Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

Jurnal Gaussian

gaussian
Website

Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

Core Subject : Education,

Other

Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.

Arjuna Subject : -

Articles 733 Documents

43 44 45 46 47

APLIKASI FUZZY ANALYTICAL HIERARCHY PROCESS UNTUK MENENTUKAN PRIORITAS PELANGGAN BERKUNJUNG KE GALERI (Studi Kasus di Secondhand Semarang) Agung Santoso; Rita Rahmawati; Sudarno Sudarno
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Entrepreneur have an important role in the development of developing countries. Entrepreneurship has many responsibilities, one of them is in making decisions concerning organizational leadership, marketing and others. Making the right decisions to support advancement a company. Analytic Hierarchy Process (AHP) is a decision support models to find the order of priority of the various alternatives in solving a problem. Weakness contained in the AHP is subjectivity. The approach to the fuzzy concept can minimize these weaknesses. The use of function Triangular Fuzzy Number (TFN) on Fuzzy AHP can clarify uncertainties in the interval assessment scale. This study aims to identifies the priority of customers visiting the gallery case study in Secondhand Semarang. The data taken by distributing questionnaires to customers have ever visiting as respondents. The results showed criteria of Barang is a top of priority with the highest priority weight is 0,341. Criteria of Produk followed with 0,245 priority weight, then criteria of Suasana with 0,211 priority weight, and the last criteria of Lingkungan with 0,201 priority weight.

PENENTUAN VALUASI PORTOFOLIO OBLIGASI DENGAN CREDIT METRICS DAN MONTE CARLO SIMULATION Arief Seno Nugroho; Di Asih I Maruddani; Sugito Sugito
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The capital market is one way to get funding for the company and as a medium to strengthen corporate finance position. One of the instruments that are traded than stocks are bonds. The advantage of this instrument because it is easy and rapid acquisition of funds to beused for the operations of the corporate and the period of payment is longer. Bond investment must be noticed valuations and credit risk, with calculating the valuation can be estimate bonds credit risk. Credit Metrics is a reduced form model to estimate the risk of displacement of ratings. The risk not only occur when corporate rating be default but also if the rating upgrade or downgrade. For the determination of the portfolio valuation can be used Monte Carlo simulation using generate scenarios corporate ratings. Empirical study can be used for three bonds there are Obligasi II Bank Danamon Tahun 2010 Seri B, Obligasi II Telkom Tahun 2010 Seri A, and Obligasi Indofood Sukses Makmur V Tahun 2009. Each has an average valuation of 1.013,039 billion, 1.179,203 billion and 2.259,284 billion. The valuation of the portfolio amounted to 4.451,52 billion and a standard deviation 70,33 billion

PERAMALAN INDEKS HARGA SAHAM GABUNGAN DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) Gayuh Kresnawati; Budi Warsito; Abdul Hoyyi
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Smooth Transition Autoregressive (STAR) Model is one of time series model used in case of data that has nonlinear tendency. STAR is an expansion of Autoregressive (AR) Model and can be used if the nonlinear test is accepted. If the transition function G(st,γ,c) is logistic, the method used is Logistic Smooth Transition Autoregressive (LSTAR). Weekly IHSG data in period of 3 January 2010 until 24 December 2017 has nonlinier tend and logistic transition function so it can be modeled with LSTAR . The result of this research with significance level of 5% is the LSTAR(1,1) model. The forecast of IHSG data for the next 15 period has Mean Absolute Percentage Error (MAPE) 2,932612%. Keywords : autoregressive, LSTAR, nonlinier, time series

IDENTIFIKASI FAKTOR-FAKTOR YANG MEMPENGARUHI TERJADINYA PREEKLAMPSIA DENGAN METODE CHAID (Studi Kasus pada Ibu Hamil Kategori Jampersal di RSUD Dr.Moewardi Surakarta) Restu Sri Rahayu; Moch. Abdul Mukid; Triastuti Wuryandari
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Pre-eclampsia is a spesific pregnancy disease in which hypertency and proteinuria occurs after 20 weeks of pregnancy . This sickness is caused by many factors. To identify the factors, We lowercase a statistical analysis that can explain the characteristics of pregnant women who has pre-eclampsia. One analysis used for segmentation is CHAID (Chi-Squared Automatic Interaction Detection). This analysis classify and view the segmentation on nominal scale dependent variable (patient’s status). CHAID analysis result indicates that the history of hypertension is the most influential independent variable. The tree diagram shows that there are seven segments of pregnant women, this study reveals that, there are three segments that need to be concerned because these segments show a high number and high index value exceeds 100% of pregnant women with pre-eclampsia. These segments need an effort to support the reduction of MMR. The three segment are segment pregnant women who has the history of hypertension; segment pregnant women of primary school degree and who are jobless, overweight, with no history of hypertension; and segment pregnant women with elementary and junior high school degree, who has jobs, and no hypertension history. Accuration of the CHAID algorithm in classifying is 78,2%. Keywords: Pre-eclampsia, Classify, CHAID, Maternal Mortality Ratio, Accuration

ANALISIS HUBUNGAN FAKTOR FAKTOR YANG MEMPENGARUHI PREDIKAT PERUSAHAAN ASURANSI UMUM DI INDONESIA PERIODE DESEMBER 2013 – NOVEMBER 2014 Tri Retnaning Nur Amanah; Tatik Widiharih; Sudarno Sudarno
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Human life often faces an uncertain situation and risks. In reducing uncertainty, human can protect themselves by choosing an insurance company. One that can be considered in the selection of protection is to observe the predicate of the insurance company. Predicate of general insurance company in Indonesia period December 2013 to November 2014, issued by the research institute Info bank categorized into 4 (four), there are very good, good, good enough and not good. Rating of predicate using factors commonly used to observe the financial performance. Those factors are the Risk Based Capital, the growth of gross premium income, the load (claims, efforts, and commissions) to net premium income, the net income (loss) before taxes compared to averages of equity, the net income (loss) comprehensive compared with the average of equity capital, the liquidity, sufficiency investments and current assets to total assets, the growth of their own capital, their own premium retention on their own capital, the underwriting results compared with net premium, the balance on investment with net premium income, investment of current assets to total assets. This study aims to determine the factors that are supposed to influence the predicate of insurance using ordinal logistic regression. Results of the analysis showed that the growth in gross premium income and load (claims, efforts, and commissions) to net premium income have significant effect (α = 5%) to predicate of insurance.Keywords: ordinal logistic, gross premium, the load to net premium, predicate of insurance.

ANALISIS HUBUNGAN ANTARA LAMA STUDI, JALUR MASUK DAN INDEKS PRESTASI KUMULATIF (IPK) MENGGUNAKAN MODEL LOG LINIER (STUDI KASUS: LULUSAN MAHASISWA FSM UNDIP PERIODE WISUDA TAHUN 2012/2013) Diah Budiati; Yuciana Wilandari; Suparti Suparti
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Graduation is the end result of the process learning during the lectures in college. One of the duties and responsibilities of the college is to produce quality graduates, which college will prepare candidates reliable scholars, achievers and have special expertise in the field. To achieve S1 degree course each student must complete his college studies load. In the process of completion of the study load many factors at play, both internal and external factors. These factors are not directly specify a person in graduation. In this study, the internal factors are long study, driveways and university grade point average (GPA) of students. The purpose of this study was to determine the relationship between the internal factors in terms of graduation. One method used to determine the relationship between the factors is log linear models. Estimating a log linear model using the Maximum Likelihood Estimation (MLE), which is followed by Newton-Raphson iteration. Selection of the best model was conducted using Backward Elimination. To test the significance of the model has been obtained to use Goodness of Fit Test. After testing on the whole, it is known that each of the factors that play a role in graduate student tested and there was an interaction between the period of study with a GPA of factors.

PEMODELAN KECEPATAN ANGIN DI KOTA SEMARANG MENGGUNAKAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) Alifah Zahlevi; Alan Prahutama; Abdul Hoyyi
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Semarang city is the one of the strategic areas located in the middle of the north coast of Java that has a tropical climate with the high humidity and temperature, so it often causes a high rainfall and strong wind. So that is way Semarang city is ever sustained the extreme weather like a Tropical Storm. Since January 2016 until 2017 there are 34 cases of Tornado and 24 incidents of fallen trees because of the gale. For helping the people to be allert the effect of the strong winds can be done by predicting the average of wind velocity by using Adaptive Neuro-Fuzzy Inference System (ANFIS) method which can predict the climate change that do not require the assumption of white noise and normal residual distribution. In addition ANFIS is a group of neural network with input that has been fuzzied on the first or second layer, but the weight of the artificial neural is not fuzzied. The identification result of stationaries obtained the plot of PACF on the first and second lag, with the result that these lag which will be a input variable on ANFIS model. The result of ANFIS by using cluster FCM, the third total membership show the smallest percentage of RMSE in-sample is 0,0048 on the first lag, and the smallest percentage of RMSE out-sample is 0,008 on the ANFIS model with the input lag 1 and three cluster. Keywords : the average of wind velocity, ANFIS, RMSE

ANALISIS REGRESI NONPARAMETRIK KERNEL MENGGUNAKAN METODE JACKKNIFE SAMPEL TERHAPUS-1 DAN SAMPEL TERHAPUS-2 (Studi Kasus: Pemodelan Tingkat Inflasi Terhadap Nilai Tukar Rupiah di Indonesia Periode 2004-2016) Putri, Agum Prafindhani; Santoso, Rukun; Sugito, Sugito
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Exchange rate is a conversion between currencies of a country to another country. Inflation can be defined as the rise of good and service’s level of price continually. The fluctuation of exchange rate is related to inflation, because inflation is the reflection of changes in the price level which happens in market and led to changes in level of money demand and supply. From the data distribution pattern which doesn’t show linearity relation, therefore the right modeling needs to be done using non-parametrical regression. Kernel Function which is used in non-parametrical component is Gaussian with optimal choice of bandwidth using the delete-1 Jackknife sample and the delete-2 Jackknife sample in Cross Validation (CV) method. This research using monthly data, 100 in sample data which taken from September 2014 until December 2012, while the number of out sample data used is 40 which taken from January 2013 until April 2014. Based on the analysis which had been done, the best kernel non-parametrical regression is the model using the delete-2 Jackknife sample because it produced the smallest Mean Absolute Percentage Error (MAPE) therefore it had better model accuracy evaluation. Keyword : Exchange Value, Non-parametrical Regression, Kernel, Jackknife Method, Cross Validation (CV)

ANALISIS VARIAN PERCOBAAN FAKTORIAL DUA FAKTOR RAKL DENGAN METODE FIXED ADDITIVE MAIN EFFECTS AND MULTIPLICATIVE INTERACTION Akhmad Zaki; Triastuti Wuryandari; Suparti Suparti
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Factorial experiment is an experiment where is in a condition (experiment unit) were attempted simultaneously from several single experiment. Two-factor factorial experiment with the basic design CRBD (Completely Randomized Block Design) is used to assess the interaction of genotype and environment on multi-location trials. The analysis can be applied in multi-location trials is AMMI analysis (additive main effects and multiplicative interaction). AMMI analysis in the calculations using analysis of variance in a factorial experiment to test the effect of the interaction and Principal Component Analysis (PCA) to elucidate the effect of the interaction with the interpretation of the results using the biplot-AMMI. Based on research with seven genotypes of rice (S382b-2-2-3, 3-2-3-1 S2389d-, S24871-65-4, S2824-1d-6, S2945f-59, Poso, and C22) and four locations (Sukamandi 94, Batang 94, Taman Bogo 94, and Garut 94) there is the influence of genotype, location, and interaction with genotype and location on rice production. Obtained three Principal Component Interactions (KUI1, KUI2 and KUI3) with the contribution of diversity respectively 78.29%, 13.94% and 7.77%. Interpretation of the AMMI Biplot is obtained genotype 1 (S382b-2-2-3) very suitable to be planted in a location 4 (Garut 94), genotype 2 (S2389d-3-2-3-1) very suitable to be planted in a location 3 (Taman Bogo 94), genotype 3 (S24871-65-4) is more suitable to be planted in locations 2 (Batang 94), genotype 4 (S2824-1d-6) are very suitable to be planted in a location 4 (Garut 94), genotype 5 (S2945f-59) is more suitable to be planted in locations 2 (Batang 94), genotype 6 (Poso) very suitable to be planted in a location 1 (Sukamandi 94) and genotype 7 (C22) is very suitable to be planted in locations 2 (Batang 94). Keywords: Factorial Experiment, CRBD, AMMI, Analysis of Variance, PCA, Biplot

VALUE AT RISK PADA PORTOFOLIO SAHAM DENGAN COPULA ALI-MIKHAIL-HAQ Delsy Nurutsaniyah; Tatik Widiharih; Di Asih I Maruddani
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Investment is one alternative to increase assets in the future. Investors can invest in a portfolio to reduce the level of risk. Value at Risk (VaR) is a measuring tool that can calculate the worst loss over a given time period at a given confidence level. GARCH (Generalized Autoregressive Conditional Heteroskedasticity) is used to model data with high volatility. The teory of copula is a powerful tool for modeling joint distribution for any marginal distributions. Ali-Mikhail-Haq copula from Archimedean copula family can be applied to data with dependencies τ between -0.1817 to 0.3333. This research uses Ali-Mikhail-Haq copula with a Monte Carlo simulation to calculate a bivariate portfolio VaR from a combination stocks of PT Pembangunan Perumahan Tbk. (PTPP), PT Bank Tabungan Negara Tbk. (BBTN), and PT Jasa Marga Tbk. (JSMR) in the period of March 3, 2014 - March 1, 2019. The results of VaR calculation on bivariate portfolio for next 1 day period obtained the lowest VaR is owned by bivariate portfolio between PTPP and JSMR with a weight of 30% and 70% at confidence level of 99%, 95%, and 90% respectively are 4.014%, 2.545%, and 1.876%.Keywords: Value at Risk, GARCH, Ali-Mikhail-Haq Copula, Monte Carlo

Page 45 of 74 | Total Record : 733

43 44 45 46 47

Filter by Year

2012 2024

Filter By Issues

All Issue Vol 13, No 1 (2024): Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian Vol 12, No 1 (2023): Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian Vol 11, No 3 (2022): Jurnal Gaussian Vol 11, No 2 (2022): Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian Vol 10, No 2 (2021): Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian Vol 7, No 4 (2018): Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian Vol 6, No 4 (2017): Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian More Issue

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275

Location
Kota semarang,

Jawa tengah

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name -

Contact Email -

Phone -

Journal Mail Official -

Editorial Address Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275

Location Kota semarang, Jawa tengah INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275

Location
Kota semarang,

Jawa tengah

INDONESIA