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IDENTIFIKASI MODEL SELF-EXCITING THRESHOLD AUTOREGRESSIVE DENGAN SWITCHING TWO REGIME (KASUS PADA DATA EKSPOR AGRIKULTUR DI INDONESIA) Riyansyah, Husnun Nur Ghiffari Putri; Saputro, Dewi Retno Sari; Winarno, Bowo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 4 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (724.939 KB) | DOI: 10.30598/barekengvol14iss4pp511-522

Abstract

A time series model that explain the structural changes associated with data in a certain time period is the Threshold Autoregressive (TAR) model. The basic of the TAR model there are some different usage regimes in autoregressive analysis. One model based on TAR is a self-exciting threshold autoregressive (SETAR) model with the same delay parameters for each regimen. The SETAR model has a linear nature in each regime but being nonlinear if the models of each regime are combined. In addition, this model can improve jump data that cannot be captured by linear time series models. This means that the SETAR model has high-level parameters through an appropriate switching regime that is applied to agricultural export data in Indonesia. The purpose of this reseach is to test the estimated SETAR parameter model and apply it to Indonesian agricultural export data. There are three methods that can be done for estimating of parameter of SETAR model, namely the conditional quadratic sequential method, ordinary least square (OLS) and nonlinear least square (NLS). In this research, the two stage parameter estimation method is used with OLS and the second stage parameter estimation is used to optimisze the parameter values ​​that are not significant in the model. In its application, the SETAR model (2,1,1) was obtained to model agricultural export data in Indonesia and the MAPE value was 25%.
HILL CLIMBING ALGORITHM ON BAYESIAN NETWORK TO DETERMINE PROBABILITY VALUE OF SYMPTOMS AND EYE DISEASE Adhitama, Ria Puan; Saputro, Dewi Retno Sari; Sutanto, Sutanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (577.687 KB) | DOI: 10.30598/barekengvol16iss4pp1271-1282

Abstract

One of the five human senses referred to as photoreceptors is the eye because the eye is very sensitive to light stimuli. Refractive abnormalities in the eyes are often experienced, which are abnormalities that occur when the eyes cannot see clearly in the open or blurred vision. An unhealthy lifestyle is a trigger for an increase in individuals who experience complaints of eye diseases. In diagnosing a disease, doctors need patient information in the form of symptoms experienced so that patients can be treated immediately. Information in the form of symptoms and types of eye diseases can be used to make conjectures about eye diseases through the structure of BN. The symptom information and type of the disease are represented through nodes, while the relationships are represented through the edge. BN is one of the Probabilistic Graphical Models (PGM) consisting of nodes and edges. BN is also known as a direct acyclic graph (DAG), which is a directed graph that does not have a cycle. The approach method used is scored based on the evaluation process with the bic scoring function. The algorithm used in this study is the HC algorithm. The research data used consisted of 52 symptoms and 15 eye diseases. The results of the study were obtained by the final structure of BN formed by the HC algorithm produced 93 edges and 65 connected nodes, and the probability value of the disease and the symptoms of the disease in the eye.
CABLE NEWS NETWORK (CNN) ARTICLES CLASSIFICATION USING RANDOM FOREST ALGORITHM WITH HYPERPARAMETER OPTIMIZATION Saputro, Dewi Retno Sari; Sidiq, Krisna
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0847-0854

Abstract

The growth of news articles on the internet occurs in a short period with large amounts so necessary to be grouped into several categories for easy access. There is a method for grouping news articles, namely classification. One of the classification methods is random forest which is built on decision tree. This research discusses the application of random forest as a method of classifying news articles into six categories, these are business, entertainment, health, politics, sport, and news. The data used is Cable News Network (CNN) articles from 2011 to 2022. The data is in form of text and has large amounts so good handling is needed to avoid overfitting and underfitting. Random forest is proper to apply to the data because the algorithm works very well on large amounts of data. However, random forest has a difficult interpretation if the combination of parameters is not appropriate in the data processing. Therefore, hyperparameter optimization is needed to discover the best combination of parameters in the random forest. This research uses search cross-validation (SearchCV) method to optimize hyperparameters in the random forest by testing the combinations one by one and validating those. Then we obtain the classification of news articles into six categories with an accuracy value of 0.81 on training and 0.76 on testing.
BIBLIOMETRIC ANALYSIS OF NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES (N-BEATS) FOR RESEARCH TREND MAPPING Saputro, Dewi Retno Sari; Prasetyo, Heri; Wibowo, Antoni; Khairina, Fadiah; Sidiq, Krisna; Wibowo, Gusti Ngurah Adhi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1103-1112

Abstract

Bibliometrics is the statistical analysis of articles, books, and other forms of publication. The bibliometrics analysis is performed with data on the number and authorship of scientific publications and articles, and citations to measure the work of individuals or groups of researchers, organizations, and countries to identify national and international networks and map developments in new multidisciplinary fields of science and technology. In addition, bibliometrics assesses and maps the research, organization, and country of researchers at a given time period. The Bibliometric analysis also has advantages which include mapping relationships between concepts, mapping research directions or trends, mapping state of the art (the novelty of the results of research conducted), and providing insights related to fields, topics, and research problems for future works. This study aims to determine the growth and development of N-BEATS publications, their distribution, variable keywords, and author collaboration using a bibliometric network. The research method used in this paper, through screening of articles obtained from the Scopus database page in 2008-2022, is used for citations in the form of metrics. At the same time, they are visualizing the metadata with VOSviewer. Data was collected from the direct science database with the keyword N-BEATS. The results show that 2022 has the highest number of publications, reaching 310 publications (14.90%). The distribution of research publications on N-BEATS shows a perfect distribution. Terms in the N-BEATS variable that often appear and are associated with other variables.
IMPLEMENTATION OF THE BIDIRECTIONAL GATED RECURRENT UNIT ALGORITHM ON CONSUMER PRICE INDEX DATA IN INDONESIA Tanjung, Andjani Ayu Cahaya; Saputro, Dewi Retno Sari; Kurdhi, Nughthoh Arfawi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0095-0104

Abstract

The Consumer Price Index (CPI) is the main index in measuring the inflation rate. Changes in the CPI from time to time reflect inflation and deflation, namely the higher the CPI value, the higher the inflation rate. This study aims to apply Birectional Gated Recurrent Unit (BiGRU) model to the CPI data in Indonesia. BiGRU comprises two GRU layers so it captures sequences that are ignored by the GRU. The research data is in the form of CPI data in Indonesia from January 2006 to December 2022 sourced from the website of the Central Bureau of Statistics totaling 204 data. The data is divided into training data and testing data. Training data was taken from January 2006 to July 2019 as many as 163 data. Data testing was taken from August 2019 to December 2022 as many as 41 data. Before the data is processed, a sliding window process is carried out by dividing the data into segments to reduce the error value. The window size value used is 10. In the sliding window process, the number of segments is 194 data segments. Based on the experiment results, it was concluded that the application of BiGRU to the CPI data was carried out in an experiment with 20 BiGRU architectures. BiGRU architecture was obtained which produced the lowest MAPE value, namely an architecture with two BiGRU layers having 256 neurons and 400 units, and one dense layer. In addition, the epochs used are 200 epochs, the ReLU activation function, and Adam optimization. The experimental results of the BiGRU architecture obtained a MAPE value of 0.24% which indicates that the architectural performance is very good.
SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS Putera Khano, Muhammad Nazhif Abda; Saputro, Dewi Retno Sari; Sutanto, Sutanto; Wibowo, Antoni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2235-2242

Abstract

Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a product, service, organization, topic, etc. Recurrent Neural Network (RNN) is one of the Natural Language Processing (NLP) algorithms that is used in sentiment analysis. RNN is a neural network that can use internal memory to process input. RNN itself has a weakness in Long-Term Memory (LTM). Therefore, this article examines the combination of Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. GRU is an algorithm that is used to make each recurrent unit able to record adaptively at different time scales. Meanwhile, LSTM is a network architecture with the advantage of learning long-term dependencies on data. LSTM can remember long-term memory information, learn long-sequential data, and form information relation data in LTM. The combination of LSTM and GRU aims to overcome RNN’s weakness in LTM. The LSTM-GRU is combined by adding GRU to the data generated from LSTM. The combination of LSTM and GRU creates a better performance algorithm for addressing the LTM problem.
A SIR-UC EPIDEMIC MODEL: THE ANALYSIS OF SUSCEPTIBLE-INFECTED-REMOVED (SIR) EPIDEMIC MODEL WITH THE COVERAGE OF HEALTH INSURANCE (UNCOVERED AND COVERED INDIVIDUALS) Sutanto, Sutanto; Saputro, Dewi Retno Sari; Christy, Alexander Yonathan; Baharum, Aslina
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0171-0178

Abstract

Susceptible-Infected-Removed (SIR) model is a widely used epidemic model that simulates the spread of infectious diseases within a population. It classifies individuals into susceptible, infected, and removed states, with the number of individuals in each state being time-dependent variables denoted by S(t), I(t), and R(t), respectively. The model considers direct contact transmission between infected and susceptible individuals. In developed countries, some people cannot afford medical treatment. In contrary, the recovery rate of infected individual in the population is directly proportional to the number of people receiving medical treatment. Affordable health insurance increases the number of people receiving medical treatment thus insurance should be considered aspect in epidemic model. The main purpose of this research is to analyze the effect of insurance on the SIR epidemic model. This research classifies individuals in both S(t) and I(t) based on their insurance coverage status. This model assumes permanent immunity for R(t), thus it is unnecessary to classify individuals in this state based on their insurance coverage status because they do not spread the disease nor have potential to be re-infected. Numerical simulation is organized to find the effect of insurance in SIR model by analyzing the equilibrium point. The result based on the equilibrium point suggests that the insurance in SIR epidemic model: (1) decrease the I(t) because it accelerate the recovery rate; (2) decrease theR(t) because there is less infected people for recovery; (3) increase the S(t) because there is less infected people to transmit the disease, compared to the SIR model without the insurance.
SPREADING PATTERN OF INFECTIOUS DISEASES: SUSCEPTIBLE INFECTED RECOVERED MODEL WITH VACCINATION AND DRUG-RESISTANT CASES (APPLICATION ON TB DATA IN INDONESIA) Widyaningsih, Purnami; Yumaroh, Siti Roqhilu; Saputro, Dewi Retno Sari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0467-0474

Abstract

Mycobacterium tuberculosis is the causative agent of the infectious illness tuberculosis (TB). Indonesia is the world's third-highest TB burden country. TB transmission is prevented by the BCG vaccination. A directly observed treatment, short-course (DOTS) treatment approach can cure TB illness. Recurrent TB may occur due to either relapse or reinfection with drug-resistant bacteria. The goals of this article are formulating the SVITR model with relapse and drug-resistant cases, applying the model to the TB data in Indonesia, determining the model accuracy, determining the spreading pattern and interpreting the result, and simulating the parameters. Literature study and application methods are used in this research. The SVITR model with relapse and drug-resistant cases is a first-order nonlinear differential equation system. The model is applied to TB in Indonesia based on annual data from Indonesian Health Profile, World Bank, and WHO. The model is solved by the fourth-order Runge-Kutta method. The model is accurate enough to explain the spread of TB in Indonesia with a MAPE value of 15,5%. The spreading pattern of tuberculosis infection is upward from 2010 to 2050. In 2050, there are still 8.115.976 TB cases in Indonesia. Hence in 2050, Indonesia's free of TB target has yet to be achieved. Simulation is conducted by increasing BCG vaccination to 95%, reducing contact with TB patients to 5%, increasing treatment to 95%, and lowering relapses and drug-resistant cases to 0.00005%, so the Indonesia free of TB target in 2050 can be achieved from 2042.
TEXT CLASSIFICATION USING ADAPTIVE BOOSTING ALGORITHM WITH OPTIMIZATION OF PARAMETERS TUNING ON CABLE NEWS NETWORK (CNN) ARTICLES Saputro, Dewi Retno Sari; Sidiq, Krisna; Rasyid, Harun Al; Sutanto, Sutanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1297-1306

Abstract

The development of the era encourages advances in communication and information technology. This resulted in the exchange of information being faster because it is connected to the internet. One platform that provides online news articles is Cabel News Network (CNN), which has been broadcasting news on its website since 1995. The number of Cabel News Network news articles continues to increase, so news articles are categorized to make it easier for readers to find articles according to the category they want. Classification is a technique for determining the class of an object based on its characteristics, where the class label is known beforehand. One of the algorithms for classification is adaptive boosting (AdaBoost). The AdaBoost algorithm performs classification by building several weighted decision trees (stumps), then the class determination is based on the number of stumps with the highest weight. The AdaBoost algorithm can be combined with parameter tuning to avoid overfitting or underfitting resulting from a weak set of stumps. Therefore, this study implements the AdaBoost algorithm with parameter tuning on CNN news article classification. The data used in this study is CNN news article data from 2011 to 2022 sourced from the Kaggle page. The data is categorized into six classes, namely business, entertainment, health, news, politics, and sports. This study uses two evaluation metrics, namely the accuracy value and the confusion matrix to measure the performance of the AdaBoost algorithm. The accuracy value obtained is 0,78763, the precision value is 0.91, the recall value is 0.85, and the F1 score value is 0.88.
KNOT OPTIMIZATION FOR BI-RESPONSE SPLINE NONPARAMETRIC REGRESSION WITH GENERALIZED CROSS-VALIDATION (GCV) Al Barra, Andre Fajry; Saputro, Dewi Retno Sari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp271-280

Abstract

Nonparametric regression is a statistical method used to model relationships between variables without making strong assumptions about the functional form of the relationship. Nonparametric regression models are flexible and can capture complex relationships that may not be adequately represented by simple parametric forms. Spline is one of the approaches used in nonparametric regression. Splines have the disadvantage of having to use optimal nodes in the data. Therefore, this article discusses the retrieval of optimal knot points using the generalized cross-validation method in the nonparametric bi-response spline regression model. The research results showed that the generalized-cross validation method is the best method for selecting nodes from other methods such as CV, AIC, BIC, RSS, or a more explicit validation-based approach method because of the development of the Cross Validation (CV) method which automatically selects the optimal number of nodes based on the balance between bias and variance. The process of optimizing knot points with Generalized Cross Validation (GCV) on bi-response spline nonparametric regression is implemented using Python can provide optimization at optimal knot points. Based on the results of the generalized cross-validation model analysis, it is concluded that GCV can effectively optimize knot points for spline fitting, ensuring a balanced and efficient model in capturing data patterns without overfitting.
Co-Authors Ade Susanti Adhitama, Ria Puan Adzakie, Haabi Luckmanoor Agung Nugroho, Tri Wahyu Agung Nugroho, Tri Wahyu Ahmad Faqihi, Ahmad Aji Hamim Wigena Al Barra, Andre Fajry Alfa Lutfiananda, Immas Metika Anik Djuraidah Antoni Wibowo Antoni Wibowo Ariati, Lia Arif Rahman Arrazaq, Khamid Muhammad Astutiningsih, Tiyas ‘Aini, Addin Zuhrotul Baharum, Aslina Budi Usodo Budi Usodo BUDIYONO Budiyono Budiyono Budiyono Budiyono Budiyono, Budiyono Cahyono, Heri Christy, Alexander Yonathan Dewi, Noviana Sukma Doni Susanto dwi hidayati Gustiasih, Restuning Harun Al Rasyid Heri Cahyono Ikawati, Nur Ikhsan Abdul Latif Indriati, Sela Putri Joko Domas, Joko Kananta, Ghaitsa Shafa Cinta Khairina, Fadiah Khamsatul Faizati, Khamsatul Khayati, Fitrotul Khomariah, Nurul Kiki Riska Ayu Kurniawati, Kiki Riska Ayu Kusuma, Nunung Fajar Kusumo, Fahri Aimar M Mardiyana, M Maharani, Swasti Marchamah Ulfa, Marchamah Mardiyana Mardiyana Mardiyana Mardiyana Mardiyana Mardiyana Mardiyana, Mardiyana Mardiyana, Mardiyana Muhamad Safa’udin, Muhamad Muslikhah, Muslikhah Musmiratul Uyun Musta'in, Ghufron Nanang Nabhar Fakhri Auliya, Nanang Nabhar Nanda Noor Fadjrin, Nanda Noor Ningrum, Hanifah Listya Ni’am, Dafi’ Ichsani Aysar Nughthoh Arfawi Kurdhi, Nughthoh Arfawi Nugroho Arif Sudibyo Nurul Khairiatin Nida Pambudi, Pangesti Arum Paryatun, Suji Paryatun, Suji Pradipta Annurwanda, Pradipta Prasetyo, Heri Pratama, Rizcka Indah Hani Prihastini Oktasari Putri Primasari, Dessy Marlinda Purnami Widyaningsih Purwaningsih, Tri Purwaningsih, Tri Putera Khano, Muhammad Nazhif Abda Putri, Diah Purwaning Putri, Matin Enggar Rahman, Arif Ramadhanti, Fajhria Budi Ririn Setyowati Riyadi Riyadi Riyansyah, Husnun Nur Ghiffari Putri Rizky Anggar Kusuma Wardani Safa’udin, Muhamad Santika, Putri Aura Sena, Arya Bima Setiyowati, Ririn Sidiq, Krisna Sujadi, Imam Sulistyaningsih Sulistyaningsih Suprapto, Suprapto Suryani, S Susanti, Ika Sutanto Sutanto Sutanto sutanto sutanto Tambunan, Nicolas Ray Amarco Tanjung, Andjani Ayu Cahaya Ummu Salamah Utami, Dwi Sari Utin Desy Susiaty Wahyu, Nugroho Lambang Wibowo, Gusti Ngurah Adhi Widiyaningsih, Purnami Winarno, Bowo YAFITA ARFINA MUTI Yekti Widyaningsih Yumaroh, Siti Roqhilu Zaidah Nurul Hasanah