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Media Statistika

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Published by Universitas Diponegoro

ISSN : - EISSN : 24770647 DOI : -

Core Subject : Science,

Decision Sciences, Operations Research & Management

Arjuna Subject : -

Articles 6 Documents

Search results for , issue "Vol 8, No 2 (2015): Media Statistika" : 6 Documents clear

ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS Haqiqi, Baiq Nurul; Kurniawan, Robert
MEDIA STATISTIKA Vol 8, No 2 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Fuzzy C-Means (FCM) is one of the most frequently used clustering method. However FCM has some disadvantages such as number of clusters to be prespecified and partition matrix to be randomly initiated which makes clustering result becomes inconsistent. Subtractive Clustering (SC) is an alternative method that can be used when number of clusters are unknown. Moreover, SC produces consistent clustering result. A hybrid method of FCM and SC called Subtractive Fuzzy CMeans (SFCM) is proposed to overcome FCMâ€™s disadvantages using SC. Both SFCM and FCM are implemented to cluster generated data and the result of the two methods are compared. The experiment shows that generally SFCM produces better clustering result than FCM based on six validity indices.Keywords : Clustering, Fuzzy C-Means, Subtractive Clustering, Subractive Fuzzy C-Means

BAGGING CLASSIFICATION TREES UNTUK PREDIKSI RISIKO PREEKLAMPSIA (Studi Kasus : Ibu Hamil Kategori Penerima Jampersal di RSUD Dr. Moewardi Surakarta) Mukid, Moch. Abdul; Wuryandari, Triastuti; Ratnaningrum, Desy; Sri Rahayu, Restu
MEDIA STATISTIKA Vol 8, No 2 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Preeclampsia is a spesific pregnancy disease in which hypertency and proteinuria occurs after 20 weeks of pregnancy. Classification Trees is a statistical method that can be used to identify potency of expectant women suffering from preeclampsia. This research aim to predict the risk of preeclampsia based on some individual variables. They are parity, work status, history of hypertension of preeclampsia, body mass index, education and income. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification Trees method. By the method, classification accuracy reach to 86%.Keywords : Pre-eclampsia, Bagging CART, Classification Accuracy

RANCANGAN D-OPTIMAL MODEL MICHAELIS MENTEN DAN EMAX DENGAN MATLAB Tatik Widiharih; Sri Haryatmi; Gunardi Gunardi; Yuciana Wilandari
MEDIA STATISTIKA Vol 8, No 2 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Michaelis Menten and Emax models are widely used in chemistry, pharmacokinetics and pharmacodynamics areas. D-optimal criteria is criteria with the purpuse to minimize the variance of the estimator of parameters in the model. In this paper will discuss the D-optimal design for Michaelis Menten and Emax models with homoscedastics error assumtion. Determination of D-optimal designs based on Generalied Equivalence Theorem Kiefer-Wolvowitz. We used minimally supported design with the proportion of each design point is uniform, lower bound of design region is design point and the others are interior points.Keywords: D-optimal, Michaelis Menten, Emax, Minimally Supported Design, Homoscedastics

MENENTUKAN MATRIKS PELUANG TRANSISI UNTUK WAKTU OKUPANSI MENGGUNAKAN TRANSFORMASI LAPLACE DAN MATRIKS EKSPONENSIAL Sudarno, Sudarno
MEDIA STATISTIKA Vol 8, No 2 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The transition probability matrix is a matrix which contains some probability among two state. It has properties that every probability is non-negative and sum by row at every state is one. This paper want to determine the transition probability matrix by Laplace transform and exponential of a matrix methods. To construct the transition probability matrix by Laplace transform depends on identity matrix and generator matrix, but by matrix exponential method depends on generator matrix only. In this research obtained result that matrix exponential method easier than Laplace transformation. Because it is aided by software and programming. The transition probability matrix can be used to predict probability each other state. It could be used to predict value of state probability on long-term or limiting behavior, too. Otherwise, the transition probability mtrix could be used to construct occupancy times matrix.Keywords: Generator matrix, Laplace transform, Exponential matrix, Occupancy times matrix.

KOMPARASI METODE PERAMALAN AUTOMATIC CLUSTERING TECHNIQUE AND FUZZY LOGICAL RELATIONSHIPS DENGAN SINGLE EXPONENTIAL SMOOTHING Endaryati, Betik; Kurniawan, Robert
MEDIA STATISTIKA Vol 8, No 2 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Automatic clustering technique and fuzzy logical relationships(ACFLR) is one of the forecasting method that used to predict time series data that can be applied in any data. Several previous studies said that this method has a good accuracy. Therefore, this study aims to compare the ACFLR methods with single exponential smoothing method and apply it to simulation data with uniform distribution. The performance of the method is measured based on MSE and MAPE. The results of the comparison of the methods showed that ACFLR has a higher forecasting accuracy than single exponential smoothing. This is evidenced by the value of MSE and MAPE of ACFLR is lower than single exponential smoothing.Keywords: Fuzzy, Forecasting, Automatic Clustering-Fuzzy Logic Relationships, Single Exponential Smoothing

PEMODELAN DATA INFLASI INDONESIA PADA SEKTOR TRANSPORTASI, KOMUNIKASI, DAN JASA KEUANGAN MENGGUNAKAN METODE KERNEL DAN SPLINE Suparti, Suparti; Tarno, Tarno
MEDIA STATISTIKA Vol 8, No 2 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In this research, we study data modeling of Indonesian inflation in theÂ transportation, communication and financial services sector using the kernel and spline models. Determination of the optimal models based on the smallest of GCVÂ value and determination of the best model based on the smallest out sampels of Mean Square Error (MSE) value. By modeling the yoy (year on year) inflation data in Indonesia in the transportation, communication and financial services sector In January 2007 to January 2015, shows that the kernel modelÂ using Gaussian kernel function obtained optimal model with a bandwidthÂ 0.24 and the optimal spline model with order 5 andÂ 4 points knots. Based on out sampels dataÂ in February to August 2015, obtained out sampelsÂ MSE value of the spline model is smaller than the kernel model. So that the spline model is better than the kernel modelÂ to analyzeÂ the inflation dataÂ of transportation, communication and financial services sector.Keywords: Inflation, Transportation, Communication and Financial Services Sector, Kernel, Spline, GCV, MSE.

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