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All Journal DIKSI Media Statistika MATEMATIKA Prosiding Seminar Biologi Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Pendidikan Dasar Nusantara Majalah Kulit, Karet, dan Plastik Jurnal Spektra Bioeksperimen: Jurnal Penelitian Biologi E-Dimas: Jurnal Pengabdian kepada Masyarakat Catharsis JURNAL KEPEMIMPINAN DAN PENGURUSAN SEKOLAH Indonesian Journal of Applied Statistics Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Pendidikan Terbuka Dan Jarak Jauh Jurnal Basicedu JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOURNAL OF ENGLISH FOR ACADEMIC JIKAP PGSD: Jurnal Ilmiah Ilmu Kependidikan International Journal for Educational and Vocational Studies JURNAL PENDIDIKAN MIPA Dinasti International Journal of Education Management and Social Science Jurnal Graha Pengabdian Indonesian Journal of Instructional Media and Model Epistema Journal of Education Research Jurnal Ilmiah Mahasiswa Manajemen, Bisnis dan Akuntansi Journal of Advanced Sciences and Mathematics Education Innovation Business Management and Accounting Journal Jurnal Basicedu JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Jurnal Edukasi Pengabdian Masyarakat: EDUABDIMAS SERIBU SUNGAI: Journal of Research and Community Service Arty: Jurnal Seni Rupa Pinisi Journal Pendidikan Guru Sekolah Dasar Jurnal Seni Tari
Suparti Suparti
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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PRODUKTIVITAS JAMUR TIRAM PUTIH (Pleurotus ostreatus) PADA MEDIA LIMBAH SEKAM PADI DAN DAUN PISANG KERING SEBAGAI MEDIA ALTERNATIF Suparti, Suparti; Marfuah, Lismiyati
Bioeksperimen: Jurnal Penelitian Biologi Vol 1, No 2: September 2015
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/bioeksperimen.v1i2.876

Abstract

Jamur tiram putih (Pleurotus ostreatus) merupakan jenis jamur pangan yang banyak dikonsumsi mengandung protein 27%. Kandungan protein pada jamur tiram putih dapat dipengaruhi oleh komposisi media tanam seperti selulosa, hemiselulosa, lignin dan nutrisi tambahan. Sekam padi dan daun pisang kering merupakan salah satu limbah organik yang dapat digunakan sebagai media alternatif untuk meningkatkan produktivitas jamur tiram putih.Tujuan penelitian ini adalah untuk mengetahui produktivitas jamur tiram putih yang ditumbuhkan pada media  limbah sekam padi dan daun pisang kering sebagai media alternatif. Jenis penelitian eksperimen dengan metode rancangan acak lengkap (RAL) dua faktor yaitu faktor 1 penambahan sekam padi dan faktor 2 daun pisang kering (0%, 5%, 10%, 15%), masing-masing  dengan empat perlakuan dan dua kali ulangan.  Hasil analisis data menunjukkan bahwa penambahan sekam padi dan daun pisang kering  15% (S3T3) memberikan pengaruh nyata terhadap lama penyebaran miselium, jumlah badan buah dan berat segar jamur tiram putih.Perlakuan yang paling baik untuk pertumbuhan jamur pada perlakuan S3T3, dengan rata-rata lama penyebaran miselium 25,5 hari, jumlah badan buah 64,5 buah dan berat segar yang dihasilkan 402,5. Hasil data tersebut lebih tinggi dibandingkan dengan perlakuan yang lain.
BIPLOT UNTUK MENGETAHUI KARAKTERISTIK KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN PRODUKSI BAWANG PUTIH, BAWANG MERAH, CABE BESAR DAN CABE RAWIT Safitri, Diah; Suparti, Suparti; Pratiwi, Esti; Estiningrum, Tyas
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 (124.049 KB) | DOI: 10.14710/medstat.7.1.47-52

Abstract

Biplot is a graphical representation of a data matrix. Garlic, onions, chili, and thai pepper are important plant in Indonesia because most people in Indonesia especially in Central Java consume garlic, onions, chili, and thai pepper every day. In this research, districts in Central Java seen characteristics are based on the productions of garlic, onions, chili, and thai pepper using biplot. There are highly correlation between chili and thai pepper, which means districts that have highly productions of chili will also tend to have highly production of thai pepper. There are some districts have the production of  garlic, onions, chili, and thai pepper relatively low, and there are some of the city has zero production of  garlic, onions, chili, and thai pepper.   Keywords: Biplot, Production of  garlic, onions, chili, thai pepper
PEMILIHAN THRESHOLD OPTIMAL PADA ESTIMATOR REGRESI WAVELET THRESHOLDING DENGAN METODE CROSS VALIDASI Suparti, Suparti; Tarno, Tarno; Hapsari, Paula Meilina Dwi
MEDIA STATISTIKA Vol 2, No 2 (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 (341.567 KB) | DOI: 10.14710/medstat.2.2.56-69

Abstract

If x is a predictor variable and y is a response  variable of  the regression model y = f (x)+ Î with  f is a regression function which not yet been known and Î is independent random variable with mean 0 and variance , hence function f can be estimated by parametric and nonparametric approach. In this paper function f is estimated with a nonparametric approach. Nonparametric approach that used is a wavelet shrinkage or a wavelet threshold method. In the function estimation with a wavelet threshold method,  the value of  threshold has  the most important role to determine  level of smoothing estimator. The small threshold give function estimation very no smoothly, while  the big value of threshold give function estimation very smoothly. Therefore the optimal value of threshold should be selected to determine the optimal function estimation. One of the methods to determine the optimal value of threshold by minimize a cross validation function. The cross validation method that be used is two-fold cross validatiaon. In this cross validation, it compute the predicted value by using a half of data set. The original data set is split  into two subsets of equal size : one containing only the even indexed data, and the other, the odd indexed data. The odd data will be used to predict the even data, and vice versa. Based on  the result of data analysis, the optimal threshold with cross validation method is not uniq, but they give the  uniq of wavelet thersholding regression estimation.   Keywords : Nonparametric Regression, Wavelet Threshold Estimator, Cross Validation.
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 PARAMETER THRESHOLD OPTIMAL DALAM ESTIMATOR REGRESI WAVELET THRESHOLDING DENGAN PROSEDUR FALSE DISCOVERY RATE (FDR) Suparti, Suparti; Tarno, Tarno; Haryono, Yon
MEDIA STATISTIKA Vol 1, No 1 (2008): 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 (235.567 KB) | DOI: 10.14710/medstat.1.1.1-9

Abstract

If X is predictor variable and Y is response  variable of following model Y = f (X) +e with function f is regression which not yet been known and e is independent random variable with mean 0 and variant , hence function of f can estimate with parametric and nonparametric approach. At this paper estimate f with nonparametric approach. Nonparametric approach that used is wavelet shrinkage or wavelet thresholding method. At function estimation with method of wavelet thresholding, what most dominant determine level of smoothing estimator is value of threshold. The small threshold give function estimation very no smoothly, while  the big value of threshold give function estimation very smoothly. Therefore require to be selected value of optimal threshold to determine optimal function estimation.               One of the method to determine the value of optimal threshold is with procedure of False Discovery Rate ( FDR). In procedure of FDR, the optimal threshold determined by selection of level of significance. Smaller mount used significance progressively smoothly its .   Keywords: Nonparametric regression, wavelet thresholding estimator, procedure of False Discovery Rate
ANALISIS DATA INFLASI DI INDONESIA PASCA KENAIKAN TDL DAN BBM TAHUN 2013 MENGGUNAKAN MODEL REGRESI KERNEL Suparti, Suparti
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 (314.708 KB) | DOI: 10.14710/medstat.6.2.91-101

Abstract

The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This study aims to analyze inflation in Indonesia after the goverment raised the price of electricity basic and fuel price in 2013 using kernel regression models. This method was good for data modeling inflation in Indonesia before. The goodness of a kernel regression model is determined by the chosen kernel function and wide bandwidth used. However, the most dominant is the selection of the wide bandwidth. In this study, determination of the optimal bandwidth by minimizing the Generalized Cross Validation (GCV). By model the annual inflation data (Indonesia) December 2006 - December 2011, the inflation target in 2012 is (4,5 + 1 )% can be achieved both exactly and predictly, while the inflation target in 2013 is (4,5 + 1 )% cannot be achieved neither exactly nor predictly. The inflation target in 2013 can’t be achieve because since the beginning of 2013, there was a government policy to raise the price of electricity and the middle of 2013, there was an increase in fuel prices. The prediction of Indonesia inflation in 2014 by Gauss kernel is 6,18%. Keywords: Inflation, Kernel Regression Models, Generalized Cross Validation
ANALISIS DATA INFLASI DI INDONESIA MENGGUNAKAN MODEL REGRESI SPLINE Suparti, Suparti
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 (651.863 KB) | DOI: 10.14710/medstat.6.1.1-9

Abstract

The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. The developed model of parametric to cope with the volatility of the data is the ARCH and GARCH models. This alternative parametric models still requires the normality assumption in the data that often cannot be satisfied by financial data. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This research aims to conduct a study in Indonesia inflation data modeling using nonparametric methods is spline regression model with truncated spline bases. Goodness of a spline regression model is determined by an orde and knots location . However, the knots location are more dominant in spline regression model. One way to get the optimal knots location are by minimizing the value of Generalized Cross Validation (GCV). By modeling the annual inflation data of Indonesia in December 2006 - December 2011, the inflation target in 2012 is 4.5% + 1% can be achieved while the inflation target in 2013 is 4.5% + 1% cannot be achieved, because that prediction in 2013 is 8.55%. It was caused by government policy to raise the price of basic electricity and the fuel prices in 2013. Keywords : Inflation, Spline Regression Model, Generalized Cross Validation.
ANALISIS DATA INFLASI INDONESIA MENGGUNAKAN MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DENGAN PENAMBAHAN OUTLIER Suparti, Suparti; Sa'adah, Alfi Faridatus
MEDIA STATISTIKA Vol 8, No 1 (2015): 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 (553.653 KB) | DOI: 10.14710/medstat.8.1.1-11

Abstract

The inflation data is one of the financial time series data which often has high volatility. It is caused by the presence of outliers in the data. Therefore, it is necessary to analyze forecasting that can make all the assumptions are fulled without having to ignore the presence of outliers. The aim of this study is analyzing the inflation data in Indonesia using ARIMA model with the outlier detection. By modeling annual inflation data in December 2006 to December 2013 there are two types of outlier that are additive outlier (AO) and level shift (LS) outlier. The results show that The ARIMA model with the addition of outlier are better than the ARIMA model without outlier. The ARIMA ([1.12], 1.0) model with the addition of 19 outliers meet to the all assumptions that are the significance parameters, normality, homoscedasticity, and independence of residuals as well as the smallest MSE value. Keywords: Inflation, ARIMA, Outlier, MSE
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI BANYAKNYA KLAIM ASURANSI KENDARAAAN BERMOTOR MENGGUNAKAN MODEL REGRESI ZERO-INFLATED POISSON (Studi Kasus di PT. Asuransi Sinar Mas Cabang Semarang Tahun 2010) Taufan, Muhammad; Suparti, Suparti; Rusgiyono, Agus
MEDIA STATISTIKA Vol 5, No 1 (2012): 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 (569.023 KB) | DOI: 10.14710/medstat.5.1.49-62

Abstract

Poisson regression is one of model that is often used to model the relationship between response variables in the form of discrete data with a set of predictor variables in the form of continuous, discrete, category, or mixture data. In Poisson regression assumes that the mean of the response variable equal to the variance (equidispersion). But in reality, sometimes found a condition called overdispersion, that the variance value is greater than the mean. One of the cause of overdispersion is excess zero in the response variable. One of model that can be used to overcome this overdispersion problem is Zero-Inflated Poisson (ZIP) regression  model. This model is applied on a case study of motor vehicle insurance in the branch of PT. Asuransi Sinar Mas in Semarang in 2010 to determine the effect of age of car and types of coverage to number of claims filed by the policyholder to the branch of PT. Asuransi Sinar Mas in Semarang. In this case, the occurrence of zeros due to many policyholders did not file a claim to the branch of PT. Asuransi Sinar Mas in Semarang. From the analytical result obtained the conclution that the age of car and types of coverage affect number of claims filed by the policyholder to the branch of PT. Asuransi Sinar Mas in Semarang in 2010.   Keywords: Poisson Regression, Overdispersion, Zero-Inflated Poisson (ZIP) Regression
PEMODELAN REGRESI NONPARAMETRIK MENGGUNAKAN PENDEKATAN POLINOMIAL LOKAL PADA BEBAN LISTRIK DI KOTA SEMARANG Suparti, Suparti; 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 (150.91 KB) | DOI: 10.14710/medstat.9.2.85-93

Abstract

Semarang is the provincial capital of Central Java, with infrastructure and economic’s growth was high. The phenomenon of power outages that occurred in Semarang, certainly disrupted economic development in Semarang. Large electrical energy consumed by industrial-scale consumers and households in the San Francisco area, monitored or recorded automatically and presented into a historical data load power consumption. Therefore, this study modeling the load power consumption at a time when not influenced by the use of electrical load (t-1)-th. Modeling using nonparametric regression approach with Local polynomial. In this study, the kernel used is a Gaussian kernel. In local polynomial modeling, determined optimum bandwidth. One of the optimum bandwidth determination using the Generalized Cross Validation (GCV). GCV values obtained amounted to 1425.726 with a minimum bandwidth of 394. Modelling generate local polynomial of order 2 with MSE value of 1408.672. Keywords: electrical load, local polinomial, gaussian kernel, GCV.
Co-Authors A. Sulaksono, A. A.A. Ketut Agung Cahyawan W Abdul Hoyyi Adhytia, Rizkyhimawan Agus Cahyono Agus Rusgiyono Agus Triyono Akbari, Windusiwi Asih Alan Prahutama Alanindra Saputra Alvita Rachma Devi Amanda Devi Paramitha Aminah Asngad Ananda, Refisa Angelia, Yuni Any Setyaningsih, Any Arianti Suhartini Arieanti, Dian Dinarafika Arief Rachman Hakim Arief Rachman Hakim Arnisa Melani Kahar Asismarta Asismarta, Asismarta AYU LESTARI Azizah, Adilla Nur Badriyah, Ratu Bahtiar Ilham Triyunanto Brillianing Pratiwi Budi Warsito C Yuwono Sumasto, C Yuwono Deden Aditya Nanda, Deden Aditya Dewi, Anggra Lita Sandra Dewi, P A R Dhea Dewanti Di Asih I Maruddani Diah Safitri Dwi Ispriyanti Dwi Sambada Dwi Wahyuningsih, Dwi Dwikoranto Eka Anisha Eka Destiyani Eka Fadilah Eka Wijayanti Erna Sulistianingsih Ernik Yuliana Esti Pratiwi Evelyna, Feby Fadilah, Eka Fitri Juniaty Simatupang, Fitri Juniaty Gina Wangsih Hanifah Nur Aini Happy Suci Puspitasari Hasbi Yasin Ihdayani Banun Afa Immawati Ainun Habibah Intaniasari, Yossinta Iut Tri Utami Iut Triutami Izzudin Khalid, Izzudin Jefferio Gusti Putratama Jody Hendrian Juwanda, Farikhin Karwanto, Karwanto Khansa Amalia Fitroh Khansa, I H Khoirunnisa, Siti Intan Khulaifiyah, Khulaifiyah Lamik Nabil Mu'affa Lanjari , Restu Lintangesukmanjaya, R T Lismiyati Marfuah, Lismiyati Lulu Maulatus Saidah Lulus Darwati, Lulus M. Noris Maman Suryaman Meiliawati Aniska Milawati Milawati Moch. Abdul Mukid Mokhamad Nurjam'i MUHAMAD SHOLEH Muhammad Sulaiman Muhammad Taufan Muqorobin, Masculine Muhammad Mustafid Mustafid Mustofa, Achmad Nastiti, Tri Dyah Netriwati Noer Rachma, Gustyas Zella Nunuk Hariyati Nurina Salma Alfiyyah Nurul Fitria Fitria Rizani Ovie Auliya’atul Faizah Paula Meilina Dwi Hapsari Prastya, Agus Puspita Kartikasari Putra, D A Rahma Dewi Hartati Rahman Kosasih, Fauzy Rahman, Syair Dafiq Faizur Rahmawati Patta, Rahmawati Rahyu Setiani Rambat Rambat, Rambat Riana Ayu Andam Pradewi Richy Priyambodo Rismawati Rismawati Rita Rahmawati Rudi Saputro Setyo Purnomo Rukun Santoso Sa'adah, Alfi Faridatus Sadjati, Ida Malati Safitri, Wardani Ana Salma Farah Aliyah Salsabila Rizkia Gusman Sania Anisa Farah Sanitoria Nadeak, Sanitoria Setiawan, Fuad Alfaridzi Setyoko Prismanu Ramadhan Setyowati, Titik Sholihah, Zaimatu Silvia Elsa Suryana Silvia Nur Rinjani Singgih Subiyantoro Siti Fadhilla Femadiyanti Sofiana Sofiana Sola Fide Sri Budiasih, Sri Sri Sumiyati Sri Wahyuni Sri Wahyuningrum Sudargo Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Sunardi Sunardi Supeno Supratmi, Nunung Surasmi, W A Surasmi, Wuwuh Asrining Syafruddin Syafruddin Syafruddin*, Syafruddin syah, naziah Syazwina Aufa Syiva Multi Fani T. Mart, T. Tarno Tarno Tarno Tarno Tatik Widiharih Tiani Wahyu Utami Triastuti Wuryandari Tyas Estiningrum Ul Haq, Hasna Faridah Dhiya Vera Handayani Victoria Dwi Murti WAHYU SUKARTININGSIH Wahyu Tiara Rosaamalia Widari Widari, Widari Yasir Sidiq YATIM RIYANTO Yon Haryono Yunianika, Ika Tri Yupitasari, Yupitasari Yusak, Suharno Zein, Secondta Habib Syarifah Zia, Nabila Ghaida Zubaidah, Lailia