal distribution, in order to obtain results as very good forecast that with each MAPE value for CSPI and JII forecast of 2,71% and 3,63%. Keywords: CPSI, JII, Brent Crude Oil, VARX, 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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