Article Per Year (5 Year)
p-Index From 2021 - 2026
4.115
P-Index
This Author published in this journals
All Journal PROSIDING SEMINAR NASIONAL Media Statistika Jurnal Ilmu Dasar Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Edukasi dan Penelitian Informatika (JEPIN) Proceeding SENDI_U Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Variance : Journal of Statistics and Its Applications LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Litbang Edusaintech Journal of Data Insights Prosiding Seminar Nasional Unimus Jurnal Meteorologi dan Geofisika
Tiani Wahyu Utami
Program Studi Statistika, Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

Published : 64 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search
Journal : PROSIDING SEMINAR NASIONAL

 1   2 
PERBANDINGAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) DAN ORDINARY LEAST SQUARE (OLS) DALAM PEMODELAN KETIMPANGAN DI PROVINSI JAWA TENGAH Lia Miftakhul Janah; Tiani Wahyu Utami
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.375 KB)

Abstract

nequality is a state where there is an imbalance between each other. Inequality indicates the unevenness of development that runs in an area.In Central Java, the problem of inequality among people still exists in daily life. Geographically Weight Regression method is a method that yields model parameter estimators that have localized properties at each point or location. While OrdinaryLeast Square method is a linear regression that doesn’t have territorial element. In this study aims to modeling the inequality problem that occurred in Central Java using Geographically Weight Regression method that has the nature of localization at the point and Ordinary Least Square method. Data taken from Central Statistics Agency (BPS) 2015. Through Geographically Weight Regression method can be concluded that 2 variables effect on imbalance with α 10% is variable of Total population (0,4078) and Labor (0,9502) . While the influential OLSmethod is the Human DevelopmentIndex and Averageper-capita expendixture.  AIC value of GWRis smaller than OLS Method (93.45184<105.1492)Which is mean GWR methodbetter than OlS in modelling inequality at Central Java.Keywords: Inequality, GWR,OLS
ANALISIS SISTEM ANTRIAN MODEL MULTI PHASE-MULTI CHANNEL PADA SENTRA PELAYANAN KIOS 3 IN 1 BBPLK SEMARANG Ujiati Suci Rahayu; Rochdi Wasono; Tiani Wahyu Utami
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.207 KB)

Abstract

The queue process is a process associated with the arrival of a customer at a service facility, then waited in a row (queue) when all services are busy, andleaving the place after getting the service. The queue process can happenanywhere, including in BBPLK Semarang. A wide variety of services such asregistration, competency testing, placement, making the rights of participantsand certificates. Therefore, it is necessary to study on the queuing system tooptimize service to customers. The purpose of this study was to determine thestatistical analysis deskriptive, making modeling a queue that services moreeffectively and efficiently and interpret the queuing models. The research in thispaper begins with a queuing system design kiosk 3 in 1 BBPLK Semarang.Then, the retrieval of data for each counter in the form of many arrivals anddepartures every 15 minutes. The collected data is then tested to determinewhether the data is distributed Poisson or not. Once known Poisson distributeddata, followed by determining a model queue at each phase and determine therate of arrivals and departures every service counter. The next step is to analyzethe size of the performance of each phase in the form of the average number ofcustomers in the system, the average number of customers in the queue, theaverage length of customer in the system, and the average length of customer inthe queue.  Keywords : queue, model multi phase-multi channel,  poisson, eksponensial
PEMODELAN ANGKA KEMATIAN BAYI DENGAN PENDEKATAN REGRESI NONPARAMETRIK SPLINE TRUNCATED Tiani Wahyu Utami
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: SEMINAR NASIONAL PENDIDIKAN SAINS DAN TEKNOLOGI
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (504.41 KB)

Abstract

Kematian bayi merupakan salah satu indikator dalam menentukan derajat kesehatan. Apabila suatu daerah memiliki kematian bayi yang tinggi maka dapat dikatakan tingkat kesehatan anak  pada daerah tersebut rendah. Angka kematian bayi juga mampu menggambarkan keadaan sosial di masyarakat.Tujuan dari penelitian ini adalah untuk memodelkan antara variabel prediktor dengan variabel respon. Variabel yang diduga adalah (Y) Angka Kematian Bayi (AKB), persentasi bayi yang diberi asi ekslusif (X1) dan persentase persalinan dengan tenaga medis (X2). Metode ini digunakan dalam penelitian ini adalah Regresi Spline Truncated, model ini cenderung mencari sendiri estimasi data. Dalam metode ini terdapat titik knot, yaitu titik yang menunjukan perubahan data. Pemilihan titik knot optimum dilakukan dengan cara memilih nilai Generalized Cross Validation (GCV) yang minimum. Niliai GCV terkecil sebesar  5578.896 dengan R2 sebesar 86,551%.Keywords : Kematian Bayi, Regresi Spline Truncated, GCV.
FAKTOR-FAKTOR DOMINAN YANG MEMPENGARUHI LAMA MENCARI PEKERJAAN DI SEMARANG MENGGUNAKAN ANALISIS REGRESI COX Anissatush Sholiha; Rochdi Wasono; Tiani Wahyu Utami
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (521.936 KB)

Abstract

Discussion on the issue of unemployment is always associated with variousfactors that affect the length of time a person needs to get a job.One commonmethod used to determine these factors is to conduct survival analysis, amongwhich commonly used is Cox Proportional Hazard Regression.The purpose ofthis study was to identify the various factors of the time needed to be employeda university fresh graduate. The variables used consist of time to be employedas the dependent variable, while the independent variables are the educationalbackground, family income, job vacancies and the work aspiration. Coxregression can be the most appropriate method because the function andpurpose of this analysis is to predict exactly what factors make a person take acertain time to get his current job. The survival function and hazard functionpresent in the cox regression method allow the estimated time required by aperson until the person experiences an event (in which case the event is gettinga job). The results obtained from the analysis of each of these variables provedto have a significant effect on the length of time seeking employment of privateworkers in the city of Semarang.Keywords: Unemployed Length, Proportional Hazard Cox Regression, Survival.
PEMODELAN MEAN SEA LEVEL (MSL) DI KOTA SEMARANG DENGAN PENDEKATAN REGRESI NONPARAMETRIK DERET FOURIER Tiani Wahyu Utami; Indah Manfaati Nur
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Publikasi Hasil-Hasil Penelitian dan Pengabdian Masyarakat
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (480.815 KB)

Abstract

The statistical method used to estimate or estimate sea level is by nonparametric regression approach of Fourier series. The problem of flooding due to rising sea levels in Semarang includes problems that have not been solved yet. This resulted in the need for modeling to predict and find out how high the average rising sea level. Fourier series have a fluctuative data pattern due to its periodic nature. This makes the Fourier series as an appropriate approach for modeling the mean sea level or called the Mean Sea Level (MSL). Before modeling the MSL data with fourier approximation approach, first determine the optimal K value, based on optimal K determination with GCV method obtained K = 277. The result of MSL modeling on tide data of Semarang City with Nonparametric Regression approach Fourier R2 obtained R2 of 95% and MSE = 4,42. Maximum MSL modeling results or average sea level experienced maximum tides occurred on 31 August 2016 and minimum MSL or so-called mean sea level experienced minimum receding occurred on March 2, 2016.Keywords: MSL, Nonparametric Regression, Fourier Series
MODELLING JAKARTA COMPOSITE INDEKS USING SPLINE TRUNCATED Alan Prahutama; Suparti Suparti; Sugito Sugito; Tiani Wahyu Utami
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: PROCEEDING 1ST INSELIDEA INTERNATIONAL SEMINAR ON EDUCATION AND DEVELOPMENT OF ASIA (INseIDEA)
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (564.334 KB)

Abstract

Regression analysis can be done by parametric and nonparametric approach. The nonparametric approach does not assume an assumption compared to parametric. One nonparametric approach is the spline truncated. Spline is a polynomial piece that provides high flexibility. Spline modeling requires spline and knots. To determine the knots using General Cross Validation (GCV). In this study modeled the value of Jakarta Composite  Index (JCI). JCI provides benefits to know the overall stock price in the stock exchange Indonesia. In this study the best spline model is linear with three knots with R square is 94.34%. Keywords: Jakarta Composite’s Index, Spline truncated, GCV.
KLASIFIKASI INDEKS PEMBANGUNAN MANUSIA KABUPATEN/KOTA SE-INDONESIA DENGAN PENDEKATAN SMOOTH SUPPORT VECTOR MACHINE (SSVM) KERNEL RADIAL BASIS FUNCTION (RBF) Fatkhurokhman Fauzi; Moh. Yamin Darsyah; Tiani Wahyu Utami
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (532.023 KB)

Abstract

Human Development Index (HDI) is a measure of human development achievementbased on basic components of quality of life. The human development index is low ifthe HDI is less than 60, moderate HDI between 60 to less than 70, high HDI between70 to less than 80, and equal to 80 and more than 80 are high. Smooth SupportVector Machine (SSVM) is a classification technique that is new. The algorithm usedis Radial Basis Function (RBF). The result of human development sperm using SSVMmethod with RBF kernel is 100%. With 41 districts / cities including low HDI. While332 districts / cities are included in medium HDI coverage, 134 districts / cities areincluded in the high HDI, and 12 districts / cities including HDI is very high. Keywords : Human Development Index, Smooth Support Vector Machine (SSVM), Radial Basis Function (RBF), accuracy, classification.
FOURIER SERIES NONPARAMETRIC REGRESSION FOR THE MODELIZING OF THE TIDAL Tiani Wahyu Utami; Indah Manfaati Nur; Ismawati -
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Proceeding 3rd ISET 2017 | International Seminar on Educational Technology 3rd 2017
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.661 KB)

Abstract

The method of statistic used to estimate the estimation of sea water level is by nonparametric regression approaching of Fourier series. The rob flood caused by sea level rise in Semarang becomes a dissolved problem until today This results the need of modeling to predict and know how high sea level is.The fourier series have fluctuative data pattern because of its periodic character. This makes Fourier series as the appropriate approaching to modelize the sea tidal. Before modelizing the sea tidal with Fourier series approaching, It is previously necessary to find the optimal K value . Based on the determination of optimal K value, with GCV method, It is obtanied K equals 277. The result of average data of the Semarang sea tidal with reggression nonparametic method showed that R 2 is 95% and MSE = 4,42. The lowest tidalestimation resulted in Semarang is on March 2, 2016. Then the highest tidal estimation in Semarang Cityoccurred on August 31, 2016. Keywords : Nonparametric Regression, Fourier Series, Tidal Sea
KERNEL NONPARAMETRIC REGRESSION FOR THE MODELIZING OF THE PRODUCTIVITY WETLAND PADDY Tiani Wahyu Utami; Martyana Prihaswati; Vega Zayu Varima
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: PROCEEDING 1ST INSELIDEA INTERNATIONAL SEMINAR ON EDUCATION AND DEVELOPMENT OF ASIA (INseIDEA)
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (376.949 KB)

Abstract

Nonparametric regression can be used when the relationship between the response variable and the predictor variables have an unknown pattern form the regression curve. One of the method that can be used to predictproductivity of the wetland paddy is a nonparametric regression kernel. In kernel regression, there are severaltypes of estimator that can be used to modelling productivity of wetland paddy in Central Java, one of which isNadaraya-Watson estimator. Variables used in the study of the productivity of rice as the response variable,while the predictor variables that harvested area, production and rainfall. Based on estimates indicate that thekernel nonparametric regression optimum bandwidth value 1.2 and GCV = 1.7577. The coefficient ofdetermination (R2) of 94.23% and MSE of 0.8560. Keywords: Kernel Nonparametric Regression, Productivity, Wetland Paddy
PEMODELAN REGRESI RIDGE PADA KASUS CURAH HUJAN DI KOTA SEMARANG Maulana Afham; Indah Manfaati Nur; Tiani Wahyu Utami
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (983.537 KB)

Abstract

Rainfall is the amount of water that falls on the surface of the flat ground for acertain period measured in units of height (mm) above the horizontal surface. Theclassification of rainfall is divided into thick, medium, and light. Based on data of2016 Semarang city rainfall for 6 years experienced a significant decrease andincrease. With the data of rainfall Semarang city is very high potential for flooding.Semarang rainfall data tend to be unstable then it will cause problems in rainfalldata. Therefore it is necessary to solve the problem in rainfall data. The purpose ofthis study is to model and know the factors that affect rainfall in the city ofSemarang. The results of multiple regression found problems in Multicollinearity. An appropriate method for overcoming multiko in multiple regression is the ridgeregression. Regression of ridge to stabilize regression coefficient value of deviationof assumption in Multicolinearity. The result of the research to select the best model using the smallest MSE value which in the regex ridge model has MSE value 1.517smaller than the value of MSE in multiple regression of 1,519. While for variables that have significant effect on rainfall is wind speed, while variable temperature, humidity, solar irradiance have a significant influence but have weak effect on rainfall in Semarang city.Keywords: Rainfall, Ridge Regression and Multiple Regression
Co-Authors Abdul Rohman Agus Rusgiyono Aisyah Lahdji, Aisyah Alan Prahutama Alan Prahutama Alwan Fadlurohman Amrullah, Setiawan Anissatush Sholiha Arianti, Irma Arini Rizky Wahyuningtyas Aulia, Syifa Aura Hisani, Zahra Ayu Wulandari Azqia Fajriyani Biru, Pelangi Langit Dannu Purwanto Devi Nurlita Dewi Ratnasari Wijaya Dhani, Oktaviana Rahma Dheanyta Alif Shafira Diana Wahyu Safitri Dwi Ispriyanti Eko Yuliyanto, Eko Elvia Nanda Sofiyanti Endah Suryaningsih Endang Tri Wahyuni Maharani Fathikatul Arnanda Fatimahthus Zahra, Diandra Fatmawati Nurjanah Fauzi, Fatkhurokhman Hanif Nur Ibrahim Hasbi Yasin Hikmah Nur Rohim, Febrian Iffah Norma Hidayati Ihsan Fathoni Amri Iis Widya Harmoko Iis Widya Harmoko, Iis Widya Imaroh Izzatun Indah Manfaati Nur Indah Sulistiya Indra Firmansyah Iqbal Kharisudin Ismawati - Juwita Rahayu Laila Khoirun Nisa Lia Miftakhul Janah M. Al Haris M. Saifudin Nur Martyana Prihaswati Maulana Afham Mifta Luthfin Alfiani Moh Yamin Darsyah Moh Yamin Darsyah Moh. Yamin Darsyah Nila Amelinda Putri Nur Chamidah Nursamsiah Nursamsiah Pranandira Rilvandri, Quinsy Prizka Rismawati Arum Rahma Dhani, Oktaviana Rahman, Budiono Rahmi, Mulya Asy-syifa Rizma Novinda Puteri Rochdi Wasono Rochdi Wasono Roosyidah, Nila Ayu Nur Salma, Nadia Khoirunnafisa Salmaa Fauziah Septi Winda Utami Setiayani, Wiwik Silvia Tri Wahyuni Sri Kustiara Sudarno Sudarno Sugito Sugito Suherdi, Andri Suparti Suparti Suparti Suparti Suparti, S. Syaifullah, Ahmad Reyhan Toha Saifudin Ujang Maulana Ujiati Suci Rahayu Ulinuha, Samikoh Vega Zayu Varima Velia Arni Widyasari Wahyu Putri Pratiwii Wisudawati, Dinda Tri Yulianita, Tanti Yuliardi, Fahrul Raditiar Yusnia Kriswanto