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All Journal PROSIDING SEMINAR NASIONAL Media Statistika MATEMATIKA Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer AdBispreneur Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Ilomata International Journal of Management Ilomata International Journal of Management International Journal of Economics, Management, Business, and Social Science Jurnal Simki Economic JURNAL ILMIAH GLOBAL EDUCATION Jurnal Manajemen dan Bisnis International Journal of Integrative Sciences Indonesian Journal of Advanced Research (IJAR) Technopreneurship and Educational Development Review (TENDER)

Rukun Santoso

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 2453849

Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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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

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 REGRESI SEMIPARAMETRIK DENGAN PENDEKATAN DERET FOURIER (Studi Kasus: Pengaruh Indeks Dow Jones dan BI Rate Terhadap Indeks Harga Saham Gabungan Laili Rahma Khairunnisa; Alan Prahutama; Rukun Santoso
Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The Composite Stock Price Index (CSPI) is a composite index all of types of shares listed on the stock exchange and their movements indicate conditions that occur in the capital market. CSPI is influenced by macroeconomic factors and foreign exchange index. Dow Jones Industrial Average has a linear relationship with CSPI and BI Rate has a repeated relationship with CSPI, so the method is used semiparametric regression with the Fourier series approach. Estimators in semiparametric regression with Fourier series approach were obtained by the Ordinary Least Square (OLS) method. This study uses monthly data which is divided into in sample data and out sample data. Semiparametric regression modelling with Fourier series approach is done by determining the optimal K value which results in a minimum General Cross Validation (GCV) value. In this study, semiparametric regression model with Fourier series approach formed by the optimal K value is 13 and GCV is 2826122. The results of the evaluation of the accuracy of the model performance and forecasting obtained the coefficient of determination is 0,9226, Mean Absolute Percentage Error (MAPE) data in sample 3,8154% and data out sample is 8,4782% which shows that the model obtained has a very accurate performance.Keywords: Composite Stock Price Index (CSPI), Semiparametric Regression, Fourier Series, OLS, GCV

MODEL ASURANSI KENDARAAN BERMOTOR MENGGUNAKAN DISTRIBUSI MIXED POISSON Tina Diningrum; Yuciana Wilandari; Rukun Santoso
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Motor vehicle insurance is a form of protection of motor vehicles owned by the insured. One of the activities in insurance companies is claim. Claim is risk of loss claim is paid by the insurance company to the insured. Analysis of motor vehicle insurance claims typically uses poisson distribution approach. Nevertheless in many cases of motor vehicle insurance claim, the value of variance greater than the mean value. In this case overdispersed has been going on the assumption poisson distribution. If the poisson distribution continued to be used when going overdispersed, so the poisson distribution is inefficient because it affects the error standard. To solve the problem can be used mixed Poisson distribution. This final project used two mixed Poisson distribution which is a mixture of gamma poison known as negative binomial distribution and poisson-exponential mixture known as a geometric distribution. Carried out on the data motor vehicle claim in PT. Jasa Asuransi Indonesia, Semarang branch year 2010 to 2011 it is estimated that of the 100 vehicle type Car policyholders aged <1 year will be 2 claims per year.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI DIVIDEND PAYOUT RATIO (DPR) MENGGUNAKAN ANALISIS REGRESI LINIER DENGAN BOOTSTRAP (Studi Kasus: PT. Unilever Indonesia, Tbk Tahun 1999-2015) Lia Safitri; Di Asih I Maruddani; Rukun Santoso
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The amount of dividend paid by the company to shareholders or dividend payout ratio is the main factor that investors pay attention to invest their capital into the company. Investors want a relative dividend, even increasing over time. Factors influencing the level of dividend payout ratio are Return on Equity (ROE), stock price, liquidity ratio, and leverage level. Based on this, multiple linear regression analysis with bootstrap is used. The purpose of this study is to analyze the factors that significantly affect the dividend payout ratio based on the best model used to predict the value of dividend payout ratio for the next period. The bootstrap method is used to overcome the occurrence of multicollinearity among independent variables due to the small sample size. Based on the simulation done with software R using PT data. Unilever Indonesia, Tbk from 1999-2015 obtained best model is bootstrap residual with 2 significant independent variable are ROE and level of leverage. Based on the best model, the predicted value of dividend payout ratio of 2016 is 41.60196 with percentage error of 7.0812%. Keywords : Regression analysis, Bootstrap, Dividend Payout Ratio, ROE, leverage

IMPLEMENTASI SUBSET AUTOREGRESSIVE MENGGUNAKAN PAKET FITAR Tomi Ardi; Rukun Santoso; Alan Prahutama
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.v6i4.30385

Time series data analysis is one of the important points in statistics that is a time-dependent analysis. The commonly used model for time series data is ARIMA (Autoregressive Integrated Moving Average) or often also called the Box-Jenkins time series method. A model of ARIMA used in time clock data forecasting is the AR subset (autoregressive). The AR subset model is suitable for a long time series with a more than 5th order lag. The statistical software used is the R. time series AR subset approach on R using the FitAR package. The main function of the FitAR package is SelectModel and FitAR. SelectModel function to get the model automatically while FitAR is used to determine the temporary suspect model. Data used in the form of dataset contained in package FitAR that is SeriesA. The SeriesA data is data about the chemical concentration process observed every 2 hours for 17 days. SeriesA is processed using FitAR package so that the best model is AR [1,2,7].Keywords : Time Series, Time Series Non-stasioner, Subset AR, FitAR Package

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

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.

PENERAPAN STRUCTURAL EQUATION MODELLING (SEM) UNTUK MENGANALISIS FAKTOR – FAKTOR YANG MEMPENGARUHI KINERJA BISNIS (STUDI KASUS KAFE DI KECAMATAN TEMBALANG DAN KECAMATAN BANYUMANIK PADA JANUARI 2019) Ade Irma Pramudita; Tatik Widiharih; Rukun Santoso
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This research is done to examine the effect of quality of service and product attractiveness toward business strategies based on service in order to improving business performance. The sample of this study were Cafe owners in Tembalang Subdistrict and Banyumanik Subdistrict, total are 116 respondents. In this Final Project, the processing of Structural Equation Modeling (SEM) is AMOS software. The results of the analysis show that service quality has a positive effect on business strategies based on service to improving business performance. The most significant factor that affecting business performance is quality of service. Quality of service is important in the performance of a café business. Cafe owners must always pay attention to the quality of café service to customers, because the quality of service is the main consideration for customers to visit cafes.

PENGUKURAN PROBABILITAS KEBANGKRUTAN OBLIGASI KORPORASI DENGAN SUKU BUNGA COX INGERSOLL ROSS MODEL MERTON (Studi Kasus Obligasi PT Indosat, Tbk) Muhammad Akhir Siregar; Mustafid Mustafid; Rukun Santoso
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Nowadays bonds become one of the many securities products that are being prefered by investors. Observing the level of the company's rating which good enough or in the criteria of investment grade can’t be a handle of investors. Investing in long-term period investors should understand the risks to be faced, one of investment credit risk on bonds is default risk, this risk is related to the possibility that the issuer fails to fulfill its obligations to the investor in due date. The measurement of the probability of default failure by the structural method approach introduced first by Black-Scholes (1973) than developed by Merton (1974). In Bankruptcy model, merton’s model assumed the company get default (bankrupt) when the company can’t pay the coupon or face value in the due date. Interest rates on the Merton model assumed to be constant values replaced by Cox Ingersoll Ross (CIR) rates. The CIR rate is the fluctuating interest rate in each period and the change is a stochastic process. The empirical study was conducted on PT Indosat, Tbk's bonds issued in 2017 with a face value of 511 Billion in payment of obligations by the issuer for 10 years. Based on simulation results done with R software obtained probability of default value equal to 7,416132E-215 Indicates that PT Indosat Tbk is deemed to be able to fulfill its obligation payment at the end of the bond maturity in 2027. Keywords: Bond, CIR Rate, Merton Model, Ekuity, Probability of default

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

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.

GRAFIK PENGENDALI MIXED EXPONENTIALLY WEIGHTED MOVING AVERAGE – CUMULATIVE SUM (MEC) DALAM ANALISIS PENGAWASAN PROSES PRODUKSI (Studi Kasus : Wingko Babat Cap “Moel”) Aulia Resti; Tatik Widiharih; Rukun Santoso
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.v10i1.30938

Quality control is an important role in industry for maintain quality stability. Statistical process control can quickly investigate the occurrence of unforeseen causes or process shifts using control charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) control chart is a tool used to monitor and evaluate whether the production process is in control or not. The MEC control chart method is a combination of the Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. Combining the two charts aims to increase the sensitivity of the control chart in detecting out of control. To compare the sensitivity level of the EWMA, CUSUM, and MEC methods, the Average Run Length (ARL) was used. From the comparison of ARL values, the MEC chart is the most sensitive control chart in detecting out of control compared to EWMA and CUSUM charts for small shifts. Keywords: Grafik Pengendali, Exponentially Weighted Moving Average, Cumulative Sum, Mixed EWMA-CUSUM, Average Run Lenght, EWMA, CUSUM, MEC, ARL

Co-Authors Abdiel Pandapotan Manullang Abdiyasti Nurul Arifa Abdul Hoyyi Achmad Soleh Ade Irma Pramudita Ade Irma Prianti Agum Prafindhani Putri, Agum Prafindhani Agus Rusgiyono Agustian, Kresnawidiansyah Aini Nurul Al Qarani, Muhammad Aqajahs Alan Prahutama Alan Prahutama Alika Ramadhani Alvita Rachma Devi Arief Rachman Hakim Aris Sugiharto Aukhal Maula Fina Aulia Resti Avida Anugraheni AYU LESTARI Bahtiar Ilham Triyunanto Brahim Abdullah Brahim Abdullah Budi Warsito Chrisentia Widya Ardianti Dhimas Bayususetyo Di Asih I Maruddani Di Asih I Maruddani Diah Aliyatus Saidah Diah Safitri Dinda Virrliana Ramadhanti Dwi Nooriqfina Emyria Natalia br Sembiring Endang Saefuddin Mubarok Erwin Permana Fauziyyah, Fida Fuadah, Alfi Gina Rosalinda Hadi, Bawa Mulyono Hana Hayati Hanum, Cholida Hasbi Yasin Hasbi Yasin Infan Nur Kharismawan Iryanto, Rivaldo Kurniawan Iyan Antono Jenesia Kusuma Wardhani Johanes Roisa Prabowo Khansa Amalia Fitroh Krismayadi Krismayadi Kurniawati, Galuh Nurvinda Laili Rahma Khairunnisa Lia Safitri Maharani, Chintya Ayu Mamuki, Emiliyan Margo Purnomo Mifta Fara Sany Mubarok, Endang Saefuddin Mubarok, Endang Saifuddin Muchammad Aziz Chusen Muhamad Syukron Muhammad Akhir Siregar Mustafid Mustafid Noer Rachma, Gustyas Zella Nor Hamidah Permana, Erwin Puspita Kartikasari Rahmat Hidayat Rahmatul Akbar Ratih Ayu Sekarini Ratna Kurniasari Ria Epelina Situmorang Ria Sulistyo Yuliani Rima Nurlita Sari Rismia, Erysta Risky Rita Rahmawati Rita Rahmawati Rosinar Siregar Saepudin, Yunus Sahara Sahara Sekarini, Ratih Ayu Setiani, Eri Shinta Karunia Permata Sari Siti Munawaroh Subagja, Asep Zamzam Subari Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Suparti Suparti Suparti Suparti Syazwina Aufa Syiva Multi Fani Tamura Rolasnirohatta Siahaan Tarno Tarno Tasrif, Mohammad Jon Tatik Widiharih Tatik Widiharih Ta’fif Lukman Afandi Thea Zulfa Adiningrumh Tina Diningrum Tita Aulia Edi Putri Tomi Ardi Uswatun Hasanah Utami, Krisdiana Nur Via Risqiyanti Wahyu Tiara Rosaamalia wardhana, galih wisnu Wijayanto, Ahmad Windianingsih, Agustin Wiwin Wiwin Wiwin, Wiwin Yuciana Wilandari Zen, Agustian

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