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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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PEMODELAN REGRESI HURDLE POISSON DALAM MENGATASI EXCESS ZEROS UNTUK KASUS PENYAKIT TETANUS NEONATORUM PADA NEONATAL DI JAWA TIMUR Cylvia Evasari Margaretha; Dwi Ispriyanti; Tatik Widiharih
Jurnal Gaussian Vol 8, No 3 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Tetanus Neonatorum is one of the infectious diseases that occur in newborns caused by Clostridium Tetani bacteria through cuts or scratches. The number of Tetanus Neonatorum cases in East Java Province in 2017 is discrete data Poisson distribution with a proportion of zero value of 73,7 percent. The amount of zero value data can result in overdispersion where the variance is greater than the mean. To overcome this problem, Hurdle Poisson regression model is a solution. To estimation of regression parameters for Hurdle Poisson regression is using the Maximum Likelihood Estimation (MLE) method and Broyden Fletcher Goldfarb Shanno (BFGS) iteration. Hurdle Poisson regression produces predictor variables that affect the number of Tetanus Neonatorum cases in East Java Province in the logit model are the percentage of pregnant women administered the K4 program, population density per and in the truncated Poisson model are the percentage of labor assisted by health workers the percentage of pregnant women administered the K4 program, population density per with the Akaike Information Criterion (AIC) value of 78,422.Keywords: Tetanus Neonatorum, Excess Zeros, Overdispersion, Hurdle Poisson Regression

PENERAPAN METODE ORDINARY KRIGING PADA PENDUGAAN KADAR NO2 DI UDARA (Studi Kasus: Pencemaran Udara di Kota Semarang) Gera Rozalia; Hasbi Yasin; Dwi Ispriyanti
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Air pollution must be addressed. Nitrogen Dioxide is one of the important factors in air pollution. To determine concentration level of the pollutant “Badan Lingkungan Hidup Kota Semarang” already take measurements at several points. However, because of blocked considerable cost, is not much point to do measurements. In this study, will be used Ordinary Kriging method to estimate at some points in Semarang. In this methode will compare the value of the eksperimental semivariogram with some theoretical semivariogram models (spherical, eksponensial, and gaussian) to get the best model that will be used in the estimation. In this study, estimate the concentration of Nitrogen Dioxide in the air in a number of village in Semarang. Based on analysis we found the best model is spherical model with Nitrogen Dioxide produces estimates is the highest in Sub Gebangsari and Nitrogen Dioxide lowest in Sub Patemon. Keywords: Ordinary Kriging, Semivariogram, Nitrogen Dioxide.

PERBANDINGAN METODE K-MEANS DAN METODE DBSCAN PADA PENGELOMPOKAN RUMAH KOST MAHASISWA DI KELURAHAN TEMBALANG SEMARANG Sisca Agustin Diani Budiman; Diah Safitri; Dwi Ispriyanti
Jurnal Gaussian Vol 5, No 4 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Students as well as community or household, as well as economic activities daily, including consumption. The student needs to choose a place to stay is also one form of consumption activities. There are many factors that affect student preferences in the selection of boarding houses, including price, amenities, location, income, lifestyle, and others. The rental price boarding and facilities offered significant positive effect on student preferences in choosing a boarding house. Based on rental rates and facilities it offered to do the grouping in order to know the condition of the student boarding house in the Village Tembalang. Grouping is one of the main tasks in data mining and have been widely applied in various fields. The method used to classify is K-Means and DBSCAN with a number of groups of three. Furthermore, the results of both methods were compared using the Silhouette index values to determine which method is better to classify the student boarding house. Based on the research that has been conducted found that the K-Means method works better than DBSCAN to classify the student boarding house as evidenced by the value of the Silhouette index on K-Means of 0.463 is higher than the value at DBSCAN Silhouette index is equal to 0.281. Keywords: student boarding houses, data mining, clustering, K-Means, DBSCAN

PENDEKATAN METODE MARKOWITZ UNTUK OPTIMALISASI PORTOFOLIO DENGAN RISIKO EXPECTED SHORTFALL (ES) PADA SAHAM SYARIAH DILENGKAPI GUI MATLAB Umiyatun Muthohiroh; Rita Rahmawati; Dwi Ispriyanti
Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i3.32805

A portfolio is a combination of two or more securities as investment targets for a certain period of time with certain conditions. The Markowitz method is a method that emphasizes efforts to maximize return expectations and can minimize stock risk. One method that can be used to measure risk is Expected Shortfall (ES). ES is an expected measure of risk whose value is above Value-at-Risk (VaR). To make it easier to calculate optimal portfolios with the Markowitz method and risk analysis with ES, an application was made using the Matlab GUI. The data used in this study consisted of three JII stocks including CPIN, CTRA, and BSDE stocks. The results of the portfolio formation with the Markowitz method obtained an optimal portfolio, namely the combination of CPIN = 34.7% and BSDE = 65.3% stocks. At the 95% confidence level, the ES value of 0.206727 is greater than the VaR value (0.15512).

APLIKASI MODEL REGRESI POISSON TERGENERALISASI PADA KASUS ANGKA KEMATIAN BAYI DI JAWA TENGAH TAHUN 2007 Nurwihda Safrida Umami; Dwi Ispriyanti; Tatik Widiharih
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Infant Mortality is one of the issues that can affect the number and age composition of the population. The Government pays special attention to reduce the amount of Infant Mortality Rate in Central Java, so the role of data and information becomes very important. Poisson regression is a nonlinear regression which is often used to model the relationship between the response variable in the form of discrete data with predictor variables in the form of continuous or discrete data. Poisson regression models have equidispersi assumption, a condition in which the mean and variance of the response variable have equal value. In practice, the assumption is sometimes violated in the analysis of discrete data in the form of overdispersi (value of variance greater than the mean value) so that Poisson regression model is not appropriate to be used. Overdispersi is a condition in which the data of response variable shows. One model that can be used to solve the overdispersi problem is generalized Poisson regression model. The regression model is an extension of the Poisson regression and part of the Generalized Linear Model (GLM) which does not require constancy of variance to test the hypothesis. From the data of Infant Mortality Rate in Central Java on 2007 known that there overdispersi. And the factors affecting Infant Mortality Rate is the number of health facilities, the number of medical personnel, and the percentage of households with clean water each county / city.

ANALISIS MULTIRESOLUSI WAVELET DENGAN TRANSFORMASI WAVELET DISKRIT BERBASIS GUI R (STUDI KASUS: INFLASI DI INDONESIA PADA PERIODE OKTOBER 2007-MEI 2018) Sania Anisa Farah; Suparti Suparti; Dwi Ispriyanti
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Lately, the wavelet applications are widely used in statistics, one of them is discrete wavelet transform (DWT) which is a non-parametric method for signal analysis, data compression, and time series analysis. As technology becomes more advanced, a software is necessary to support the statistical analysis by such method, one of them being the open source based R. It is often used in statistical computing with command line interface (CLI) which requires the R user to remember the names of syntaxes and functions. It becomes less effective when there are many related statistical analysis involved, so graphical user interface (GUI) is needed to access all of them easily. The testing of multiresolution analysis by DWT for Haar, Daublets, and Coiflets filters with levels 1-6 had been performed by using the inflation data in Indonesia during October 2007-May 2018 taken from Bank Indonesia website. The result shows that the sixth level of DWT gives the best estimation for each filters, and Daublets 20 is the best filter for overall estimation with MSE, MAPE, and MASE values are 0.05755, 3.40678, and 0.35343 respectively. The packages for GUI construction in R are wavelets and shiny. Based on its usage, the GUI is capable of processing the chosen analysis and showing the valid output.

ANALISIS SENTIMEN REVIEW APLIKASI CRYPTOCURRENCY MENGGUNAKAN ALGORITMA MAXIMUM ENTROPY DENGAN METODE PEMBOBOTAN TF, TF-IDF DAN BINARY Fadhilla Atansa Tamardina; Hasbi Yasin; Dwi Ispriyanti
Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v11i1.34004

Pandemi COVID-19 yang belum berhenti menyebabkan kondisi ekonomi Indonesia kian memburuk. Masyarakat yang terkena dampak pemotongan upah akibat pandemi harus mencari cara untuk mendapatkan pendapatan pasif. Salah satu cara untuk mendapatkan hal tersebut adalah berinvestasi. Cryptocurrency adalah salah satu instrumen investasi berbasis aplikasi yang memiliki return tinggi. Aplikasi Pintu adalah aplikasi pertama yang menyediakan fasilitas mobile apps pada penggunanya. Aplikasi yang dirilis pada tahun 2020 ini sudah memiliki banyak ulasan yang diberikan oleh penggunanya. Ulasan ini dibutuhkan untuk mengetahui apakah ulasan yang diberikan bersifat positif atau negatif. Analisis sentimen pada aplikasi Pintu dipilih untuk melihat sentimen pengguna yang akan dibagi menjadi dua kelas sentimen yaitu positif dan negatif. Klasifikasi dilakukan dengan algoritma Maximum Entropy dengan perbandingan metode pembobotan kata Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IDF) dan Binary. Model klasifikasi terbaik dilihat berdasarkan nilai akurasi yang dievaluasi dengan 5-Fold Cross Validation. Hasil klasifikasi model Maximum Entropy dengan Binary memiliki tingkat akurasi sebesar 83,21% sedangkan hasil klasifikasi model Maximum Entropy dengan Term Frequency hanya sebesar 83,01% dan model Maximum Entropy dengan Term Frequency-Inverse Document Frequency hanya sebesar 83,20%. Hal ini menunjukkan bahwa tidak terdapat perbedaan yang signifikan pada model algoritma Maximum Entropy dengan metode pembobotan kata Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IDF) dan Binary. Keywords: Cryptocurrency, Binary, Term Frequency, Term Frequency-Inverse Document Frequency, Maximum Entropy

PENENTUAN CADANGAN DISESUAIKAN DENGAN METODE ILLINOIS PADA ASURANSI JIWA ENDOWMEN SEMIKONTINU Marlia Aide Revani; Yuciana Wilandari; Dwi Ispriyanti
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Semicontinuous endowment insurance is a kind of insurance with a periodic premium payments which gives two benefits, payment of death benefit at the moment of death if the insured dies during a certain period of years or payment of living benefit if the insured survives to the end of the period. The insurer’s obligation of insured’s premium payments, provides net level premium reserves for benefit payment in the future. The insurer needs expenses for it’s operate and in fact, the first year expenses usually exceed the loading. This means that an insurance company have to find funds to cover the first year expenses. The funds can be obtained by modified reserve system. To get information of modified reserve value for semicontinuous life insurance, the study of determination of modified reserve value using Illinois method has been done. The full net level reserves are lesser than the reserves under the Illinois method before the end of min(n, 20) years and both of these reserves will be equal at the the end of min(n, 20) years, with n is premium period.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENERIMA BERAS RASKIN MENGGUNAKAN REGRESI LOGISTIK BINER DENGAN GUI R Agustinus Salomo Parsaulian; Tarno Tarno; Dwi Ispriyanti
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30934

The Rice Subsidy Program for Low-Income Communities or the Raskin Program is one of the government's programs to eradicate poverty. However, in practice, determining the criteria for Raskin recipients is a complicated problem. The Raskin program is a cross-sectoral national program both horizontally and vertically, to help meet the rice needs of low-income citizens. Determining the criteria for Raskin recipients is often a complicated issue. This study aims to analyze the classification of the Target Households (RTS) for the Raskin Program. The method used is binary logistic regression by utilizing R GUI. Binary logistic regression method is a method to find the relationship between independent and dependent variables, with a binary or dichotomous dependent variable. The data used is the March 2018 National Socio-Economic Survey (Susenas) data for Brebes Regency. The independent variables used in this study are the criteria for determining poor households, namely the area of the house, floor type of the house, wall type of the house, defecation facilities, lighting used, fuel used, ability to buy meat/milk, education level of the head of the household, and the capacity of installed electricity in the main residence. The results of the analysis show that in the final model, the variables that significantly affect the classification of RTS are the ability to eat healthy food, the capacity of installed electricity in the main residence, the education level of the head of the household, and defecation facilities with an accuracy value of 85.4%.Keywords: Raskin Program, Binary Logistic Regression, R GUI

PENENTUAN MODEL KEMISKINAN DI JAWA TENGAH DENGAN MULTIVARIATE GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) Sindy Saputri; Dwi Ispriyanti; Triastuti Wuryandari
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The problem of poverty is a fundamental problem faced in a number of regions in Indonesia, to determine significant indicators on poverty by taking into account the spatial variation in the province of Central Java can use multivariate models Geographically Weighted Regression (MGWR). In the model MGWR model parameter estimation is obtained by using Weighted Least Square (WLS). Selection of the optimum bandwidth using Cross Validation (CV). The study looked for the best model among MGWR with multivariate regression and create distribution maps counties and cities in the province of Central Java based variables significantly to poverty. The results of testing the suitability of the model shows that there is no influence of spatial factors on the percentage of poor and non-poor in the province of Central Java. Variables expected to affect the percentage of poor people is a variable percentage of expenditures for food, while the percentage of the non-poor is a variable percentage of expenditure on food and the percentage of heads of household education level less than SD. Based on the AIC and the MSE obtained the best model is the model MGWR with AIC value of 44.4603 and MSE 0.454.Keywords: Cross Validation, MGWR, Poverty, Weighted Least Square

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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