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PEMODELAN BIVARIATE POLINOMIAL LOKAL PADA JUMLAH KEMATIAN IBU DAN BAYI DI JAWA TENGAH Prahutama, Alan; Suparti, Suparti; Ispriyanti, Dwi; Utami, Tiani Wahyu
Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 1 (2018)
Publisher : Program Studi Statistika, FMIPA, Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1082.222 KB)

Abstract

Analisis regresi merupakan analisis dalam metode statistika untuk memodelkan hubungan antara variabel respon dengan variabel prediktor. Analisis regresi dapat dilakukan secara parametrik dan nonparametrik. Analisis regresi nonparametrik dilakukan apabila bentuk kurva regresinya tidak diketahui. Salah satu metode dalam analisis regresi nonparametrik adalah polinomial lokal. Polinomial lokal dilakukan berdasarkan pembobotan kernel, sehingga membutuhkan bandwidth. Pemilihan bandwidth optimal menggunakan Generalized Cross Validation (GCV). Pada penelitian ini dikembangkan model regresi bivariate polinomial lokal pada kasus pemodelan jumlah kematian ibu dan bayi di Jawa Tengah. Variabel prediktor yang digunakan adalah jumlah tenaga kesehatan. Nilai bandwidth optimla yang didapatkan adalah 1. Nilai MSE yang dihasilkan dari model jumlah kematian ibu adalah 1.017741 dan Nilai MSE yang dihasilkan dari model jumlah kematian bayi adalah 1.380833. Keywords: Bivariate, Polinomial Lokal, Jumlah kematian ibu, Jumlah kematian bayi.
KOMPUTASI GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION BERBASIS GRAPHICAL USER INTERFACE (GUI) Yasin, Hasbi; Warsito, Budi; Ispriyanti, Dwi; Suparti, Suparti; Hakim, Arief Rachman
Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 1 (2018)
Publisher : Program Studi Statistika, FMIPA, Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1575.013 KB)

Abstract

Geographically and Temporally Weighted Regression (GTWR) merupakan salah satu metode spatio temporal yang dikembangkan pada model regresi linier. Pengembangan dilakukan dengan menambahkan unsur spasial yang direpresentasikan dengan lokasi geografis dan penambahan unsur temporal yang diwakili oleh waktu pengamatan.  Dengan metode GTWR akan diperoleh parameter bersifat lokal menurut lokasi dan waktu pengamatan. Perkembangan teknologi telah memunculkan berbagai alat bantu dalam proses analisis data. Salah satunya berkembangnya software statistik yang berbasis antarmuka berupa Graphical User Interface (GUI) untuk memudahkan pengguna. Hasil penelitian ini adalah sebuah sistem komputasi untuk proses analisis data menggunakan model GTWR baik estimasi parameter maupun inferensinya. Hasil penelitian menunjukkan bahwa dengan dengan menggunakan GUI GTWR pengguna akan sangat dimudahkan dalam proses analisis data spasial menggunakan metode GTWR. Hasil penelitian menunjukkan bahwa model spatio temporal GTWR lebih baik digunakan untuk pemodelan Indeks Standar Pencemar Udara (ISPU) dengan pembobot Bisquare karena mempunyai nilai R2 terbesar dengan MSE dan AIC yang terkecil bila dibandingkan dengan pembobot yang lain. Kata kunci :  Antar Muka Grafis, ISPU, GTWR, Spasial, Temporal.
CLUSTERING DATA PENCEMARAN UDARA SEKTOR INDUSTRI DI JAWA TENGAH DENGAN KOHONEN NEURAL NETWORK Warsito, Budi; Ispriyanti, Dwi; Widayanti, Henny
Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Vol 4, No 1 (2008): Vol 4, No 1 (2008)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (45.724 KB) | DOI: 10.14710/presipitasi.v4i1.17-22

Abstract

Industrial clustering in Central Java based on polutan yielded to be intended in order to obtaine an industrial group as information in development wisdom specially at Central Java Province. The  method that  is  selected  in  industrial  clustering  is  Kohonen  Artificial  Neural  Network.  An Artificial Neural Network is configured for a specific application, such as pattern recognition or data classification, through a learning process. Kohonen Neural Network can be used in data clustering through unsupervised learning. This network will divide the input pattern into some cluster, based on trained weight. Then this weight will be updated until it can classified itself into the class needed. This paper will present the result of the air contamination data clustering at industrial sector in Central Java at the year 2006 using Kohonen Neural Network. The result of this clustering is industrial clustering, based on polutan yielded, become three clusters.
ANALISIS TINGKAT STRESS WANITA KARIR DALAM PERAN GANDANYA DENGAN REGRESI LOGISTIK ORDINAL (Studi Kasus pada Tenaga Kerja Wanita di RS. Mardi Rahayu Kudus) Nova, Nova; Ispriyanti, Dwi
MEDIA STATISTIKA Vol 5, No 1 (2012): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (658.586 KB) | DOI: 10.14710/medstat.5.1.37-48

Abstract

Currently, the role of women has shifted from traditional to modern roles. From only a traditional role to bear children and run the household, women now have a social role which can be a career with supported higher education. This can result in conflict dual role as worker and housewife for women who have a family, so easy to cause stress. Several factors are thought to affect levels of stress, especially for career women is child care, housekeeping assistance, communication and interaction with children and husband, time for family, determining priorities, career pressures and family pressures, and the husband's view of the dual role of women. Based on the test independence of variables, seven variables have a relationship with the level of stress career woman.By using the likelihood ratio test and Wald test is found to be two factors affect the stress levels of women workers in Mardi Rahayu Kudus hospital are a time for family and the support of her husband in a career.   Keywords: Stress Level, Dual Role Conflict, Ordinal Logistic Regression, Mardi Rahayu Hospital.
PENANGANAN OVERDISPERSI PADA MODEL REGRESI POISSON MENGGUNAKAN MODEL REGRESI BINOMIAL NEGATIF Simarmata, Rio Tongaril; Ispriyanti, Dwi
MEDIA STATISTIKA Vol 4, No 2 (2011): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (413.945 KB) | DOI: 10.14710/medstat.4.2.95-104

Abstract

Poisson regression is the most popular tool for modeling the relationship between a discrete data in the response variable and a set of predictors with continue, discrete, categoric or mix data. Response variable with discrete data, however, may overdispersed or underdispersed, not conductive to Poisson regression which assumed that the mean value equals to variance  (equidispersed). One of the model that be used to overdispersed the discrete data is a regression model based on mixture distribution namely Poisson-gamma mixture which result negative binomial distribution. This regression model usually known as binomial negative regression. Using Generalized Linier Model (GLM) approach, the given model, parameter estimate, diagnostics, and interpretation of negative binomial regression can be determined.   Keyword: Negative Binomial Distribution, Dispersion, Generalized Linier Model
PENERAPAN REGRESI LOGISTIK MULTINOMIAL PADA PEMILIHAN ALAT KONTRASEPSI WANITA (Studi Kasus di Desa Tonggara Kecamatan Kedungbanteng Kabupaten Tegal) Sulistio, Erna; Ispriyanti, Dwi
MEDIA STATISTIKA Vol 3, No 1 (2010): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (418.864 KB) | DOI: 10.14710/medstat.3.1.31-40

Abstract

The development of a growing population, causing many problems within national development, so the program necessary to reduce the population of family planning program, one of the programs is Contraceptive Services. A variety of contraceptive choices provided by the government especially for women, including: pill, injection, IUD, implant, tissue KB, tubectomy, cream, jelly, and foam. The selection of contraceptives for women have to weigh various factors. So we want to know the factors which influence women in choosing a particular contraceptive. By testing the significance of the multinomial logistic regression model through the G test statistic can be shown there are four factors that influence contraceptive use, namely maternal age, number of living children, age of last child, and pregnancy plans. Keywords: Contraception, Multinomial Logistic Regression
DISTRIBUSI INVERS GAMMA PADA INFERENSI BAYESIAN Sugito, Sugito; Ispriyanti, Dwi
MEDIA STATISTIKA Vol 3, No 2 (2010): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.831 KB) | DOI: 10.14710/medstat.3.2.59-68

Abstract

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.   Keywords: Gamma Inverse Distribution, Posterior Distribution, Non-Informatif Prior
OPTIMISASI MULTIOBJEKTIF UNTUK PEMBENTUKAN PORTOFOLIO Hoyyi, Abdul; Ispriyanti, Dwi
MEDIA STATISTIKA Vol 8, No 1 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.978 KB) | DOI: 10.14710/medstat.8.1.31-39

Abstract

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. Keywords: Portofolio, Multi Objective Optimization
PEMODELAN DATA KEMISKINAN DI PROVINSI SUMATERA BARAT DENGAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) Maggri, Ilham; Ispriyanti, Dwi
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (681.466 KB) | DOI: 10.14710/medstat.6.1.37-49

Abstract

Counting the number of poor have often been modeled as a function of a global regression, which meant that the regression coefficient value applied to all geographic regions. Though this assumption was not always valid because of the differences in geographic locations most likely causing the spatial heterogeneity. In case of spatial heterogeneity, the regression parameters would vary spatially, so if the global regression model was applied, would produce an average value of those regression parameters which vary spatially. This study uses the method Geographically Weighted Regression (GWR) to analyze data that contains spatial heterogeneity. In GWR model estimation, the model parameters are obtained by using the Weighted Least Square (WLS) which gives a different weighting in each location. This study discusses the factors that influence the level of poverty in the province of West Sumatra. Suitability test of the model results shows that there is no influence of spatial factors on the level of poverty in the province of West Sumatra. The results shows that there are four variables that are assumed to affect the level of poverty in the province of West Sumatra, they are the variable of floor space, the facility to defecate, ability to pay the cost of health center / clinic and education  levels of household head. The four variables have a similar effect in every city and county.Keywords : Poverty, Spatial Heterogeneity, Geographically Weighted Regression
ANALISIS KLASIFIKASI MASA STUDI MAHASISWA PRODI STATISTIKA UNDIP dengan METODE SUPPORT VECTOR MACHINE (SVM) dan ID3 (ITERATIVE DICHOTOMISER 3) Ispriyanti, Dwi; Hoyyi, Abdul
MEDIA STATISTIKA Vol 9, No 1 (2016): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (642.835 KB) | DOI: 10.14710/medstat.9.1.15-29

Abstract

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. Keywords:  SVM, ID3
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 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 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