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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

Author-ID : 408851

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

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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

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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

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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

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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

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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

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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

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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 KETAHANAN PANGAN KEDELAI (GLYSINE SOYA MAX (LENUS&MERRIL)) DI PROVINSI JAWA TENGAH DENGAN PENDEKATAN SPATIAL REGRESSION Fathikatul Arnanda; Yusnia Kriswanto; Imaroh Izzatun; Devi Nurlita; Azqia Fajriyani; Tiani Wahyu Utami
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 3, No 1 (2015): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

Masalah pangan merupakan salah satu masalah nasional. Kedelai merupakan salah satu sumber bahan komoditas pangan yang telah lama dibudidayakan di Indonesia, yang saat ini tidak hanya diposisikan sebagai bahan baku industri pangan, namun juga ditempatkan sebagai bahan baku industri non-pangan. Beberapa produk yang dihasilkan antara lain tempe, tahu, es krim, susu kedelai, tepung kedelai, minyak kedelai, pakan ternak ,dan bahan baku industri. Sifat multiguna yang ada pada kedelai menyebabkan tingginya permintaan kedelai di dalam negeri. Selain itu, manfaat kedelai sebagai salah satu sumber protein murah membuat kedelai semakin diminati. Variabel penelitian yang digunakan adalah variabel endogenous, yakni nilai total produktifitas kedelai (Y) berdasarkan Kabupaten-Kota di Jawa Tengah dan Variabel Exogenous luas panen kedelai di Kabupaten-Kota di Jawa Tengah (X) dan total produksi kedelai di Kabupaten-Kota di Jawa Tengah(X₂) Penelitian ini mengkaji efek dependensi spasial dengan menggunakan pendataan area. Spatial regression dengan lag di variable independen dinamakan Spatial Lag X (SLX). Model SLX merupakan model regresi linier lokal yang menghasilkan dugaan parameter model regresi yang bersifat lokal.Kata Kunci : Ketahanan Pangan, Kedelai, Spasial Regresi, SLX

PEMODELAN VECTOR AUTOREGRESIVE EXOGENOUS (VARX) PADA NILAI INFLASI TERHADAP PDRB DI JAWA TENGAH Alan Prahutama; Agus Rusgiyono; Tiani Wahyu Utami
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 7, No 2 (2019): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

Analisis time series dapat dilakukan secara univariat maupun multivariat. Pemodelan time series univariat menggunakan model ARIMA (Autoregressive Integrated Moving Average), sedangkan pemodelan multivariat dapat menggunakan VAR (Vector Autoregressive). Baik model ARIMA ataupun VAR memiliki prosedur yang mirip antaralain stasioneritas data, penentuan orde dari model, checking diagnostic. Model VAR merupakan pengembangan dari model AR (Autoregressive). apabila model univariat time series dipengaruhi oleh variabel eksogen dapat dimodelkan menggunakan ARIMAX, sedangkan time series multivariate dapat dimodelkan menggunakan VARX.Pada penelitian ini dimodelkan nilai inflasi di kota Semarang, kota Surakarta dan kota Purwokerto berdasarkan nilai PDRB Jawa Tengah. Berdasarkan hasil analisis yang didapat, nilai inflasi setiap wilayah dipengaruhi lag ke-(t-1) dengan wilayahnya sendiri ataupun dengan wilayah yang lain. Nilai PDRB tdak signifikan hanya di wilayah Surakarta, tetapi di wilayah lainnya signifikan. Nilai AIC model mencapai 976.876.Kata kunci : VARX, inflasi, PDRB

PEMODELAN REGRESI ROBUST M-ESTIMATOR DALAM MENANGANI PENCILAN (STUDI KASUS PEMODELAN JUMLAH KEMATIAN IBU NIFAS DI JAWA TENGAH Alan Prahutama; Agus Rusgiyono; Dwi Ispriyanti; Tiani Wahyu Utami
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.35-39

Regression analysis is statistical method that used to model between predictor variables and response variables. In the regression model, the residual assumed normal distribution, non-autocorrelation, and homoscedasticity. When the assumptions doesn’t fulfilled, then the measurement of goodness not well enough. One of the causes may be outlier of data. Handling the outlier can be used robust regression, which one of method is robust M-estimator. In this article, we purposed modelling the number of maternal postpartum in Central Java province with predictor variables are the percentage of pregnant who consumed Fe tablet (X1), the percentage of household whom applied clean and health lifestyle(X2), and the percentage of pregnant who First visited to midwife of doctor (K1) (X3). In the multiple regression only X3 was significantly with R-square was 14.25209%, and Mean Square Error (MSE) was 20.4177. Moreover, in outlier detection, there were two outlier in the data, then modelled with Robust M-estimator. The measurement of goodness used R-square of regression robust M-estimator was 21.74% with MSE was 15.02766. Robust M-estimator regression resulted better model than multiple regression to model the number of maternal postpartum in Central Java Province.

SMOOTH SUPPORT VECTOR MACHINE (SSVM) UNTUK PENGKLASIFIKASIAN INDEKS PEMBANGUNAN MANUSIA KABUPATEN/KOTA SE-INDONESIA Fatkhurokhman Fauzi; Moh. Yamin Darsyah; Tiani Wahyu Utami
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 5, No 2 (2017): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Indeks Pembangunan Manusia (IPM) adalah mengukur capaian pembangunan manusia berbasis sejumlah komponen dasar kualitas hidup. Indeks pembangunan manusia dikatakan rendah jika IPM kurang dari 60, IPM sedang antara 60 sampai kurang dari 70,IPM tinggi antara 70 sampai kurang dari 80, dan sama dengan 80 dan lebih dari 80 tergolong IPM tinggi. Smooth Support Vector Machine (SSVM) merupakan teknik pengklasifikasian yang tergolong baru. Algoritma yang digunakan adalah Newton Armijo dengan pendekatan kernel linier, polynomial, dan Radial Basis Function (RBF). Hasil klasifikasi indeks pembangunan manusia dengan metode SSVM dengan kernel linier menunjukan keakuratan prediksi sebesar 84.77%, kernel polynomial 61.65%, dan kernel RBF sebesar 100%. Dengan jumlah klasifikasi 440 kabupaten/ kota untuk kernel linier,kernel polynomial 320, dan kernel RBF 519 kabupaten/kota yang dibagi menjadi 4 klasifikasi menurut BPS. Dari ketiga kernel yang digunakan kernel Radial Basis Function (RBF) merupakan kernel yang paling akurat dalam memperdiksi serta IPM.Kata kunci: Indeks Pembangunan Manusia, Smooth Support Vector Machine (SSVM), kernel, akurasi, klasifikasi

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 Budiono Rahman 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 Fatkhurokhman Fauzi Fatkhurokhman Fauzi 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 Rizma Novinda Puteri Rochdi Wasono Rochdi Wasono Roosyidah, Nila Ayu Nur Salmaa Fauziah Samikoh Ulinuha 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 Vega Zayu Varima Velia Arni Widyasari Wahyu Putri Pratiwii Wisudawati, Dinda Tri Yulianita, Tanti Yuliardi, Fahrul Raditiar Yusnia Kriswanto

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