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All Journal Media Statistika MATEMATIKA Jurnal Gaussian
Triastuti Wuryandari
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Decision Sciences, Operations Research & Management Mathematics Other

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PEMILIHAN ARSITEKTUR OPTIMAL MODEL NN DENGAN METODE KONTRIBUSI INCREMENT Wuryandari, Triastuti
MATEMATIKA Vol 9, No 3 (2006): JURNAL MATEMATIKA
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (175.6 KB)

Abstract

Neural Network is an information processing system that has certain characteristic in common with biological neural network. In development NN has been many applied in several surface, one of them is for forecasting. For the best application of NN, architecture has determined. One of methode to get optimal architecture NN is incremental contribution methods. This methods will to determine the size of hidden and input cell in the network with excluding respectively. One of the unit cell with a low incremental contribution will be exclution from network. The result shows that the incremental contribution methods is capable reducing the size of the network is propozed, so getting optimal architecture from network.
METODE UNWEIGHTED MEANS UNTUK FAKTORIAL TAK SEIMBANG DISPROPORSIONAL WURYANDARI, TRIASTUTI
MATEMATIKA Vol 10, No 1 (2007): JURNAL MATEMATIKA
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (62.155 KB)

Abstract

A factorial design should be used when there are several factors of interest in experiment. The problem which arises when the factorial experiment does not contain equal sized samples in the cells is that the design is nonorthogonal. In other words, the total sums of squares in the ANOVA table cannot be decomposed into a series of additive components which permit the analysis of the separate effect. There are possible situations when the cell sample sized are unequal, allocated proportionately or disproportionately. On the allocated disproportionately, approximate F test using the method of unweighted means.  
PENGAMBILAN SAMPEL BERDASARKAN PERINGKAT PADA ANALISIS REGRESI LINIER SEDERHANA Wijayanti, Pritha Sekar; Ispriyanti, Dwi; Wuryandari, Triastuti
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (625.919 KB) | DOI: 10.14710/j.gauss.v2i3.3666

Abstract

Ranked Set Sampling and Ranked Set Sampling concomitant are more efficient than Simple Random Sampling. This can be determined by calculating the Relative Precision which is a ratio value from the variance of the mean from each sampling technique. From the research of Ranked Set Sampling, obtained ,  and  so Ranked Set Sampling is more efficient than Simple Random Sampling. For the research of Ranked Set Sampling concomitant, obtained ,  and  so Ranked Set Sampling concomitant is more efficient than Simple Random Sampling, and for simple linear regression analysis obtained , , ,  so simple linear regression model of Ranked Set Sampling is more efficient than simple linear regression model of Simple Random Sampling
ANALISIS ANTRIAN PENGUNJUNG DAN KINERJA SISTEM DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL KOTA SEMARANG Astrelita, Fahra Pracendi; Sugito, Sugito; Wuryandari, Triastuti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (486.7 KB) | DOI: 10.14710/j.gauss.v4i4.10138

Abstract

Department of Population and Civil Registration (Dispendukcapil) has the duty of assistance in the field of population and civil registration. Civil registration services such as services related to birth, death, marriage, and divorce. As a service provider, Dispendukcapil of Semarang has the motto "No Day Without Service Quality Improvement". Queuing problem is that often occur and must be considered. The queue situation occurs because the number of visitors to a service facility exceeds the available capacity to perform such services. A system is always trying to serve visitors well in accordance with the rate of arrival of each visitor. Therefore please note the size of the system's performance on each section on service system. Dispendukcapil queuing system at Semarang city located on the Legalized, Change Data, Birth, Death, Divorce/Marriage, and Decision Act. Based on the results obtained and the analysis of models of queuing at the counter is Legalized (G/G/2):(GD/∞/∞), while the counter is Birth (G/G/3):(GD/∞/∞), the Change the counter Data, Death, Divorce / Marriage is (M/G/1):(GD/∞/∞) and Decision Deed is (G/G/1):(GD/∞/∞).  Keywords: Queuing System, Dispendukcapil, Dispendukcapil of Semarang, Legalized, Birth, Death, Divorce, Marriage.    
ANALISIS KUALITAS PELAYANAN DENGAN MENGGUNAKAN FUZZY SERVQUAL, KUADRAN IPA, DAN INDEKS PGCV Rosyidah, Hanik; Wuryandari, Triastuti; Rusgiyono, Agus
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.578 KB) | DOI: 10.14710/j.gauss.v4i4.10223

Abstract

Quality of service (service quality) require attention in the field of service. A service is considered and perceived as good if it can meet the customer’s requirement and expectation. This study aims to determine the suitability and student’s expectation of existing services and to determine which services should be prioritized to be improved . The method used is the Servqual gap scores to determine the level of customer satisfaction or provided service based on expectations and performance. The Importance-Performance Analysis’s method and Potential Gain Customer Value (PGCV) to determine the priority of criteria of the service that must be improved. Servqual calculation results indicate a mismatch between perceptions and student’s expectation which is -0,0724. By using IPA quadrant shows that the main indicators for priority services is an indicator of the school environment’s cleanliness. PGCV shows that there are nine indicators of service which becomes the next priorities. Keywords : Service quality, IPA, PGCV, satisfaction, expectation, performance
KLASIFIKASI RUMAH LAYAK HUNI DI KABUPATEN BREBES DENGAN MENGGUNAKAN METODE LEARNING VECTOR QUANTIZATION DAN NAIVE BAYES Simatupang, Fitri Juniaty; Wuryandari, Triastuti; Suparti, Suparti
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (659.428 KB) | DOI: 10.14710/j.gauss.v5i1.11033

Abstract

House is a very basic need for everyone besides food and clothing. House can reflect the level of welfare and the level of health of its inhabitants. The advisability of a house as a good shelter can be seen from the structure and facilities of buildings.  This research aims to analyze the classification of livable housing and determine the criteria of houses uninhabitable. The statistical method used are the Learning Vector Quantization and Naive Bayes. The data used in this final project are data of Survei Sosial Ekonomi Nasional (Susenas) Kor Keterangan Perumahan in 2014 Quarter 1 district of Kabupaten Brebes. In this research, the data divided into training data and testing data with the proportion that gives the highest accurate is 95% for training data and 5% for testing data. Training data will be used to generate the model and pattern formation, while testing data used to evaluate how accurate the model or pattern formed in classifying data through confusion tables. The results of analysis showed that the Learning Vector Quantization method gives 71,43% of classification accuracy, while Naive Bayes method gives 95,24% of classification accuracy. The Naive Bayes method has better classification accuracy than the Learning Vector Quantization method.Keywords: House, Learning Vector Quantization, Naive Bayes, Classification
PERBANDINGAN ANALISIS DISKRIMINAN FISHER DAN NAIVE BAYES UNTUK KLASIFIKASI RISIKO KREDIT (Studi Kasus Debitur di Koperasi Jateng Amanah Mandiri Cabang Sukorejo Kendal) Abdur Rofiq; Triastuti Wuryandari; Rita Rahmawati
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (963.688 KB) | DOI: 10.14710/j.gauss.v5i1.10907

Abstract

Credit is a form of money lending to debitors conducted by financial institutions such as cooperatives. In practice there are obstacles in the form of bad credit. Analyze by Fisher discriminant analysis method and Naive Bayes is used to classify the debitors fall into the category bad debitorr or not. This study uses data from  the Debitors of Cooperative of Central Java Amanah Independent in Sukorejo Kendal Branch. The data obtained is used for classification by Fisher discriminant analysis and Naive Bayes method. Data obtained has  multivariate normal distribution, has the same of variance-covariance matrix and has metric scale. Fisher discriminant analysis and Naive Bayes calculated and compared to the level of accuracy. From this research, the degree of accuracy of each method, namely 90% for Fisher Discriminant Analysis and 83.33% for the Naive Bayes. Having tested using the proportion test, Fisher discriminant analysis method is no different accuracy when compared with Naive Bayes to classify credit risk. Keywords: debitors, credit risk, Fisher discriminant analysis, Naive Bayes.
PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHEN UNTUK PERAMALAN HARGA PALADIUM Anes Desduana Selasakmida; Tarno Tarno; Triastuti Wuryandari
Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i3.32782

Abstract

Palladium is one of the precious metal commodities with the best performance since 3 years ago. Palladium has many benefits, including being used in the electronics, medical, jewelry and chemical industries. The benefits of palladium in the chemical field are that it can help speed up chemical reactions, filter out toxic gases in exhaust gases, and convert the gas into safer substances, so palladium is usually used as a catalyst for cars. Forecasting is a process of processing past data and projected for future interest using several mathematical models. The model used in this study is the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods. The process of forecasting palladium prices using monthly data from January 2011 to December 2020 with the Double Exponential Smoothing Holt method and the Fuzzy Time Series Chen method will be carried out in this study to describe the performance of the two methods. Based on the results of the analysis, it can be concluded that the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods have equally good performance with sMAPE values of 6.21% for Double Exponential Smoothing Holt and 9.554% for Fuzzy Time Series Chen. Forecasting for the next 3 periods using these two methods generally produces forecasting values that are close to the actual data. 
PENERAPAN ANALISIS KLASTER METODE WARD TERHADAP KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN PENGGUNA ALAT KONTRASEPSI Yogi Isna Hartanto; Agus Rusgiyono; Triastuti Wuryandari
Jurnal Gaussian Vol 6, No 4 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v6i4.30387

Abstract

The cluster analysis of the Ward method is a cluster forming method based on minimizing the loss of information due to the incorporation of objects into clusters. An Error Sum of Square (ESS) is used as an objective function. Two objects will be combined if they have the smallest objective function among possibilities. The similarity measure used is the Euclidean distance. In this experiment used data from the number of users of contraceptives in Central Java Province. Contraceptives that can be detected its use is IUD, MOW, MOP, condoms, implants, injections, and pills. The results of cluster analysis of Ward method were obtained as many as 3 clusters. First cluster consists of 9 districts/cities with the number of use of most contraceptives, namely Cilacap, Banyumas, Pati, Pemalang, Tegal, Jepara, Grobogan, Demak, and Semarang City. Second cluster consists of 21 districts/cities with the number of use of medium contraceptives, namely Purbalingga, Banjarnegara, Kendal, Wonogiri, Pekalongan, Blora, Brebes, Kebumen, Wonososbo, Boyolali, Karanganyar, Sragen, Magelang, Klaten, Semarang, Purworejo, Temanggung , Sukorejo, Rembang, Batang, and Kudus. Third cluster consists of 5 districts/cities with the number of use of contraceptives a little, namely Magelang City, Salatiga City, Surakarta City, Pekalongan City, and Tegal City. Keywords: Contraceptives, Cluster Analysis, Ward Methods, Euclidean Distance
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT KEMISKINAN DENGAN METODE REGRESI PROBIT ORDINAL (Studi Kasus Kabupaten/ Kota di Jawa Tengah Tahun 2013) Alin Citra Suardi; Triastuti Wuryandari; Sugito Sugito
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v5i1.10908

Abstract

According to BPS, Central Java is third in terms of the number of poor people in Indonesia. The overall number of poor people in Central Java in 2013 was 4.811.300 inhabitants. Factors that influence the level of poverty can be derived from the employment factor, economic factors, or educational factors. Based on these three factors independent variables were selected which supposed to influence the poverty level. There are inflation, City Minimum Wage by Regency/City, Gross Regional Domestic Product at constant market prices, Unemployment Rate, Mean Years of Schooling, and Illiteracy Rate. The poverty level is categorized into three categories. There are Prosperous, Medium and Poor. The independent variables were analyzed its effect on poverty levels that have been categorized by the Ordered Probit Regression method. The complete model of the ordered probit regression is tested the significance of the parameters by likelihood ratio test and Wald test. Based on ordered probit regression analysis, variables that affect the level of poverty in Central Java in 2013 was Inflation, City Minimum Wage by Regency/City, and Mean Years of Schooling (MYS).Keywords : Poverty Level, Central Java, Ordered Probit Regression.
Co-Authors Abdul Hoyyi Abdur Rofiq Agung Waluyo Agus Rusgiyono Alin Citra Suardi Amanda Devi Paramitha Anes Desduana Selasakmida Annisa Rahmawati Aprilia Sekar Khinanti Arief Rachman Hakim Damayanti, Sindi Danardono Danardono Desy Ratnaningrum Desy Ratnaningrum, Desy Dewi Setya Kusumawardani Dwi Ispriyanti DWI RAHMAWATI Etik Setyowati, Etik Fahra Pracendi Astrelita, Fahra Pracendi Fahrur Rozzi Iskak Faiqotul Himmah Fitri Juniaty Simatupang, Fitri Juniaty Hanik Rosyidah, Hanik Iut Tri Utami Moch. Abdul Mukid Muhammad Rizky Yuliyanto Mustafid Mustafid Noor Afifah Oktaviana Prayudhani Pritha Sekar Wijayanti Restu Sri Rahayu, Restu Ridho, Wahyu Anwar Rita Rahmawati Rosilawati, Elsa Dwi Sayekti Dewi Anggraini Sri Haryatmi Kartiko Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Suparti Suparti Tarno Tarno Tarno Tarno Tatik Widiharih Tuan Hanni Ulya Tsaniya Viona Alliza Diandra Putri Yogi Isna Hartanto Yuciana Wilandari Zega, Nesty Novita Sari