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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

Author-ID : 422426

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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IMPLEMENTASI JARINGAN SYARAF TIRUAN BACKPROPAGATION DENGAN ALGORITMA CONJUGATE GRADIENT UNTUK KLASIFIKASI KONDISI RUMAH (Studi Kasus di Kabupaten Cilacap Tahun 2018) Johanes Roisa Prabowo; Rukun Santoso; hasbi Yasin
Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

House is one aspect of the welfare of society that must be met, because house is the main need for human life besides clothing and food. The condition of the house as a good shelter can be known from the structure and facilities of buildings. This research aims to analyze the classification of house conditions is livable or not livable. The method used is artificial neural networks (ANN). ANN is a system information processing that has characteristics similar to biological neural networks. In this research the optimization method used is the conjugate gradient algorithm. The data used are data of Survei Sosial Ekonomi Nasional (Susenas) March 2018 Kor Keterangan Perumahan for Cilacap Regency. The data is divided into training data and testing data with the proportion that gives the highest average accuracy is 90% for training data and 10% for testing data. The best architecture obtained a model consisting of 8 neurons in input layer, 10 neurons in hidden layer and 1 neuron in output layer. The activation function used are bipolar sigmoid in the hidden layer and binary sigmoid in the output layer. The results of the analysis showed that ANN works very well for classification on house conditions in Cilacap Regency with an average accuracy of 98.96% at the training stage and 97.58% at the testing stage.Keywords: House, Classification, Artificial Neural Networks, Conjugate Gradient

KOMPUTASI METODE MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING AVERAGE (MEWMA) UNTUK PENGENDALIAN KUALITAS PROSES PRODUKSI MENGGUNAKAN GUI MATLAB (STUDI KASUS: PT. Pismatex Textile Industry Pekalongan) Riza Fahlevi; Hasbi Yasin; Dwi Ispriyanti
Jurnal Gaussian Vol 9, No 3 (2020): 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.v9i3.28908

Control chart is one of the effective statistical tools to overcome the problem of process quality in a production. Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is an effective quality control tool in processes with more than one variable and correlated (multivariate). The MEWMA control chart has a weight value (λ) which makes this chart more sensitive in detecting small shifts process mean. The weight (λ) has values ranging from 0 to 1 ( ), where this weight will be given to each data. The MEWMA control chart in this study was used to form a control chart by the product defects percentage of grade B and grade B at PT. Pismatex Textile Industry Pekalongan. In this study, GUI Matlab was formed to assist the computational process in forming MEWMA control charts to control the quality of production at PT. Pismatex Textile Industry Pekalongan. Based on the result, the optimal weight is obtained at the weight value λ = 0.9. Keywords: Multivariate Exponentially Weighted Moving Average (MEWMA), Weight (λ), GUI Matlab, Percentage of product defects.

PENANGANAN KLASIFIKASI KELAS DATA TIDAK SEIMBANG DENGAN RANDOM OVERSAMPLING PADA NAIVE BAYES (Studi Kasus: Status Peserta KB IUD di Kabupaten Kendal) Reza Dwi Fitriani; Hasbi Yasin; Tarno Tarno
Jurnal Gaussian Vol 10, No 1 (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.v10i1.30243

The Family Planning Program (KB) launched by the Government of Indonesia to address the problem of population control does not always produce the desired program results. In 2017, there were 7 users of the IUD contraceptive type of contraceptive who failed from 1,102 new IUD users in Kendal Regency so that the ratio of success and failure to the IUD KB program when compared to users of the new IUD KB is 0.64%: 99.36% . The ratio of success and failure of family planning programs which tend to be unbalanced makes it difficult to predict. One of the handling imbalanced data is oversampling, for example using Random Oversampling (ROS). Naive Bayes is used for classification because it’s easy and efficient learning model. The data in this study used 14 independent variables and 1 dependent variable. The results of this study indicate that the G-mean of Naive Bayes is less than 60%. The G-mean of ROS-Naive Bayes is 96.6%. It can be concluded that in this research, the ROS-Naive Bayes method is better than the Naive Bayes method for detecting the success status of IUD family planning in Kendal Regency. Keywords: Naive Bayes, Random Oversampling, G-mean

KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) Isna Wulandari; Hasbi Yasin; Tatik Widiharih
Jurnal Gaussian Vol 9, No 3 (2020): 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.v9i3.27416

The recognition of herbs and spices among young generation is still low. Based on research in SMK 9 Bandung, showed that there are 47% of students that did not recognize herbs and spices. The method that can be used to overcome this problem is automatic digital sorting of herbs and spices using Convolutional Neural Network (CNN) algorithm. In this study, there are 300 images of herbs and spices that will be classified into 3 categories. It’s ginseng, ginger and galangal. Data in each category is divided into two, training data and testing data with a ratio of 80%: 20%. CNN model used in classification of digital images of herbs and spices is a model with 2 convolutional layers, where the first convolutional layer has 10 filters and the second convolutional layer has 20 filters. Each filter has a kernel matrix with a size of 3x3. The filter size at the pooling layer is 3x3 and the number of neurons in the hidden layer is 10. The activation function at the convolutional layer and hidden layer is tanh, and the activation function at the output layer is softmax. In this model, the accuracy of training data is 0.9875 and the loss value is 0.0769. The accuracy of testing data is 0.85 and the loss value is 0.4773. Meanwhile, testing new data with 3 images for each category produces an accuracy of 88.89%. Keywords: image classification, herbs and spices, CNN.

ANALISIS METODE BAYESIAN PADA SISTEM ANTREAN RAWAT JALAN DI RSUP Dr. KARIADI DENGAN DISTRIBUSI SAMPEL POISSON DAN GEOMETRIK Nur Azizah; Sugito Sugito; Hasbi Yasin
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.v10i3.32801

Hospital service facilities cannot be separated from queuing events. Queues are an unavoidable part of life, but they can be minimized with a good system. The purpose of this study was to find out how the queuing system at Dr. Kariadi. Bayesian method is used to combine previous research and this research in order to obtain new information. The sample distribution and prior distribution obtained from previous studies are combined with the sample likelihood function to obtain a posterior distribution. After calculating the posterior distribution, it was found that the queuing model in the outpatient installation at Dr. Kariadi Semarang is (G/G/c): (GD/∞/∞) where each polyclinic has met steady state conditions and the level of busyness is greater than the unemployment rate so that the queuing system at Dr. Kariadi is categorized as good, except in internal medicine poly.

PEMODELAN DATA KEMISKINAN PROVINSI JAWA TENGAH MENGGUNAKAN FIXED EFFECT SPATIAL DURBIN MODEL Siska Alvitiani; Hasbi Yasin; Mochammad Abdul Mukid
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Based on data from the Central Statistics Agency, Central Java has 4,20 million people (12,23%) poor population in 2017 with Rp333.224,00 per capita per month poverty line. So, Central Java has got the second rank after East Java as the province which has the highest poor population in indonesia in 2017. In this research use the fixed effects spatial durbin model method for modeling poor population in each city in Central Java at 2014-2017. The spatial durbin model is a spatial regression model which contains a spatial dependence on dependent variable and independent variable. If the spatial dependence on dependent variable or independent variables is ignored, the resulting coefficient estimator will be biased and inconsistent. The fixed effect is one of the panel data regression models which assumes a different intercept value at each observation but fixed at each time, and slope coefficient is constant. The advantage of using fixed effects in spatial panel data regression is able to know the different characteristics in each region. The dependent variable used is poor population in each city in Central Java, and the independent variable is Minimum Wage, Life Expectancy, School Participation Rate 16-18 Years, Expected Years of Schooling, Total Population, and Per Capita Expenditure. The results of the analysis shows that the fixed effects spatial durbin model is significant and can be used. The variables that significantly affect the model are the Life Expectancy and Expected Years of Schooling, and the coefficient of determination (R2) is 99.95%. Keywords: Poverty, Spatial, Panel Data, Fixed Effects Spatial Durbin Model

PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH Eko Siswanto; Hasbi Yasin; Sudarno Sudarno
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

PEMODELAN INDEKS HARGA KONSUMEN DI JAWA TENGAH DENGAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR) Mega Fitria Andriyani; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 7, No 4 (2018): 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.v7i4.28859

The Generalized Space Time Autoregressive (GSTAR) model with Seemingly Unrelated Regression (SUR) estimation method or often called GSTAR-SUR is more efficient to be used for residual correlation than Ordinary Least Square (OLS) estimation method. The SUR estimation method utilizes residual correlation information to improve the estimated efficiency resulting in a smaller standard error. The purpose of this research is to get the GSTAR-SUR model according to Consumer Price Index (CPI) data in four regencies or cities in Central Java namely Purwokerto, Surakarta, Semarang, and Tegal. Based on the assumed white noise assumption, the smallest MAPE and RMSE averages, the best model chosen in this research is the GSTAR-SUR(11)I(1) model with the heavy of normalized cross-correlation with the average MAPE value of 0.4455% and RMSE value of 0.80582. The best model obtained explains that the CPI data in Purwokerto, Semarang, and Tegal not only influenced by the previous time but also influenced by the locations. Meanwhile, the CPI data in Surakarta is only influenced by the previous time, but it is not affected by other locations. Keywords: SUR, OLS, Consumer Price Index

PEMILIHAN SMARTPHONE TERBAIK PENUNJANG KEGIATAN AKADEMIS MENGGUNAKAN METODE BWM DAN PENGEMBANGAN AHP Mochammad Iffan Zulfiandri; Hasbi Yasin; Sudarno Sudarno
Jurnal Gaussian Vol 10, No 1 (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.v10i1.30542

Multi-Criteria Decision Making (MCDM) is a decision-making method to determine the best alternative from several alternatives based on several certain criteria. One of the alternative decision-making methods that can be used is the Best Worst Method (BWM) and the Analytical Hierarchy Process (AHP). BWM makes structured pairwise comparisons and AHP breaks down complex problems into hierarchical structures. One of the decision-making problems that can be solved by the BWM and AHP methods is the problem of choosing a smartphone. Smartphones are one of the most widely used Information and Communication Technology (ICT) devices by Indonesians. The use of smartphones as ICT devices has benefits for the academic community, especially as a means of supporting academic activities. However, various types and mereks of smartphones are circulating, making users confused about choosing the best smartphone according to their needs. Therefore, a reliable method is needed to make it easier for users to choose the best smartphone, especially in supporting academic activities, namely by using a combination of the BWM method and AHP development. The BWM method is used to calculate the optimal weight of the criteria and the AHP method that has been developed is used to calculate the alternative optimal weight based on the criteria. The combination of the two is used to calculate the final optimal weight for each alternative. The results of the calculation of the optimal weight of the criteria show that the RAM criterion has the highest weight, which is 0.290 and the Screen Size criterion has the lowest weight, which is 0.047. The final result obtained is a smartphone type OPPO Find X2 with a final optimal weight of 0.153 to be the best alternative among other alternatives. Keywords: Multi-Criteria Decision Making (MCDM), Best Worst Method (BWM), Analytical Hierarchy Process (AHP), Information and Communication Technology (ICT), Smartphones, Academic Activities

PEMODELAN REGRESI ROBUST S-ESTIMATOR UNTUK PENANGANAN PENCILAN MENGGUNAKAN GUI MATLAB (Studi Kasus : Faktor-Faktor yang Mempengaruhi Produksi Ikan Tangkap di Jawa Tengah) Dhea Kurnia Mubyarjati; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multiple Linear Regression can be solved by using the Ordinary Least Squares (OLS). Some classic assumptions must be fulfilled namely normality, homoskedasticity, non-multicollinearity, and non-autocorrelation. However, violations of assumptions can occur due to outliers so the estimator obtained is biased and inefficient. In statistics, robust regression is one of method can be used to deal with outliers. Robust regression has several estimators, one of them is Scale estimator (S-estimator) used in this research. Case for this reasearch is fish production per district / city in Central Java in 2015-2016 which is influenced by the number of fishermen, number of vessels, number of trips, number of fishing units, and number of households / fishing companies. Approximate estimation with the Ordinary Least Squares occur in violation of the assumptions of normality, autocorrelation and homoskedasticity this occurs because there are outliers. Based on the t- test at 5% significance level can be concluded that several predictor variables there are the number of fishermen, the number of ships, the number of trips and the number of fishing units have a significant effect on the variables of fish production. The influence value of predictor variables to fish production is 88,006% and MSE value is 7109,519. GUI Matlab is program for robust regression for S-estimator to make it easier for users to do calculations. Keywords: Ordinary Least Squares (OLS), Outliers, Robust Regression, Fish Production, GUI Matlab.

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 Hendrian, Jody Hidayatul Musyarofah Hindun Habibatul Mubaroroh Ika Chandra Nurhayati Inas Hasimah Inayati, Syarifah Indah Suryani Innosensia Adella Intan Monica Hanmastiana Isna Wulandari Ispriyansti, Dwi 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 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 Sari, Indri Puspita Satriyo Adhy Setiawan Setiawan Setyoko Prismanu Ramadhan Siahaan, Rina Br 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 Wulandari, Isna Youngjo Lee Yuciana Wilandari Yudha Subakti, Yudha Zulfa Wahyu Mardika, Zulfa Wahyu

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