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All Journal Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Cahaya Mandalika Innovative: Journal Of Social Science Research International Journal of Health and Medicine
I WAYAN SUMARJAYA
Faculty of Mathematics and Natural Sciences, Udayana University
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Transportation Other

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Forecasting Monthly Inflation Rate in Denpasar Using Long Short-Term Memory Sumarjaya, I Wayan; Susilawati, Made
Jurnal Matematika Vol 13 No 1 (2023)
Publisher : Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2023.v13.i01.p157

Abstract

One of indicators of economic stability of a country is controlled inflation. In general, inflation provides information about the rise of goods and services in a region within certain period which has strongly related with people’s ability to purchase. The Covid-19 pandemic has affected almost any sectors especially the consumer price in-dex. Bali, as a major tourist destination in Indonesia, has severely affected by the pandemic. Information about future inflation rate plays important role in determining the correct decision regarding economic policy. The aim of this research is to fore-cast inflation rate in Denpasar using deep learning method for time series. Deep learning, a part of machine learning, consists of layers of neurons that are designed to learn complex patterns and is able to make forecasting. In this research we de-ployed a special type of recurrent neural networks called long-short term memory (LSTM) that is suitable for use in time series analysis. We stacked the networks into two, three, and four layers to add capacity and to build deep networks for inflation rate series. A grid search for each layer is conducted to obtain optimal hyperparame-ters setting. We conclude that the optimum architecture for setting for this deep net-work is stacked two LSTM layers. The monthly inflation rate forecasts suggest the in-flation for 2022 fluctuates, but below one percent.
A Comparative Analysis of Deep Autoregressive, Deep State Space, Simple Feed Forward, and Seasonal Naive in Forecasting Indonesia’s Inflation Rate Sumarjaya, I Wayan; Susilawati, Made
Jurnal Matematika Vol 14 No 1 (2024)
Publisher : Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2024.v14.i01.p170

Abstract

Information about inflation plays important role in economic policy. The government of the Republic Indonesia has put a great deal of effort to control inflation rate. The aims of this research are to forecast Indonesia’s inflation rate using deep auto-regressive networks and to compare it with other models such as deep state space, simple feed forward, and naïve seasonal. In this study we compare eighteen deep au-toregressive networks. Each model differs only in its hyperparameters settings such as the number of epochs, the number of layers, the number of cells, and the number of batch sizes. In order to check for consistency each model is replicated ten times. In total there are 180 runs for each of configuration including the replication. The results show that the deep autoregressive model with 50 epochs, 4 layers, 40 cells, 32 batch sizes produces the smallest root mean squared error at 0.218565. This root mean squared error also the smallest among the other models such as deep state space (0.28734), simple feed forward (0.350449), and naïve seasonal (0.336056). In conclusion, the median forecasts fluctuates but below 1 percent.
A Confirmation Factor Analysis of Public Compliance in Implementing Health Protocols Post Covid-19 Pandemic Susilawati, Made; Sumarjaya, I Wayan; Octavanny, Made Ayu Dwi; Diva, Made Tresia Pramasta
International Journal of Health and Medicine Vol. 2 No. 1 (2025): January : International Journal of Health and Medicine
Publisher : Asosiasi Riset Ilmu Kesehatan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijhm.v2i1.227

Abstract

The COVID-19 pandemic severely impacted Indonesia, leading to substantial socio-economic disruptions and widespread social anxiety. In response, the government implemented a range of measures, including restrictions and mandatory health protocols, to control the virus. Although the pandemic is officially declared over, the risk of infection remains. Therefore, continued adherence to health protocols is essential. This study aimed to identify the key factors driving public compliance with health protocols in the post-pandemic period within Indonesia, providing valuable insights for policymakers to prevent future outbreaks. The findings revealed that knowledge emerged as the most significant determinant of community adherence to ongoing health protocol implementation. Consequently, the government must consistently emphasize the importance of adhering to health protocols whenever individuals are outside their homes.
Aspect-Based Sentiment Analysis of Reviews for Pandawa Beach Using Naive Bayes and SVM Methods Putri, Made Ayu Asri Oktarini; Sumarjaya, I Wayan; Wijayakusuma, I Gusti Ngurah Lanang
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9083

Abstract

The presence of digital technology, especially online platforms such as Google Maps, has changed the way people search for information about tourist destinations, including reviews and ratings from previous visitors. Aspect-based sentiment analysis becomes a very useful tool to understand people's views and feelings towards a place or product based on the reviews given and identify aspects of interest to tourists visiting Pandawa Beach, by utilizing Naive Bayes and Support Vector Machine (SVM) methods. The main objective of this research is to identify sentiment patterns based on aspects such as attraction, accessibility, amenities, and ancillary. Data was collected and labeled according to sentiment and aspects, then processed using preprocessing techniques, extracted by bag-of-words method, and chi-square feature selection. The model evaluation results showed that SVM produced the highest F1-Score value of 79,625%, while the Naive Bayes method reached 73.29%.
ANALYSIS OF SOCIO-ECONOMIC IMPACTS OF THE COVID-19 PANDEMIC USING FACTOR ANALYSIS Susilawati, Made; Sumarjaya, I Wayan; Srinadi, IGAM; Nilakusmawati, DPE; Suciptawati, NLP
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1235-1244

Abstract

The purpose of this study is to identify the factors that influence the socio-economic impact of the Covid-19 pandemic. This study uses explanatory factor analysis that is an analysis that forms new random factors in which the later formed factors or constructs can be interpreted. The case study was conducted in Sawan Village, Sawan District, Buleleng Bali, with six variables explaining the economic impact, and 16 variables explaining the social impact. The results of the study show that there are three factors that explain the economic impact due to Covid-19. They are the income factor, the purchase of quotas and gadgets, and the expenditure factor with the total variance described being 82,178 percent. Meanwhile, the social impact due to the Covid-19 pandemic is explained by three factors, namely the fear of interacting in public places, the fear factor of doing activities outside the home, and the fear of using public facilities with a total variance that can be explained is 73,609 percent.
Seleksi Fitur Information Gain Untuk Klasifikasi Kualitas Susu Sapi Menggunakan Metode K-Nearest Neighbor Dan Naïve Bayes Ali, Fauzi Wardah; Sumarjaya, I Wayan; Kencana, Eka N.
Innovative: Journal Of Social Science Research Vol. 5 No. 1 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i1.17477

Abstract

Kemajuan dalam komputasi modern, khususnya klasifikasi, telah membantu manusia dalam mengklasifikasikan berbagai tugas yang memakan waktu dan komputasi yang mahal, salah satunya adalah klasifikasi kualitas susu sapi. Pengklasifikasian ini penting dilakukan untuk mengurangi kemungkinan beredarnya susu dengan kualitas buruk di masyarakat. Data pada penelitian ini terdiri dari 429 susu kualitas rendah, 374 susu kualitas menengah, dan 256 susu kualitas tinggi. Penelitian ini menguji performa algoritma klarifikasi K-nearest neighbor dan naïve Bayes dengan menggunakan teknik seleksi fitur information gain dan tanpa seleksi fitur information gain. Hasil penelitian ini menunjukkan performa KNN mengalami peningkatan rata-rata akurasi sebesar 1,04 persen dengan perhitungan jarak Euclid dan 0,92 persen dengan perhitungan jarak Manhattan ketika menggunakan lima fitur. Sedangkan performa naïve Bayes mengalami penurunan akurasi sebesar 3,48 persen. Perlakuan yang berbeda tersebut memiliki perbedaan akurasi yang signifikan.
ESTIMASI TAIL VALUE AT RISK SAHAM BLUE CHIPS MENGGUNAKAN COPULA ALI-MIKHAIL-HAQ Candrasuari, Ni Luh Putu Diah Ayu; Sumarjaya, I Wayan; Sari, Kartika
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.v5i1.2142

Abstract

When making investments, investors definitely want high returns with low risk. However, high returns are usually accompanied by high risks and vice versa. Value at Risk (VaR) and other measurement tools help manage risk. VaR measures possible losses. However, VaR has weaknesses, thus Tail Value at Risk (TVaR) can be used to evaluate the likelihood of larger losses than VaR. Copula can be used in risk management because it does not require normal distribution assumptions so it is well applied to financial data. The purpose of this research is to use the Ali-Mikhail-Haq copula to estimate TVaR value of blue chip stock portfolios, including those of BRI, BCA, and Bank Mandiri. Data used is the closing price of daily stocks for the period Jan 1 2021 to Jun 30 2023. The results of calculating TVaR value at the 90%, 95%, and 99% confidence level of the combination of BMRI and BBRI stocks are 0,0574; 0,0668; dan 0,0807. At a confidence level of 90%, 95% and 99% TVaR value of the combination of BMRI and BBCA stocks is 0,0569; 0,0669; dan 0,0886. The combination of BBRI and BBCA stocks resulted in TVaR at 90%, 95% and 99% confidence levels are 0,0238; 0,0283; 0,0376.
Co-Authors Ali, Fauzi Wardah Candrasuari, Ni Luh Putu Diah Ayu Diva, Made Tresia Pramasta Kartika Sari Kencana, Eka N. Made Susilawati Ni Luh Putu Suciptawati Nilakusmawati, DPE Octavanny, Made Ayu Dwi Pramesti, Ni Kadek Lia Cahyani Putri, Made Ayu Asri Oktarini Srinadi, IGAM Suciptawati, NLP TRESNAWARDANI, NI PANDE LUH Widiastuti, Ratna Sari Wijayakusuma, I Gusti Ngurah Lanang