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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang

Triastuti Wuryandari

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Author-ID : 452063

Decision Sciences, Operations Research & Management Other

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ANALISIS REGRESI KEGAGALAN PROPORSIONAL DARI COX PADA DATA WAKTU TUNGGU SARJANA DENGAN SENSOR TIPE I (Studi Kasus di Fakultas Sains dan Matematika Universitas Diponegoro) Oka Afranda; Triastuti Wuryandari; Dwi Ispriyanti
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of the goals of studying in Higher Education Institutionis to obtain a job as soon as possible. A graduate is not required to be an unemployed. In Indonesia, the average period of waiting time for undergraduate (S1) to get the first job is 0 (zero) to 9 (nine) months. There are several factors have influenced the length of an undergraduate to get a job. They are Grade Point Average (GPA), Length of Study, etc. Therefore, it is important to know the factors influencing the waiting time of undergraduates to get a job. One method that can be used is the analysis of survival. Survival analysis is the analysis of survival time data from the initial time of the study until certain events occur. One method of survival analysis is Cox Proportional Hazard Regression. It is used to determine the relationship between one or more independent variables and the dependent variable. Cases raised in this study were the factors influencing the waiting time of graduates of the Faculty of Science and Mathematics, University of Diponegoro by using Type I data censoring. The conclusions state that the factors influencing the waiting time of graduates are Organization, Department, and Gender.Keywords: Waiting time of undergraduate, survival analysis, Cox Proportional Hazard, Regression, University of Diponegoro.

PENERAPAN DIAGRAM KONTROL IMPROVED GENERALIZED VARIANCE PADA PROSES PRODUKSI HIGH DENSITY POLYETHYLENE (HDPE) Rahma Kurnia Widyawati; Hasbi Yasin; Triastuti Wuryandari
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Dalam suatu industri manufaktur, pengendalian kualitas yang sesuai standar dari perusahaan terhadap produk yang dihasilkan sangat diperlukan. Biasanya pengendalian kualitas tersebut hanya menggunakan metode sederhana, sehingga diperlukan adanya analisis lebih lanjut, yaitu dengan menggunakan salah satu metode statistika inferensia. Penelitian dilakukan pada CV. Garuda Plastik Karangawen untuk mengetahui keadaan proses produksi High Density PolyEthylene (HDPE). Pengendalian kualitas yang dilakukan melibatkan dua karakteristik kualitas yaitu Panjang dan Berat HDPE. Kualitas pada umumnya diukur menggunakan beberapa karakteristik, sehingga diperlukan metode pengendalian kualitas multivariat dalam melakukan monitoring. Pengendalian kualiatas mean proses menggunakan diagram kontrol T2Hotelling, sedangkan pengendalian kualitas variabilitas proses menggunakan diagram kontrol Improved Generalized Variance. Penelitian ini dilakukan dalam dua tahap. Pada Tahap I diketahui bahwa proses produksi HDPE belum stabil dalam variabilitas maupun meannya. Pada Tahap II diketahui bahwa proses produksi HDPE belum stabil dalam variabilitasnya tetapi sudah stabil dalam mean, artinya proses produksi Tahap II sudah dilakukan perbaikan. Berdasarkan hal tersebut yang menyebabkan proses produksi tidak stabil adalah sistem kejar target produksi sehingga berpengaruh pada bahan baku, pengaturan mesin dan suhu mesin yang sering berubah-ubah sehingga mengakibatkan ukuran roll HDPE menjadi beragam

PREDIKSI DATA HARGA SAHAM HARIAN MENGGUNAKAN FEED FORWARD NEURAL NETWORKS (FFNN) DENGAN PELATIHAN ALGORITMA GENETIKA (Studi Kasus pada Harga Saham Harian PT. XL Axiata Tbk) Ira Puspita Sari; Triastuti Wuryandari; Hasbi Yasin
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Artificial neural network (ANN) or Neural Network (NN) is an information processing system that has characteristics similar to biological neural networks. One of the ANN models have network is quite simple and can be applied to time series data prediction is Feed Forward Neural Networks (FFNN). In general, FFNN trained using Backpropagation algorithm to obtain weights, but performance will decrease and trapped in a local minimum when applied to data that have great complexity like financial data. The solution to this problem is to train FFNN using Genetic Algorithm (GA). GA is a search algorithm that is based on the mechanism of natural selection and genetics to determine the global optimum. Training FFNN using GA is a good solution but the problem is how to understand the workings of FFNN training using the GA, the determination of the combination of the probability of crossover (), number of populations, number of generations, and the size of the tournament (k) on the AG to produce predictive value approaching actual value. One possible option is to use the technique of trial-end-error by experimenting for some combination of these four parameters. Of the 64 times the application of the AG test results to train FFNN models on daily stock price data PT. XL Axiata Tbk obtained results are sufficiently accurate predictions indicated by the proximity of the target to the output of the crossover probability () 0.8, a population of 50, the number of generations 20000 and tournament size of 4 produces the testing RMSE 107.4769.

PERHITUNGAN DAN ANALISIS PRODUK DOMESTIK REGIONAL BRUTO (PDRB) KABUPATEN/KOTA BERDASARKAN HARGA KONSTAN (Studi Kasus BPS Kabupaten Kendal) Fitriani Fitriani; Agus Rusgiyono; Triastuti Wuryandari
Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Gross Regional Domestic Product (GRDP) is technical term that always we heard in the civil government or in the public society. According to Statistics Indonesia, GRDP is total number of added value who producting by effort unit in that domestic area. GRDP is one of economics growth indicator in the domestic area. If GRDP is higher, then people economics prosperity must be high too, and do also that opposite. GRDP contains of 2 methods, that is GRDP at Current Market Prices and GRDP at Constant Prices. In this report will discuss about GRDP at Constant Prices with GRDP the Kendal Regency at 2000 Constant Prices in 2010 for example. Arranging GRDP at Constant Prices has purpose to find out economics condition from year to year by discern the GRDP every year. The methods to arranging GRDP at Constant Prices are revaluasi, ekstrapolasi, and deflasi. After doing the accounting by Statistics Indonesia, we obtainable GRDP the Kendal Regency at Constant Prices in 2010 in million rupiahs is 5.394.079,31. And according the analysis, GRDP from 1983 to 2011 show the linear graph that has model GRDP = -986933 + 220901 (X). This model, can use to forecasting for GRDP the Kendal Regency at Constant Prices over the next years.

Co-Authors Abdul Hoyyi Agus Rusgiyono Akhmad Zaki Alif Hartati Angga Saputra Desti Annisa Ayu Wulandari Annisa Intan Mayasari ANNISA RAHMAWATI Artha Ida Sri Anggriyani Arya Fendha Ibnu Shina Avida Nugraheni C. Ayu Hapsari Budi Utami Bellina Ayu Rinni Danardono Danardono Desriwendi Desriwendi Desy Rahmawati Ningrat Di Asih I Maruddani Diah Safitri Dian Ika Pratiwi Dwi Ispriyanti Fitriani Fitriani Friska Irnas Adiyani Galuh Riani Putri Gian Kusuma Diah Tantri Haryanti Novitasari Hasbi Yasin Ibnu Athoillah Indradewa, Rhian Ira Puspita Sari Ishlahul Kamal Landong Panahatan Hutahaean Lintang Ratri Wardhani Maralika Yundya Sari Moch. Abdul Mukid Mohamad Reza Pahlevi Mustafid Mustafid Nova Yanti Gultom Novia Dian Ariyani Oka Afranda Puti Cresti Ekacitta Rahma Kurnia Widyawati Restu Sri Rahayu Rita Rahmawati Rizky Ade Putranto Rosmalia Safitri Sarita Budiyani Purnamasari Sindy Saputri Solly Aryza Sudarno Sudarno Sugito Sugito Suparti Suparti Susi Ekawati Syarah Widyaningtyas Tarno Tarno Tatik Widiharih Tri Murda Agus Raditya Tyas Ayu Prasanti Vina Riyana Fitri Wulan Safitri Yuciana Wilandari Yuyun Naifular

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