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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika
Hasbi Yasin
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Other

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Journal : Jurnal Gaussian

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (720.967 KB) | DOI: 10.14710/j.gauss.v3i1.4782

Abstract

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
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI BAYI BERAT LAHIR RENDAH DENGAN MODEL REGRESI LOGISTIK BINER MENGGUNAKAN METODE BAYES (Studi Kasus di Rumah Sakit Umum Daerah Kota Semarang) Laily Nadhifah; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 1, No 1 (2012): 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 (677.932 KB) | DOI: 10.14710/j.gauss.v1i1.900

Abstract

This study aims to elucidate several factors which affect low-birth-weight (LBW) infants in Semarang General Hospital (RSUD) in the period from July to December 2011. With regard to the MilleniumDevelopment Goals’s targets, which are predominantly intended to reduce the child mortality rate, serious investigations are highly needed to identify the factors that determine the rate of babies born with the low-birth-weight category. This problem can be solved with the Binary Logistic Regression model,using the Bayesian method. The Bayesian method is one of the parameter estimation technique which employ prior value as initial knowledge. The conducted research is to argue that both factors of age and the maternal hemoglobin level considerably give influence on LBW birth. Based on the research analysis, it is extremely recommended that mother to be pays much attention not to be pregnant at relatively young age and maintain the secure level of hemoglobin during pregnancy. 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (668.363 KB) | DOI: 10.14710/j.gauss.v3i3.6455

Abstract

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.  
ANALISIS SUMBER-SUMBER PENDAPATAN DAERAH KABUPATEN DAN KOTA DI JAWA TENGAH DENGAN METODE GEOGRAPHICALLY WEIGHTED PRINCIPAL COMPONENTS ANALYSIS (GWPCA) Alfiyatun Rohmaniyah; Hasbi Yasin; Yuciana Wilandari
Jurnal Gaussian Vol 3, No 3 (2014): 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 (595.422 KB) | DOI: 10.14710/j.gauss.v3i3.6438

Abstract

The districts/cities  sources of revenue  in Central Java consists of Natural Revenue District (PAD), the equalization fund (DAPER), and other local income. PAD consists of four variables namely local tax (X1) , retribution (X2) , the results of regional company and wealth management that is separated (X3) , and other legal PAD (X4). DAPER consists of four variables namely sharing of tax revenue (X5) , sharing of non-tax revenue (X6) , the general allocation fund (X7) , and the special allocation fund (X8). Other region revenues (X9) is a source of local income that is not included in the PAD or DAPER. Sources of local revenue variables are mutually correlated multivariate data and have spatial effect. Therefore Geographically Weighted Principal Components Analysis (GWPCA) is suitable for analyzing sources of local revenue variables. GWPCA is a multivariate analysis method that is used to eliminate multicolliniearity in the multivariate data that have spatial effect. The result of this study is that the variables of revenue sources on each location can be replaced by three new variables called PC1, PC2, and PC3 which is independent each other. Variance Cumulative Proportion that can be explained by those new variables is approximately 80%. Based on the first principal component (PC1) that have variance proportion approximately 50%, there are three groups which has different carracteristics. The first group is the region that the revenue have influenced by variables X9 followed by X1. The second group is the region that the revenue have influenced by variables X9 followed by X2. The third group is the region that the revenue have influenced by variables X9 followed by X5. It is also seen that Kudus District has the most distinct characteristics which the revenue are influenced by variables X5 followed by X9.
Co-Authors Abdul Hoyyi Agus Rusgiyono Ahmat Dhani Riau Bahtiyar Ajeng Arum Sari Alan Prahutama Aldila Abid Awali Alfiyatun Rohmaniyah Alvita Rachma Devi Andreas Pedo Baluk Arief Rachman Hakim Budi Warsito Cakra Kurniawan Catra Aditya Wisnu Aji Christa Monica David Yuliandar Depy Veronica Diah Safitri Dody Apriliawan Dwi Ispriyanti Ely Fitria Rifkhatussa'diyah Erfan Sofha Firda Shintia Dewi Ilham Indra Bakti Al-Irsyad Indra Satria Indra Satria Ira Puspita Sari Ishlahul Kamal Johan Adi Wicaksana Khusnul Yeni Widiyanti Laily Nadhifah Lenti Agustina Lianasari Tambunan Lina Irawati Mamuroh Mamuroh Mega Fitria Andriyani Moch. Abdul Mukid Muh Najib Hilmi Muhammad Hilman Rizki Abdullah Muryanto Muryanto Muryanto Muryanto Novian Trianggara Novika Pratnyaningrum Nurmalita Sari Nursihan Nursihan Putri Aulia Netra Putri Aulia Wahyuningsih Rahafattri Ariya Fauzannissa Rahma Kurnia Widyawati Rahma Nurfiani Pradita Restu Dewi Kusumo Astuti Rezzy Eko Caraka Rima Nurlita Sari Rita Rahmawati Rizky Amanda Rukun Santoso Safitri Daruyani Sarita Budiyani Purnamasari Sudarno Sudarno SUGITO Sugito Sugito Suparti Suparti Syaifudin Karyadi Tarno Tarno Tatik Widiharih Tika Dhiyani Mirawati Triastuti Wuryandari Vita Dwi Rachmawati Wayaning Apsari Yuciana Wilandari