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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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PENCARIAN JALUR TERPENDEK MENGGUNAKAN METODE ALGORITMA “ANT COLONY OPTIMIZATION” PADA GUI MATLAB (Studi Kasus: PT Distriversa Buana Mas cabang Purwokerto) Via Risqiyanti; Hasbi Yasin; Rukun Santoso
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

For company, shortest distribution route is an important thing to be developed in order to obtain effectiveness in the distribution of products to consumers. One way of development is to find the shortest route with Ant Colony Optimization algorithm. This algorithm is inspired by the behavior of ant colonies that can find the shortest path from the nest to the food source. One example of a distribution company is PT Distriversa Buana Mas, also known as DBM. DBM is a physical distribution company covering the entire Indonesian archipelago specialized in the distribution of pharmaceuticals and consumer goods such as personal care, cosmetic and food products. DBM uses land transportation in 18 brances spread across Indonesia. One branch of DBM is in the Purwokerto region that distributes products to 29 stores in the Purbalingga region. This research is done with the help of GUI as a computation tool. Based on test results, the GUI system that has been built able to simplify and speed up the selection process of finding the shortest route for distribute product of DBM in the Purbalingga region. Keywords: Travelling Salesman Problem, Distriversa Buana Mas, Algorithm, Ant Colony Optimization, GUI

KLASIFIKASI PERUSAHAAN DI INDONESIA DENGAN MENGGUNAKAN PROBABILISTIC NEURAL NETWORK (Studi Kasus: Perusahaan yang Terdaftar di Bursa Efek Indonesia Tahun 2016) Adi Waridi Basyirudin Arifin; Hasbi Yasin; Budi Warsito
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.v6i4.30383

Classification of company performance can be judged by looking at it’s financial status, whether poor or good state. In order to classifying the financial status, annual financial report will be required. By learning financial status of company, it would be useful to evaluate the performance of the company itself from management cause, or as an investor, making strategy for investment to certain company would be easier. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric method. One of the models in Artificial Neural Network is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclid distance and each class share the same values as their weights. By using the holdout method as an evaluation in honesty, the results show that modeling the company performance with PNN model has a very high accuracy. This is confirmed by the level of accuracy of the data model built on the training data is 100%, while trial data also got 100% accuracy. Keywords : Classification of Company Performance, PNN, Holdout.

PEMODELAN ANGKA HARAPAN HIDUP PROVINSI JAWA TENGAH MENGGUNAKAN ROBUST SPATIAL DURBIN MODEL Maghfiroh Hadadiah Mukrom; Hasbi Yasin; Arief Rachman Hakim
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.v10i1.30935

Spatial regression is a model used to determine relationship between response variables and predictor variables that gets spatial influence. If there are spatial influences on both variables, the model that will be formed is Spatial Durbin Model. One reason for the inaccuracy of the spatial regression model in predicting is the existence of outlier observations. Removing outliers in spatial analysis can change the composition of spatial effects on data. One way to overcome of outliers in the spatial regression model is by using robust spatial regression. The application of M-estimator is carried out in estimating the spatial regression parameter coefficients that are robust against outliers. The aim of this research is obtaining model of number of life expectancy in Central Java Province in 2017 that contain outliers. The results by applying M-estimator to estimating robust spatial durbin model regression parameters can accommodate the existence of outliers in the spatial regression model. This is indicated by the change in the estimating coefficient value of the robust spatial durbin model regression parameter which can increase adjusted R2 value becomes 93,69% and decrease MSE value becomes 0,12551.Keywords: Outliers, M-estimator, Spatial Durbin Model, Number of Life Expectancy.

PEMODELAN FIXED EFFECT GEOGRAPHICALLY WEIGHTED PANEL REGRESSION UNTUK INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH Siti Maulina Meutuah; Hasbi Yasin; Di Asih I Maruddani
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Human development index is an indicator for assessing the quality of human resources and measure the results of human development. The achievements of the human development index is not enough if conducting observations in each cities in just one particular time, but the observations need to be made in some period of time. The distribution in each cities is also a concern, because the conditions are so diverse that led to their spatial effects. Therefore, it is necessary to study these variables in some time periods that affect human development index taking into account the spatial effects. Statistical methods used to overcome their spatial effects, especially in the problem of spatial heterogeneity in the data type of panel is Geographically Weighted Panel Regression (GWPR). This study focused on the establishment of GWPR model with fixed effects using fixed exponential kernel on the human development index data cities in Central Java in 2010-2015. The results of this study indicate that the fixed effect model GWPR differ significantly on panel data regression model, and the model generated for each location will be different from one another. In addition, cities in Central Java has five groups based on variables that are significant. In the fixed effect model GWPR generates R2 value of 92.27%.Keywords: Human Development Index, Panel Data, Spatial Effects, Fixed Effect, Fixed Exponential Kernel, Geographically Weighted Panel Regression, R2.

PEMODELAN GEOGRAPHICALLY WEIGHTED GENERALIZED POISSON REGRESSION (GWGPR) PADA KASUS KEMATIAN IBU NIFAS DI JAWA TENGAH Wahyu Sabtika; Alan Prahutama; Hasbi Yasin
Jurnal Gaussian Vol 10, No 2 (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.v10i2.30946

Maternal mortality is one indicator to describing prosperity in a country and indicator of women's health. Most of the maternal mortality caused by postpartum maternal mortality. The number of postpastum maternal mortality is events that the probability of the incident is small, where the incident depending on a certain time or in a certain regions with the results of the observation are variable diskrit and between variable independent each other that follows the Poisson distribution, so that the proper statistical method is Poisson regression. However, in Poisson regression model analysis sometimes assumptions can occur violations, where the value of variance is greater than the mean value called overdispersion. Generalized Poisson Regression (GPR) is one model that can be used to handle overdispersion problems. This modeling produces global parameters for all locations (regions), so to overcome this we need a method of statistical modeling with due regard to spatial factors. The analytical method used to determine the factors that influence the number of postpartum maternal mortality in Central Java that have overdispersion and there are spatial factors, is Geographically Weighted Generalized Poisson Regression (GWGPR) using the Maximum Likelihood Estimation method and Adaptive Bisquare weighting. Poisson regression and GPR modeling produces a variable percentage of pregnant women doing K1 which has a significant effect on the number of postpartum maternal mortality, while for GWGPR modeling is divided into four cluster in all regency/city in Central Java based on the same significant variable. From the comparison of AIC values, it was found that the GWGPR model is better for analyzing postpartum maternal mortality in Central Java because it has the smallest AIC value.Keywords: The Number of Postpartum Maternal Mortality, Overdispersion, Generalized Poisson Regression, Spatial, Geograpically Weighted Generalized Poisson Regression, AIC

PEMODELAN REGRESI RIDGE ROBUST S,M, MM-ESTIMATOR DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi Kemiskinan di Jawa Tengah Tahun 2020) Anggun Perdana Aji Pangesti; 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.v10i3.32799

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linier regression parameters. If there is a violation of assumptions such as multicolliniearity especially coupled with the outliers, then the regression with OLS is no longer used. One method can be used to solved the multicollinearity and outliers problem is Ridge Robust Regression. Ridge Robust Regression is a modification of ridge regression method used to solve the multicolliniearity and using some estimators of robust regression used to solve the outlier, the estimator including : Maximum likelihood estimator (M-estimator), Scale estimator (S-estimator), and Method of moment estimator (MM-estimator). The case study can be used with this method is data with multicollinearity and outlier, the case study in this research is poverty in Central Java 2020 influenced by life expentancy, unemployment number, GRDP rate, dependency ratio, human development index, the precentage of population over 15 years of age with the highest education in primary school, mean years school. The result of estimation using OLS show that there is a multicollinearity and presence an outliers. Applied the ridge robust regression to case study prove that ridge robust regression can improve parameter estimation. The best ridge robust regression model is Ridge Robust Regression S-Estimator. The influence value of predictor variabels to poverty is 73,08% and the MSE value is 0,00791.

OPTIMASI PARAMETER MODel AUTOREGRESSIVE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION Setyoko Prismanu Ramadhan; Hasbi Yasin; Suparti Suparti
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Box-Jenkins ARIMA method is a linear model in time series analysis which is widely used in various fields. One estimation method for Box-Jenkins ARIMA model is OLS method which aims to minimize the number of squared errors. This method is not effective when applied to time series data that is random, nonlinear and non-stationary. In this study discussed the alternative method of the PSO algorithm as an parameter optimization of the ARIMA model. PSO algorithm is an optimization method based on the behavior of a flock of birds or fish. The main advantage of the PSO algorithm is having a simple, easy to implement and efficient concept in calculations. This method is applied to data from PT Perusahaan Gas Negara shares. The results of both methods will be compared. In the AR model (1) the value of MSE is 0.532 and MAPE is 0.993. Meanwhile, the PSO algorithm obtained MSE 0.531 and MAPE 0.988. It was found that the PSO algorithm resulted in smaller MSE and MAPE values and could provide better results.Keywords : Time Series Analysis, Autoregressive, PSO

PEMODELAN JUMLAH KASUS DEMAM BERDARAH DENGUE (DBD) DI JAWA TENGAH DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) Indah Suryani; Hasbi Yasin; Puspita Kartikasari
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.v10i1.29400

Dengue Hemorrhagic Fever (DHF) is one of the diseases with unsual occurrence in Central Java and spread throughout the regency/city. The number sufferers of this disease is still high because the mortality rate is still above the national target. Regarding the less handling of DHF spread, it is necessary to make a plan by identify the factors that allegedly affect that case. Characteristics of data the DHF cases is count data, so this research is carried out using poisson regression. If in poisson regression there is overdispersion, it can be overcome using negative binomial regression. Meanwhile to see the spatial effect, we can use the Geographically Weighted Negative Binomial Regression (GWNBR) method. GWNBR modeling uses a fixed exponential kernel for weighting function. GWNBR is better at modeling the number of DHF cases because it has the smallest AIC value than poisson regression and negative binomial regression. The results of research with poisson regression obtained three variables that have a significant effect on dengue cases. For negative binomial regression, two variables have a significant effect on DHF cases. While the GWNBR method obtained two groups of districts/cities based on significant variables. The variables affecting the number of DHF cases in all districts/cities in Central Java are the percentage of healthy houses, the percentage of clean water quality, and the ratio of medical personnel.Keywords: DHF, GWNBR, Poisson Regression, Binomial Negative Regression, Fixed Exponential Kernel

KLASIFIKASI CALON DEBITUR KREDIT PEMILIKAN RUMAH (KPR) MULTIGUNA TAKE OVER MENGGUNAKAN METODE k NEAREST NEIGHBOR DENGAN PEMBOBOTAN GLOBAL GINI DIVERSITY INDEX Inas Hasimah; Moch. Abdul Mukid; Hasbi Yasin
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

House credit (KPR) is a credit facilities for buying or other comsumptive needs with house warranty. The warranty for KPR is the house that will be purchased. The warranty for KPR multiguna take over is the house that will be owned by debtor, and then debtor is taking over KPR to another financial institution. For fulfilled the credit to prospective debtor is done by passing through the process of credit application and credit analysis. With the credit analysis, will acknowledge the ability of debtor for repay a credit. Final decision of credit application is classified into approved and refused. k Nearest Neighbor by attributes weighting using Global Gini Diversity Index is a statistical method that can be used to classify the credit decision of prospective debtor. This research use 2443 data of KPR multiguna take over’s prospective debtor in 2018 with credit decision of prospective debtor as dependent variable and four selected independent variable such as home ownership status, job, loans amount, and income. The best classification result of k-NN by Global Gini Diversity Index weighting is when using 80% training data set and 20% testing data set with k=7 obtained APER value 0,0798 and accuracy 92,02%. Keywords: KPR Multiguna Take Over, Classification, KNN by Global Gini Diversity Index weighting, Evaluation of Classification

PERBANDINGAN METODE REGRESI LOGISTIK BINER DAN METODE BACKPROPAGATION DALAM MENENTUKAN MODEL TERBAIK UNTUK KLASIFIKASI PENGGUNA PROGRAM KELUARGA BERENCANA Muhammad Mujahid; Hasbi Yasin; Moch. Abdul Mukid
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesia is one of the highest population density in the world has high birth level. One of the regulation to get the population density lower than before that is used by Government is Family Planning Program. On the reality, not all of the productive age join this program. The method is Binary Logistic Regression and Backpropagation. The predictor variables that is researched are husband’s age, wife’s age, age of the last child, count of children, husband’s education, wife’s education, husband’s job, wife’s job and the level of family prosperity. The aim of the research is to compare the classification accuracy between Binary Logistic Regression and Backpropagation. The result of the research by binary logistic regression method, shows the variables that affect the status of KB user is age of the last child and wife’s education with the classification accuracy are 66.98%, and the classification accuracy of Backpropagation are 67,30%. The conclution based on the research that is the Backpropagation is better than Binary Logistic Regression when classification the status of KB user in Semarang on March 2013 until Januari 2014. Keywords : Binary Logistic Regression, Backpropagation, Keluarga Berencana, Classification

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 Arumningtyas, Felinda 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 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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