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All Journal PROSIDING SEMINAR NASIONAL Media Statistika Jurnal Ilmu Dasar Jurnal Statistika Universitas Muhammadiyah Semarang Proceeding SENDI_U Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Litbang Edusaintech Journal of Data Insights Prosiding Seminar Nasional Unimus
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 Education Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Nursing Public Health

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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.
PEMODELAN REGRESI BERGANDA DAN GEOGRAPHICALLY WEIGHTED REGRESSION PADA TINGKAT PENGANGGURAN TERBUKA DI JAWA TENGAH Utami, Tiani Wahyu; Rohman, Abdul; Prahutama, Alan
MEDIA STATISTIKA Vol 9, No 2 (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 (303.285 KB) | DOI: 10.14710/medstat.9.2.133-147

Abstract

The problems in employment was the growing number of Open Unemployment Rate (OUR). The open unemployment rate is a number that indicates the number of unemployed to the 100 residents are included in the labor force. The purpose of this study is mapping the data OUR in Central Java and the suspect and identify linkages between factors that cause OUR in the District / City of Central Java in 2014. Factors that allegedly include population density (X1), Inflation (X2), the GDP value (X3), UMR Value (X4), the percentage of GDP growth rate (X5), Hope of the old school (X6), the percentage of the labor force by age (X7) and the percentage of employment (X8). Geographically Weighted Regression (GWR) is a method for modeling the response of the predictor variables, by including elements of the area (spatial) into the point-based model. This research resulted in the conclusion that the OLS regression models have poor performance because the residual variance is not homogeneous. There were no significant differences between GWR models with OLS model or in other words generally predictor variables did not affect the response variable (rate of unemployment in Central Java) spatially. However, GWR model could captured modelling in each region. Keywords: multiple linear regression, geographiically weighted regression, open unemployement rate in Central Java.
PREDIKSI HARGA SAHAM MENGGUNAKAN SUPPORT VECTOR REGRESSION DENGAN ALGORITMA GRID SEARCH Yasin, Hasbi; Prahutama, Alan; Utami, Tiani Wahyu
MEDIA STATISTIKA Vol 7, No 1 (2014): 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 (335.209 KB) | DOI: 10.14710/medstat.7.1.29-35

Abstract

The stock market has become a popular investment channel in recent years because of the low return rates of other investment. The stock price prediction is in the interest of both private and institution investors. Accurate forecasting of stock prices is an appealing yet difficult activity in the business world. Therefore, stock prices forecasting is regarded as one of the most challenging topics in business. The forecasting techniques used in the literature can be classified into two categories: linear models and non linear models.  One of forecasting techniques in nonlinear models is support vector regression (SVR). Basically, SVR adopts the structural risk minimization principle to estimate a function by minimizing an upper bound of the generalization. The optimal parameters of SVR can be use Grid Search Algorithm method. Concept of this method is using cross validation (CV). In this paper, the SVR model use linear kernel function. The accurate prediction of stock price, in telecommunication, is 92.47% for training data and 83.39% for testing data.   Keywords: Stock price, SVR, Grid Search, Linear kernel function.
PRINCIPAL COMPONENT ANALYSIS SUPPORT VECTOR MACHINE (PCA-SVM) UNTUK KLASIFIKASI KESEJAHTERAAN RUMAH TANGGA DI KABUPATEN BREBES Utami, Tiani Wahyu; Arianti, Irma
Proceeding SENDI_U 2020: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

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Abstract

Kesejahteraan merupakan berbagai tindakan yang dilakukan manusia untuk mencapai tingkat kehidupanmasyarakat yang lebih baik. Kesejahteraan masyarakat dapat diukur dari terpenuhinya kebutuhan dasarmanusia. Rumah tangga yang tidak mampu dalam pemenuhan kebutuhan dasarnya, maka dikategorikan dalamkemiskinan. Berdasarkan PPLS 2008, terdapat 13 indikator dalam penentuan kemiskinan yang diperoleh darihasil Survei Sosial Ekonomi Nasional yang laksanakan oleh Badan Pusat Statistik. Penelitian ini menggunakanmetode kombinasi Principal Component Analysis (PCA) dan Support Vector Machine (SVM) untukmengklasifikasikan kesejahteraan rumah tangga di Kabupaten Brebes tahun 2018 dengan kategori miskin. PCAdigunakan untuk mereduksi data dan data terbaru diproses menggunakan SVM untuk diklasifikasikan. Hasilklasifikasi kesejahteraan rumah tangga di Kabupaten Brebes tahun 2018 menggunakan PCA-SVM secarakeseluruhan lebih baik daripada menggunakan SVM.
ANALISIS SENTIMEN TERHADAP DAMPAK COVID-19 PADA PERFORMA TOKOPEDIA MENGGUNAKAN SUPPORT VECTOR MACHINE Wisudawati, Dinda Tri; Utami, Tiani Wahyu; Arum, Prizka Rismawati
Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 2 (2020)
Publisher : Program Studi Statistika, FMIPA, Universitas Negeri Makassar

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Abstract

Tokopedia merupakan e-commerce populer di Indonesia. Hal tersebut didukung dengan rating Tokopedia yang tinggi pada Google Play. Diperlukan sebuah metode yang mampu mengkategorikan reviews pengguna secara otomatis, apakah tergolong ke dalam klasifikasi positif atau negatif. Analisis Sentimen menggunakan Support Vector Machine (SVM) merupakan metode yang digunakan.Konsep SVM merupakan usaha mencari hyperplane terbaik yang berfungsi sebagai pemisah dua buah kelas pada input space dengan memaksimalkan jarak antar kelas. Sehingga SVM dapat menjamin kemampuan generalisasi yang tinggi untuk data-data yang akan datang. Klasifikasi menggunakan SVM pada periode sebelum munculnya Covid-19 di Indonesia (Februari 2020) menghasilkan akurasi sebesar 87% dan 84% pada periode sesudah munculnya Covid-19 (April 2020). Hasil menunjukkan bahwa walaupun Covid-19 muncul di Indonesia, performa Tokopedia masih tetap terjaga dan pengguna masih tetap memberikan penilaian suka sekali.Hal ini dibuktikan dengan penurunan jumlah review negatif dari 43% pada Februari 2020 menjadi 27% pada April 2020. Kata Kunci: Review, Google Play, Tokopedia, Support Vector Machine, Analisis Sentimen
ANALISIS SENTIMEN DALAM PENANGANAN COVID-19 DI INDONESIA MENGGUNAKAN NAIVE BAYES CLASSIFIER Yulianita, Tanti; Utami, Tiani Wahyu; Al Haris, M.
Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 2 (2020)
Publisher : Program Studi Statistika, FMIPA, Universitas Negeri Makassar

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Abstract

Kasus Coronavirus Disease (covid-19) di Indonesia telah berdampak dalam segala lapisan kehidupan. Salah satu kebijakan yang dilakukan oleh pemerintah menjadi sorotan di media sosial yaitu tentang adanya kebijakan Pembatasan Sosial Berskala Besar (PSBB). Banyaknya tanggapan masyarakat tentang kebijakan tersebut sangat beragam terutama di media sosial Twitter. Penelitian ini bertujuan mengetahui bagaimana sentimen masyarakat terhadap kebijakan PSBB melalui tanggapan di media sosial twitter. Data yang digunakan dengan rentang waktu April – Juni 2020. Data tersebut diklasifikasikan menggunakan algoritma Naïve Bayes Classifier. Hasil akurasi yang didapatkan dengan menggunakan Confussion Matrix untuk algoritma Naïve Bayes Classifier sebesar 89.13%. Sedangkan peluang kesalahan klasifikasi yang dihasilkan oleh kedua metode tersebut dengan menggunakan APER (Apparent Error Rate) dengan hasil Naïve Bayes Classifier sebesar 10.87%. Kata Kunci:Analisis Setimen,Covid-19, Naive Bayes Classifier, PSBB, Twitter
MODELING OF LOCAL POLYNOMIAL KERNEL NONPARAMETRIC REGRESSION FOR COVID DAILY CASES IN SEMARANG CITY, INDONESIA Utami, Tiani Wahyu; Lahdji, Aisyah
MEDIA STATISTIKA Vol 14, No 2 (2021): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.14.2.206-215

Abstract

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which was recently discovered. Coronavirus disease is now a pandemic that occurs in many countries in the world, one of which is Indonesia. One of the cities in Indonesia that has found many COVID cases is Semarang city, located in Central Java. Data on cases of COVID patients in Semarang City which are measured daily do not form a certain distribution pattern. We can build a model with a flexible statistical approach without any assumptions that must be used, namely the nonparametric regression. The nonparametric regression in this research using Local Polynomial Kernel approach. Determination of the polynomial order and optimal bandwidth in Local Polynomial Kernel Regression modeling use the GCV (Generalized Cross Validation) method. The data used this research are data on the number of COVID patients daily cases in Semarang, Indonesia. Based on the results of the application of the COVID patient daily cases in Semarang City, the optimal bandwidth value is 0.86 and the polynomial order is 4 with the minimum GCV is 3179.568 so that the model estimation results the MSE is 2922.22 and the determination coefficient is 97%. The estimation results show the highest number of Corona in the Semarang City at the beginning of July 2020. After the corona case increased in July, while the corona case in August decreased.
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

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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

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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

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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.
Co-Authors Abdul Rohman Agus Rusgiyono Aisyah Lahdji, Aisyah Al Haris, M. Alan Prahutama Alan Prahutama Alwan Fadlurohman Anissatush Sholiha Arianti, Irma Arini Rizky Wahyuningtyas Azqia Fajriyani Devi Nurlita Dewi Ratnasari Wijaya Dheanyta Alif Shafira Diana Wahyu Safitri Dwi Ispriyanti Endah Suryaningsih Fathikatul Arnanda Fatkhurokhman Fauzi Fatmawati Nurjanah Hanif Nur Ibrahim Hasbi Yasin Iffah Norma Hidayati Imaroh Izzatun Indah Manfaati Nur Indah Manfaati Nur Indra Firmansyah Ismawati - Juwita Rahayu Lia Miftakhul Janah M. Al Haris 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 Prizka Rismawati Arum Rizma Novinda Puteri Rochdi Wasono Rochdi Wasono Salmaa Fauziah Septi Winda Utami Silvia Tri Wahyuni Sri Kustiara Sudarno Sudarno Sugito Sugito Suparti Suparti Suparti Suparti Ujang Maulana Ujiati Suci Rahayu Vega Zayu Varima Wahyu Putri Pratiwii Wisudawati, Dinda Tri Yulianita, Tanti Yusnia Kriswanto