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All Journal INDONESIAN JOURNAL OF LEGAL AND FORENSIC SCIENCES Jurnal Matematika Journal on Education Udayana Journal of Social Sciences and Humanities (UJoSSH) Jurnal Cahaya Mandalika Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa International Journal of Applied Mathematics and Computing. E-Jurnal Matematika Jurnal Matematika
I Wayan Sumarjaya
Prodi Matematika FMIPA Universitas Udayana
Humanities Chemistry Computer Science & IT Dentistry Earth & Planetary Sciences Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Social Sciences

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Forecasting Foreign Tourist Visits to Bali Using Bayesian Vector Autoregression with Normal-Inverse-Wishart Prior I Wayan Sumarjaya; Ni Ketut Tari Tastrawati
Udayana Journal of Social Sciences and Humanities Vol 1 No 2 (2017): UJoSSH, September 2017
Publisher : Research and Community Services Institutes of Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (320.689 KB) | DOI: 10.24843/UJoSSH.2017.v01.i02.p02

Abstract

As a major tourist destination, Bali has become an icon for tourism in Indonesia. In general, the number of foreign tourist visits shows an increasing trend. However, there is considerable fluctuation in the number of visits which is affected by season. In another word, there is a stochastic trend in the number of tourist visits. Policy makers need a method to predict this tourist visits. A commonly used method to predict tourist visits is time series analysis. Time series analysis has been used in various fields such as finance, business, engineering, meteorology, geophysics, and tourism, to name but a few. Research on forecasting tourist visits usually uses univariate data. This research aims to forecast the number of foreign tourist visits from four major countries such as Asia Pacific, ASEAN, America, and Europe simultaneously using Bayesian vector autoregression with normal-inverse-Wishart prior. First data is plotted to see its characteristics. Then the data is modeled using Bayesian vector autoregression. In this stage normal-inverse-Wishart prior is used. Next, Markov chain Monte Carlo is conducted to make a prediction from the posterior distribution. The forecast suggests that the number of tourist visits in general increased, albeit some fluctuation in some months.
Penerapan Metode Regresi Logistik Bayes dalam Menentukan Faktor-Faktor yang Memengaruhi Kurangnya Minat Masyarakat Menggunakan QRIS Ni Ketut Linda Aryani; I Wayan Sumarjaya; Kartika Sari
Journal on Education Vol 5 No 3 (2023): Journal on Education: Volume 5 Nomor 3 Tahun 2023
Publisher : Departement of Mathematics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The economy is an aspect that is most susceptible to change. As a countermeasure to these changes, the government making efforts with a policy on using digital money. The digital money transaction tool issued by the Indonesian government is QRIS (Quick Response Code Indonesian Standard). However, the fact is that public interest in using QRIS is still low. This study aims to determine the factors that influence it significantly and obtain a model of the lack of public interest using QRIS, using the Bayesian logistic regression method. The Bayesian logistic regression method can generate parameter estimates by combining the likelihood function of the sample data with the prior distribution and the results are referred to as the posterior. In addition, the predictor variables used in this study are age, shopping frequency, consumer income, and the number of digital payment applications and for the response variable is a lack of interest in QRIS, for the sampling method used is accidental sampling and the sample used is primary data sourced from the results of filling out questionnaires distributed to consumer communities in Badung, Kreneng, and Galang Ayu Markets. Estimation from the Bayes method was obtained using a Markov Chain Monte Carlo (MCMC) simulation. The results of this study indicate that the variables of age and the number of digital payment applications have a significant effect on people's lack of interest in using QRIS.
Image Classification Comparison Using Neural Network and Support Vector Machine Algorithm With VGG16 As Feature Extraction Method Aulia Wicaksono; I Putu Eka Nila Kencana; I Wayan Sumarjaya
International Journal of Applied Mathematics and Computing Vol. 1 No. 4 (2024): October: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.29

Abstract

Image classification is widely used in everyday life such as in car steering, closed-circuit television (CCTV), traffic cameras, etc. The implementation of image classification can be done using several methods, including neural network and support vector machine models. The neural network method is able to find the right weights that allow the network to show the desired behaviour while the support vector machine method has many dimensions and can overcome linear and non-linear data. In this research, feature extraction was carried out using VGG16 to increase accuracy. This research aims to find out how to implement the neural network and SVM algorithms to classify images and determine the results of analyzing the performance of the two methods. The data used in this study is secondary data consisting of 10 types of large wild cats with a total of 2339 training image datasets and 50 testing image datasets. The research stages consist of data augmentation, model design, model training, and model evaluation. Classification with the neural network model produced an accuracy of 96% and the support vector machine model produced an accuracy of 96%, which means that in a consistent training environment, the two models have the same performance.
PERAMALAN FAKTOR-FAKTOR PEREKONOMIAN YANG MEMENGARUHI NILAI EKSPOR MINYAK KELAPA SAWIT INDONESIA MENGGUNAKAN VECM Ni Putu Manik Maharani; I Wayan Sumarjaya; I Gusti Ayu Made Srinadi
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 5 No. 1 (2024)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v5i1.2881

Abstract

Abstract: Forecasting is predicting future events. The forecasting process involves the use of time series data. Time series models that are often used for forecasting are vector autoregressive (VAR) models and vector error correction models (VECM). The aim of this research is to determine the model and forecasting results on Indonesian palm oil production, CPO prices on the international market, inflation, money supply, USD exchange rate, and the value of Indonesian palm oil exports. The data used comes from BPS, BI, and Index Mundi, covering the period January 2017 to December 2021. Based on the optimum lag of the VECM model, the research results show that the VECM(1) ​​model is suitable for use. In parameter estimation, there is a long-term relationship between the Indonesian palm oil export value variable and the other five variables, and there is a short-term relationship between the observed variables. The average monthly forecasting results for Indonesian palm oil production is 4.211.539 tons, the price of CPO on the international market is 2.583,96 USD/metric ton, inflation is 0,1365938333 percent, the M1 money supply is 3.051.982 billion rupiah, the USD exchange rate is 14.115,48 rupiah and the export value of Indonesian palm oil is 4.641.307 thousand US dollars. Keywords: Forecasting, VECM, Economic Factors, CPO. Abstrak: Peramalan adalah memprediksi peristiwa pada masa depan. Proses peramalan melibatkan penggunaan data deret waktu (time series). Model deret waktu yang sering digunakan untuk peramalan adalah model vector autoregressive (VAR) dan vector error correction model (VECM). Tujuan dari penelitian ini adalah untuk mengetahui model dan hasil peramalan pada produksi minyak kelapa sawit Indonesia, harga CPO di pasar internasional, inflasi, jumlah uang beredar M1, kurs USD, dan nilai ekspor minyak kelapa sawit Indonesia. Data yang digunakan bersumber dari BPS, BI, dan Index Mundi, mencakup periode Januari 2017 hingga Desember 2021. Berdasarkan lag optimum model VECM, hasil penelitian menunjukkan model VECM(1) cocok digunakan. Dalam estimasi parameter, terdapat hubungan jangka panjang antara variabel nilai ekspor minyak kelapa sawit Indonesia dengan kelima variabel lainnya, dan terdapat hubungan jangka pendek antar variabel yang diamati. Hasil peramalan rata-rata bulanan untuk produksi minyak kelapa sawit Indonesia adalah 4.211.539 ton, harga CPO di pasar Internasional adalah 2.583,96 USD/metrik ton, inflasi adalah 0,1365938333 persen, jumlah uang beredar M1 adalah 3.051.982 milyar rupiah, kurs USD adalah 14.115,48 rupiah dan nilai ekspor minyak kelapa sawit Indonesia adalah 4.641.307 ribuan dolar US. Kata Kunci: Peramalan, VECM, Faktor Ekonomi, CPO
IMPLEMENTASI METODE VECTOR AUTOREGRESSIVE DALAM PERAMALAN JUMLAH PRODUKSI PADI DI KABUPATEN BADUNG Margaretha Ratih Dyah Novitasari; I Wayan Sumarjaya; I Gusti Ayu Made Srinadi
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 5 No. 1 (2024)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v5i1.2122

Abstract

Forecast is a process to predict something in the future using past data. One of common model used in forecast is time series data that is vector autoregressive (VAR) model. The research purpose is to know the model and amount of rice production in Badung regency. It is used seconder data get from the BPS office Bali and BMKG Denpasar, that are rice production data, harvest area, and rainfall from Januari 2018 till December 2022. Base on lag optimum model VAR, the research result show that the VAR(1) model is suitable being used. Therefore, base on MAPE forecast criteria the VAR model in this research show the result less accurate. Eventhough the forecast pattern for rice production, harvest area, and rainfall show the stability. Beside that in the first period there is shocking in the IRF analysis but finally reach the stabil condition. Keywords: Forecast, VAR models, Rice Production, Harvest Area, Rainfall
PERAMALAN DURASI ETHEREUM MENGGUNAKAN MODEL AUTOREGRESSIVE CONDITIONAL DURATION I WAYAN SUMARJAYA; RENOVAR JOJOR DELIMA SIMANULLANG; RATNA SARI WIDIASTUTI
E-Jurnal Matematika Vol. 14 No. 3 (2025)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2025.v14.i03.p484

Abstract

Forecasting is the process of estimating future events using past data. Financial time series forecasting often prioritizes stock price variables. Apart from the stock price variable, inter-transaction time or duration is also an important variable to predict, because the timing of changes in financial prices cannot be predicted. Duration modeling and forecasting can be done using the autoregressive conditional duration (ACD) model. In this research, modeling and forecasting using the ACD model was carried out on Ethereum. This research aims to predict the duration of Ethereum in order to help traders know the time needed to reach the next price change. Several ACD models with four distributions, i.e., exponential, Weibull, Burr, and generalized gamma were fit to the Ethereum duration. The research results suggest that the Burr-ACD model produces the smallest AIC value compared to other distributed ACD models. However, the forecast results using the Burr-ACD models show increasing duration and hence are less accurate. The generalized gamma-ACD (2,2) model was then chosen as an alternative for forecasting Ethereum duration, showing that Ethereum duration forecast results are less than one second, which indicates the high frequency of transactions that occur on Ethereum.
PERAMALAN PENGGUNAAN LISTRIK DI PROVINSI BALI MENGGUNAKAN METODE ARIMA I GEDE GANA ARIAWAN; I WAYAN SUMARJAYA; MADE SUSILAWATI
E-Jurnal Matematika Vol. 14 No. 3 (2025)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2025.v14.i03.p487

Abstract

This study aims to forecast electricity consumption in the Province of Bali using the ARIMA (Autoregressive Integrated Moving Average) method. The forecasting process is based on monthly electricity usage data spanning from January 2015 to June 2024. The initial analysis revealed a significant upward trend, with a notable decline in usage during 2020, coinciding with the COVID-19 pandemic. To address the issue of non-stationarity in the data, a differencing process was applied until stationarity was achieved, as confirmed by the Augmented Dickey-Fuller (ADF) test. Model identification was conducted using ACF and PACF plots, and several ARIMA models were evaluated based on their Akaike Information Criterion (AIC) values. The ARIMA(0,1,1) model was selected as the most suitable model due to its lowest AIC value and its compliance with diagnostic assumptions, including uncorrelated residuals (verified by the Ljung-Box test) and normally distributed residuals (confirmed by the Shapiro-Wilk test). The forecasting results demonstrated that the selected model provides stable predictions for the subsequent 12 months. This study is expected to contribute to effective planning and management of electricity demand in the Bali region.
ESTIMASI VALUE AT RISK PORTOFOLIO VALUTA ASING PADA KONDISI PANDEMI COVID-19 MENGGUNAKAN COPULA ANJAR ANGGRAINI; KOMANG DHARMAWAN; I WAYAN SUMARJAYA
E-Jurnal Matematika Vol. 14 No. 4 (2025)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2025.v14.i04.p490

Abstract

The Coronavirus disease (Covid-19) has been officially declared a pandemic by the World Health Organization (WHO). This pandemic affects not only the health of the population but also weakens the rupiah exchange rate. Fluctuations in exchange rate changes can affect the investment value, so investors need to take risk measurements. This study discusses the measurement of portfolio loss risk which is composed of a combination of USD, JPY, GBP, and EUR currency exchange rates using the value at risk (VaR) risk measure. Dependent structure analysis was carried out using the Gumbel, Clayton, and Frank copulas approach from the Archimedean copula family. The results obtained from this study are based on portfolio calculations composed of USD-GBP, JPY-GBP, and EUR-USD currency exchange rates at , , and  confidence levels in the next one-day period. The highest VaR of  is achieved by the EUR-USD portfolio at a  confidence level using the Gumbel copula. Meanwhile, the lowest estimated VaR of  is achieved by the EUR-USD portfolio at a  confidence level using the Gumbel copula.
Prediksi Jumlah Sepeda yang Melintasi Willianmsburg Bridge Menggunakan Regresi Binomial Negatif Berdasarkan Variabel Cuaca: Suhu dan Curah Hujan Mulyani, Luh Sukma; Stefani Putri Wulandari; Marcellina Layata; Ni Kadek Trisnawati; I Wayan Sumarjaya
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 3 No. 6 (2025): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v3i6.859

Abstract

Negative Binomial Regression is a statistical modeling approach used to analyze count data with overdispersion, where the variance exceeds the mean. This study applies the method to examine the influence of weather factors on the daily number of cyclists crossing the Williamsburg Bridge in New York City. The independent variables used in the analysis include maximum temperature, minimum temperature, and precipitation. The dataset was obtained from the NYC Department of Transportation through the Kaggle platform and covers the period from April 1 to April 30, 2016. The analysis began with a Poisson Regression model; however, the presence of overdispersion was identified, indicated by a high AIC value of 8598.19, suggesting that the model was not suitable. The alternative Negative Binomial Regression model was then employed and produced a significantly lower AIC value of 518.77, demonstrating a superior fit. The findings indicate that maximum temperature has a positive effect on the number of cyclists, while precipitation shows a significant negative effect. Conversely, minimum temperature does not exhibit a meaningful influence. These results highlight the importance of considering weather conditions when planning bicycle-based transportation systems and support the development of sustainable mobility strategies in urban environments.
Small Area Estimation Untuk Mengestimasi Persentase Kebutuhan Rumah Sederhana di Kabupaten Buleleng Luh Devi Maharani Mecker; I Komang Gde Sukarsa; I Wayan Sumarjaya
Jurnal Matematika Vol. 15 No. 2 (2025)
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Science, Udayana University Gedung UKM, Ruang UKM 8 Lt 1, Kampus Bukit Jimbaran, Badung-Bali.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2025.v15.i02.p193

Abstract

Data is a source of information to support the decision-making process of the object under study so that the availability of data is important to fulfill. Survey as one of the techniques used to provide data has weaknesses such as area parameters outside the design cannot be explained by the statistics generated. Small area estimation (SAE) is an indirect estimator method that can overcome the problems of survey data. SAE is used to estimate population or subpopulation parameters with limited sample size to produce statistics that are as precise as direct estimators. The general approach used in SAE is EBLUP. The topic in this research is the need for simple houses in Buleleng Regency. By estimating the percentage of simple housing needs for each sub-district in Buleleng through direct estimation and EBLUP, the results show that the estimation value of the direct estimator is not more precise than EBLUP indicated by the MSE output of the direct estimator which is greater than EBLUP. Based on the output of the estimation results with the EBLUP method, the results show that the highest and lowest percentage of housing needs are in Sukasada Sub-district at 67.05% and Banjar Sub-district at 37.19%, respectively.
Co-Authors AA Sudharmawan, AA ADE KUSUMA DEWI Ade Widyaningsih ALEXANDER HIRO WIBISONO ANAK AGUNG ISTRI AGUNG CANDRA ISWARI ANJAR ANGGRAINI AULIA ATIKA PRAWIBTA SUHARTO Aulia Wicaksono Chairun Nisa Chrisna Anzella Jacob COKORDA BAGUS YUDISTIRA DESAK AYU WIRI ASTITI Desak Putu Eka Nilakusmawati Dewa Ken Budiputra DIAN RAHMAN EKA N. KENCANA FITRI ANANDA DITA SARASWITA G. K. GANDHIADI GILANG BIMASAKTI ANDHIKA GUSTI AYU PUTU YULIANDARI HERLINA HIDAYATI HIRZI FIRDAUSI I GEDE DICKY ARYA BRAMANTA I GEDE GANA ARIAWAN I GEDE MAHA HENDRA PRATAMA I Gusti Ayu Made Srinadi I GUSTI AYU MEIGAYONI LESTARI I Ketut Darmana I KETUT PUTRA ADNYANA I KOMANG GDE SUKARSA I MADE BUDIANTARA PUTRA I MADE PRABA ESHA SUKSEMAWAN I Nyoman Sama I Nyoman Widana I Putu Eka Nila Kencana I PUTU GEDE DIAN GERRY SUWEDAYANA I Wayan Suirta IRENE MAYLINDA PANGARIBUAN JUITA HARYATI SIDADOLOG JULIANTARI JULIANTARI KADEK DITA SUGIARI Kartika Sari KASTIN DWILEN PONG SUMAE Ketut Jayanegara KHOSYI RUKITO KOMANG CANDRA IVAN Komang Dharmawan KOMANG KOKOM SUCAHYATI DEWI P Luh Devi Maharani Mecker LUH GEDE UDAYANI LUH HENA TERECIA WISMAWAN PUTRI LUH PUTU ARI DEWIYANTI LUH PUTU IDA HARINI MADE NITA DWI SAWITRI MADE NONIK PRAMESTI KARANA Made Susilawati MAHMUDATUL AQIBAH Marcellina Layata Margaretha Ratih Dyah Novitasari MIRA AYU NOVITA SARI Mulyani, Luh Sukma NATASYA WIDIA PUTRI NI KADEK JULIARINI Ni Kadek Trisnawati NI KADEK YUNI DEWIANTARI Ni Ketut Linda Aryani Ni Ketut Tari Tastrawati NI KOMANG DEBY ARIANI Ni Luh Putu Ayu Fitriani Ni Luh Putu Suciptawati Ni Made Asih NI MADE LASTI LISPANI NI MADE RARA KESWARI Ni Made Sri Wahyuni NI MADE SURYA JAYANTI NI PUTU AYU DEWI CAHYANTARI NI PUTU DEVIYANTI Ni Putu Manik Maharani NI PUTU MEILING UTAMI NI PUTU MIRAH SRI WAHYUNI NI PUTU NIA IRFAGUTAMI NI PUTU SRI YULI ARTINI NI PUTU WIDYA ISWARI DEWI NI PUTU YULIKA TRISNA WIJAYANTI NI WAYAN DIAH SIHMAWATI Ni Wayan Merry Nirmala Yani NI WAYAN UCHI YUSHI ARI SUDINA NOVIAN ENDI GUNAWAN NUR FAIZA NURMA ALIYUWANINGSIH NYOMAN KRISHNA PRATIWI DANGIN PUTU AMANDA SETIAWANI PUTU EKA ARIWIJAYANTHI PUTU GDE BUDHA WIRYADANA RAMADHAN LENGGU RAMLI RATNA SARI WIDIASTUTI RENALDO EVIPANIA RENOVAR JOJOR DELIMA SIMANULLANG SITI RAHAYU NINGSIH Stefani Putri Wulandari TJOK GDE SAHITYAHUTTI RANANGGA Tjokorda Bagus Oka TRISNA RAMADHAN ULYATIL AENI VINSENTIA REVICA BELLA ROSSARY WILDAN FATTURAHMAN MUJTABA WIMAS ASTARI YUDA