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All Journal International Journal of Science and Engineering (IJSE) Media Statistika Jurnal Ilmu Dasar Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang E-Dimas: Jurnal Pengabdian kepada Masyarakat Indonesian Journal of Applied Statistics Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Surya Masyarakat Jurnal Varian Jurnal Surya Masyarakat

Alan Prahutama

Departemen Statistika, FSM, Universitas Diponegoro, Jl. Prof Soedharto SH Tembalang, Semarang

Author-ID : 344091

Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Mathematics Public Health Social Sciences Other

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PEMODELAN DEFORESTASI HUTAN LINDUNG DI INDONESIA MENGGUNAKAN MODEL GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION (GTWR) Thea Zulfa Adiningrumh; Alan Prahutama; Rukun Santoso
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical analysis method that is used to modeling the relationship between dependent variables and independent variables. In the linear regression model only produced parameter estimators are globally, so it’s often called global regression. While to analyze spatial data can be used Geographically Weighted Regression (GWR) method. Geographically and Temporally Weighted Regression (GTWR) is the development of GWR model to handle the instability of a data both from the spatial and temporal sides simultaneously. In this GWR modeling the weight function used is a Gaussian Kernel, which requires the bandwidth value as a distance parameter. Optimum bandwidth can be obtained by minimizing the CV (cross validation) coefficient value. By comparing the R-square, Mean Square Error (MSE) and Akaike Information Criterion (AIC) values in both methods, it is known that modeling the level of deforestation in protected forest areas in Indonesia in 2013 through 2016 uses the GTWR method better than global regression. With the R-square value the GTWR model is 25.1%, the MSE value is 0.7833 and AIC value is 349,6917. While the global regression model has R-square value of 15.8%, MSE value of 0.861 and AIC value of 361,3328. Keywords : GWR, GTWR, Bandwidth, Kernel Gaussian

IMPLEMENTASI METODE SIX SIGMA MENGGUNAKAN GRAFIK PENGENDALI EWMA SEBAGAI UPAYA MEMINIMALISASI CACAT PRODUK KAIN GREI Ayudya Tri Wahyuningtyas; Mustafid Mustafid; Alan Prahutama
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The quality being a very important aspect for consumer to choose products beside price that competes. In production process grey fabric there are several kinds of defects, the defects can cause to decrease of grade fabric produced. Six sigma method is a method that can be used to analyze defect rate to approach zero defect products. A procedure used for quality improvement toward the target that the concept of six sigma DMAIC. This study aims to implement six sigma method and EWMA control chart in quality control of product quality cloth of grey. The results obtained in this study is one the whole production process produces DPMO value of 24790.97 with sigma quality level of 3.464 means that the product of one million cloth of grey there are 24790.97 meters of product that does not fit in production. In the calculation process capability, process capability ratio value obtained more than 1 means that the process is going well and meets the specifications that have been established, but it is still possible to be improved so that the products resulting better. Keywords: Quality, Quality Control, Six Sigma, EWMA

PEMODELAN HARGA EMAS DUNIA MENGGUNAKAN METODE NONPARAMETRIK POLINOMIAL LOKAL DILENGKAPI GUI R Jody Hendrian; Suparti Suparti; Alan Prahutama
Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i4.33103

Investing in gold is a flexible choice because it can be sold at any time and used as an emergency fund. Investors should have the knowledge to predict data from time to time to achieve investment goals. One of the statistical methods for time series data modeling is ARIMA. The ARIMA model is strict with the assumptions that the data must be stationary, the residuals must be normally distributed, independent, and with constant variance, so an alternative model is proposed, namely nonparametric regression model, which has no modeling assumptions requirement. In this study, the daily world gold price data will be modeled using a local polynomial nonparametric model as an alternative because the assumptions in the ARIMA are not fulfilled. The data is divided into 2 parts, namely in sample data from January 2, 2020 to November 30, 2020 to form a model and out sample data from December 1, 2020 to December 31, 2020 used for evauation of model performance based on MAPE values. The chosen best model is the local polynomial model with Gaussian kernel function of degree 5, bandwidth of 373, and local point of 1744 with an MSE value of 482.6420. The local polynomial model out sample data MAPE value is 0.61%, indicating that the model has excellent forecasting capability. In this study, Graphical User Interface (GUI) using R software with the help of shiny package is also built, making data analyzing easier and generating more interactive display output.

PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) DENGAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK MENGGUNAKAN GUI MATLAB Rizki Brendita Br Tarigan; Hasbi Yasin; Alan Prahutama
Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v7i4.28872

Capital market Indonesia is one of the important factors in the development of the national economy, proved to have many industries and companies that use these institutions as a medium to absorb investment to strengthen its financial position. The recent years, Jakarta Composite Index (JCI) in Capital Market tend to strengthen. JCI data are the time series data obtained from the past to predict the future with caracteristics of JCI data are non stationary and non linier. Neural network is a computational method that imitate the biological neural network. There are several types of methods that can be used in neural network that is: Radial Basis Function Neural Network (RBFNN) Generalized Regression Neural Network (GRNN), dan Probabilistic Neural Network (PNN). Model of Radial Basis Function Neural Network is suitable for time series data. This model has a network architecture in the form of input layer, hidden layer and output layer. This research is done with the help of GUI as a computation tool. The results of analysis by using GUI conducted on the size sample of data as much as 1211 taken as 100 the data thus obtained value of 2315,6 MSE training and training MAPE value of 0,72%, while for the testing of 28886,7 MSE and MAPE testing value is 0,70%. Based on the results of forecasting, JCI values on January 02, 2018 until January 08, 2018 at 6499,922 every day. Keywords: Radial Basis Function Neural Network (RBFNN), Jakarta Composite Index (JCI), MSE, MAPE, Time Series, GUI.

PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH DENGAN REGRESI KOMPONEN UTAMA ROBUST Tsania Faizia; Alan Prahutama; Hasbi Yasin
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Robust principal component regression is development of principal component regression that applies robust method at principal component analysis and principal component regression analysis. Robust principal component regression does not only overcome multicollinearity problems, but also overcomes outlier problems. The robust methods used in this research are Minimum Covariance Determinant (MCD) that is applied when doing principal component analysis and Least Trimmed Squares (LTS) that is applied when doing principal component regression analysis. The case study in this research is Human Development Index (HDI) in Central Java in 2017 which is influenced by labor force participation rates, school enrollment rates, percentage of poor population, population aged 15 years and over who are employed, health facilities, gross enrollment rates, and net enrollment rates. The model of HDI in Central Java in 2017 using robust principal component regression MCD-LTS provides the most effective result for handling multicollinearity and outliers with Adjusted R2 value of 0.6913 and RSE value of 0.469. Keywords: Robust Principal Component Regression, Multicollinearity, Outliers, Minimum Covariance Determinant (MCD), Least Trimmed Squares (LTS), Human Development Index (HDI).

PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) DAN WEIGHTED PRODUCT (WP) DALAM SISTEM PENUNJANG PEMILIHAN LAPTOP TERFAVORIT MENGGUNAKAN GUI MATLAB Abdiel Pandapotan Manullang; Alan Prahutama; Rukun Santoso
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Laptops have become an important requirement for most students is to support educational activities and business activities. The number of brands of laptops or types of laptops that exist makes consumers especially students have their own preferences in choosing a laptop. The method can be used to select the favorite laptop are SAW (Simple Additive weighting) and WP (Weighted Product). Both of these methods are the methods used to solve the problem of MADM (Multi Attribute Decision Making). There are 30 types of laptops that will be used in the selection of the favorite laptops.For the selection criteria for the type of laptop that is priced, RAM (Random Access Memory), HDD (hard drive), a processor, a VGA (Video Graphics Array), weight, color, screen size, service centers, warranty, availability of spare parts, battery capacity, equipped with OS and application software. Selection of the favorite type of laptop is done with the help of MATLAB (Graphical User Interface) GUI (Matrix Laboratory) as a computing tool. SAW method and WP, in this research showed the same results that the most favored type of laptop laptop mode DEL INSPIRON 15Z-5523 with a value preference for SAW method amounted to 0.9518 while the WP method amounted to 0.9511.Keywords: SAW, WP, Laptop, favorite, GUI

ANALISIS METODE BAYESIAN MENGGUNAKAN NON-INFORMATIF PRIOR UNIFORM DISKRIT PADA SISTEM ANTREAN PELAYANAN GERBANG TOL MUKTIHARJO Dini Febriani; Sugito Sugito; Alan Prahutama
Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i3.32783

The growth rate of the traffic that is high resulting in congestion on the road network system. One of the government's efforts in addressing the issue with the build highways to reduce congestion, especially in large cities. One of the queuing phenomena that often occurs in the city of Semarang is the queue at the Toll Gate Muktiharjo, that the queue of vehicles coming to make toll payment. This study aims to determine how the service system at the Toll Gate Muktiharjo. This can be known by getting a queue system model and a measure of system performance from the distribution of arrival and service. The distribution of arrival and service are determined by finding the posterior distribution using the Bayesian method. The bayesian method combine the likelihood function of the sample and the prior distribution. The likelihood function is a negative binomial. The prior distribution used a uniform discrete. Based on the calculations and analysis, it can be concluded that the queueing system model at the Toll Gate Muktiharjo is a (Beta/Beta/5):(GD/∞/∞). The queue simulation obtained that the service system Toll Gate Muktiharjo is optimal based on the size of the system performance because busy probability is higher than jobless probability.

PEMBENTUKAN DAN PENGUKURAN KINERJA PORTOFOLIO EFISIEN DENGAN METODE CONSTANT CORRELATION MODEL MENGGUNAKAN GUI MATLAB (Studi Kasus: Kelompok Saham pada Indeks JII, LQ45, dan INFOBANK15) Muhammad Zidan Eka Atmaja; Alan Prahutama; Dwi Ispriyanti
Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i2.28940

Investment is an important part of financial management that is widely known by the public. One example of an investment is a stock, stock is favored by investors because many of companies issue stock investment. investors goal from investment are to get funds that have been invested. Besides advantage, investors also have to face risks that can befall on him. Risk in investment can be minimized by diversification, for example by forming a portfolio. A good portfolio is an efficient portfolio, which is a portfolio that has a high rate of return with minimal risk. One of the way to to form an efficient portfolio is the Constant Correlation Model (CCM) method. The CCM method focuses on Excess return to Standard Deviation (ERS) and correlation between paired stocks. And to measure the portfolio formed can be measured by the Sharpe Ratio. GUI MATLAB program was formed to make it easier to find portfolio from the CCM method. This research uses stock data on the stock index JII, LQ45, and INFOBANK15 with interest rate of SBI period 2 October 2017-30 December 2019. Based on the results and discussion with manual calculations and GUI MATLAB, it can be concluded that percentage of weight, expected return, risk, and Sharpe index produce the same numbers. Keywords: Stock, Efficient Portfolio, Constant Correlation Model, Sharpe Ratio

ANALISIS PORTOFOLIO OPTIMAL MENGGUNAKAN MULTI INDEX MODEL (Studi Kasus: Kelompok Saham IDX30 periode Januari 2014 – Desember 2018) Bramadita Kunni Fauziyyah; Alan Prahutama; Sudarno Sudarno
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Investment is the placement of a number of funds at this time in the hope of making a profit in the future. The purpose of investors investing is to get many profit by understanding that there is a possibility of losses. But, the higher the expected return then the risk also greater. The method to minimize risk is portfolio. One of the optimum portfolio method is Multi Index Model. Multi Index Model is model that use more than one index or factor that affects the return on stock. The stock in this research is 10 stocks of IDX30 period January 2014 – December 2018. Index in this research is IHSG, Hang Seng Index and DJIA. Multi Index Model has assumptions: residual variance of stock i equals , variance of index j equals , E(ci) = 0, covariance between index equals 0, covariance between the residual for stock and index equals 0 and covariance between the residual for stock equals 0. The result of this research is there are 4 stocks that fulfill the assumpions to be made as the optimum portfolio, that is GGRM (Gudang Garam Tbk) 23.67%, UNVR (Unilever Indonesia Tbk) 37.09%, BBCA (Bank Central Asia Tbk) 25.15% dan ASII (Astra International Tbk) 14.09% with a value of expected return portfolio is 1.19% and risk of portfolio is 3.79%. Keywords: Investment, Optimum Portfolio, Multi Index Model

KAJIAN RELIABILITAS PADA SISTEM SERI-PARALEL DENGAN EMPAT KOMPONEN Farhah Izzatul Jannah; Sudarno Sudarno; Alan Prahutama
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Reliability analysis is the analysis of the possibility that the product or service will function properly for a certain period of time under operating conditions without failure. One configuration of components that can be formed is a series-parallel system on a filter capacitor circuit using 4 components consisting of 2 rectifier diodes, 1 capacitor, and 1 load resistor. The data used to obtain the value of system reliability is the time of failure based on the assumption of failure of the independent component. The function of the form on the system can be expressed by Ф(x)= x1x3 + x1x4 + x2x3 + x2x4 - x1x3x4 - x2x3x4 - x1x2x3 - x1x2x4 + x1x2x3x4. The parameter values of each distribution are calculated using the Median Rank Regression Estimation (MRRE) and Maximum Likelihood Estimation (MLE) methods. To test the data following a certain distribution or not, the calculation is manually done with the Anderson-Darling (AD) test so that it is known that the failure time data of rectifier diode 1 follows the weibull distribution with parameters and , failure time data of rectifier diode 2 follows weibull distribution with parameters and , failure time data of capacitors follow normal distribution with parameters and , and the failure time data of the load resistor following the gamma distribution with parameters and . From the calculation of system reliability, it shows that the higher the intensity of the system fails it will affect the value of reliability to be lower. A serial system from a parallel system functions if there is at least one component j in one subsystem that functions. Keywords: Reliability, Series-Parallel, MRRE, MLE, AD.

Co-Authors Abdiel Pandapotan Manullang Abdul Hoyyi Abdul Rohman Agus Rusgiyono Alifah Zahlevi Alvita Rachma Devi Ana Kristiana, Ana Anak Agung Gede Sugianthara Arafa Rahman Aziz Arbella Maharani Putri Arief Rachman Hakim Arief Rachman Hakim Arkadina Prismatika Noviandini Taryono Ayudya Tri Wahyuningtyas Bramadita Kunni Fauziyyah Budi Warsito Dede Zumrohtuliyosi Dhea Dewanti Dini Febriani Dwi Ispriyanti Farhah Izzatul Jannah Felinda Arumningtyas Hasbi Yasin Helmi Panjaitan Izzudin Khalid, Izzudin Jefferio Gusti Putratama Jody Hendrian Laili Rahma Khairunnisa Lingga Bayu Prasetya Lutfia Nuzula Lutfiah Maharani Siniwi Moch. Abdul Mukid Muhammad Zidan Eka Atmaja Mustafid Mustafid Nabila Surya Wardani Nahdliyah, Milla Alifatun Nur Walidaini Pandu Anggara Pramestiara Dewiga, Pramestiara Puspita Kartikasari Rafida Zahro Hasibuan Ratna Kurniasari Rezzy Eko Caraka Rita Rahmawati Rizki Brendita Br Tarigan Rosyidah, Haniatur Rukun Santoso Shofi Andari Sudargo Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Suparti Suparti Suparti, S. Tamura Rolasnirohatta Siahaan Tarno Tarno Tatik Widiharih Thea Zulfa Adiningrumh Tiani Wahyu Utama Tiani Wahyu Utami Tiara Sukma Valentina Tomi Ardi Tresno Sayekti Nuryanto Tsania Faizia Ulfi Nur Alifah Wahyu Sabtika Widi Rahayu Yuciana Wilandari

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