Pemodelan Regresi Untuk Rancangan Percobaan Faktor Tunggal

---Metode Statistik yang sering digunakan dalam percobaan adalah analisis ragam. Dalam tulisan ini akan dibahas analisis ragam dengan pengaruh tetap diselesaikan dengan pendekatan metode regresi,hal itu dapat dilakukan kalau modelnya diindetifikasi secara benar dan kalau langkah-langkah pencegahan telah diambil agar diperoleh persamaan normal yang bebas. Suatu ciri analisis ragam adalah bahwa model analisis ini terparameterisasi secara berlebih (Overparameterized), sehingga perlu membuat kendala terhadap parameter-parameternya. Pendekatan model regresi terhadap masalah analisis ragam mengharuskan peubah bebas X dalam bentuk katagori, yaitu nol dan satu

Penentuan Model Antrian Bus Antar Kota di Terminal Mangkang

In daily activities, we often face in a situation of queueing. The Queue is dull. Most people have experienced in a queue situation or a waiting situation. It is a part of the state that occurs in a series of operations that are random in a service facility. The Queue can be found easily in a human life, for example bus queue in Terminal Mangkang. It means that a bus wait to be dispatched and from the bus that will go to the service station. Therefore make an arrival and departure of buses not on schedule which resulted in the accumulation of customers in the terminal. To analyze the extent of the effectiveness of terminal Mangkang particularly inter-city terminal Queue theory it is used in the service system in the terminal

Distribusi Invers Gamma pada Inferensi Bayesian

One of the methods which can be used in statistical inferences is Bayesian inference. It is combine sample distribution and prior distribution, that can be resulted posterior distribution. In this article, sample distribution use univariate normal distribution. If prior distribution for variance with known mean is gamma inverse distribution, then posterior distribution is formed gamma inverse distribution. If Prior distribution use non-informative prior, then have the posterior distribution, by the marginal distribution of mean and varian. Also posterior distribution formed by gamma inverse distribution

Analisis Klasifikasi Masa Studi Mahasiswa Prodi Statistika Undip dengan Metode Support Vector Machine (Svm) dan Id3 (Iterative Dichotomiser 3)

Graduation is the final stage of learning process activities in college. Undergraduate study period in UNDIP's academic regulations is scheduled in 8 semesters (4 years) or less and maximum of 14 semesters (7 years). Department of Statistics is one of six departments in the Faculty of Science and Mathematics UNDIP. Study period in this department can be influenced by many factors. Those factor are Grade Point Average (GPA) or IPK, gender, scholarship, parttime, organizations, and university entrance pathways. The aim of this paper is to determine the accuracy factors classification. We use SVM (Support Vector Machine method) and ID3 (Iterative Dichotomiser 3). The comparison of SVM and ID3 method, both for training and testing the data generate good accuracy, namely 90%. Especially ID3 training data gives better result than SVM

Optimisasi Multiobjektif untuk Pembentukan Portofolio

Investing in asset such as stock; besides generate profit (return), it is also deal with a risk of loss, so that portofolio diversification is needed to reduce the risk. In the establishment of stock portofolio, the investors seeking to maximize the expected return of investment with a certain level of risk that still can be accepted. Portofolios that can achieve the above objectives called optimal portofolios. The application of multiobjective optimization on the establishment of the optimal portofolio is to maximize the return and minimize the risk at the same time. The aim of this research is to analize the proportion of each stock in order to form an optimal portofolio and to analyze the level of benefits and risks of the portofolio which is formed in accordance with the preferences of investors. The data used are monthly stock data of ASII, TLKM, SMGR, LPKR and BBNI. The optimal portofolio for risk seeker investors is a portofolio that used coefficient k =0,01, namely by investing in SMGR whilst the optimal portofolio for risk indifference investors is a portfolia which has coefficient 1 ≤ k ≤ 100 namely by investing in ASII, TLKM, SMGR, LPKR, and BBNI. Whereas, the optimal portofolio for risk averse investors is a portfolio which has coefficient k =1000 that is by investing in ASII, TLKM, SMGR, LPKR, and BBNI