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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Jurnal Kesehatan Lingkungan indonesia Media Statistika JURNAL SISTEM INFORMASI BISNIS Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Sains dan Teknologi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika WARTA Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer JTP - Jurnal Teknologi Pendidikan Indonesian Journal of Community Services Journal of Applied Data Sciences Jurnal Riset Teknologi Pencegahan Pencemaran Industri Indonesian Journal of Librarianship Proceeding Biology Education Conference Media Pustakawan STATISTIKA Journal of Bioresources and Environmental Sciences Scientific Journal of Informatics

Budi Warsito

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 413102

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 91 Documents Claim Missing Document

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GREY FORECASTING MODEL IMPLEMENTATION FOR FORECAST OF CAPTURED FISHERIES PRODUCTION muhammad shodiq; Budi Warsito; Rachmat Gernowo
Jurnal Ilmiah Kursor Vol 9 No 4 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i4.170

The increasing need for fish causes problems related to production in the fisheries sector. In fisheries production all information related to (fishing ground) is well known, but on the other hand it is not easy to predict the amount of production due to unclear information. This is also related to the number of ships that make trips, the length (time) of the trip, the type of fishing gear, weather conditions, the quality of human resources, natural environmental factors, and others. The purpose of this study is to apply Grey forecasting model or GM (1,1) to predict fisheries production. Grey forecasting models are used to build forecast models with limited amounts of data with short-term forecasts that will produce accurate forecasts. This study employs the data of captured fish from 2010 to 2018 to analyze calculations using the GM model (1,1). The results showed that the Grey forecasting model or GM (1.1) produced accurate forecasts with an ARPE error value of 9.60% or the accuracy of the forecast model reached 90.39%.

A FUZZY TIME SERIES-MARKOV CHAIN MODEL TO FORECAST FISH FARMING PRODUCT Bagus Dwi Saputra; Rachmat Gernowo; Budi Warsito
Jurnal Ilmiah Kursor Vol 9 No 4 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i4.167

Price is one of the important things that need to concern as defining factor of the profit or loss of product selling as the result of price fluctuations that are very difficult to control. Price fluctuations are caused by many factors including weather, stock availability, demand and others. One of the steps to solve the price fluctuations problem is by making a forecast of fish incoming prices. The purpose of this study is to apply Markov chain’s fuzzy time series to forecast farming fish prices. Markov chain fuzzy time series is one of the prediction methods to predict time series data that has advantages in the implentation of historical data, flexible, and high level of data forecasting accuracy. This study used fish prices at November 2018. The results showed that markov chain fuzzy time series showed very accurate forecasting results with a mean error percentage of absolute percentage error (MAPE) of 1.4% so the accuracy of the Markov chain fuzzy time series method is 98, 6%.

ANALISIS KOMODITAS UNGGULAN PERIKANAN BUDIDAYA PROVINSI JAWA TENGAH TAHUN 2012-2016 MENGGUNAKAN METODE LOCATION QUOTIENT DAN SHIFT SHARE Dian Mariana L Manullang; Agus Rusgiyono; Budi Warsito
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Condition of capture fisheries is currently stagnating, even tended to decline, which is indicated by the decrease of production in some fishery development areas in Indonesia. Aquaculture is one solution that can be done. Central Java Province is a province that has a large aquaculture potential, therefore of course Central Java province has leading commodities that become the sector of regional economic development. This research discusses about the potential location for the development of each leading commodities in Central Java Province as a recommendation related to the centre of fisheries production. Analytical methods in this research are Location Quotient (LQ) dan Shift share. It used to see how big these locations have a potential in the development of aquaculture production and to identify spatial autocorrelation in the amount of aquaculture production using Moran’s index. The analysis of LQ and shift share shows that each district has a different potential in the development of leading commodities production. The value of the Moran’s index obtained equal to -0.1381, that is in the range of -1 <I ≤ 0, indicating that the presence of spatial autocorrelation is negative but small because of near to zero. It can be concluded that there is no similarity of the values between the districts or indicate that amount of aquaculture production among the districts in Central Java are not correlated.Keywords: Leading Commodities, Location Quotient (LQ), Shift Share, Moran’s Index

ANALISIS METODE BAYESIAN PADA SISTEM ANTREAN PELAYANAN LOKET TIKET STASIUN TAWANG SEMARANG Aurum Anisa Salsabela; Sugito Sugito; Budi Warsito
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.v10i2.29410

Jamming is one of the serious problem in Indonesia caused by the increase of vehicle. The government has made solution for this situation for example was public transportation. Train is one of the suitable public transportation because of the ticket price was cheap. Tawang Railway Stasion Semarang was the biggest railway station in Semarang. In the specific day such long holiday or celebrating day, many people have chosen train to bring them. This make a queuing situation on the counter of station. Queue theory models provide the random of arrival and service time. The Bayesian theory suits to handle the problem of queuing that has been working for several times. Based on the analysis of the queue models for customer service, self-print tickets, cancellation and ordering are (G/G/c):(GD/∞/∞) from the posterior distribution with combination from prior distribution and likelihood sample. The combination of prior distribution and likelihood sample used in this research is Poisson distribution for all ticket counter except the arrival for cancellation counter which Normal distribution. The likelihood sample used Poissonn distribution for all ticket counter, except for self-print tickets which Diskrit Uniform Distribution. Queue models can be used to count the size of the system performance. Based on the calculations and analysis, it can be concluded that the queueing system to the customer service, self-print tickets, cancellation and ordering have been good because its steady state and busy probability is higher than jobless probability. Keywords: Tawang Railway Station, Queue, Bayesian, size of the system performance

PREDIKSI HARGA EMAS MENGGUNAKAN FEED FORWARD NEURAL NETWORK DENGAN METODE EXTREME LEARNING MACHINE Nisa Afida Izati; Budi Warsito; Tatik Widiharih
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The prediction of gold price aims to find out the gold price in the future on the basis of historical data on gold prices in the past, so it can be used as a consideration by gold investors to investing in gold. Prediction methods that do not require assumptions, one of which is Artificial Neural Networks. In this study, using Artificial Neural Networks, Feed Forward Neural Network with Extreme Learning Machine (ELM). ELM is a non-iterative algorithm so ELM has advantages in process speed. The input weight and bias for this method are determined randomly. After that, to find the final weight using the Moore-Penrose Generalized Inverse calculation on the hidden layer output matrix. The best model selection criteria uses the Mean Absolute Percentage Error (MAPE). This study shows that the results of the training and testing process from the model 1 input neuron and 7 hidden neurons are very good, because it produces MAPE training = 0.6752% and MAPE testing = 0.8065%. Also gives a very good prediction result because it has MAPE = 0.5499% Keywords: gold price, Extreme Learning Machine, MAPE

PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT Ekky Rosita Singgih Wigati; Budi Warsito; Rita Rahmawati
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords: artificial neural network, cascade forward, exchange rate, MAPE

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

MODEL FEED FORWARD NEURAL NETWORK (FFNN) DENGAN ALGORITMA PARTICLE SWARM SEBAGAI OPTIMASI BOBOT (Studi Kasus : Harga Daging Sapi dari Bank Dunia Periode Januari 2007 – Desember 2018) Faisal Fikri Utama; Budi Warsito; Sugito Sugito
Jurnal Gaussian Vol 8, No 1 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Beef is one of the important food commodities to fulfill the nutritional adequacy of humans. The World Bank notes the beef prices that are exported worldwide every month. For this reason, those data becomes a predictable series for the next period. Feed Forward Neural Network is a non-parametric method that can be used to make predictions from time series data without having to be bound by classical assumptions. The initiated weight will be evaluated by an algorithm that can minimize errors. Particle Swarm Optimization (PSO) is an optimization algorithm based on particle speed to reach the destination. The FFNN model will be combined with PSO to get predictive results that are close to the target. The best architecture on FFNN is obtained with 2 units of input, 1 unit of bias, 3 hidden units, and 1 unit of output by producing MAPE training 11.7735% and MAPE testing 8.14%. According to Lewis (1982) in Moreno et. al (2013), the MAPE value below 10% is highly accurate forecasting. Keywords: Feed Forward Neural Network (FFNN), Particle Swarm Optimization (PSO), neurons, weights, predictions.

PEMODELAN JUB DAN BI RATE TERHADAP INFLASI DAN KURS RUPIAH MENGGUNAKAN REGRESI SEMIPARAMETRIK BIRESPON BERDASARKAN ESTIMATOR PENALIZED SPLINE Siti Fadhilla Femadiyanti; Suparti Suparti; Budi Warsito
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Some indicators of the Indonesian economy are inflation and the exchange rate of rupiah against US dollar. Inflation and the rupiah exchange rate are thought to be influenced by the money supply (JUB) and the BI Rate. The money supply has a nonparametric relationship pattern to inflation and the rupiah exchange rate, while the BI Rate has a parametric relationship pattern to inflation and the rupiah exchange rate. The right method for detecting the relationship between inflation and the exchange rate with JUB and BI Rate is birespon semiparametric regression with a splined penalized estimator. The semiparametric regression coefficient of birespon spline penalized is estimated using the Weighted Least square (WLS) method which is determined based on the degree of polynomials, the number and location of the optimal knot points, and the optimal lambda determined based on the minimum of Generalized Cross Validation (GCV). This research uses the R Program. Based on the results of the analysis, the best spline penalized birespon semiparametric regression model is located in the number of knots is 5 at the knot points of 5257,783; 6649,469; 8976,871; 11099,19 and 13535,51 found in the first degree of response is 1 and the second degree of response is 2 with an optimal lambda of 99,99. The results of the performance evaluation of the model produce value of is 99,9007%, meaning that the model's performance is very good for out samples of the data and the MAPE value of 2.89169% is less than 10% which means the model's performance is very good.

KLASIFIKASI PERUSAHAAN DI INDONESIA DENGAN MENGGUNAKAN PROBABILISTIC NEURAL NETWORK (Studi Kasus: Perusahaan yang Terdaftar di Bursa Efek Indonesia Tahun 2016) Adi Waridi Basyirudin Arifin; Hasbi Yasin; Budi Warsito
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.v6i4.30383

Classification of company performance can be judged by looking at it’s financial status, whether poor or good state. In order to classifying the financial status, annual financial report will be required. By learning financial status of company, it would be useful to evaluate the performance of the company itself from management cause, or as an investor, making strategy for investment to certain company would be easier. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric method. One of the models in Artificial Neural Network is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclid distance and each class share the same values as their weights. By using the holdout method as an evaluation in honesty, the results show that modeling the company performance with PNN model has a very high accuracy. This is confirmed by the level of accuracy of the data model built on the training data is 100%, while trial data also got 100% accuracy. Keywords : Classification of Company Performance, PNN, Holdout.

Co-Authors . Widayat Abdul Hoyyi Adi Waridi Basyirudin Arifin Adi Wibowo Adi Wibowo Agus Rusgiyono Agus Winarno, Agus Ahmad Lubis Ghozali Ahmed, Kamil Alan Prahutama Anindita Nur Safira Arafa Rahman Aziz Arbella Maharani Putri Arief Rachman Hakim Arief Rachman Hakim Arief Rachman Hakim Aris Sugiharto Arsyil Hendra Saputra Atmaja, Dinul Darma Atur Ekharisma Dewi Aurum Anisa Salsabela Bagus Dwi Saputra Bayastura, Shahnilna Fitrasha Bayu Surarso Bimastyaji Surya Ramadhan Budiyono Budiyono Calvin, Esagu John Catur Edi Widodo Chrisna Suhendi Cintika Oktavia Di Asih I Maruddani Di Mokhammad Hakim Ilmawan Dian Mariana L Manullang Dinar Mutiara Kusumo Nugraheni Dwi Ispriyanti Dyna Marisa Khairina eka rahmawati Ekky Rosita Singgih Wigati Endang Fatmawati Endang Fatmawati Fachry Abda El Rahman Faisal Fikri Utama Faliha Muthmainah Fath Ezzati Kavabilla Fatiya Nur Umma Ferry Hermawan Fiqria Devi Ariyani Firdonsyah, Arizona Gayuh Kresnawati Gertrude, Akello Ghifar Rahman Handayani, Sri Hanif Kusumasasmita Haritsa, Rifda Tsaqifarani Harjum Muharam Hasbi Yasin Hendri Setyawan Henny Widayanti, Henny Heriyanto Hizkia Christian Putra Setiadi Indra Jaya Infan Nur Kharismawan Intan Monica Hanmastiana Jafron Wasiq Hidayat Junta Zeniarja Kadarrisman, Vincensius Gunawan Slamet Kiswanto Kiswanto M. Afif Amirillah M. Andang Novianta Maharani, Chintya Ayu Mahrus Ali Maori, Nadia Annisa Maryono Maryono Maryono Maryono Masruroh, Fitriana Maulida Najwa, Maulida Mifta Ardianti Moch. Abdul Mukid Mochamad Arief Budihardjo Moh Ali Fikri mohamad jamil muhammad shodiq Muliyadi Muliyadi Munji Hanafi Mustafid Mustafid Mustaqim Mustaqim, Mustaqim Nisa Afida Izati Noor Azizah Nur Fitriyah Nurcahyanti, Tri Meida Nurul Hidayati Oktavia, Cintika Oky Dwi Nurhayati Pandu Anggara Paul, Gudoyi M Perdana, Ery Purwanto Purwanto Puspita Kartikasari Putri, Nitami Lestari R Rizal Isnanto R. Rizal Isnanto Rachmat Gernowo Rachmat Gernowo Rahmat Gernowo Rahmatul Akbar Ratna Kencana Putri Rini Nuraini Rita Rahmawati Rita Rahmawati Riva Amrulloh Riza Rizqi Robbi Arisandi Royani, Noorhanida Rukun Santoso Rully Rahadian Safitri, Adila Salma Farah Aliyah Sang Nur Cahya Widiutama Sari, Juwita Dwinda Silvia Elsa Suryana Siti Fadhilla Femadiyanti Sri Endah Moelya Artha Sri Sumiyati Sudarno Sudarno Sudarno Sudarno Sudarno utomo Sugito Sugito Sulardjaka Sulardjaka Suparti Suparti Syafrudin Syafrudin Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Ta’fif Lukman Afandi Tri Yani Elisabeth Nababan Ummayah, Putri Qodar Vincensius Gunawan Slamet Kadarrisman Wahyul Amien Syafei Whisnumurti Adhiwibowo Wibowo, Catur Edi Widiyatmoko, Carolus Borromeus Winahyu Handayani Yanuar Yoga Prasetyawan Yundari, Yundari

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