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Jurnal Gaussian

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Published by Universitas Diponegoro

ISSN : - EISSN : 23392541 DOI : -

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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.

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Articles 733 Documents

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METODE BOOTSTRAP AGGREGATING REGRESI LOGISTIK BINER UNTUK KETEPATAN KLASIFIKASI KESEJAHTERAAN RUMAH TANGGA DI KOTA PATI Ridha Ramandhani; Sudarno Sudarno; Diah Safitri
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Kesejahteraan merupakan salah satu aspek yang cukup penting untuk menjaga dan membina terjadinya stabilitas sosial dan ekonomi. Berbagai penelitian yang telah dilakukan mengenai kesejahteraan mengindikasikan bahwa banyak faktor yang mempengaruhi kesejahteraan rumah tangga. Faktor-faktor yang mempengaruhi kesejahteraan rumah tangga antara lain jenis kelamin kepala rumah tangga, usia kepala rumah tangga, lapangan usaha kepala rumah tangga, jumlah anggota rumah tangga, bahan bakar utama untuk memasak, pengalaman membeli raskin dan ada atau tidaknya anggota keluarga yang menguasai penggunaan telepon seluler/HP. Dalam penelitian ini dilakukan kajian tentang klasifikasi kesejahteraan rumah tangga di Kota Pati dengan tujuan untuk mengidentifikasi faktor-faktor apa saja yang mempengaruhi kesejahteraan rumah tangga di Kota Pati. Dari hasil kajian dengan menggunakan metode Bootstrap Aggregating (Bagging) regresi logistik biner diperoleh tiga variabel prediktor yang berpengaruh signifikan terhadap kesejahteraan rumah tangga di Kota Pati, yaitu jenis kelamin kepala keluarga, jumlah anggota rumah tangga, dan penguasaan telepon seluler dengan tingkat akurasi sebesar 79,87%. Hasil analisis bagging regresi logistik biner dengan replikasi bootstrap sebesar 50, 60, 70, 80, 90, 100, 150, 200, 626, dan 1000 kali menunjukkan bahwa terdapat konsistensi pada setiap pengulangan. Kata Kunci : Klasifikasi, Regresi Logistik Biner, Bootstrap Aggregating

PERAMALAN VOLATILITAS MENGGUNAKAN MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY IN MEAN (GARCH-M) (Studi Kasus pada Return Harga Saham PT. Wijaya Karya) Ratnasari, Dwi Hasti; Tarno, Tarno; Yasin, Hasbi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Stock return volatility in the markets of developing countries (emerging markets) is generally much higher than the markets of developed countries. High volatility illustrates the level of high risk faced by investors due to reflect fluctuations in stock price movement. Therefore, it is probable, stock investments that are carried in Indonesia have a high risk opportunity. Important properties are often owned by time series data in the financial sector in particular to return data that the probability distribution of returns is fat tails and volatility clustering or often referred to as a case of heteroscedasticity.Time series models that can be used to model this condition are ARCH and GARCH. One form of ARCH/GARCH is Generalized Autoregressive Conditional Heteroscedasticity In Mean (GARCH-M). The purpose of this study is to predict volatility by using GARCH-M model in the return data analysis of daily stock price closing of Wijaya Karya (Persero) Tbk from October 18, 2012 until March 14, 2014 by using the active days (Monday to Friday). The best model is used for forecasting the volatility case in the stock price return of PT. Wijaya Karya is ARIMA (0,0, [35]) GARCH (1,1)-M. Keywords: Stocks, Volatility, Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M)

ANALISIS PREFERENSI KONSUMEN PENGGUNA JASA MASKAPAI PENERBANGAN UNTUK RUTE SEMARANG-JAKARTA DENGAN METODE CHOICE-BASED CONJOINT (FULL PROFILE) Vierga Dea Margaretha Sinaga; Diah Safitri; Agus Rusgiyono
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Airline services nowadays become one of the highly coveted options by many consumers for long-distance transportation. The increasing numbers of users makes airlines tightly compete each other to attract consumers’ interest. Thus, analysis to consumer preference has always been the starting point in market research as reference in creating new innovation. This research uses the choice-based conjoint analysis with the full profile as method of presentation. Conjoint analysis is a multivariate analysis method that can be used as a measurement for the level of preference. In the instrument, consumers were asked to choose one among three attribute combination of each choice set within 9 choice sets. Utility values were obtained by conditional logic model. The results show that for each attribute the order of preference is Price-Airport tax-Class-Facility. Judging from the value of its usefulness, the most preferred attribute by consumer is Airport tax and that Include is preferably from Exclude. For Price attribute, lower than 500 thousand rupiahs is the most preferred categories among others. In Class attribute, Business is more preferable than other categories. And for Facility attribute, entertainment is the most preferred one of other categories. Keywords: preferences, airlines, choice-based conjoint

SIMULASI PENGUKURAN KETEPATAN MODEL VARIOGRAM PADA METODE ORDINARY KRIGING DENGAN TEKNIK JACKKNIFE Kusumawardani, Dewi Setya; Sudarno, Sudarno; Yasin, Hasbi
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Kriging adalah metode yang digunakan untuk mengestimasi besarnya nilai yang mewakili suatu titik yang tidak tersampel berdasarkan titik-titik tersampel yang berada disekitarnya. Pada Ordinary Kriging pendugaan suatu nilai variabel pada titik tertentu dilakukan dengan cara mengamati data yang sejenis pada daerah lain, pada setiap titik yang tidak diketahui nilainya, maka akan diestimasi dengan menggunakan kombinasi linier terboboti (weighted linier combination). Data yang dibangkitkan adalah data kandungan besi (%). Data tersebut merupakan data random hasil simulasi berdasarkan model variogram Spherical dan Eksponensial. Nilai dugaan diperoleh melalui sistem Ordinary Kriging dengan menggunakan teknik Jackknife. Ketepatan model variogram spherical dan eksponensial dihitung berdasarkan nilai tengah kesalahan persentase absolut (Mean Absolut Percentage Error). Berdasarkan hasil perhitungan untuk variogram spherical persentase kesalahan yang diperoleh yaitu 0,0417%, sedangkan persentase kesalahan untuk model variogram eksponensial yaitu 0,0776%. Kedua nilai MAPE tersebut berada dibawah 10%, dengan demikian dapat disimpulkan bahwa teknik jackknife dapat digunakan untuk menentukan nilai dugaan dari sistem ordinary kriging dari model variogram spherical dan eksponensial.

PENENTUAN HARGA OPSI PUT DAN CALL TIPE EROPA TERHADAP SAHAM MENGGUNAKAN MODEL BLACK-SCHOLES Marthin Nosry Mooy; Agus Rusgiyono; Rita Rahmawati
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Option is a contract that gives the right, but not obligation, to individuals to buy (call) or sell (put) certain stocks by a certain price at a specified date. One method that can be used to estimate option price is by using Black-Scholes Model. This model is introduced by Fisher Black and Myron Scholes in 1973. Black-Scholes Model was derived in certain assumptions, such as no dividens, no transaction cost, free-risked interest rates, the option is “European”, and stock price follows a random walk in continuos time, thus the distribution of possible stock prices is lognormal. Application of Black-Scholes Model on Honda Motor Company, Ltd.’s stocks shows that investors can get profits by investing on certain contracts, which is call options with the price of 10,1 US$; 8,9 US$; and 1,15 US$, and also put option with the price of 6,12 US$, all with maturity date at January 20th 2017. Keywords: Option, call option, put option, stock, Black-Scholes model.

PENGELOMPOKAN PASIEN DEMAM BERDARAH RSUD dr. SOEHADI PRIJONEGORO DENGAN METODE ANALISIS KELAS LATEN Nurhayati, Noviana; Mukid, Moch. Abdul; Ispriyanti, Dwi
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The degree of disease dengue patients in early at the hospital is latent or unknown directly. Therefore it needs an indicator variables such as the examination of hematocrit, leukocytes and platelets to classify patients with dengue fever into classes according to the degree of disease. In this study, the method used to classify patients with dengue fever is a latent class analysis method. The purpose of this study is to establish a latent class model and describes profile of the class on cases of grouping dengue fever patients in dr. Soehadi Prijonegoro Sragen. The results from latent class analysis showed that the latent class model formed is two latent class model. There are two classes formed is class 0 for disease dengue infection with danger signs have criteria a normal hematocrit, abnormal leukocyte and platelet abnormal and class 1 for disease dengue infection without signs of danger have criteria a normal hematocrit, normal leukocytes and normal platelets.Keyword : dengue fever, latent class analysis, latent variables

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

PENGAMBILAN SAMPEL BERDASARKAN PERINGKAT PADA ANALISIS REGRESI LINIER SEDERHANA Wijayanti, Pritha Sekar; Ispriyanti, Dwi; Wuryandari, Triastuti
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Ranked Set Sampling and Ranked Set Sampling concomitant are more efficient than Simple Random Sampling. This can be determined by calculating the Relative Precision which is a ratio value from the variance of the mean from each sampling technique. From the research of Ranked Set Sampling, obtained , and so Ranked Set Sampling is more efficient than Simple Random Sampling. For the research of Ranked Set Sampling concomitant, obtained , and so Ranked Set Sampling concomitant is more efficient than Simple Random Sampling, and for simple linear regression analysis obtained , , , so simple linear regression model of Ranked Set Sampling is more efficient than simple linear regression model of Simple Random Sampling

PREDIKSI HARGA EMAS MENGGUNAKAN FEED FORWARD NEURAL NETWORK DENGAN METODE EXTREME LEARNING MACHINE Nisa Afida Izati; Budi Warsito; Tatik Widiharih
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The prediction of gold price aims to find out the gold price in the future on the basis of historical data on gold prices in the past, so it can be used as a consideration by gold investors to investing in gold. Prediction methods that do not require assumptions, one of which is Artificial Neural Networks. In this study, using Artificial Neural Networks, Feed Forward Neural Network with Extreme Learning Machine (ELM). ELM is a non-iterative algorithm so ELM has advantages in process speed. The input weight and bias for this method are determined randomly. After that, to find the final weight using the Moore-Penrose Generalized Inverse calculation on the hidden layer output matrix. The best model selection criteria uses the Mean Absolute Percentage Error (MAPE). This study shows that the results of the training and testing process from the model 1 input neuron and 7 hidden neurons are very good, because it produces MAPE training = 0.6752% and MAPE testing = 0.8065%. Also gives a very good prediction result because it has MAPE = 0.5499% Keywords: gold price, Extreme Learning Machine, MAPE

ANALISIS PENGENDALIAN PERSEDIAAN PRODUK OLI MENGGUNAKAN METODE ECONOMIC ORDER QUANTITY PROBABILISTIK DENGAN MODEL (q,r) (Studi Kasus di bengkel Maju Jaya Tuban) Werti, Wetty Anggun; Sudarno, Sudarno; Mukid, Moch. Abdul
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Inventory has an important role for the continuity of the trading business. In the trading business, consumer demand for the product is usually random. Consumer demand opportunities are aspects that need to be considered in the process of inventory management. Economic Order Quantity (EOQ) probabilistic model (q,r) is the method used when consumer demand is random and the time between ordering until the product comes (lead time) is not equal to zero. This research aims to apply methods EOQ probabilistic model (q,r) in determining the total cost savings in the inventories of oil products in Maju Jaya Tuban workshop. The oil products analyzed were Top 1 and Yamalube oil products. These results indicate that the method EOQ probabilistic model (q,r) has a total inventory cost less than the policy Maju Jaya Tuban workshop. Total inventory cost savings when the ordering cost (10%) and holding cost (1%) is Rp 4.313,- for Top 1 oil products and Rp 3.086,- for Yamalube oil products.Keywords: Oil Demand, EOQ Probabilistic (q,r), Cost Savings

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Jawa tengah

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