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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.
ANALYSIS OF MULTILINGUAL OPINION POLARIZATION WITH CROSS-LINGUAL LANGUAGE MODEL-ROBUSTLY OPTIMIZED BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS APPROACH (XLM-ROBERTA) Kananta, Ghaitsa Shafa Cinta; Saputro, Dewi Retno Sari; Sutanto, Sutanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1709-1718

Abstract

The rapid growth of digital communication has intensified opinion exchanges across languages and cultures on social media, enriching public discourse while also increasing the risk of polarization that deepens social divisions. Conventional sentiment analysis methods that rely on translation often distort meaning, overlook emotional nuances, and fail to capture rhetorical devices such as irony and sarcasm, thereby limiting their reliability in multilingual contexts. This study examines the capability of XLM-RoBERTa, a multilingual transformer model pretrained on more than 100 languages, to address these challenges by generating consistent semantic representations and accommodating linguistic and cultural diversity without translation. The research employs bibliometric analysis using VOSviewer on 357 Scopus-indexed publications from 2020 to 2025 to map research trends, combined with a literature review that evaluates XLM-RoBERTa in sentiment and opinion analysis. The findings reveal that although XLM-RoBERTa has been widely employed for sentiment classification, text categorization, and offensive language detection, research explicitly focused on multilingual opinion polarization remains limited. Benchmark evaluations further indicate that XLM-RoBERTa surpasses earlier multilingual models, achieving 79.6% accuracy on XNLI and an 81.2% F1-score on MLQA, confirming its robustness in capturing semantic nuances, cultural variations, and rhetorical complexity without translation. The novelty of this research lies in integrating trend-mapping with methodological evaluation, thereby establishing XLM-RoBERTa as a reliable framework for real-time monitoring of global public opinion, supporting evidence-based policymaking, and advancing scholarly understanding of multilingual communication dynamics in the digital era.
HYBRID INTEGRATION OF BERT AND BILSTM MODELS FOR SENTIMENT ANALYSIS Tambunan, Nicolas Ray Amarco; Saputro, Dewi Retno Sari; Widyaningsih, Purnami
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1719-1730

Abstract

The rapid growth of sentiment analysis research has driven increasing interest in deep learning models, particularly transformer-based architectures such as BERT and recurrent neural networks like BiLSTM. While both approaches have shown substantial success in text classification tasks, each presents distinct strengths and limitations. This study aims to analyze the integration of BERT and BiLSTM models to enhance sentiment classification performance by combining contextual and sequential learning. A bibliometric analysis was conducted using VosViewer based on Scopus-indexed publications from 2020 to 2025, identifying four major thematic clusters related to transformer modeling, recurrent architectures, hybrid integration, and methodological advancements. Comparative findings from benchmark datasets, including SST-2, IMDb, and Yelp Reviews, indicate that hybrid BERT–BiLSTM models achieve superior accuracy compared to single models, reaching up to 97.67% on the IMDb dataset. However, this improvement is associated with increased computational complexity. The proposed framework reinforces the integration between BERT’s contextual embeddings and BiLSTM’s sequential modeling, offering a foundation for developing adaptive, and multilingual sentiment analysis systems. The results highlight future directions in optimizing hybrid architectures for efficiency, cross-lingual adaptability, and domain-specific sentiment understanding.
Lung Nodule Segmentation Accuracy in CT Images Using YOLO, 3D-CNN, and Ensemble ViT-UNETR U-Net Reyga Ferdiansyah Putra; Antoni Wibowo; Dewi Retno Sari Saputro
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 2 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i2.308

Abstract

Background: Lung cancer is the leading cause of cancer-related mortality globally, with over 2.2 million new cases and 1.8 million deaths reported annually (WHO, 2022). Pulmonary nodule detection through low-dose computed tomography (LDCT) screening is the most effective method for early lung cancer identification. However, automated systems still face significant challenges: high false positive rates, limited sensitivity for micronodules (<5 mm), and poor segmentation accuracy for nodules with irregular morphology or juxtapleural attachment. Objective: Lung nodules early discovery is key to treating lung carcinoma, but even conventional systems' micronodules still have high false positives and low accuracy. Method: This study presents an end-to-end hybrid pipeline that uses the LUNA16 database to tackle this issue. The initial stage is to make use of YOLOv12 for Region of Interest (ROI) extraction, with 3D-CNN carrying out false positive filtering through volumetric verification as a gate. The final phase conducts pixel-level precision segmentation using Adaptive Bayesian Fusion on U-Net Residual 3D ensemble (local texture features) and ViT-UNETR (global anatomical context). Results: Experiments showed superior performance level 99.99% Accuracy, Mean Dice Similarity Coefficient (DSC) at 93.88% and IoU is 90.45%. The system was very robust, reaching 97.33% DSC in the micro nodule category (<5 mm). Conclusion: In summary, this integrated architecture delivers an objective, efficient and high-quality solution for automated Diagnosis.
EKSPERIMENTASI MODEL PEMBELAJARAN KOOPERATIF TIPE GROUP INVESTIGATION (GI) DAN TIPE THINK PAIR SHARE (TPS) DENGAN PENDEKATAN SAINTIFIK PADA MATERI POKOK HIMPUNAN DITINJAU DARI ADVERSITY QUOTIENT (AQ) Ikawati, Nur; Mardiyana, Mardiyana; Sari Saputro, Dewi Retno
Journal of Mathematics and Mathematics Education Vol 6, No 1 (2016): Journal of Mathematics and Mathematics Education (JMME)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jmme.v6i1.10036

Abstract

Abstract: The aim of the research was to know the effect of learning models on mathematics learning achievement viewed from the student Adversity Quotient (AQ). The learning models compared were the cooperative learning model of the Group Investigation with scientific approach (GI-S), the cooperative learning model of the Think Pair Share with scientific approach (TPS-S), and model of classical learning with scientific approach (K-S). The type of the research was a quasi experimental research with the factorial design of 3 x 3. The population were all students of Junior High School in Bantul regency on academic year 2014/2015. The samples of  the research were taken by using the stratified cluster random sampling. The instruments used were mathematics achievement test and questionnaires of AQ. The hypotheses of the research were analyzed by using the two-way analysis of variance with unbalanced cells at the significance level of . The results of the research are as follows. 1) GI-S gives better mathematics achievements than TPS-S, and both gives better mathematics achievements than K-S. 2) Students with AQ type of climber have better mathematics achievements than students with AQ type of camper and type of quitter, while students with AQ type of camper have better mathematics achievements than students with AQ type of quitter. 3) For GI-S and TPS-S, students with AQ type of climber and type of camper have the same mathematics achievements, and students with AQ type of climber and type of camper gives better mathematics achievements than students with AQ type of quitter. For K-S, students with AQ type of climber have better mathematics achievements than students with AQ type of camper and type of quitter, and both have the same mathematics achievements. 4) For students with AQ type of climber and type of quitter, GI-S, TPS-S, and K-S gives the same mathematics achievement. For students with AQ type of camper, GI-S and TPS-S gives the same mathematics achievements, and both gives better mathematics achievements than K-S.Keywords: GI, TPS, Classical, Scientific Approach, AQ 
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 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 Ikawati, Nur Ikhsan Abdul Latif Imam Sujadi 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 Muhamad Safa’udin, Muhamad Muslikhah, Muslikhah Musmiratul Uyun Musta'in, Ghufron Mu’ti, Yafita Arfina 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 Reyga Ferdiansyah Putra 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 Sulistyaningsih Sulistyaningsih Suprapto Suprapto 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