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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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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.

ANALISIS PENGELOMPOKAN DAERAH MENGGUNAKAN METODE NON-HIERARCHICAL PARTITIONING K-MEDOIDS DARI HASIL KOMODITAS PERTANIAN TANAMAN PANGAN (Studi Kasus Kabupaten/Kota Se-Jawa Tengah Tahun 2009 – 2013) Etik Setyowati; Agus Rusgiyono; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Non-Hierarhical K-Medoids Partitioning is a clstering method for classifying objects based on the characteristics possessed by the object, wherein the object k randomly selected to be medoids is the center of the cluster. After medoids selected then other objects that have similarities with medoids made in one cluster. Medoids is the object which is considered to represent a cluster. Similarity between objects is calculated using euclidean distance. One application grouping method Non-Hierarhical K-Medoids Partitioning is to classify District in Central Java is based on the production of rice and pulses. Grouping Regency / City in Central Java using Non-Hierarhical Partitioning K-Medoids obtained information that rice production by Regency / City in Central Java can be grouped into seven clusters, but because of a case in 2010 and in 2011 the number of clusters that formed are two clusters, while the production of food crops by Regency / City in Central Java can be grouped into two clusters.Keywords: k-medoids, Non-Hierarhical, Euclidean distance, Similarities.

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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