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All Journal Media Statistika MATEMATIKA SAINS DAN MATEMATIKA Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang

Agus Rusgiyono

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 366013

Decision Sciences, Operations Research & Management Mathematics Other

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IDENTIFIKASI LAMA STUDI BERDASARKAN KARAKTERISTIK MAHASISWA MENGGUNAKAN ALGORITMA C4.5 (Studi Kasus Lulusan Fakultas Sains dan Matematika Universitas Diponegoro Tahun 2013/2014) Bramaditya Swarasmaradhana; Moch. Abdul Mukid; Agus Rusgiyono
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Based on academics regulation No. 209/PER/UN7/2012, the study period of students in Diponegoro University has been scheduled for 4 years. In this study the graduation status of students that graduate under or equal to 4 years categorized as graduate on time, meanwhile students that graduate over 4 years categorized as graduate out of time. Hence, it is important to understand the profile of students who graduate on time and out of time based on gender, majors, GPA, organizational experience, part time experience, scholarship, students origin and pathways scholar. The purpose of this study is to identify those students profiles using Algorithm C4.5. Algorithm C4.5 contructs a decision tree that able to handle missing values, able to handle continues attribute and able to simplify the trees by pruning. The accuration of the Algorithm C4.5 is 84.475% and the number of the nodes are 20 nodes where 13 nodes are leaf nodes. The students profile that identified graduate on time are students of Physics who had received scholarship and a woman; students of Chemistry with GPA > 3.06; students of Statistics with GPA > 3.43 from SNMPTN also PSSB and students of Mathematics with GPA > 2.96. Keywords: Study Period, Algorithm C4.5, Decision Tree.

ANALISIS DISKRIMINAN FISHER POPULASI GANDA UNTUK KLASIFIKASI NASABAH KREDIT Ungu Siwi Maharunti; Moch. Abdul Mukid; Agus Rusgiyono
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is the biggest asset carried out by a bank and become the most dominant contributor to the bank income. However, the activity to distribute the credit takes a risk which can influence health and continuance of bank business. The credit risk which potentially occurs can be measured and controlled by analyzing directly whichever the credit client categorized to. The credit risk categorized to current credit, in specific concern credit, less current credit, doubtful credit and bad credit based on Bank Indonesia Regulation No.: 7/2/PBI/2005. The independent variables used in this research are nominal credit, principal balance, in time being bank client, time period, and bank interest. Fisher multiple discriminant analysis is a method whose assumption equality of covariance matrices. The result from using the Fisher multiple discriminant analysis in data of credit client from bank “X” in Pati shows that variable principal balance, in time being bank client, time period, and bank interest significant to measure credit risk. The classification using the Fisher multiple discriminant analysis in data of credit client from bank “X” in Pati gives the accurate 64,33%. Keywords: credit, classification, fisher multiple discriminant analysis

PERBANDINGAN MODEL ARIMA DAN FUNGSI TRANSFER PADA PERAMALAN CURAH HUJAN KABUPATEN WONOSOBO Siti Lis Ina Atul Hidayah; Agus Rusgiyono; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Rainfall is one of the things that affect agricultural production. The highest amount of rainfall will cause perturbation in the pollination of flowers and caused zalacca palm to produce fruits no season of the year. Zalacca palm is growing well in heavy rainfall area.. There are some factors which influence rainfall; those are: humidity, solar energy, wind direction and velocity as well as air temperature. The application of ARIMA (Autoregressive Integrated Moving Average) and multi input transfer function was intended to model the rainfall which would be forecasted based on the best model chosen. There were two kinds of variables used in this study. Those were rainfall as the output series while humidity and air temperature as the input series during January 2009 to October 2014. The result showed that ARIMA ([3], 1, [12]) had a fewer Schwart’z Bayesian Criterion (SBC) value 293.199 than multi input transfer function model (0,0,0) (0,1,0) with the result 906.9632.Keywords: Rainfall, ARIMA, Transfer Function

ANALISIS PENGELOMPOKAN DAERAH MENGGUNAKAN METODE NON-HIERARCHICAL PARTITIONING K-MEDOIDS DARI HASIL KOMODITAS PERTANIAN TANAMAN PANGAN (Studi Kasus Kabupaten/Kota Se-Jawa Tengah Tahun 2009 – 2013) Etik Setyowati; Agus Rusgiyono; Moch. Abdul Mukid
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Non-Hierarhical K-Medoids Partitioning is a clstering method for classifying objects based on the characteristics possessed by the object, wherein the object k randomly selected to be medoids is the center of the cluster. After medoids selected then other objects that have similarities with medoids made in one cluster. Medoids is the object which is considered to represent a cluster. Similarity between objects is calculated using euclidean distance. One application grouping method Non-Hierarhical K-Medoids Partitioning is to classify District in Central Java is based on the production of rice and pulses. Grouping Regency / City in Central Java using Non-Hierarhical Partitioning K-Medoids obtained information that rice production by Regency / City in Central Java can be grouped into seven clusters, but because of a case in 2010 and in 2011 the number of clusters that formed are two clusters, while the production of food crops by Regency / City in Central Java can be grouped into two clusters.Keywords: k-medoids, Non-Hierarhical, Euclidean distance, Similarities.

APLIKASI METODE PUNCAK AMBANG BATAS MENGGUNAKAN PENDEKATAN DISTRIBUSI PARETO TERAMPAT DAN ESTIMASI PARAMETER MOMEN-L PADA DATA CURAH HUJAN (Studi Kasus : Data Curah Hujan Kota Semarang Tahun 2004-2013) Tyas Estiningrum; Agus Rusgiyono; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The rainfall with very high intensity cause a lot of problem like flood, landslide and be a factor restricting of flight aircraft at the airport. One of the methods that can be use to analyze such extreme events is Peak Over Threshold (POT) with distribution approach Generalized Pareto Distribution (GPD) include in the Extreme Value Theory (EVT). L-Moment method used for estimation of scale and shape parameter from GPD. In this research, data used is daily rainfall data of the Semarang city in 2004-2013 that recorded at the Meteorological Station of Class II Ahmad Yani Semarang. Daily rainfall data is analyzed each year during the rainy season. Result of analysis of the data shows rainfall there are heavy tail that indicates there is a possibility of occurrence extreme value. Return level obtained indicated occurrence of precipitation with very high intensity for the period of rainy season in 2006/2007, 2009/2010, 2010/2011, 2011/2012, 2012/2013 and 2013/2014 with intensity of rainfall 117,1905730 mm/day, 118,6389421 mm/day, 106,5032441 mm/day, 107,2133094 mm/day, 108,2262353 mm/day dan 111,2356887 mm/day.Keyword : Rainfall, Peak Over Threshold, Generalized Pareto Distribution, Extreme Value Theory, L-Moment, Return level.

PERHITUNGAN DAN ANALISIS PRODUK DOMESTIK REGIONAL BRUTO (PDRB) KABUPATEN/KOTA BERDASARKAN HARGA KONSTAN (Studi Kasus BPS Kabupaten Kendal) Fitriani Fitriani; Agus Rusgiyono; Triastuti Wuryandari
Jurnal Gaussian Vol 2, No 2 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Gross Regional Domestic Product (GRDP) is technical term that always we heard in the civil government or in the public society. According to Statistics Indonesia, GRDP is total number of added value who producting by effort unit in that domestic area. GRDP is one of economics growth indicator in the domestic area. If GRDP is higher, then people economics prosperity must be high too, and do also that opposite. GRDP contains of 2 methods, that is GRDP at Current Market Prices and GRDP at Constant Prices. In this report will discuss about GRDP at Constant Prices with GRDP the Kendal Regency at 2000 Constant Prices in 2010 for example. Arranging GRDP at Constant Prices has purpose to find out economics condition from year to year by discern the GRDP every year. The methods to arranging GRDP at Constant Prices are revaluasi, ekstrapolasi, and deflasi. After doing the accounting by Statistics Indonesia, we obtainable GRDP the Kendal Regency at Constant Prices in 2010 in million rupiahs is 5.394.079,31. And according the analysis, GRDP from 1983 to 2011 show the linear graph that has model GRDP = -986933 + 220901 (X). This model, can use to forecasting for GRDP the Kendal Regency at Constant Prices over the next years.

PERAMALAN HARGA EMAS DUNIA DENGAN MODEL GLOSTEN-JAGANNATHAN-RUNCLE GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY Uswatun Hasanah; Agus Rusgiyono; Rukun Santoso
Jurnal Gaussian Vol 11, No 2 (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.v11i2.35477

Gold investment is considered safer and has less risk than other types of investment. One of the important knowledge in investing in gold is predicting the price of gold in the future through modeling the price of gold in the past. The purpose of this study is to model the gold price in the past so that it can be used to predict gold prices in the future. The world gold price data is a time series data that has heteroscedasticity properties, so the time series model used to solve the heteroscedasticity problem is GARCH. This study has an asymmetric effect, so the asymmetric GARCH model is used, namely the Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) model to model the world gold price data. The data is divided into in-sample data from January 3, 2012 to December 31, 2018 to create a world gold price model and out-sample data from January 1, 2019 to December 31, 2020, which is used to evaluate model performance based on MAPE values. The best model is the ARIMA(1,1,0) GJR-GARCH(1,1) model with a MAPE data out sample value of 18,93% which shows that the performance of the model has good forecasting abilities.

ANALISIS SENTIMEN DATA ULASAN APLIKASI RUANGGURU PADA SITUS GOOGLE PLAY MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER DENGAN NORMALISASI KATA LEVENSHTEIN DISTANCE Hindun Habibatul Mubaroroh; Hasbi Yasin; Agus Rusgiyono
Jurnal Gaussian Vol 11, No 2 (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.v11i2.35472

One form of technological development in education is the increasing number of online based learning. More than that, during this period of Covid-19 pandemic distance education was tried by the government that requires learning are done online. The online learning application that is the implementation of this technological development continues to show its existence. Many non-formal educational companies are available, one of which is the Ruangguru, getting a nickname as a number one learning application requires the Ruangguru to continue and improve the performance. Users of the Ruangguru application can communicate a response to Ruangguru through the review feature available on the google play site. The reviews that have been written can be analyzed how the user sentiment is whether positive or negative using Multinomial Naïve Bayes. This method is used because it is easy to use with simple structures and gives high accuracy values. The model will be selected using 10-fold cross validation method to get the model with the best accuracy. The normalization phase of words was also perfected using Levenshtein Distance method that was proven to add accuracy value. Performance result using Multinomial Naïve Bayes by adding Levenshtein Distance method to fix the words gives an average accuracy value of 88,20% with the 8th fold as the fold with the best accuracy value of 94%.

ANALISIS METODE ANTREAN DAN SIMULASI MONTE CARLO PADA ANTREAN DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL (DISDUKCAPIL) KOTA SALATIGA DILENGKAPI GUI-R Diyah Rahayu Ningsih; Sugito Sugito; Agus Rusgiyono
Jurnal Gaussian Vol 11, No 3 (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.11.3.418-428

One of the services that often occurs in everyday life is the queue service. Queues can arise due to delays in a service system in providing a service, resulting in a row of a group of people to get a service. The queue analyzed in this study is a queue in The Salatiga City Disdukcapil. The parameters on which this research is based are the number of arrivals (λ) and service time (μ) of visitors who arrive. The methods used are queue analysis and Monte Carlo simulation. The Monte Carlo method provides more effective results at each counter than using queue analysis. The result of this study is a decrease in the utilization rate of service facilities, so that it is accompanied by a decrease in the size of system performance for the calculation of Lq, Ls, Wq, and Ws. Decreases in utilization rates and system performance measures at each counter make an increase in the probability of idle systems at each counter. The model generated by the sample data with the Monte Carlo simulation data tends to be the same, namely for counter 1,2,3,4, counter 5 model (G/G/c):(GD/¥/¥), and for counter 6 with queuing model ( G/M/1):(GD/¥/¥).

PERBANDINGAN METODE HOLT WINTER’S EXPONENTIAL SMOOTHING DAN EXTREME LEARNING MACHINE UNTUK PERAMALAN JUMLAH BARANG YANG DIMUAT PADA PENERBANGAN DOMESTIK DI BANDARA UTAMA SOEKARNO HATTA Kevin Togos Parningotan Marpaung; Agus Rusgiyono; Yuciana Wilandari
Jurnal Gaussian Vol 11, No 3 (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.11.3.439-446

The loading of goods carried out at the airport is an essential part of the transporting goods system. In this regard, it is necessary to have a prediction to make the right policy or to solve the problems that occur. Holt Winter's Exponential Smoothing, which one of the classic methods of analyzing time series data, and Extreme Learning Machine which is part of the artificial neural network method, are methods that can be used as a tool for forecasting problems. Holt Winter's Exponential Smoothing uses three times of smoothing on related data, which are level smoothing, trend smoothing, and season smoothing, while Extreme Learning Machine goes through three stages, which are normalization, training, and denormalization. In measuring the error rate in related forecasting, the symmetric Mean Absolute Percentage Error (sMAPE) value is used. The Holt Winter's Exponential Smoothing method Additive model produces a sMAPE value of 26.14%; while the Multiplicative model with the same method resulted in the sMAPE value of 25.69%. For the Extreme Learning Machine method, the sMAPE value is 49.85%. Based on the accuracy test using the sMAPE value, Holt Winter's Exponential Smoothing method Multiplicative model is the better method than Extreme Learning Machine

Co-Authors Abdul Hoyi Abdul Hoyyi Agustina Sunarwatiningsih Alan Prahutama Alan Prahutama Andreanto Andreanto Anggita, Esta Dewi Anifa Anifa Anindita Nur Safira ANNISA RAHMAWATI Annisa Rahmawati Arief Rachman Hakim Aulia Putri Andana Aulia Rahmatun Nisa Bagus Arya Saputra Bayu Heryadi Wicaksono Bellina Ayu Rinni Besya Salsabilla Azani Arif Bramaditya Swarasmaradhana Budi Warsito Dede Zumrohtuliyosi Dermawanti Dermawanti Desy Tresnowati Hardi Di Asih I Maruddani Diah Safitri Diah Safitri Dian Mariana L Manullang Dini Anggreani Diyah Rahayu Ningsih Dwi Asti Rakhmawati Dwi Ispriyansti Dwi Ispriyanti Eis Kartika Dewi Ely Fitria Rifkhatussa'diyah Elyasa, Fatiya Rahmita Enggar Nur Sasongko Etik Setyowati Etik Setyowati, Etik Farisiyah Fitriani fatimah Fatimah Febriana Sulistya Pratiwi Feby Kurniawati Heru Prabowo Fitriani Fitriani Hana Hayati Hanik Malikhatin Hanik Rosyidah, Hanik Hasbi Yasin Hasbi Yasin Hildawati Hildawati Hindun Habibatul Mubaroroh Ika Chandra Nurhayati Ilham Muhammad Imam Desla Siena Inas Husna Diarsih Iwan Ali Sofwan Kevin Togos Parningotan Marpaung Listifadah Listifadah M. Afif Amirillah M. Atma Adhyaksa Marthin Nosry Mooy Maryam Jamilah An Hasibuan Maulana Taufan Permana Merlia Yustiti Moch. Abdul Mukid Moch. Abdul Mukid Muhammad Rizki Muhammad Taufan Mustafid Mustafid Mustafid Mustafid Mustofa, Achmad Nabila Chairunnisa Nor Hamidah Noveda Mulya Wibowo Novie Eriska Aritonang Nur Khofifah Nur Walidaini Octafinnanda Ummu Fairuzdhiya Puji Retnowati Puspita Kartikasari Putri Fajar Utami Rengganis Purwakinanti Revaldo Mario Ria Sulistyo Yuliani Riana Ikadianti Riszki Bella Primasari Rita Rahmawati Rita Rahmawati Rizal Yunianto Ghofar Rizky Aditya Akbar Rosita Wahyuningtyas Rukun Santoso Salsabila Rizkia Gusman Setiyowati, Eka Shella Faiz Rohmana Siti Lis Ina Atul Hidayah Sudargo Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Sugito Sugito Suparti Suparti Suparti Suparti Susi Ekawati sutimin sutimin Tarno Tarno Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Tiani Wahyu Utami Tika Dhiyani Mirawati Tika Nur Resa Utami, Tika Nur Resa Titis Nur Utami Tri Ernayanti Tri Yani Elisabeth Nababan Triastuti Wuryandari Triastuti Wuryandari Tyas Ayu Prasanti Tyas Estiningrum Ulfi Nur Alifah Ungu Siwi Maharunti Uswatun Hasanah Vierga Dea Margaretha Sinaga Viliyan Indaka Ardhi Winastiti, Lugas Putranti Yogi Isna Hartanto Yuciana Wilandari Yuciana Wilandari Yuciana Wilandari

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