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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 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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IMPLEMENTASI MODEL ACCELERATED FAILURE TIME (AFT) BERDISTRIBUSI LOG-LOGISTIK PADA PASIEN PENYAKIT JANTUNG BAWAAN Dwi Nooriqfina; Sudarno Sudarno; 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.v10i3.32796

Log-Logistic Accelerated Failure Time (AFT) model is survival analysis that is used when the survival time follows Log-Logistic distribution. Log-Logistic AFT model can be used to estimate survival time, survival function, and hazard function. Log-Logistic AFT model was formed by regressing covariates linierly against the log of survival time. Regression coefficients are estimated using maximum likelihood method. This study uses data from Atrial Septal Defect (ASD) patients, which is a congenital disease with a hole in the wall that separates the top of two chambers of the heart by using sensor type III. Survival time as the response variable, that is the time from patient was diagnosed with ASD until the first relapse and uses age, gender, treatment status (catheterization/surgery), defect size that is the size of the hole in the heart terrace, pulmonary hypertension status, and pain status as predictor variables. The result showed that variable gender, treatment status, defect size, pulmonary hypertension status, and pain status affect the first recurrence of ASD patients, so it is found that category of female, untreated patient, defect size ≥12mm, having pulmonary hypertension, having chest pain tend to have first recurrence sooner than the other category.

PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) DAN WEIGHTED PRODUCT (WP) DALAM SISTEM PENUNJANG PEMILIHAN LAPTOP TERFAVORIT MENGGUNAKAN GUI MATLAB Abdiel Pandapotan Manullang; Alan Prahutama; Rukun Santoso
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Laptops have become an important requirement for most students is to support educational activities and business activities. The number of brands of laptops or types of laptops that exist makes consumers especially students have their own preferences in choosing a laptop. The method can be used to select the favorite laptop are SAW (Simple Additive weighting) and WP (Weighted Product). Both of these methods are the methods used to solve the problem of MADM (Multi Attribute Decision Making). There are 30 types of laptops that will be used in the selection of the favorite laptops.For the selection criteria for the type of laptop that is priced, RAM (Random Access Memory), HDD (hard drive), a processor, a VGA (Video Graphics Array), weight, color, screen size, service centers, warranty, availability of spare parts, battery capacity, equipped with OS and application software. Selection of the favorite type of laptop is done with the help of MATLAB (Graphical User Interface) GUI (Matrix Laboratory) as a computing tool. SAW method and WP, in this research showed the same results that the most favored type of laptop laptop mode DEL INSPIRON 15Z-5523 with a value preference for SAW method amounted to 0.9518 while the WP method amounted to 0.9511.Keywords: SAW, WP, Laptop, favorite, GUI

KLASTERISASI PROVINSI DI INDONESIA BERDASARKAN FAKTOR PENYEBARAN COVID-19 MENGGUNAKAN MODEL-BASED CLUSTERING t-MULTIVARIAT Nor Hamidah; Rukun Santoso; Agus Rusgiyono
Jurnal Gaussian Vol 11, No 1 (2022): 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.v11i1.33999

The spread of Covid-19 had a significant impact in all sectors. Enforcement policies from the government that are appropriate with the conditions for the spread of the virus that are needed to prevent a bigger impact. Clusteritation by province based on data on the spread of Covid-19 is important for the government to set appropriate policies in order to prevent the spread of Covid-19. The data used include data on population density, testing rate, proportion of population 50 years and over, and proportion of population diligently hand-washing in each province. The data factors for the spread of Covid-19 tend to overlap and there are outliers in the data which causes the data not normally distributed. In this study, Model-Based Clustering t-multivariate was used for data clustering. The results show that using Integrated Completed Likelihood, two groups of optimal cluster were obtained. The second cluster has a higher risk of spreading Covid-19 than the first cluster. Keywords : Covid-19, Clustering, Model-Based Clustering t-Multivariat

ANALISIS DAMPAK SHOCK VOLUME PERDAGANGAN SAHAM PADA INDEKS HARGA SAHAM CONSUMER GOODS DENGAN STRUCTURAL VECTOR AUTOREGRESSIVE (SVAR) Infan Nur Kharismawan; Rukun Santoso; Budi Warsito
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The stock trading in the capital market will result daily volume of trading stock that impact on stock price. One of the indicators that describes the stock price movement is stock index. There are many types of stock index, one of them is consumer goods stock index. Stock index is a sensitive economic variable affected by shock and need a restriction to form its economic model. Based on that, Structural Vector Autoregressive (SVAR) is used to describe its economic model. SVAR is formed by a stable VAR, fulfilled white noise, k-variate normal distribution. The purpose of this study are to forecast data on each variables and analyze the impact of the shock through the descriptions of variance decomposition. VAR used as the basis for SVAR is VAR(8) whose the forming variable stationary at the first different degree. Performances of forecasting SVAR using MAPE (Mean Absolute Percentage Error) for in sample data are 13.87434% (volume of trading stock) and 0.87045% (consumer goods stock index) and for out sample data are 14.22964% (volume of trading stock) and 1.76054% (consumer goods stock index). Response of consumer goods stock index to the impact of the volume of trading stock shock shown by proportion of variance decomposition tends to increase, while the shock by itself has decreased until reach its equilibrium point. Keywords:cosumer goods stock index, SVAR, variance decomposition, volume of trading stock

PERAMALAN INDEKS HARGA SAHAM MENGGUNAKAN ENSEMBLE EMPIRICAL MODE DECOMPOSITION (EEMD) Rosinar Siregar; Rukun Santoso; Puspita Kartikasari
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.v10i2.29919

Stock price fluctuations make investors tend to hesitate to invest in stock markets because of an uncertain situation in the future. One method that can solve these problems is to use forecasting about the stock prices in the future. Generally, the huge size of data non linear and non stationary, and it is difficult to be interpreted in concrete. This problem can be solved by performing the decomposition process. One of decomposition method in time series data is Ensemble Empirical Mode Decomposition (EEMD). EEMD is process decomposition data into several Intrinsic Mode Function (IMF) and the IMF residue. In this research, this concept applied to data Stock Price Index in Property, Real Estate, and Construction from July 1, 2019 to July 30, 2020 as many as 272 data. Based on the results of data processing, as many as 6 IMF and IMF remaining were used as IMF forecasting and the IMF remaining in the future. The forecast was performed by choosing the best model of each IMF component and IMF remaining, used ARIMA and polynomial trend. Keywords: Time Series Data, Stock Price Index, EEMD, ARIMA, Polynomial Trend.

PERAMALAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA DI KEPULAUAN RIAU DENGAN MENGGUNAKAN MODEL FUNGSI TRANSFER Tamura Rolasnirohatta Siahaan; Rukun Santoso; Alan Prahutama
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Transfer function models is a data analysis model that combines time series and causal approach, in another words, transfer function models is a method that ilustrates that the predicted value in teh future is affected by the past value time series and based on one or more related time series. In this research, an analysis of the number of tourist arrival and rainfall in several regions in Kepulauan Riau from January 2013 until December 2017 was aimed at obtaining a transfer function model and forecasting the number of tourist arrival in several regions of the Kepulauan Riau for next periods. Based on the result of the analysis, rainfall in Tanjung Pinang does not affect the visit of tourist with the values of MAPE is 13,63494%. Rainfall in Batam also does not affect the visit of tourist with the values of MAPE is 7,977151%. While in Tanjung Balai Karimun, tourist arrivals was affected by rainfall with the values of MAPE is 10,32777%.

PEMBENTUKAN PORTOFOLIO SAHAM DENGAN METODE MARKOWITZ DAN PENGUKURAN VALUE AT RISK BERDASARKAN GENERALIZED EXTREME VALUE (Studi Kasus: Saham Perusahaan The IDX Top Ten Blue 2017) Ria Epelina Situmorang; Di Asih I Maruddani; Rukun Santoso
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In financial investment, investors will try to minimize risk and increase returns for portfolio formation. One method of forming an optimal portfolio is the Markowitz method. This method can reduce the risk and increase returns. The performance portfolio is measured using the Sharpe index. Value at Risk (VaR) is an estimate of the maximum loss that will be experienced in a certain time period and level of trust. The characteristics of financial data are the extreme values that are alleged to have heavy tail and cause financial risk to be very large. The existence of extreme values can be modeled with Generalized Extreme Value (GEV). This study uses company stock data of The IDX Top Ten Blue 2017 which forms an optimal portfolio consisting of two stocks, namely a combination of TLKM and BMRI stocks for the best weight of 20%: 80% with the expected return rate of 0.00111 and standard deviation of 0.01057. Portfolio performance as measured by the Sharpe index is 1,06190 indicating the return obtained from investing in the portfolio above the average risk-free investment return rate of -0,01010. Risk calculation is obtained based on Generalized Extreme Value (GEV) if you invest both of these stocks with a 95% confidence level is 0,0206 or 2,06% of the current assets. Keywords: Portfolio, Risk, Heavy Tail, Value at Risk (VaR), Markowitz, Sharpe Index, Generalized Extreme Value (GEV).

PERBANDINGAN METODE SMOTE RANDOM FOREST DAN SMOTE XGBOOST UNTUK KLASIFIKASI TINGKAT PENYAKIT HEPATITIS C PADA IMBALANCE CLASS DATA Muhamad Syukron; Rukun Santoso; 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.v9i3.28915

Hepatitis causes around 1.4 million people die every year. This number makes hepatitis to be the largest contagious disease in the number of deaths after tuberculosis. Liver biopsy is still the best method for diagnosing the stage of hepatitis C, but this method is an invasive, painful, expensive, and can cause complications. Non-invasively method needs to be developed, one of non-invasif method is machine learning. Random Forest and XGboost are classification methods that are often used, since they have many advantages over classical classification methods. The SMOTE algorithm can be used to improve the accuracy of predictions from imbalanced data. the data in this study have 24 independent variables in the form of patients self-data, hepatitis C symptoms, and laboratory test results. The dependent variable in this study is a binary category, namely the level of hepatitis C disease (fibrosis and cirrhosis). The results showed that the random forest and XGboost had an accuracy of around 74% but the recall value was less than 2%. SMOTE random forest dan SMOTE XGboost have an accuracy & recall value more than 75%. SMOTE random forest has a higher accuracy for predicting fibrosis class while SMOTE XGboost is better in cirrhosis class. Variables that are more influental in determining hepatitis C stage are variables from laboratory test. Keyword : Fibrosis, Cirrhosis, Random Forest, SMOTE, XGboost

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 CALON PENDONOR DARAH MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER (Studi Kasus : Calon Pendonor Darah di Kota Semarang) Dhimas Bayususetyo; Rukun Santoso; Tarno Tarno
Jurnal Gaussian Vol 6, No 2 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Classification is the process of finding a model or function that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. There are some methods that are included in the classification methods, one of them is Naïve Bayes. Naïve Bayes is a prediction technique that based simple probabilistic are based on the application of Bayes theorem with strong independence assumption. On this study carried out correction to the Naïve Bayes method in calculating the conditional probability of each feature using two approaches, normal density function and cumulative distribution function approaches. These two approaches are used to classify prospective blood donors in Semarang City. The predictor variables used are hemoglobin level, upper blood pressure, lower blood pressure, and weight. The result of this study shows that both approaches have the same Matthews Correlation Coefficient (MCC) values, 0.8985841 or close to +1. It means that both approaches equally well doing classification.Keywords: Classification, Naïve Bayes, Normal Density Function, Cumulative Distribution Function, Blood Donors, Matthews Correlation Coefficient (MCC).

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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