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All Journal COPING (Community of Publishing in Nursing) Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Pendas : Jurnah Ilmiah Pendidikan Dasar Review of Accounting and Taxation Jurnal Riset Ilmu Kesehatan Umum dan Farmasi

Diah Safitri

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Author-ID : 2977048

Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Nursing Public Health Social Sciences Other

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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

KLASIFIKASI PENERIMA PROGRAM BERAS MISKIN (RASKIN) DI KABUPATEN WONOSOBO DENGAN METODE SUPPORT VECTOR MACHINE MENGGUNAKAN LibSVM Yogi Setiyo Pamuji; Diah Safitri; Alan Prahutama
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Beras Miskin (Raskin) Program is a program of social protection, as supporters of other programs such as nutrition improvement, healthy increase, education and productivity improvement of Poor Households. According to Badan Pusat Statistika, there were 14 criteria to determine a household is classified as poor households. Based on these criteria it will be classified of recipient households and non-recipient households of Beras Miskin (Raskin) Program by Support Vector Machine (SVM) method using LibSVM. The concept of classification by SVM is search for the best hyperplane which serves as a separator of two classes of data in the input space. Kernel function is used to convert the data into a higher dimensional space to allow a separation. LibSVM is a package program created by Chih-Chung Chang and Chih-Jen Lin from Department of Computer Science at National Taiwan University. The method used by LibSVM to obtain global solution of duality lagrange problem is decomposition method. To determine the best parameters of kernel function, used k-vold cross validation method and grid search algorithm. In this classification by SVM method using LibSVM, obtain the best accuracy value as 83,1933%, which is the kernel function Radial Basis Function (RBF). Keywords : Beras Miskin (Raskin) Program, Classification, Support Vector Machine (SVM), LibSVM, Kernel Function

VALUASI COMPOUND OPTION PUT ON CALL TIPE EROPA PADA DATA SAHAM FACEBOOK Muhammad Sunu Widianugraha; Di Asih I Maruddani; Diah Safitri
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Option is a contract that gives the right to individuals to buy (call options) or sell (put options) the underlying asset by a certain price for a certain date. One type of options that are traded is compound options. Compound option model is introduced by Robert Geske in 1979. Compound option is option on option. Compound option put on a call is put option where the underlying asset are call option. An empirical study using compound option put on a call stocks of Facebook. It has strike price compound option US$ 77.5 and strike price call option US$ 80, with the first exercise date on September 26, 2014 and the second exercise date on October 31, 2014. The theoritical price of compound option put on call on stocks of Facebook is US$ 75.65048. Keywords: Compound option, put on a call, option stocks of Facebook, Black-Scholes model, theoritical price.

KLASIFIKASI WILAYAH DESA-PERDESAAN DAN DESA-PERKOTAAN WILAYAH KABUPATEN SEMARANG DENGAN SUPPORT VECTOR MACHINE (SVM) Mekar Sekar Sari; Diah Safitri; Sugito Sugito
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This research will be carry out classification based on the status of the rural and urban regions that reflect the differences in characteristics/ conditions between regions in Indonesia with Support Vector Machine (SVM) method. Classification on this issue is working by build separation functions involving the kernel function to map the input data into a higher dimensional space. Sequential Minimal Optimization (SMO) algorithms is used in the training process of data classification of rural and urban regions to get the optimal separation function (hyperplane). To determine the kernel function and parameters according to the data, grid search method combined with the leave-one-out cross-validation method is used. In the classification using SVM, accuracy is obtained, which the best value is 90% using Radial Basis Function (RBF) kernel functions with parameters C=100 dan γ=2-5. Keywords : classification, support vector machine, sequential minimal optimization, grid search, leave-one-out, cross validation, rural, urban

Co-Authors Abdul Hoyyi Afianti Sonya Kurniasari Agus Rusgiyono Alan Prahutama Amelia Crystine Amir, Yufitriana Annisa Pratiwi Artha Ida Sri Anggriyani Aryono Rahmad Hakim Aulia Putri Andana Bisri Merluarini Budi Warsito Desy Trishardiyanti Adiningtyas Dhinda Amalia Timur Di Asih I Maruddani Dimas Kevin Natanael Dwi Ispriyanti Erin Padilla Siregar Erna Musri Arlita FANDI AHMAD Galuh Ayu Prameshti Hasbi Yasin Indri Puspitasari Lailly Rahmatika Mekar Sekar Sari Mertika Moch. Abdul Mukid Muhammad Abid Muhyidin Muhammad Sunu Widianugraha Mulyasari, Windu Nana Nofianti, Nana Ndaru Dian Darmawanti Nindy Citroresmi Nur Musrifah Rohmaningsih Nuril Faiz Onny Kartika Hitasari Puti Cresti Ekacitta Rahma Nurfiani Pradita Revaldo Mario Ridha Ramandhani Rita Rachmawati Rita Rahmawati Rizal Yunianto Ghofar Rizka Asri Brilliani Sherly Candraningtyas Sisca Agustin Diani Budiman Sudarno Sudarno Sugito Sugito Suparti Suparti Syilfi Syilfi Trianita Resmawati Triastuti Wuryandari Vierga Dea Margaretha Sinaga Vina Riyana Fitri Wella Rumaenda Yani Puspita Kristiani Yogi Setiyo Pamuji Yuciana Wilandari Yulia Irvani Dewi

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