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All Journal Jurnal Matematika International Journal of Science and Engineering (IJSE) Media Statistika SAINS DAN MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Science and Technology E-Dimas: Jurnal Pengabdian kepada Masyarakat Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Riset Teknologi Pencegahan Pencemaran Industri STATISTIKA
Hasbi Yasin
Department Of Statistics, Faculty Of Sciences And Mathematics, Diponegoro University
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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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.
ANALISIS DATA INFLASI INDONESIA MENGGUNAKAN METODE FOURIER DAN WAVELET MULTISCALE AUTOREGRESIVE Suparti, Suparti; Santoso, Rukun; Prahutama, Alan; Yasin, Hasbi; Devi, Alvita Rachma
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 (884.754 KB)

Abstract

Analisis regresi merupakan metode statistika untuk mengetahui hubungan antara variabel prediktor dan variabel respon. Pendekatan regresi dapat dilakukan dengan  pendekatan parametrik dan nonparametrik. Pendekatan parametrik ketat dengan asumsi dan harus dipenuhi untuk mendapatkan model yang baik. Sementara pendekatan nonparametrik tidak ketat dengan asumsi karena metode tersebut didasarkan pada pendekatan kurva yang tidak diketahui bentuknya. Pendekatan nonparametrik dapat dilakukan dengan beberapa pendekatan diantaranya metode Fourier dan Wavelet. Metode Fourier merupakan metode yang didasarkan pada deret cosinus atau sinus. Metode Fourier sangat sesuai untuk data yang mengalami pola berulang atau stasioner. Sedangkan pada pemodelan wavelet tidak hanya terbatas pada data berulang atau stasioner saja, akan tetapi juga mampu memodelkan data yang tidak stasioner. Pada penelitian ini dimodelkan nilai Inflasi di Indonesia dari Januari 2007 sampai Agustus 2017.  Variabel responnya adalah nilai inflasi, sedangkan variabel prediktornya adalah waktu. Metode Fourier dengan K=100 menghasilkan MSE sebesar 0,846216 dan R2 sebesar 80,12%. Model Wavelet menggunakan Multiscale Autoregresive dengan filter Haar, J=4 dan Aj = 2  mempunyai MSE sebesar 0,312 dengan R2  sebesar  96,91%.  Pada model Fourier dengan K=100 diperlukan parameter sebanyak 102 buah sedangkan model wavelet dengan J=4 dan Aj = 2 hanya diperlukan parameter sebanyak 10 buah. Jadi model wavelet sangat efisien dengan kinerja yang lebih bagus dibandingkan dengan model Fourier. Kata Kunci: Inflasi, nonparametrik, Fourier, Wavelet, MSE
Klasifikasi Data Berat Bayi Lahir Menggunakan Weighted Probabilistic Neural Network (WPNN) (Studi Kasus di Rumah Sakit Islam Sultan Agung Semarang) Yasin, Hasbi; Ispriyansti, Dwi
MEDIA STATISTIKA Vol 10, No 1 (2017): 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 (759.328 KB) | DOI: 10.14710/medstat.10.1.61-70

Abstract

Low Birthweight (LBW) is one of the causes of infant mortality. Birthweight is the weight of babies who weighed within one hour after birth. Low birthweight has been defined by the World Health Organization (WHO) as weight at birth of less than 2,500 grams (5.5 pounds). There are several factors that influence the BWI such as maternal age, length of gestation, body weight, height, blood pressure, hemoglobin and parity. This study uses a Weighted Probabilistic Neural Network (WPNN) to classify the birthweight in RSI Sultan Agung Semarang based on these factors. The results showed that the birthweight classification using WPNN models have a very high accuracy. This is shown by the model accuracy of 98.75% using the training data and 94.44% using the testing data.Keywords:Birthweight, Classification, LBW, WPNN.
ANALISIS VARIABEL KANONIK BIPLOT UNTUK BANK UMUM DI JAWA TENGAH Yasin, Hasbi; Rusgiyono, Agus
MEDIA STATISTIKA Vol 6, No 2 (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 (525.945 KB) | DOI: 10.14710/medstat.6.2.71-80

Abstract

Bank Competition in Indonesia increase due to good economic growth and the improvement of the social middle class in Indonesia. Increased bank raises the fierce competition between banks and internal banks themselves. This makes the management of the bank should work seriously to maintain its existence. In this case the assessment of the bank become very important in the banking business to survive in today's banking industry. This study was conducted to determine the competitive commercial banks operating in Central Java with the Canonical Variate Analysis (CVA) Biplot. This analysis can be applied to find out information about the relative position, the similarity between the object characteristics and diversity of variables in the three groups of commercial banks in Central Java, namely state-owned banks, private banks and private banks Non Foreign Exchange, based on the health aspects of the bank. The results obtained are the banks in each group had different characteristics shown in the relative position of the already well-separated in the resulting biplot. Variables that tend to influence the grouping of commercial banks are Capital Adequacy Ratio (CAR). The total assets is variable with the highest level of prediction accuracy on each bank.   Keywords: Health Aspects of the Bank, Commercial Banks, Canonical Variate Analysis (CVA) Biplot.
ESTIMASI REGRESI NON PARAMETRIK DENGAN METODE WAVELET SHRINKAGE NEURAL NETWORK PADA MODEL RANCANGAN TETAP Yasin, Hasbi
MEDIA STATISTIKA Vol 2, No 1 (2009): 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 (371.536 KB) | DOI: 10.14710/medstat.2.1.1-10

Abstract

If X is a predictor variable and Y is a response  variable of following model Y = g(X) +e with function g is a regression which not yet been known and e is an independent random variable with mean 0 and variant . The function of g can be estimated by parametric and nonparametric approach. In this paper, g is estimated by nonparametric approach that is named wavelet shrinkage neural network  method. At this method, the smoothly function estimation is depending on shrinkage parameter’s that are threshold value and level of wavelet that be used. It also depending on the number of neuron in the hidden layer and the number of epoch that be used in feed forward neural network. Therefore, it is required to be select the optimal value of threshold, level of wavelet, the number of neuron and the number of epoch to determine optimal function estimation.   Keywords: Nonparametric Regression, Wavelet Shrinkage Neural Network
TIME SERIES ANALYSIS USING COPULA GAUSS AND AR(1)-N.GARCH(1,1) Caraka, Rezzy Eko; Yasin, Hasbi; Sugiarto, Wawan; Ismail, Kadi Mey
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 (786.146 KB) | DOI: 10.14710/medstat.9.1.1-13

Abstract

In this case, the Gaussian Copula is used to connect the data that correlates with the time and with other data sets. Most often, practitioners rely only on the linear correlation to describe the degree of dependence between two or more variables; an approach that can lead to quite misleading conclusions as this measure is only capable of capturing linear relationships. Correlation doesn’t mean causation, prediction using Copula is built on three things that the marginal distribution function, the kernel function, and the function of the Copula. Gaussian Copula involves the covariance matrix are approximated by using kernel functions. Kernel acts as the correlation between the approach of the data values that have the same characteristics. In this case, the characteristics used is the time. The advantage of the kernel function is able to calculate the correlation between random variables that have a realization using data characteristics. The advantage of using the kernel based Copula able to capture the dependencies between data and process data that have the same characteristics with time. Another benefit is that it allows a sequence of random variables have a joint distribution function so that the conditional probability of the prediction can be calculated. Keywords: Binding, Copula, GARCH, Gauss, Time Series
PEMETAAN PENYAKIT DEMAM BERDARAH DENGUE DENGAN ANALISIS POLA SPASIAL DI KABUPATEN PEKALONGAN Yasin, Hasbi; Saputra, Ragil
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 (560.845 KB) | DOI: 10.14710/medstat.6.1.27-36

Abstract

The number of dengue haemorrhagic fever (DHF) incidence in Pekalongan from year to year is very volatile. In 2006, there was 352 cases, 718 cases occurred in 2007, 2008 saw 403 cases, 2009 there were 753 cases, whereas in 2010 a decline to 223 cases. This is possible due to the lack of information about the place, time and location of the incident spread of dengue in Pekalongan. Various efforts have been made to address these issues both society and government but the incidence of this disease has not been effectively suppressed. The results of data analysis showed that the incidence of dengue in Pekalongan mostly occurs during the rainy season is the period from January to June. The DHF incidence tends to be higher in Kedungwuni. Highest incidence of DHF occurred in April 2010. In addition, there are some months that indicate the spatial relationships in the incidence of dengue in Pekalongan, ie January, February, July, October and December. The sub-district that has a positive autocorrelation is  Kedungwuni, Wonopringgo, and Tirto. While the sub-district has a negative autocorrelation is Karangdadap. Most of the sub-districts in Pekalongan status is still endemic for dengue.   Keywords: DHF, Moran’s Index, Spatial Pattern
PEMODELAN VOLATILITAS UNTUK PENGHITUNGAN VALUE AT RISK (VaR) MENGGUNAKAN FEED FORWARD NEURAL NETWORK DAN ALGORITMA GENETIKA Yasin, Hasbi; Suparti, Suparti
MEDIA STATISTIKA Vol 7, No 2 (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 (580.333 KB) | DOI: 10.14710/medstat.7.2.53-61

Abstract

High fluctuations in stock returns is one problem that is considered by the investors. Therefore we need a model that is able to predict accurately the volatility of stock returns. One model that can be used is a model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). This model can serve as a model input in the model Feed Forward Neural Network (FFNN) with Genetic Algorithms as a training algorithm, known as GA-Neuro-GARCH. This modeling is one of the alternatives in modeling the volatility of stock returns. This method is able to show a good performance in modeling the volatility of stock returns. The purpose of this study was to determine the stock return volatility models using a model GA-Neuro-GARCH on stock price data of PT. Indofood Sukses Makmur Tbk. The result shows that the determination of the input variables based on the ARIMA (1,0,1) -GARCH (1,1), so that the model used FFNN consists of 2 units of neurons in the input layer, 5 units of neurons in the hidden layer neuron layer and 1 unit in the output layer. then using a genetic algorithm with crossover probability value of 0.4, was obtained that the Mean Absolute Percentage Error (MAPE) of 0,0039%. Keywords: FFNN, Genetic Algorithm, GARCH, Volatility
PEMILIHAN VARIABEL PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION Yasin, Hasbi
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 (439.595 KB) | DOI: 10.14710/medstat.4.2.63-72

Abstract

Regression analysis is a statistical analysis that aims to model the relationship between response variable with some predictor variables. Geographically Weighted Regression (GWR) is statistical method used for analyzed the spatial data in local form of regression. One of the problems in GWR is how to choose the significant variables. The number of predictor variables will allow the violation of assumptions about the absence of multicollinearity in the data. Therefore, this needs a method to reduce some of the predictor variables which not significant to the response variable. This paper will discuss how to select significant variables by stepwise method. This method is a combination of forward selection method and the backward elimination method. Keywords:   Geographically Weighted Regression, Backward Elimination, Forward Selection, Stepwise Method
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.
Co-Authors Abdul Hoyyi Achmad Choiruddin Adi Waridi Basyiruddin Adi Waridi Basyirudin Arifin Agus Rusgiyono Ajeng Arum Sari Alan Prahutama Alvita Rachma Devi Amanda Lucky Berlian Andreanto Andreanto Anggun Perdana Aji Pangesti Arief Rachman Hakim Arief Rachman Hakim Baluk, Andreas Pedo Bens Pardamean Budi Warsito Budi Warsito Danang Chandra Pradana, Danang Chandra Dani Al Mahkya Darwanto Darwanto Devi Wijayanti Dewi Setya Kusumawardani Dharmawan, Bagus Dwiky Dhea Kurnia Mubyarjati Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diah Safitri Dwi Hasti Ratnasari Dwi Ispriyanti Eko Siswanto Fadhilla Atansa Tamardina Felinda Arumningtyas Fiqria Devi Ariyani Gera Rozalia Hanien Nia H Shega Hari Susanta Nugraha Hidayatul Musyarofah Hindun Habibatul Mubaroroh Ika Chandra Nurhayati Inas Hasimah Inayati, Syarifah Indah Suryani Indri Puspita Sari Innosensia Adella Intan Monica Hanmastiana Isna Wulandari Isna Wulandari Ispriyansti, Dwi Jody Hendrian Johanes Roisa Prabowo Kadi Mey Ismail Kurniawan, Isma Dwi Lutfia Septiningrum Maghfiroh Hadadiah Mukrom Maria Odelia Mas'ad, Mas'ad Maulana Taufan Permana Mega Fitria Andriyani Meilia Kusumawardani, Meilia Moch. Abdul Mukid Mochammad Iffan Zulfiandri MUHAMMAD HARIS Muhammad Mujahid Muhammad Tahmid Muryanto Muryanto Muryanto, Muryanto Mustafid Mustafid Mutiara, Dinar Nova Delvia Nur Azizah Nur Indah Yuli Astuti, Nur Indah Yuli Pandu Anggara Purhadi Purhadi Puspita Kartikasari Ragil Saputra Rahmasari Nur Azizah Reza Dwi Fitriani Rezzy Eko Caraka Riama Oktaviani Samosir, Riama Oktaviani Rifki Adi Pamungkas, Rifki Adi Rina Br Siahaan Rita Rahmawati Rita Rahmawati Riza Fahlevi Rizki Brendita Br Tarigan Rose Debora Julianisa, Rose Debora Rukun Santoso Rung Ching Chen Saepudin, Yunus Sakhinah Abu Bakar Salma Farah Aliyah Sari, Ajeng Arum Satriyo Adhy Setiawan Setiawan Setyoko Prismanu Ramadhan Siska Alvitiani Siti Maulina Meutuah Sri Endah Moelya Artha Sudarno Sudarno Sudarno Sudarno Sugito Sugito - Sugito Sugito Suhartono Suhartono Suparti Suparti Tarno Tarno Tarno Tarno Tatik Widiharih Tiani Wahyu Utami Tsania Faizia Ubudia Hiliaily Chairunnnisa Via Risqiyanti Wahyu Sabtika Wawan Sugiarto, Wawan Wulandari, Heni Dwi Youngjo Lee Yuciana Wilandari Yudha Subakti, Yudha Zulfa Wahyu Mardika, Zulfa Wahyu