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All Journal Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Media Statistika MATEMATIKA SAINS DAN MATEMATIKA Jurnal Ilmu Dasar Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Indonesian Journal of Applied Statistics Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan

Dwi Ispriyanti

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 3131513

Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Transportation Other

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PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG Octaviani, Pusphita Anna; Wilandari, Yuciana; Ispriyanti, Dwi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Accreditation is the recognition of an educational institution given by a competent authority, that is Badan Akreditasi Nasional Sekolah/Madrasah (BAN - S/M) after it is assessed that the institution has met the eight components of the accreditation assessment. An elementary school, as one of the compulsory basic education, should have the status of accreditation to ensure the quality of education. This study aimed to apply the classification method Support Vector Machine (SVM) on the data accreditation SD in Magelang. Support Vector Machine (SVM) is a method that can be used as a predictive classification by using the concept of searching hyperplane (separator functions) that can separate the data according to the class. SVM using the kernel trick for non-linear problems which can transform data into a high dimensional space using a kernel function, so that the data can be classified linearly. The results of this study indicate that the prediction accuracy of SVM classification using Gaussian kernel function RBF is 93.902%. It is calculated from 77 of 82 elementary schools that are classified correctly with the original classes. Keywords : Accreditation, Classification, Support Vector Machine (SVM), hyperplane, Gaussian RBF Kernel, Accuracy

PERAMALAN HARGA SAHAM DENGAN METODE EXPONENTIAL SMOOTH TRANSITION AUTOREGRESSIVE (ESTAR) (Studi Kasus pada Harga Saham Mingguan PT United Tractors) Rahmayani, Dwi; Ispriyanti, Dwi; Mukid, Moch. Abdul
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The stock price data series of PT United Tractors in the period of December 1th 2008 to December 29th 2014 is fluctuative. To model data nonlinear time series one method that can be used is Smooth Transition Autoregressive (STAR), if the function of an exponential transition then a method that can be used is Exponential Smooth Transition Autoregressive (ESTAR). In modelling ESTAR determined transition variable ( of transition function ). Of the research result obtained model ESTAR (1,1). With significance level of 5% obtainedthe value of the stock price data for pt united tractors in the next four to the original. It was also strengthened by Mean Absolute Percentage Error (MAPE) 0,768233 % are relatively small. Keywords : Autoregressive,time series, nonlinearity, ESTAR, MAPEThe stock price data series of PT United Tractors in the period of December 1th 2008 to December 29th 2014 is fluctuative. To model data nonlinear time series one method that can be used is Smooth Transition Autoregressive (STAR), if the function of an exponential transition then a method that can be used is Exponential Smooth Transition Autoregressive (ESTAR). In modelling ESTAR determined transition variable ( of transition function ). Of the research result obtained model ESTAR (1,1). With significance level of 5% obtainedthe value of the stock price data for pt united tractors in the next four to the original. It was also strengthened by Mean Absolute Percentage Error (MAPE) 0,768233 % are relatively small. Keywords : Autoregressive,time series, nonlinearity, ESTAR, MAPE

PEMODELAN JARINGAN SYARAF TIRUAN DENGAN ALGORITMA ONE STEP SECANT BACKPROPAGATION DALAM RETURN KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT Najwa, Maulida; Warsito, Budi; Ispriyanti, Dwi
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Exchange rate is the currency value of a country that is expressed by the value of another country's currency. Changes in exchange rates indicate risks or uncertainties that would return obtained by investors. With the predicted value of return, investors can make informed decisions when to sell or buy foreign currency to gain an advantage. Forecasting of return values can be using artificial neural network with backpropagation. In backpropagation procedure, data is divided into two pairs, namely training data for training process and testing data for testing process. In the training process, the network is trained to minimize the MSE. One of optimization method that can minimize the MSE is one step secant backpropagation. In this research, the data used is the return of the exchange rate of rupiah against US dollar in the period of January 1st, 2015 until December 31st, 2015. The results were obtained architecture best model neural network that was built from 8 neurons in the hidden layer, 1 unit of input layer with input xt-1 and 1 unit of output layer. The activation function used in the hidden layer and output layer are bipolar sigmoid and linear, respectively. The architecture chosen based on the smallest MSE of testing data is 0.0014. After obtaining the best model, data is foreseen in the period of November 2016 produce MAPE=153.23%.Keyword : Artificial Neural Network, Backpropagation, One Step Secant, Time Series, Exchange Rate.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA (IPM) MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL DAN REGRESI PROBIT ORDINAL (Studi Kasus Kabupaten/Kota di Jawa Tengah Tahun 2014) Nurmalasari, Ratih; Ispriyanti, Dwi; Sudarno, Sudarno
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Human Development Index (HDI) is one of the most important indicator to observe another dimensions of human development. The HDI is a measurement for achievement levels of the quality of human development. This study analyze HDI in the Districts/Cities of Central Java in 2014. The Central Java’s HDI data is categorized as low, medium, and high. The HDI presumed to be affected by many factors, such as high school participation rates, middle school graduates percentage, percentage of household with clean water access, numbers of health facility, open unemployment rate,and labour force participation rate. This study used the ordinal logistic regression and the ordinal probit regression as its statical analysis method. The result showed that factors affecting HDI in the Districts/Cities of Central Java in 2014 are percentage of household with clean water access and numbers of health facility. To evaluate the performance of ordinal logistic regression and the ordinal probit regression, researcher uses classification accuracy and AIC. Based on reasearch classification accuracy and AIC of each methods, the result showed that both the ordinal logistic regression and the ordinal probit regression has good result in analyzing factors affecting Human Development Index in the Districts/Cities of Central Java in 2014.Keywords: HDI, Ordinal Logistic Regression, Ordinal Probit Regression, Classification Accuracy, AIC

PERAMALAN JUMLAH WISATAWAN YANG BERKUNJUNG KE OBJEK WISATA DI JAWA TENGAH MENGGUNAKAN VARIASI KALENDER ISLAM REGARIMA Jesica, Haniela Puja; Ispriyanti, Dwi; Tarno, Tarno
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Tourism is one of the most strategically controlled areas that have been developed.The number of tourists in Central Java is constantly rising in the month of Eid Al-Fitr caused by holiday and mudik to hometown. The shift of the Eid Al-Fitr month on the data will form a seasonal pattern with an unequal period, then called moving holiday effect.One of the calendar variationsare often used to remove the moving holiday effect is RegARIMA model. RegARIMA is a combination of the linier regression and ARIMA, which a weight was used as a regression variable and error of regression model was used a variable in the ARIMA process. Based on the analysis carried out on the monthly number of tourists visiting tourist attractions in Central Java data for the period January 2011 to December 2017, the RegARIMA (1,1,1) (0,0,1)12model as the best model because it have the lowest AIC value than other model. The forecasting results in 2018 shows an increase on number of tourists data on June 2018 which coincided with the Eid Al-Fitr holiday on 15 June 2018. sMAPE value is 23,298%.Keyowrds:Time Series, Tourists, RegARIMA, Moving Holiday Effect

PERBANDINGAN ANALISIS DISKRIMINAN LINIER KLASIK DAN ANALISIS DISKRIMINAN LINIER ROBUST UNTUK PENGKLASIFIKASIAN KESEJAHTERAAN MASYARAKAT KABUPATEN/KOTA DI JAWA TENGAH Kartikawati, Ana; Mukid, Moch. Abdul; Ispriyanti, Dwi
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Discriminant analysis is a statistics method which is used to classify an individual or object into certain group which has determined based on its independent variables. Discriminant analysis that commonly used is classical discriminant analysis which consist of classical linear discriminant analysis and classical quadratic discriminant analysis. In classical linear discriminant analysis there are two assumptions to be fulfilled i.e. independent variables have to be normal multivariate distributed and the covariance matrix from the two observed objects should be the same. Classical discriminant analysis cannot work properly if the data which being analyzed consists of many outliers. In order to make discriminant analysis works optimally within the classification though in the condition of data which contains of many outliers, robust estimator is needed. The robust discriminant analysis is used to get the high classification accuracy for data which contains of many outliers. Fast-MCD estimator is one of the robust estimators which is aimed to get the smallest determinant of covariance matrices. The robust linear discriminant analysis with fast-MCD method in this graduating paper is implemented to determine the prosperity status of the people in the regencies or towns in Central Java. The total proportion of classification accuracy using robust linear discriminant analysis method on the data of Central Java people prosperity is 77.14 percent. It is equal with the result from classic linear discriminant analysis which is also 77.14 percent. It is caused by the few amount of outlier on the data of Central Java people prosperity.

PERBANDINGAN METODE KLASIFIKASI REGRESI LOGISTIK BINER DAN NAIVE BAYES PADA STATUS PENGGUNA KB DI KOTA TEGAL TAHUN 2014 Rajagukguk, Nanci; Ispriyanti, Dwi; Wilandari, Yuciana
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesia is a country that includes having the highest population density in the world.It is because the Indonesian state has a birth rate is so high. One of the efforts to control that population growth can be controlled by using the Keluraga Berencana program. In this study, the method used is the Binary Logistic Regression and Naive Bayes. To perform classification KB User Status in Tegal 2014, the variable used is the wife’s age, the age of first marriage, type of wife’s job, type of husband’s job, wife's education, husband's education, and number of children. The training data comparison testing is 70:30. Based on the research results using binary logistic regression showed that a significant predictor variables that affect the status of keluarga Berencana user are wife’s age, type of wife’s job, and number of children with a classification accuracy of testing data 83.33% .While with the Naive Bayes method obtained classification accuracy of 81.75%. From this analysis it can be concluded that the Binary Logistic Regression method is better than the Naive Bayes in classifying the status of KB users in Tegal 2014. Keywords : Binary Logistic Regression, Naive Bayes, Keluarga Berencana, Classification.

PENGGUNAAN PENDEKATAN CAPITAL ASSET PRICING MODEL DAN METODE VARIANCE-COVARIANCE DALAM PROSES MANAJEMEN PORTOFOLIO SAHAM (Studi Kasus: Saham-Saham Kelompok Jakarta Islamic Index) Ikhsan, Aulia; Ispriyanti, Dwi; Rahmawati, Rita
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The great amount of risk arising from stock investment make investors create a portfolio in order to minimize it. To achieve this aim, a portfolio management in which consist of several processes is required. There are three important processes in portfolio management. First, the selection of stocks that will be selected into the portfolio by Capital Asset Pricing Model (CAPM). Second, portfolio optimization by defining the weight of fund allocation for every stock in portfolio by Mean Variance Efficient Portofolio (MVEP), and third, estimating the risk of the optimal portfolio by Variance-Covariance. There are seven stocks picked into portfolio through the research done by Jakarta Islamic Index (JII) group, where the biggest fund allocation given to stock of EXCL (PT XL Axiata, Tbk) and the smallest fund allocation given to stock of ITMG (PT Indo Tambangraya Megah, Tbk). The amount of loss that estimated on 95% confidence level is 2,65% from initial capital invested on stock portfolio during one day holding period after portfolio were created.

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

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

Co-Authors A Rusgiyono Abdul Hoyyi Agus Rusgiyono Agustinus Salomo Parsaulian Ain Hafidita Ajeng Dwi Rizkia Alan Prahutama Alan Prahutama Alvi Waldira Ana Kartikawati Anisa Septi Rahmawati Anjan Setyo Wahyudi Annisa Ayu Wulandari Arief Rachman Hakim Arkadina Prismatika Noviandini Taryono Arya Despa Ihsanuddin Arya Huda Arrasyid Atika Elsadining Tyas Aulia Ikhsan Avia Enggar Tyasti Azizah Mulia Mawarni Berta Elvionita Fitriani Bitoria Rosa Niashinta Budi Warsito Budi Warsito Cylvia Evasari Margaretha Dedi Nugraha Di Asih I Maruddani Di Asih I Maruddani Diah Safitri Diah Safitri Diah Wulandari Dita Ruliana Dwi Rahmayani, Dwi Dyan Anggun Krismala Dydaestury Jalarno Eis Kartika Dewi Endah Fauziyah Erna Sulistianingsih Erna Sulistio Evi Yulia Handaningrum Fadhilla Atansa Tamardina Firda Dinny Islami Firdha Rahmatika Pratami Fithroh Oktavi Awalullaili Gandhes Linggar Winanti Gera Rozalia Ghina Nabila Saputro Putri Hanifah Nur Aini Hasbi Yasin Hasbi Yasin Henny Widayanti, Henny Ilham Maggri Imam Desla Siena Innosensia Adella Irawati Tamara Iut Tri Utami Jesica, Haniela Puja Kishatini Kishartini Lifana Nugraeni Lingga Bayu Prasetya M. Ali Ma'sum Marlia Aide Revani Masfuhurrizqi Iman Maulida Azkiya, Maulida Maulida Najwa, Maulida Merinda Pangestikasari Moch. Abdul Mukid Moch. Abdul Mukid Muhammad Fitri Lutfi Anshari Muhammad Rosyid Abdurrahman Muhammad Zidan Eka Atmaja Mustafid Mustafid Mustafid Mustafid Nanci Rajagukguk, Nanci Nandang Fahmi Jalaludin Malik Nida Adelia Nidaul Khoir Nova Nova Noviana Nurhayati Nurwihda Safrida Umami Oka Afranda Pandu Anggara Pritha Sekar Wijayanti Puput Ramadhani Pusphita Anna Octaviani Puspita Kartikasari Putri Fajar Utami Rafida Zahro Hasibuan Rahafattri Ariya Fauzannissa Rahmah Merdekawaty Rahmaniar, Ratna Rany Wahyuningtias Ratih Nurmalasari, Ratih Ratna Pratiwi Ria Sutitis Rio Tongaril Simarmata Riszki Bella Primasari Rita Rahmawati Rita Rahmawati Riza Adi Priantoro Riza Fahlevi Sa'adah, Alfi Faridatus Sania Anisa Farah Setiani Setiani Sherly Candraningtyas Sindy Saputri Sisca Agustin Diani Budiman Sri Maya Sari Damanik Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Sugito Sugito Suhendra, Muhammad Arif Suparti Suparti Suparti Suparti Suparti, S. Suryaningrum, Fahlevi Syilfi Syilfi Sylvi Natalia P P Tarno Tarno Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Tatik Widiharih Tiani Wahyu Utami Triastuti Wuryandari Triastuti Wuryandari Trimono Trimono Ulya Tsaniya Umiyatun Muthohiroh Warsito Budi Yani Puspita Kristiani Yashmine Noor Islami Yuciana Wilandari Yuciana Wilandari

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