Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

1.734

P-Index

This Author published in this journals

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

Published : 58 Documents Claim Missing Document

Claim Missing Document

Articles

Title

1 2 3 4

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.

PERAMALAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA DI KEPULAUAN RIAU DENGAN MENGGUNAKAN MODEL FUNGSI TRANSFER Tamura Rolasnirohatta Siahaan; Rukun Santoso; Alan Prahutama
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Transfer function models is a data analysis model that combines time series and causal approach, in another words, transfer function models is a method that ilustrates that the predicted value in teh future is affected by the past value time series and based on one or more related time series. In this research, an analysis of the number of tourist arrival and rainfall in several regions in Kepulauan Riau from January 2013 until December 2017 was aimed at obtaining a transfer function model and forecasting the number of tourist arrival in several regions of the Kepulauan Riau for next periods. Based on the result of the analysis, rainfall in Tanjung Pinang does not affect the visit of tourist with the values of MAPE is 13,63494%. Rainfall in Batam also does not affect the visit of tourist with the values of MAPE is 7,977151%. While in Tanjung Balai Karimun, tourist arrivals was affected by rainfall with the values of MAPE is 10,32777%.

PEMODELAN GEOGRAPHICALLY WEIGHTED GENERALIZED POISSON REGRESSION (GWGPR) PADA KASUS KEMATIAN IBU NIFAS DI JAWA TENGAH Wahyu Sabtika; Alan Prahutama; Hasbi Yasin
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.v10i2.30946

Maternal mortality is one indicator to describing prosperity in a country and indicator of women's health. Most of the maternal mortality caused by postpartum maternal mortality. The number of postpastum maternal mortality is events that the probability of the incident is small, where the incident depending on a certain time or in a certain regions with the results of the observation are variable diskrit and between variable independent each other that follows the Poisson distribution, so that the proper statistical method is Poisson regression. However, in Poisson regression model analysis sometimes assumptions can occur violations, where the value of variance is greater than the mean value called overdispersion. Generalized Poisson Regression (GPR) is one model that can be used to handle overdispersion problems. This modeling produces global parameters for all locations (regions), so to overcome this we need a method of statistical modeling with due regard to spatial factors. The analytical method used to determine the factors that influence the number of postpartum maternal mortality in Central Java that have overdispersion and there are spatial factors, is Geographically Weighted Generalized Poisson Regression (GWGPR) using the Maximum Likelihood Estimation method and Adaptive Bisquare weighting. Poisson regression and GPR modeling produces a variable percentage of pregnant women doing K1 which has a significant effect on the number of postpartum maternal mortality, while for GWGPR modeling is divided into four cluster in all regency/city in Central Java based on the same significant variable. From the comparison of AIC values, it was found that the GWGPR model is better for analyzing postpartum maternal mortality in Central Java because it has the smallest AIC value.Keywords: The Number of Postpartum Maternal Mortality, Overdispersion, Generalized Poisson Regression, Spatial, Geograpically Weighted Generalized Poisson Regression, AIC

PERBANDINGAN METODE MOORA DAN TOPSIS DALAM PENENTUAN PENERIMAAN SISWA BARU DENGAN PEMBOBOTAN ROC MENGGUNAKAN GUI MATLAB Rafida Zahro Hasibuan; Alan Prahutama; Dwi Ispriyanti
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

MAN Asahan is an educational institution that selects new students every year. MAN Asahan sets certain criteria in choosing new students so that selected students are of high quality. The criteria determined are the Al-Qur'an test scores, national exam scores, Academic Potential Test scores and achievement certificates. In selecting new students who were accepted as many as 271 of the 530 registrants the school still used the manual process so that it needed accuracy and a long time. In this study a decision support system was created that could be a solution to assist the selection process according to school criteria. The system will applied is MOORA (Multi-Objective Optimization on the Base of Ratio Analysis) method and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) with the weighting method of ROC (Rank Order Centroid). Then the sensitivity analysis is done to determine the appropriate method to be chosen to obtain optimal results. This research was conducted with the help of the MATLAB GUI as a computing tool. The GUI that is built can simplify and speed up the selection process. Based on the results of the study, the average percentage value of sensitivity for the MOORA method is -1.61% while the TOPSIS method is -7.96%. With the existence of sensitivity analysis it can be known the most appropriate method for this case is the MOORA method.Keywords: Students, MOORA, TOPSIS, ROC, Sensitivity, GUI Matlab

PERBANDINGAN NILAI KORELASI PADA KANONIK ROBUST (METODE MINIMUM COVARIANCE DETERMINANT) DAN KANONIK KLASIK (Studi Kasus Data Struktur Ekonomi dan Kesejahteraan Rakyat di Jawa Barat 2016) Widi Rahayu; Sudarno Sudarno; Alan Prahutama
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Canonical correlation analysis is a multivariate statistical analysis that aims to examine the correlation between two groups of variabels in a way to maximize the value of correlation between variabels. The outlier in the data affect the covariance matrix is generated, So that use robust multivarat. There is robust multivariate approach to the analysis of canonical robust with MCD method (Minimum Covariance Determinant). This final project aims to determine comparison between correlation value of robust canonical with MCD and canonical classical methods. With a data theres containing of outliers in the case studies of people's welfare and economic structures in West Java in 2016. Used a set of variabels welfare of people consist of 6 variabel (Y) and a set of variabels economic structure which consists of four variabels (X). Based on the analysis results obtained that robust canonical correlation values better explain the correlation between two sets of variabels, the correlation value 0.99552, =0.91228, =0.71529, =0.63174, While the correlation value on classical canonical are 0.931489, 0.538672, 0.387099, 0.259318, Canonical robust can be interpreted more because it meets the test of significance are partially and directly, while the classical canon can not be interpreted further because it does not meet the test of the significance of the function. Keywords : Classical canonical correlation, canonical correlation robust correlation value, Minimum Covariance Determinant (MCD)

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

Title Search

Found 33 Documents Search Journal : Jurnal Gaussian

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 33 Documents
Search
Journal : Jurnal Gaussian