Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

P-Index

This Author published in this journals

All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika

Hasbi Yasin

Unknown Affiliation

Author-ID : 452055

Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Other

Published : 47 Documents Claim Missing Document

Claim Missing Document

Articles

Title

1 2 3 4 5

GUI MATLAB UNTUK KOMBINASI METODE ANALYTIC HIERARCHY PROCESS (AHP) DAN TOPSIS DALAM PEMILIHAN CAFE TERFAVORIT (STUDI KASUS : Pemilihan Cafe Terfavorit di Daerah Tembalang, Semarang) Putri Aulia Netra; Tatik Widiharih; Hasbi Yasin
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Tembalang is an area that has many culinary business. One of them is cafe bussiness. This condition causes high competition in attracting consumers to gain profit. According to this situation, we need a method to asses the most favourite cafe based on consumer taste to create cafe as they expected. The methods used in choosing the most favourite cafe are Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both of method are the methods used to solve the Multi-Attribute Decision Making (MADM) problem. AHP is used as a method of weighting each criteria by forming pairwise comparison matrix, normalizing pairwise comparison matrix, weighting and testing the consistency of the weight that was gained. Whereas TOPSIS is used to rank the most favorite cafe by calculating the weighted-normalized decision matrix MADM, determining the positive and negative ideal solution, calculating the distance between each alternative with positive and negative ideal solution and calculating the value of preference for each alternatives. There are eight cafes and fourteen criterias. The criterias are the taste of foods and drinks, price, site accessibility, wifi, the neatness of waiters, the hospitality of waiters, waiters’s knowledge about menu, the accuracy of the preparation of the foods and drinks, transaction convenience, varian of menu, the safety and cleanliness of area, handling against misstatement, layout and decoration, and serving. The result of this research is: the most preferred cafe has 0.84322 of preference value. Preference value which calculated manually has similar result with Graphical User Interface (GUI) Matlab.Keywords: AHP, TOPSIS, cafe, favorite, preference

PEMODELAN PROPORSI PENDUDUK MISKIN KABUPATEN DAN KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION Khusnul Yeni Widiyanti; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Regression analysis is a statistical analysis that aims to quantify the effect of predictor variables on the response variable. Geographically Weighted Regression (GWR) is a local form of regression and a statistical method used to analyze spatial data. Geographically and Temporally Weighted Regression (GTWR) is the development of GWR models to handle data that is not stationary both in terms of spatial and temporal simultaneously. In obtaining estimates of parameters of the GTWR model can be used Weighted Least Square method (WLS). Selection of the optimum bandwidth used method of Cross Validation (CV). Conformance testing global regression and GTWR models approximated by the distribution of F, whereas the partial testing of the model parameters using the t distribution. Application GTWR models at the level of poverty in Central Java province in 2008 to 2012 showed GTWR models differ significantly from the global regression model. Based on R2 and Mean Squared Error (MSE) value between the global regression model and GTWR models, it is known that the GTWR model with exponential weighting kernel function is the best model is used to analyze proportion of poor people in Central Java province in 2008 to 2012 because it has a value of R2 larger and MSE is the smallest. Keywords: Bandwidth, Cross Validation, Exponential Kernel Functions, Geographically and Temporally Weighted Regression, Weighted Least Square, R2, Mean Squared Error.

ANALISIS SUPPORT VECTOR REGRESSION (SVR) DALAM MEMPREDIKSI KURS RUPIAH TERHADAP DOLLAR AMERIKA SERIKAT Rizky Amanda; Hasbi Yasin; Alan Prahutama
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In economy, the global markets have an important role as a forum for international transactions between countries in selling or purchasing goods or services on an international scale. Money as legal tender in the trading activities, but the problem is the difference between the state of the currency, the exchange rate will be established. Exchange rate is the value of a country's currency is expressed in another country's currency value. Fluctuations in foreign exchange rates greatly affect the Indonesian economy, so the determination of the exchange rate should be beneficial to a country can run the economy well. To predict the exchange rate of the Rupiah against the United States dollar in this study used methods of Support Vector Regression (SVR) is a technique to predict the output in the form of continuous data. SVR aims to find a hyperplane (line separator) in the form of the best regression function is used to predict the exchange rate against the United States dollar with linear kernel and polynomial functions. Criteria used in measuring the goodness of the model is the MAPE (Mean Absolute Percentage Error) and R2 (coefficient of determination). The results of this study indicate that both the kernel function gives very good accuracy in the prediction results of the exchange rate with R2 of 99.99% with MAPE 0.6131% in the kernel linear and R2 result of 99.99% with MAPE 0.6135% in the kernel polynomial. Keyword : Exchange rate, Support Vector Regression (SVR), Hyperplane, Linear Kernel, Polynomial Kernel, ε-insensitive, Accuracy

ANALISIS BIPLOT KOMPONEN UTAMA PADA BANK UMUM (COMMERCIAL BANK) YANG BEROPERASI DI JAWA TENGAH Ely Fitria Rifkhatussa'diyah; Hasbi Yasin; Agus Rusgiyono
Jurnal Gaussian Vol 3, No 1 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Competition among banks in Indonesia nowadays are getting higher due to the good economic growth and increasing of middle social class in Indonesia. The number of banks cause high competition among banks and internal bank itselves. This high competition makes the management of the bank should think seriously to maintain its existence. In this case the assessment of the bank become very important in the banking business to survive in today's banking industry. This study was conducted to determine how competitive the Commercial Bank are operating in Central Java by a method of Principal Component Biplots. This analysis can be applied to find out information about the relative position, the similarity between objects and characteristic of variables in the three categories of commercial banks operating in Central Java based on their health aspects. The results of this study are the banks from each category have a distinct predominance in every aspect of health assessment variable. In addition, the biplots can give information on the variability more than 70% which means that principal component biplot explains the overall data well.

PELATIHAN FEED FORWARD NEURAL NETWORK MENGGUNAKAN ALGORITMA GENETIKA DENGAN METODE SELEKSI TURNAMEN UNTUK DATA TIME SERIES David Yuliandar; Budi Warsito; Hasbi Yasin
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

ABSTRAK Pemodelan time series seringkali dikaitkan dengan proses peramalan suatu nilai karakteristik tertentu pada periode mendatang. Salah satu metode peramalan yang berkembang saat ini adalah menggunakan artificial neural network atau yang lebih dikenal dengan neural network.Penggunaan neural network dalam peramalan time series dapat menjadi solusi yang baik, namun yang menjadi masalah adalah arsitektur jaringan dan pemilihan metode pelatihan yang tepat. Salah satu pilihan yang mungkin adalah menggunakan algoritma genetika. Algoritma genetika adalah suatu algoritma pencarian stokastik berdasarkan cara kerja melalui mekanisme seleksi alam dan genetik yang bertujuan untuk mendapatkan solusi dari suatu masalah. Algoritma ini dapat digunakan sebagai metode pembelajaran dalam melatih model feed forward neural network. Penerapan algoritma genetika dan neural network untuk peramalan time series bertujuan untuk mendapatkan bobot-bobot yang optimum dengan meminimumkan error. Dari hasil pelatihan dan pengujian pada data kurs Dolar Australia terhadap Rupiah didapatkan nilai RMSE sebesar 117.3599 dan 82.4917. Model ini baik untuk digunakan karena memberikan hasil prediksi yang cukup akurat yang ditunjukkan oleh kedekatan target dengan output.

ANALISIS LAMA KAMBUH PASIEN HIPERTENSI DENGAN SENSOR TIPE III MENGGUNAKAN REGRESI COX KEGAGALAN PROPORSIONAL (Studi Kasus di RSUD Kartini Jepara) Ishlahul Kamal; Triastuti Wuryandari; Hasbi Yasin
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Hypertension is a disease that silently kills the patients because they do not realize that they get hypertension until they check their blood pressure. It is important for hypertensive patients to know the factors that lead to the relapse time. To determine the relationship between the relapse time on hypertensive patients with the influencing factors is using regression analysis, the dependent variable is the failure time so to determine the relationship is using regression Cox proportional hazard. This research uses the medical records of hypertensive patients in period January to December 2014 in RSUD Kartini Jepara. The results indicate that the factors which affect relapse time of hypertension are kidney disease and stroke. The hypertensive patients that also suffer from kidney disease have relapse time sooner than patients who do not suffer from kidney disease. The hypertensive patients that also suffer from stroke have relapse time sooner than patients who do not suffer from a stroke. Keywords: Hypertension, Survival Analysis, Regression Cox Proportional Hazards

OPTIMASI VALUE AT RISK PADA REKSA DANA DENGAN METODE HISTORICAL SIMULATION DAN APLIKASINYA MENGGUNAKAN GUI MATLAB Christa Monica; Tarno Tarno; Hasbi Yasin
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Value at Risk (VaR) is a method used to measure financial risk within a firm or investment portfolio over a specific time period at certain confidence interval level. Historical Simulation is used in this research to compute VaR of stock mutual fund at 5% confidence interval level, with one day time period and Rp 100.000.000,00 startup investment fund. Historical Simulation ia a non parametric method where the formula doesn’t require any asumption. Portfolio optimization is done by calculating the weight of allocation fund for each asset in the portfolio using Mean Variance Efficient Portfolio (MVEP) method. The data in this research are divided into four mutual fund asset. To make VaR become easier for people to understand, an application is made using GUI in Matlab. The smallest risk value for single investment asset is obtained by Valbury Equity I stock mutual fund and the smallest risk value for two-asset portfolio is obtained by the combination assets of Pacific Equity Fund and Valbury Equity I. Meanwhile for three-asset portfolio, the combination assets of Pacific Equity Fund, Valbury Equity I, and Millenium Equity Prima Plus have the smallest risk value. The test result of VaR with Basel Rules shows that the usage of VaR is legitimate to measure loses potency in mutual fund investment.Keywords: Value at Risk (VaR), Historical Simulation, Mutual Fund, Risk.

PROBABILISTIC NEURAL NETWORK BERBASIS GUI MATLAB UNTUK KLASIFIKASI DATA REKAM MEDIS (Studi Kasus Penyakit Diabetes Melitus di Balai Kesehatan Kementerian Peridustrian Jakarta) Johan Adi Wicaksana; Hasbi Yasin; Sudarno Sudarno
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Neural Network (NN) system is an information-processing that has characteristics similar to the neural network in living beings. A model of Neural Network is used for classification is Probabilistic Neural Network (PNN). PNN structured by four layers, the input layer, layer pattern, the summation layer and output layer. One of classification problems that can be solved by PNN is a classification of Diabetes Mellitus’s status. Diabetes mellitus is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin produced. To facilitate the classification of diabetes mellitus, it is used a software-based Graphical User Interface (GUI) of Matlab to build a software of PNN. GUI that is formed can do PNN classification and predict the status of one’s Diabetes Mellitus. PNN structure that is formed resulting the highest accuracy 0.9143548 on the training process and 0.919512 on the testing process obtained by the percentage of training data than testing data by 90%:10% with holdout accuracy evaluation method, and a smoothing value of 1. This classification resulting 23 patients were classified as negative diabetes and 18 patients were classified as positive diabetes.Keywords: Neural Network, Probabilistics Neural Network, diabetes mellitus, GUI, holdout, smoothing parameter.

OPTIMASI WAKTU EFEKTIF APLIKASI HERBISIDA PADA TANAMAN KELAPA SAWIT (ELAEIS GUINEENSIS JACQ.) DENGAN FUNGSI ESTIMASI DENSITAS KERNEL (Studi Kasus di Perkebunan Sawit PT SMART Tbk, Libo Estate, Riau) Putri Aulia Wahyuningsih; Tatik Widiharih; Hasbi Yasin
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Palm oil agribusiness is one of potential source to accelerate economic growth in Indonesia. Palm oil is the raw material to produce CPO (Crude Palm Oil) which is source of vegetable oil that is needed by all people. This research used a combination of 16 treatments of type and dose of herbicide on oil palm trees. Purposes of this research are to determine the optimal timing of herbicide applications and determine the treatment that maximizes efficacy of weed. Optimal timing of herbicide applications to the palm trees is determined through the largest mean of bootstrap resample and plot of kernel epanechnikov density estimation. Optimal treatment is determined through the largest mean of bootstrap resample, the smallest variance resample, the smallest range of bootstrap percentile confidency interval, and coverage probability that close to 1-α. Result obtained is the optimal timing of herbicide applications to oil palm trees is 8 weeks after applications. And optimal treatment is Tricalon 318 EC at a dose of 1500 cc.

PEMODELAN STATUS KESEJAHTERAAN DAERAH KABUPATEN ATAU KOTA DI JAWA TENGAH MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION SEMIPARAMETRIC Firda Shintia Dewi; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Welfare in society is one of the most important aspects in ensuring the realization of the social where people have a good level of welfare. Benchmarks achieved prosperity is the fulfillment of basic needs of society as feasible. Statistical methods have been developed for the analysis of spatial data by taking into account factors that Geographically Weighted Logistic Regression Semiparametric (GWLRS). GWLRS is a local form of the logistic regression where there are parameters that are influenced by the location (Geographically varying coefficient) and the parameters that are not influenced by the location (fixed coefficient). Selection of the optimum bandwidth using Cross Validation (CV). Model GWLRS Welfare Status district or city in Central Java showed that GWLRS models differ significantly from the logistic regression model. And models generated for each area will be different from each other. To get the best models, the number of models to be evaluated. One method for selecting the best model is the value of the Akaike Information Criterion (AIC). Based on AIC obtained the best model is the model GWLRS because it has the smallest AIC value of 46.11213 with a classification accuracy of 77.14%. Keywords: Welfare, Geographically Weighted Logistic Regression Semiparametric, Cross Validation, Akaike Information Criterion

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

Title Search

Found 44 Documents Search Journal : Jurnal Gaussian

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 44 Documents
Search
Journal : Jurnal Gaussian