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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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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).

ANALISIS KEPUASAN MASYARAKAT TERHADAPPELAYANAN PUBLIK MENGGUNAKAN PENDEKATANPARTIAL LEAST SQUARE (PLS) (Studi Kasus: Badan Arsip dan Perpustakaan Daerah Provinsi Jawa Tengah) Emyria Natalia br Sembiring; Abdul Hoyyi; Rukun Santoso
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Public service in Indonesia has grown into a strategic policy issue. The implementation of public services in the field of library is intended to provide library services to the public quickly, precisely and accurately. This research aims to analyze the effect between service quality, collection quality, employee performance, user needs (community) on community satisfaction and its implications for community loyalty or interest in library utilization. The use of SEM based covariance with parametric assumption that research variable must fulfill normal multivariate distribution assumption. However, the research variables do not meet the assumptions of normality then used Partial Least Square (PLS). The research was conducted at the Regional Library of Central Java Province. The method of testing instrument used SPSS Software 22.00 and hypothesis testing using Structural Equation Modeling (SEM) by SmartPLS 3.00 software. Characteristics of respondents are women (65,5%), age 21-25 years (53,5%), and job as student (69,5%). The results showed that the variable quality of service, collection quality, and community give a positive and significant effect, while employee performance variable gives positive effect but not significant to the satisfaction of society. The variable of satisfaction has a positive and significant effect on community loyalty. Keywords: Public Service, Library, Partial Least Square (PLS)

IMPLEMENTASI JARINGAN SYARAF TIRUAN BACKPROPAGATION DENGAN ALGORITMA CONJUGATE GRADIENT UNTUK KLASIFIKASI KONDISI RUMAH (Studi Kasus di Kabupaten Cilacap Tahun 2018) Johanes Roisa Prabowo; Rukun Santoso; hasbi Yasin
Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

House is one aspect of the welfare of society that must be met, because house is the main need for human life besides clothing and food. The condition of the house as a good shelter can be known from the structure and facilities of buildings. This research aims to analyze the classification of house conditions is livable or not livable. The method used is artificial neural networks (ANN). ANN is a system information processing that has characteristics similar to biological neural networks. In this research the optimization method used is the conjugate gradient algorithm. The data used are data of Survei Sosial Ekonomi Nasional (Susenas) March 2018 Kor Keterangan Perumahan for Cilacap Regency. The data is divided into training data and testing data with the proportion that gives the highest average accuracy is 90% for training data and 10% for testing data. The best architecture obtained a model consisting of 8 neurons in input layer, 10 neurons in hidden layer and 1 neuron in output layer. The activation function used are bipolar sigmoid in the hidden layer and binary sigmoid in the output layer. The results of the analysis showed that ANN works very well for classification on house conditions in Cilacap Regency with an average accuracy of 98.96% at the training stage and 97.58% at the testing stage.Keywords: House, Classification, Artificial Neural Networks, Conjugate Gradient

KOMPUTASI METODE SAW DAN TOPSIS MENGGUNAKAN GUI MATLAB UNTUK PEMILIHAN JENIS OBJEK WISATA TERBAIK (Studi Kasus : Pesona Wisata Jawa Tengah) Rima Nurlita Sari; Rukun Santoso; Hasbi Yasin
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Multi-Attribute Decision Making (MADM) is a method of decision-making to establish the best alternative from a number of alternatives based on certain criteria. Some of the methods that can be used to solve MADM problems are Simple Additive Weighting (SAW) Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). SAW works by finding the sum of the weighted performance rating for each alternative in all criteria. While TOPSIS uses the principle that the alternative selected must have the shortest distance from the positive ideal solution and the farthest from the negative ideal solution. Both of these methods were applied in making the selection of the best tourist attractions in Central Java. There are 15 tourist attractions and 7 criteria: location, infrastructure, beauty, atmosphere, tourist interest, promotion, and cost. This primary research employed a questionnaire that passed the questionnaire testing, namely its validity and reliability test. The result of this study shows that the best type of tourism according to the government is temple tour. While water sports tourism is favored by tourism observers. As for college students, the preferred tourist destination is religious tourism. This study also produced a GUI Matlab programming application that can help users in performing data processing using SAW and TOPSIS to select the best attraction in Central Java. Keywords: MADM, SAW, TOPSIS, GUI, tourism

PERBANDINGAN DIAGRAM KONTROL MEWMA DAN DIAGRAM KONTROL T2 HOTELLING UNTUK PENGENDALIAN KUALITAS PRODUK KAIN POLYESTER (Studi Kasus : PT Daya Manunggal Kota Salatiga) Abdiyasti Nurul Arifa; Rukun Santoso; Tatik Widiharih
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Fabrics is one of the most important needs of human life, so demand for clothing is greatly increased. Polyester fabric is a superior product at PT Daya Manunggal Salatiga because it has good quality. The quality of the fabric is very important because it is very influential in the competition to attract consumer interest. To maintain the consistency of the quality of the products produced in accordance with specifications, it is necessary to control the quality of the production process. The quality characteristics used in the production process of polyester fabric are thick layers, thin layers, two weft threads partially and two weft threads one more interconnected with one another, so multivariate control diagrams are used. Multivariate Exponentially Weighted Moving Average (MEWMA) and T2 Hotelling are control diagrams for monitoring mean process. The results showed that the MEWMA control diagram with lambda 0.7 yielded controlled results with a BKA value of 14.56021. Whereas in the Hotelling T2 control diagram a data reduction of four revisions was made to achieve controlled results with a final BKA value of 10.10928. The controlled production process obtained multivariate process capability values of 0.9672105 <1 which means the process is not capable. Comparison of results from the two methods shows that the MEWMA control diagram is more sensitive than the T2 Hotelling control diagram.Keywords: Fabric, Multivariate Exponentially Weighted Moving Average (MEWMA), Hotelling T2, Process Capability Analysis

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 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.v10i4.33099

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.

PERAMALAN OUTFLOW UANG KARTAL DI BANK INDONESIA WILAYAH JAWA TENGAH DENGAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) Aukhal Maula Fina; Tarno Tarno; Rukun Santoso
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Generalized Space Time Autoregressive (GSTAR) model is a method that has interrelation between time and location or called with space time data. This model is generalization of Space Time Autoregressive (STAR) model where GSTAR more flexible for data with heterogeneous location characteristics. The purposes of this research are to get the best GSTAR model that will be used to forecast the outflow in the Bank Indonesia Office (BIO) Semarang, Solo, Purwokerto and Tegal. The best model obtained in this study is GSTAR (11) I(1) using the inverse distance weighting locations. This model has an average value of MAPE 35.732% and RMSE 440.52. The best model obtained explains that the outflow in BIO Semarang, Solo and Purwokerto are affected by two time lag before while for outflow in BIO Tegal is affected by two time lag befor and outflows in three other BIO. Keywords: GSTAR, Space Time, Outflow, Currency

PEMODELAN INDEKS HARGA PROPERTI RESIDENSIAL DI INDONESIA MENGGUNAKAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE Syazwina Aufa; Rukun Santoso; Suparti Suparti
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.v11i1.34001

Generalized Space Time Autoregressive (GSTAR) is a model used for space time data analysis. Space time data is data related to events at previous times and different locations. GSTAR is an expansion of the Space Time Autoregressive (STAR) method. The STAR method is only suitable for homogeneous locations while GSTAR can be used for heterogeneous locations. This research uses Residensial Property Price Index (IHPR) data. IHPR data is in the form of a multivariate time series consisting of 18 cities/regions with a certain time span. In this study, the analysis of IHPR data is carried out by looking at the relationship between the previous time and other cities/regions. Therefore, the method that can be used is GSTAR method. Analysis of IHPR data in each city/region can help increase the supply of housing, thereby reducing the number of backlogs. The backlog of houses in Indonesia is still relatively high. Backlog is an indicator that is often used by the government to measure the number of housing needs in Indonesia. Based on the fulfillment of the assumptions and the smallest MSE value, the best model obtained is GSTAR(4;1,1,1,1) using cross-correlation normalized weight. The largest IHPR data on forcasting results is in the cities of Makassar, Manado, and Surabaya while the smallest IHPR data is in the city of Balikpapan. The GSTAR method produces forcasted data that is close to the actual data so it is good to use.Keywords : GSTAR, OLS, IHPR

PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN ADAPTIVE BOOSTING PADA KASUS KLASIFIKASI MULTI KELAS Ade Irma Prianti; Rukun Santoso; Arief Rachman Hakim
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.v9i3.28924

The company's financial health provides an indication of company’s performance that is useful for knowing the company's position in industrial area. The company's performance needs to be predicted to knowing the company's progress. K-Nearest Neighbor (KNN) and Adaptive Boosting (AdaBoost) are classification methods that can be used to predict company's performance. KNN classifies data based on the proximity of the data distance while AdaBoost works with the concept of giving more weight to observations that include weak learners. The purpose of this study is to compare the KNN and AdaBoost methods to find out better methods for predicting company’s performance in Indonesia. The dependent variable used in this study is the company's performance which is classified into four classes, namely unhealthy, less healthy, healthy, and very healthy. The independent variables used consist of seven financial ratios namely ROA, ROE, WCTA, TATO, DER, LDAR, and ROI. The data used are financial ratio data from 575 companies listed on the Indonesia Stock Exchange in 2019. The results of this study indicate that the prediction of company’s performance in Indonesia should use the AdaBoost method because it has a classification accuracy of 0,84522 which is greater than the KNN method’s accuracy of 0,82087. Keywords: company’s performance, classification, KNN and AdaBoost, classification accuracy.

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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