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 7 Documents

Search results for , issue "Vol 5, No 2 (2016): Jurnal Gaussian" : 7 Documents clear

KOMPUTASI METODE SAW DAN TOPSIS MENGGUNAKAN GUI MATLAB UNTUK PEMILIHAN JENIS OBJEK WISATA TERBAIK (Studi Kasus : Pesona Wisata Jawa Tengah) Rima Nurlita Sari; Rukun Santoso; Hasbi Yasin
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multi-Attribute Decision Making (MADM) is a method of decision-making to establish the best alternative from a number of alternatives based on certain criteria. Some of the methods that can be used to solve MADM problems are Simple Additive Weighting (SAW) Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). SAW works by finding the sum of the weighted performance rating for each alternative in all criteria. While TOPSIS uses the principle that the alternative selected must have the shortest distance from the positive ideal solution and the farthest from the negative ideal solution. Both of these methods were applied in making the selection of the best tourist attractions in Central Java. There are 15 tourist attractions and 7 criteria: location, infrastructure, beauty, atmosphere, tourist interest, promotion, and cost. This primary research employed a questionnaire that passed the questionnaire testing, namely its validity and reliability test. The result of this study shows that the best type of tourism according to the government is temple tour. While water sports tourism is favored by tourism observers. As for college students, the preferred tourist destination is religious tourism. This study also produced a GUI Matlab programming application that can help users in performing data processing using SAW and TOPSIS to select the best attraction in Central Java. Keywords: MADM, SAW, TOPSIS, GUI, tourism

OPTIMASI VALUE AT RISK PADA REKSA DANA DENGAN METODE HISTORICAL SIMULATION DAN APLIKASINYA MENGGUNAKAN GUI MATLAB Christa Monica; Tarno Tarno; Hasbi Yasin
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Value at Risk (VaR) is a method used to measure financial risk within a firm or investment portfolio over a specific time period at certain confidence interval level. Historical Simulation is used in this research to compute VaR of stock mutual fund at 5% confidence interval level, with one day time period and Rp 100.000.000,00 startup investment fund. Historical Simulation ia a non parametric method where the formula doesn’t require any asumption. Portfolio optimization is done by calculating the weight of allocation fund for each asset in the portfolio using Mean Variance Efficient Portfolio (MVEP) method. The data in this research are divided into four mutual fund asset. To make VaR become easier for people to understand, an application is made using GUI in Matlab. The smallest risk value for single investment asset is obtained by Valbury Equity I stock mutual fund and the smallest risk value for two-asset portfolio is obtained by the combination assets of Pacific Equity Fund and Valbury Equity I. Meanwhile for three-asset portfolio, the combination assets of Pacific Equity Fund, Valbury Equity I, and Millenium Equity Prima Plus have the smallest risk value. The test result of VaR with Basel Rules shows that the usage of VaR is legitimate to measure loses potency in mutual fund investment.Keywords: Value at Risk (VaR), Historical Simulation, Mutual Fund, Risk.

PERBANDINGAN KLASIFIKASI PENYAKIT HIPERTENSI MENGGUNAKAN REGRESI LOGISTIK BINER DAN ALGORITMA C4.5 (Studi Kasus UPT Puskesmas Ponjong I, Gunungkidul) Wella Rumaenda; Yuciana Wilandari; Diah Safitri
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Hypertension is a major problem in the world today. In Indonesia prevalence of hypertension is still high. There are two types of hypertension based on cause, primary and secondary hypertension. In this thesis focused on the classification of types of hypertension based on the cause using binary logistic regression and C4.5 algorithms with case studies in UPT Puskesmas Ponjong I, Gunungkidul of October-November 2015. Binary logistic regression is a method that describes the relationship between the response variable and several predictor variables with the variable equal to 1 to declare the existence of a characteristic and the value 0 to declare the absence of a characteristic. C4.5 algorithm is one method of classification of data mining is used to create a decision tree. The predictor variables were used in this thesis are gender, age, systolic blood pressure, diastolic blood pressure, treatment history, as well as diseases and or other complaints. Based on this analysis, classification of hypertension by binary logistic regression method obtained value APER=27,4648% and 72,5352% of accuracy, while the value obtained using the algorithm C4.5 APER=35,9155% and the accuracy 64,0845 %. In two different test proportion was found that there were significant differences of the two methods.Keywords : Types of Hypertension, Classification, C4.5 Algorithm, Biner Logistic Regression, APER

PENGHITUNGAN PREMI ASURANSI LONG TERM CARE UNTUK MODEL MULTI STATUS Gumauti, Chrysmandini Pulung; Wilandari, Yuciana; Rahmawati, Rita
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Health insurance is insurance that provides health benefit in the form of a cash compensation for the cost of treatment and care. One of the health insurance’s products is Long Term Care (LTC) insurance. LTC insurance guarantees nursing and medical expense, preferred for elderly people in the future. Proper calculation the cost of premiums is needed to maintain the reserve fund appropriate for insurance company to fulfill the policy agreement. In this final project will be discussed about calculation of premiums for LTC insurance products Annuity as A Rider Benefit as a multi-state models (three states), which is based on Markov transition probability matrix. Data used is the data prevalence rate of heart disease in the United Kingdom in 2014. By calculating premiums of multi-state models, insurance products are expected to be able to guarantee health care expense according insured’s needs. Result of this premiums calculation is the older someone takes insurance, greater the annual net premium to be paid. Keywords: health insurance, Long Term Care insurance, multiple state models, Annuity as A Rider Benefit product, Markov transition.

KLASIFIKASI PERUBAHAN HARGA OBLIGASI KORPORASI DI INDONESIA MENGGUNAKAN METODE NAIVE BAYES CLASSIFICATION Khotimatus Sholihah; Di Asih I Maruddani; Abdul Hoyyi
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bond is a medium-long term debt securities which can be sold and contains a pledge from the issuer to pay interest for a certain period and repayment of the principal debt at a specified time to the bonds buyer. Bonds price changes any time, it could be beneficial or give disadvantage to investors. Investors should know the best conditions to buy bonds on a discount, or sell them at a premium price. By classify the changing of bonds price, it could help investors to gain optimum return. One method is Naive Bayes classification. In theory, It has the minimum error rate in comparison to all other classifiers. Bayes is a simple probabilistic-based prediction technique which based on the application of Bayes theorem with strong independence assumptions. Before classifying, preprocessing data is required as a stage feature selection. In this case, the Mann Whitney test can be done to choose the independent features of each class. Validation technique in use is k-fold cross validation. Based on analysis, we gained average accuracy at 78,52% and 21,8% error. With high accuracy and quite low error, it means that the Naïve Bayes method works quite well on classifying the corporate bonds price changes in Indonesia. Keywords: bonds, classification, k-fold cross validation, Naive Bayes

VALUASI KUPON OBLIGASI PT. BPD LAMPUNG TBK. MENGGUNAKAN OPSI MAJEMUK CALL ON CALL TIPE EROPA Revaldo Mario; Diah Safitri; Agus Rusgiyono
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

A bond is a debt capital market instrument issued by a borrower, who is then required to repay to the lender/investor the amount borrowed plus interest at maturity, and also known as fixed-income securities, and therefore the bond is an attractive investment in the financial sector. Most theories about the financial statistics is based on the bond without coupon bonds. Whereas, in fact most companies issue bonds with a coupon. Option is an agreement or contract which provides the right and not an obligation for the holder of a contract to buy (call option) or sell (put option) a particular asset at a price and time have been set. Underlying assets can be stocks, bonds, warrants and more. One type of option trading is a European type option is an option that can be used only at the time of maturity. The approach used in the valuation of bond coupons is to use the theory of Europe style compound option call on call. European style compound option call on call is the type of European call options with underlying assets are call options. Final project aims to get the value of equity and the value of liabilities on the bonds PT BPD Lampung Tbk with a coupon rate when the bond before maturity (compound option strike price) and a coupon rate of the bond at maturity (the strike price of the call option). The current bond coupon payments prior to maturity was conducted on July 9, 2017 and a coupon payment at maturity conducted on 9 October 2017. Based on the results of data processing with the help of open source software R 3.1.1, the value of the equity is greater than the value of liabilities.Keywords: bond, call option, compound option, coupon bond, equity, liability

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.

Page 1 of 1 | Total Record : 7

Filter by Year

2016 2016

Filter By Issues

All Issue Vol 12, No 3 (2023): Jurnal Gaussian (Forthcomming Issue) 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

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

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