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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275
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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
Other
Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.
Arjuna Subject : -
Articles 733 Documents
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PENERAPAN k-MODES CLUSTERING DENGAN VALIDASI DUNN INDEX PADA PENGELOMPOKAN KARAKTERISTIK CALON TKI MENGGUNAKAN R-GUI Hanik Malikhatin; Agus Rusgiyono; Di Asih I Maruddani
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.v10i3.32790

Abstract

Prospective TKI workers who apply for passports at the Immigration Office Class I Non TPI Pati have countries destinations and choose different PPTKIS agencies. Therefore, the grouping of characteristics prospective TKI needed so that can be used as a reference for the government in an effort to improve the protection of TKI in destination countries and carry out stricter supervision of PPTKIS who manage TKI. The purpose of this research is to classify the characteristics of prospective TKI workers with the optimal number of clusters. The method used is k-Modes Clustering with values of k = 2, 3, 4, and 5. This method can agglomerate categorical data. The optimal number of clusters can be determined using the Dunn Index. For grouping data easily, then compiled a Graphical User Interface (GUI) based application with RStudio. Based on the analysis, the optimal number of clusters is two clusters with a Dunn Index value of 0,4. Cluster 1 consists of mostly male TKI workers (51,04%), aged ≥ 20 years old (91,93%), with the destination Malaysia country (47%), and choosing PPTKIS Surya Jaya Utama Abadi (37,51%), while cluster 2, mostly of male TKI workers (94,10%), aged ≥ 20 years old (82,31%), with the destination Korea Selatan country (77,95%), and choosing PPTKIS BNP2TKI (99,78%). 
Penerapan Text Mining untuk Melakukan Clustering Data Tweet Akun Blibli Pada Media Sosial Twitter Menggunakan K-Means Clustering Syiva Multi Fani; Rukun Santoso; Suparti Suparti
Jurnal Gaussian Vol 10, No 4 (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.v10i4.30409

Abstract

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.
PEMODELAN BAYESIAN KONSUMSI RUMAH TANGGA AGREGAT MENGGUNAKAN PRIOR ZELLNER Muhammad Fajar
Jurnal Gaussian Vol 10, No 4 (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.v10i4.30871

Abstract

In the development of statistics, there are two views of parameters, namely frequentist and Bayesian. In Bayesian, the parameter is a random variable, not a constant like a frequentist view. The research aims to estimate the function or model of household consumption agrees using the Bayesian method. The data used in this study are GDP (y) and household consumption (x) at constant prices (2000) for the 1983Q1 - 2016Q4 period sourced from the Statistics-Indonesia. This study results that the Bayesian regression modeling of the household consumption function agrees with Zellner's previous use. The income coefficient in this model is significant and gets a marginal propensity to consume the value of 0.5702. This implies that more than half of people's income is used for consumption purposes.  
ANALISIS TECHNOLOGY ACCEPTANCE MODEL PADA APLIKASI PLATFORM SHOPEE DENGAN PENDEKATAN PARTIAL LEAST SQUARE (STUDI KASUS PADA MAHASISWA UNIVERSITAS DIPONEGORO) Ovie Auliya’atul Faizah; Suparti Suparti; Abdul Hoyyi
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.v10i3.32802

Abstract

E-commerce refers to business transactions using digital networks such as the internet. Based on the rank on the Appstore and Playstore, Shopee places the first rank. In 2019, Shopee had 56 million visitors. Meanwhile, in the same year, it had 3,225 workers. The imbalance between the number of Shopee visitors and Shopee employees allows users to be disappointed with Shopee's services, but on the other hand, there are also many users who are happy with its services. With both positive and negative responses to the services provided by Shopee, this study analyzes the factors affecting the acceptance of Shopee Apps on students of Universitas Diponegoro Semarang. The analysis was based on the Technology Acceptance Model (TAM). It used the Structural Equation Modeling with the Partial Least Square (SEM-PLS) approach. The study used primary data obtained by distributing questionnaires to students of Universitas Diponegoro. The result showed 28 valid indicators, 5 deal inner models, and 8 significant pathways. All the causality between latent variables contained in the Technology Acceptance Model (TAM) have a positive and significant effect, it's just that the results of integrating trust variables on TAM, namely the latent variable between trust and interest in usage behavior, have no significant effect. 
ANALISIS KECENDERUNGAN LAPORAN MASYARAKAT PADA “LAPORGUB..!” PROVINSI JAWA TENGAH MENGGUNAKAN TEXT MINING DENGAN FUZZY C-MEANS CLUSTERING Ratna Kurniasari; Rukun Santoso; Alan Prahutama
Jurnal Gaussian Vol 10, No 4 (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.v10i4.33101

Abstract

Effective communication between the government and society is essential to achieve good governance. The government makes an effort to provide a means of public complaints through an online aspiration and complaint service called “LaporGub..!”. To group incoming reports easier, the topic of the report is searched by using clustering. Text Mining is used to convert text data into numeric data so that it can be processed further. Clustering is classified as soft clustering (fuzzy) and hard clustering. Hard clustering will divide data into clusters strictly without any overlapping membership with other clusters. Soft clustering can enter data into several clusters with a certain degree of membership value. Different membership values make fuzzy grouping have more natural results than hard clustering because objects at the boundary between several classes are not forced to fully fit into one class but each object is assigned a degree of membership. Fuzzy c-means has an advantage in terms of having a more precise placement of the cluster center compared to other cluster methods, by improving the cluster center repeatedly. The formation of the best number of clusters is seen based on the maximum silhouette coefficient. Wordcloud is used to determine the dominant topic in each cluster. Word cloud is a form of text data visualization. The results show that the maximum silhouette coefficient value for fuzzy c-means clustering is shown by the three clusters. The first cluster produces a word cloud regarding road conditions as many as 449 reports, the second cluster produces a word cloud regarding covid assistance as many as 964 reports, and the third cluster produces a word cloud regarding farmers fertilizers as many as 176 reports. The topic of the report regarding covid assistance is the cluster with the most number of members. 
ANALISIS KETAHANAN HIDUP PENDERITA DENGUE HEMORRHAGIC FEVER (DEMAM BERDARAH) DENGAN REGRESI COX KEGAGALAN PROPORSIONAL (Studi Kasus : Rumah Sakit Islam Nahdlatul Ulama Demak) Ummayah, Putri Qodar; Sudarno, Sudarno; Warsito, Budi
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.v10i3.32793

Abstract

Dengue hemorrhagic fever is an acute febrile disease caused by the dengue virus, which enters the human bloodstream through the bite of a mosquito of the genus Aedes Aegypti or Aedes Albopictus. Based on World Health Organization (WHO) records, it is estimated that 500,000 dengue hemorrhagic fever patients require hospital treatment every year and most of the sufferers are children. To analyze the relationship between recovery time in dengue fever patients and the factors that influence it using regression analysis, the dependent variable is the failure time and the function of the response variable tends to fail constant so to find out the relationship using Cox proportional hazard regression. Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is a method used to describe data analysis in terms of time from the time of origin defined until a certain event occurs. In this study, the recovery time of dengue fever patients as a function of failure is proportional. The observations used by the researchers for each patient were not the same. The population of this study were all patients with dengue fever. The data used was obtained from the medical record section for data on the length of hospitalization of patients regarding the recovery of patients with dengue fever. The conclusion of the research shows that the factors that affect the recovery time of dengue fever patients are hematocrit, platelets, immunoglobulin G, and immunoglobulin M. 
KLASIFIKASI REGRESI LOGISTIK MULTINOMIAL DAN FUZZY K-NEAREST NEIGHBOR (FK-NN) DALAM PEMILIHAN METODE KONTRASEPSI DI KECAMATAN BULAKAMBA, KABUPATEN BREBES, JAWA TENGAH Rismia, Erysta Risky; Widiharih, Tatik; Santoso, Rukun
Jurnal Gaussian Vol 10, No 4 (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.v10i4.33095

Abstract

The characteristics of society in choosing contraceptive methods are also the crucial factors for the government to prepare the family planning services needed at Bulakamba District, Brebes Regency, Central Java. In this case, a classification process needs to be done to assist the process of classifying the characteristics of society in the selection of contraceptive methods. Multinomial Logistic Regression classification is good in exploring data information  meanwhile Fuzzy K Nearest Neighbor (FK-NN) classification is good for handling big data and noise. These two methods used in this study because they are relevant to the data applied and will be compared their classification accuracy through APER and Press's Q calculations.The classification accuracy results obtained based on the APER calculation for Multinomial Logistic Regression is 88,25% and Fuzzy K Nearest Neighbor (FK-NN) is 88,92%.  Meanwhile, the Press's Q value of both methods are 9600,945 and 9518,014 greater than χ 2𝛼,1 which is 3,841, so that it is statistically accurate. Based on the results obtained, it can be concluded that Multinomial Logistic Regression classification method has a better classification accuracy than Fuzzy K Nearest Neighbor (FK-NN) method. 
IMPLEMENTASI PAKET SHINY PADA PEMODELAN MULTISCALE AUTOREGRESSIVE UNTUK DATA HARGA SAHAM BBRI Bahtiar Ilham Triyunanto; Suparti Suparti; Rukun Santoso
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.v10i3.32781

Abstract

Stocks are an investment that attract people because they can earn large profits by having claim rights to the company's income and assets so investors have to observe stock price movements in the future to achieve investment goals. One of the statistical methods for time series data modeling is ARIMA. However, modeling assumptions must be fulfilled to use that method so an alternative model is proposed, namely nonparametric regression model, which has no modeling assumptions requirement. In this study, the nonparametric regression multiscale autoregressive (MAR) with two different filter and decomposition level J are compared to choose the best model and forecast it. The data are closing stock price, high stock price and low stock price of BBRI’s stocks that divided into 2 parts, namely in sample data from March 19, 2020 to February 4, 2021 to form a model and out sample data from February 5, 2021 to March 23, 2021 used for evaluation of model performance based on MAPE values. The chosen best model for each stock price are the MAR model with  wavelet haar filter and decomposition level 5 for the closing stock price which produces a MAPE value of 1.194%, the MAR model with wavelet haar filter and decomposition level 5 for the high stock price which produces a MAPE value of 1.283%, and the MAR model with a wavelet haar filter and decomposition level 5 for the low stock price which produces a MAPE value of 1.141%, indicating that the models have excellent forecasting capability. In this study, Graphical User Interface (GUI) using R software with the help of shiny package is also built, making data analyzing easier and generating more interactive display output.
PENERAPAN GRADIENT BOOSTING DENGAN HYPEROPT UNTUK MEMPREDIKSI KEBERHASILAN TELEMARKETING BANK Silvia Elsa Suryana; Budi Warsito; Suparti Suparti
Jurnal Gaussian Vol 10, No 4 (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.v10i4.31335

Abstract

Telemarketing is another form of marketing which is conducted via telephone. Bank can use telemarketing to offer its products such as term deposit. One of the most important strategy to the success of telemarketing is opting the potential customer to create effective telemarketing. Predicting the success of telemarketing can use machine learning. Gradient boosting is machine learning method with advanced decision tree. Gardient boosting involves many classification trees which are continually upgraded from previous tree. The optimal classification result cannot be separated from the role of the optimal hyperparameter.  Hyperopt is Python library that can be used to tune hyperparameter effectively because it uses Bayesian optimization. Hyperopt uses hyperparameter prior distribution to find optimal hyperparameter. Data in this study including 20 independent variables and binary dependent variable which has ‘yes’ and ‘no’ classes. The study showed that gradient boosting reached classification accuracy up to 90,39%, precision 94,91%, and AUC 0,939. These values describe gradient boosting method is able to predict both classes ‘yes’ and ‘no’ relatively accurate.
ANALISIS METODE BAYESIAN PADA KINERJA SISTEM ANTREAN INSTALASI RAWAT JALAN RSUP DR. KARIADI (Studi Kasus: Poliklinik Mata, Poliklinik THT, Laboratorium, dan Pendaftaran) Eny Sulistyowati; Sugito Sugito; Di Asih I Maruddani
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.v10i3.32804

Abstract

Indonesian people’s awareness of the importance of health has increased significantly so that it has a positive impact on the development of the health sector in Indonesia. The largest service facility in Central Java Province is RSUP Dr. Kariadi. The number of patients who came for an examination at Dr. Kariadi’s arrival rate is unpredictable. This can cause the service system to be busy and result in queues. The purpose of this study was to find out how the service system in Dr. Kariadi especially eye polyclinic, ENT polyclinic, laboratory, and registration. Queue theory has random arrivals and services. Bayesian method is used to analyze the queue system, that has been running for a long time by combining the prior and likelihood distribution of samples. Prior distribution is obtained from previous research, namely the Poisson distribution. Meanwhile, the likelihood of the sample obtained from the current study is the Poisson distribution and the Negative Binomial distribution. The resulting queue models for the eye polyclinic are (GAMM/BETA/4):(GD/∞/∞), ENT polyclinic (GAMM/GAMM/2):(GD/∞/∞), laboratory (GAMM/GAMM/4):(GD/∞/∞), and registration (GAMM/GAMM/3):(GD/∞/∞). Based on the results of the study, it was found that the patient care system at the eye polyclinic, ENT polyclinic, laboratory, and registration met steady state condition, meaning that the service system was running well. The value of the unemployment rate at the eye polyclinic is 96,36%; ENT polyclinic 31,86%; laboratory 34,87% and registration 32.85%. Thus, at the eye polyclinic, the unemployment rate is greater than the busy level. Meanwhile, in ENT polyclinics, laboratories, and registration is the opposite occurs. 

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