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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi International Journal of Advances in Intelligent Informatics JOIN (Jurnal Online Informatika) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Inferensi ESTIMASI: Journal of Statistics and Its Application MATHunesa: Jurnal Ilmiah Matematika Jurnal Info Kesehatan
Khikmah, Khusnia Nurul
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Energy Engineering Health Professions Immunology & microbiology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Simulation and Empirical Studies of Long Short-Term Memory Performance to Deal with Limited Data Khikmah, Khusnia Nurul; Sadik, Kusman; Notodiputro, Khairil Anwar
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1356

Abstract

This research is proposed to determine the performance of time series machine learning in the presence of noise, where this approach is intended to forecast time series data. The approach method chosen is long short-term memory (LSTM), a development of recurrent neural network (RNN). Another problem is the availability of data, which is not limited to high-dimensional data but also limited data. Therefore, this study tests the performance of long short-term memory using simulated data, where the simulated data used in this study are data generated from the functional autoregressive (FAR) model and data generated from the functional autoregressive model of order 1 FAR(1) which is given additional noise. Simulation results show that the long short-term memory method in analyzing time series data in the presence of noise outperforms by 1-5% the method without noise and data with limited observations. The best performance of the method is determined by testing the analysis of variance against the mean absolute percentage error. In addition, the empirical data used in this study are the percentage of poverty, unemployment, and economic growth in Java. The method that has the best performance in analyzing each poverty data is used to forecast the data. The comparison result for the empirical data is that the M-LSTM method outperforms the LSTM in analyzing the poverty percentage data. The best method performance is determined based on the average value of the mean absolute percentage error of 1-10%.
Prediction of Air Temperature in East Java using Spatial Extreme Value with Copula Approach Sofro, A'yunin; Habibulloh, Wildan; Khikmah, Khusnia Nurul
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.25436

Abstract

The increase in world temperature or global warming is a form of imbalance in the average temperature on Earth. The increase in air temperature will increase the risk of disasters, which will occur more frequently in the future. Rising global temperatures are expected to cause changes that can have fatal consequences. To anticipate the dangers are predicted by predicting the future air temperature increase. One of the methods that can be used is spatial extreme value theory, which uses the Gaussian copula model approach and Student's t copula, where the choice of these two methods was based on the flexibility they offer in capturing tail dependencies due to their capacity to describe the dependence structure between many variables simultaneously. . This makes it possible to get a return level or predicted value of air temperature by considering the elements of location in it. This research discusses both approaches and uses the maximum likelihood estimation (MLE) and pseudo maximum likelihood estimation (PMLE) methods to estimate the parameters. In addition, since spatial elements need to be considered, the trend surface model is also used. Akaike information criterion (AIC) is used to determine the best model for predicting air temperature based on extreme n air temperature data in East Java Province from nine air temperature observation stations. The results show that the highest air temperature value is around the Banyuwangi temperature observation station located in Banyuwangi Regency in the next two-year return period. The AIC results show that the best model produced is the Gaussian copula approach with a smaller AIC value than the student's t-copula approach, which is 8.0174. This value used to compare the relative quality of different statistical models with a lower AIC value generally indicates a better-fitting model.This value with a lower AIC value generally indicates a better-fitting model.
Analisis Pelaksanaan Pelatihan Penulisan Karya Tulis Ilmiah di MGMP Matematika SMP Kabupaten Lumajang Sofro, A'yunin; Khikmah, Khusnia Nurul; Fuad, Yusuf; Maulana, Dimas Avian; Lukito, Agung; Auliya, Elok Rizqi
Aksiologiya: Jurnal Pengabdian Kepada Masyarakat Vol 8 No 2 (2024): Mei
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/aks.v8i2.13610

Abstract

Wabah Covid-19 merupakan ancaman nyata bagi kesehatan global dan menjadi beban dan tantangan serius bagi semua negara. Covid-19 berdampak pada melemahnya perekonomian, tetapi dampaknya juga dirasakan dalam dunia pendidikan. Keprofresionalan seorang guru sangatlah dibutuhkan untuk menghadapi berbagai tantangan. Guru memegang peranan yang sangat penting dalam mendukung program pemerintah khususnya peningkatan kualitas pendidikan, terutama di masa pandemi saat ini. Seorang guru yang profesional juga diharapkan selalu melakukan penelitian yang dituangkan dalam suatu karya tulis ilmiah. Untuk mendukung kualitas dari karya tulis ilmiah, analisis data dalam penelitian juga sangat diperlukan. Di sisi lain, MGMP Matematika SMP Kabupaten Lumajang membutuhkan pelatihan untuk meningkatkan kinerja guru. Sehingga, menggiatkan guru untuk melakukan penulisan karya ilmiah dengan analisis statistika adalah salah satu solusi yang tepat dilakukan. Dari hasil yang didapatkan bahwa kriteria keberhasilan dari sisi output telah terpenuhi. Lebih dari 90 persen kelompok telah mencapai target kinerja yang ditetapkan. Sedangkan dari sisi proses, sekitar 80 persen lebih peserta memberikan kesan positif terhadap workshop yang telah dilakukan. Dengan adanya pelatihan tersebut juga ada peningkatan kinerja guru dalam penulisan karya ilmiah sebesar 82 persen.
COMPARISON OF ARIMA AND GARMA'S PERFORMANCE ON DATA ON POSITIVE COVID-19 CASES IN INDONESIA Sofro, A'yunin; Khikmah, Khusnia Nurul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (417.061 KB) | DOI: 10.30598/barekengvol16iss3pp919-926

Abstract

The development of methods in statistics, one of which is used for prediction, is overgrowing. So it requires further analysis related to the goodness of the method. One of the comparisons made to the goodness of this model can be seen by applying it to actual cases around us. The real case still being faced by people worldwide, including in Indonesia, is Covid-19. Therefore, research comparing the autoregressive integrated moving average (ARIMA) and the Gegenbauer autoregressive moving average (GARMA) method in positive confirmed cases of Covid-19 in Indonesia is essential. Based on the results of this research analysis, it was found that the best model with the Aikake's Information Criterion measure of goodness that was used to predict positive confirmed cases of Covid-19 in Indonesia was the Gegenbauer autoregressive moving average (GARMA) model.
TRANSFER FUNCTION AND ARIMA MODEL FOR FORECASTING BI RATE IN INDONESIA Khikmah, Khusnia Nurul; Sadik, Kusman; Indahwati, Indahwati
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/barekengvol17iss3pp1359-1366

Abstract

Fluctuating gold prices can have an impact on various sectors of the economy. Some of the impacts of rising and falling gold prices are inflation, currency exchange rates, and the value of the Bank Indonesia benchmark interest rate (BI Rate). The data was taken from the Indonesian Central Statistics Agency's official website (BPS) for the Bank Indonesia benchmark interest rate (BI Rate) value. Therefore, research on the value of the Bank Indonesia benchmark interest rate (BI Rate) is essential with the gold price as a control. The purpose of this study is to forecast the value of the Bank Indonesia reference interest rate (BI Rate) with a transfer function model where the input variable used is the price of gold and forecast the value of the Bank Indonesia benchmark interest rate (BI Rate) with the ARIMA model. The analysis results show that the best model for forecasting the Bank Indonesia reference interest rate (BI Rate) is a transfer function model with a value of , , , and a noise series model with the MAPE value is
ANALYSIS OF RAINFALL IN INDONESIA USING A TIME SERIES-BASED CLUSTERING APPROACH Sofro, A'yunin; Riani, Rosalina Agista; Khikmah, Khusnia Nurul; Romadhonia, Riska Wahyu; Ariyanto, Danang
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/barekengvol18iss2pp0837-0848

Abstract

Indonesia has a tropical climate and has two seasons: dry and rainy. Prolonged drought can cause drought disasters, and rain can cause floods and landslides. According to information from the Meteorology, Climatology, and Geophysics Agency (BMKG), natural disasters such as floods and landslides due to heavy rains have been a severe problem in Indonesia for the past five years. Different regional characteristics can affect the intensity of rain that falls in every province in Indonesia. It can be grouped to determine which provinces have similar characteristics to natural disasters due to rainfall. Later, it can provide information to the government and the public so that they are more aware of natural disasters. So, it is necessary to research and classify provinces in Indonesia for rainfall with cluster analysis. The data used is secondary rainfall data taken from the official BMKG website. Cluster analysis of rainfall in 34 provinces in Indonesia used hierarchical and non-hierarchical methods in this study. The approach that is used in this research limits our clustering of the data. Further research with a machine learning approach is recommended. For the clustering method, the agglomerative hierarchical method includes single, average, and complete linkage. The non-hierarchical method includes k-medoids and fuzzy c-means. The cluster analysis results show that the dynamic time warping (DTW) distance measurement method with the average linkage method has the most optimal cluster results with a silhouette coefficient value of 0.813.
Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java Khikmah, Khusnia Nurul; Sartono, Bagus; Susetyo, Budi; Dito, Gerry Alfa
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1012

Abstract

This study aims to compare the classification performance of the random forest, gradient boosting, rotation forest, and extremely randomized tree methods in classifying the food insecurity experience scale in West Java. The dataset used in this research is based on the Socio-Economic Survey by Statistics Indonesia in 2020. The novelty of this research is comparing the performance of the four methods used, which all are the tree ensemble approaches. In addition, due to the imbalance class problem, the authors also applied three imbalance handling techniques in this study. The results show that the combination of the random-forest algorithm and the random-under sampling technique is the best classifier. This approach has a balanced accuracy value of 65.795%. The best classification method results show that the food insecurity experience scale in West Java can be identified by considering the factors of floor area (house size), the number of depositors, type of floor, health insurance ownership status, and internet access capabilities.
The Influence of Women’s Empowerment on The Preference for Contraceptive Methods in Indonesia: A Multinomial Logistic Regression Modelling Fulazzaky, Tahira; Indahwati, Indahwati; Fitrianto , Anwar; Erfiani, Erfiani; Khikmah, Khusnia Nurul
JURNAL INFO KESEHATAN Vol 22 No 3 (2024): JURNAL INFO KESEHATAN
Publisher : Research and Community Service Unit, Poltekkes Kemenkes Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31965/infokes.Vol22.Iss3.1213

Abstract

The concept of women's empowerment encompasses enabling women to take control of their own lives, independently make choices, and fulfill their complete capabilities. Numerous research studies examined the correlation between the empowerment of women and their reproductive health. In Indonesia, female labor force participation is relatively low. As a result, research on the influence of empowering women on contraceptive method preference in Indonesia makes sense. This research aims to find the multinomial logistic regression model in choosing contraceptive methods for married women in Indonesia and to identify the women’s empowerment traits that most impact contraceptive method choice.  For this study, the researchers utilized secondary data obtained from the 2017 Indonesian Demographic and Health Survey (IDHS). The participants consisted of women between the ages of 15 and 49 who were married. The total number of respondents sampled was 49,216. Variables that significantly affect contraceptive method use include the respondent's current employment, the respondent has bank account or other financial institution accounts, the cumulative count of offspring previously born and beating justified if the wife argues with her husband. The analysis is obtained using the multinomial logistic regression test, independency, multicollinearity, and parameter test, and the selection is made by considering either the smallest value of Akaike's information criterion or the option that achieves the highest level of accuracy. Findings highlight four significant variables: Firstly, employed women are more likely to use contraceptives than the unemployed. Secondly, access to banking services correlates with a higher likelihood of contraceptive use. Thirdly, women with more children tend to prefer long-acting reversible contraceptives. Lastly, endorsement of spousal violence justifiability is linked to conventional contraceptive selection. These results emphasize the roles of employment, financial access, family size, and gender-based violence perceptions in shaping contraceptive choices in Indonesia. Model 3 emerges as the most accurate predictor of preferences after eliminating six variables based on rigorous testing and multicollinearity considerations. These findings underscore the importance of addressing economic empowerment and gender-related issues in Indonesian reproductive health programs and policies. Such a comprehensive approach can enhance women's autonomy, enabling them to make crucial life choices and ultimately improving their overall well-being.         
Enhanced diabetes and hypertension prediction using bat-optimized k-means and comparative machine learning models Sofro, A'yunin; Ariyanto, Danang; Prihanto, Junaidi Budi; Maulana, Dimas Avian; Romadhonia, Riska Wahyu; Maharani, Asri; Oktaviarina, Affi; Kurniawan, Ibnu Febry; Khikmah, Khusnia Nurul; Al Akbar, Muhammad Mahdy
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.1816

Abstract

This research aims to develop an analytical approach to classification statistics. The proposed approach combines machine learning with optimization. Considering the urgency of research related to exploring the best methods to apply to sports data. This study proposes a novel framework that combines the k-means clustering results with the bat algorithm to optimize performance prediction for athletes in Indonesia. The proposed method aims to explore the data by comparing the classification performance of random forests, extremely randomized trees, and support vector machines. We conducted a case study using primary data from 200 respondents at Surabaya State University and the East Java National Sports Committee. The accuracy results in this study indicate that, based on the performance evaluation metric, the best approach is random forest clustering using k-means with bat algorithm optimization, achieving 81.25% accuracy, compared with other machine learning approaches. This research contributes to the field of classification statistics by introducing a novel hybrid framework that integrates machine learning, clustering, and optimization techniques to improve predictive accuracy, particularly in sports analytics. Beyond sports science, the proposed approach can be adapted to other domains that require robust performance prediction and decision support, such as health analytics, educational assessment, and human resource selection.
TRADITIONAL LOGISTIC REGRESSION AND MACHINE LEARNING APPROACHES OF SOCIODEMOGRAPHIC AND ANTHROPOMETRIC FACTORS INFLUENCING HYPERTENSION IN ATHLETES Sofro, A'yunin; Maharani, Asri; Mustafidah, Mutia Eva; Khikmah, Khusnia Nurul; Oktaviarina, Affiati; Ariyanto, Danang
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/barekengvol20iss2pp1125-1138

Abstract

The type and intensity of exercise performed by athletes play an important role in affecting blood pressure stability, putting them at risk of developing hypertension. Hypertension, or high blood pressure, is a medical condition in which the blood pressure in the arteries rises above normal limits. Hypertension in athletes becomes an essential factor in real cases if not detected early. Therefore, this study aims to model and analyse the sociodemographic and anthropometric factors that influence the incidence of hypertension. The data used in this study are primary data from 200 athlete selection participants at the University of Surabaya and the Indonesian National Sports Committee (INSC) of East Java. This research method proposes to compare the traditional approach with machine learning to prove the accuracy comparison of the model's goodness, where both approaches are proposed by considering the novelty proposed through the machine learning approach but still maximizing the traditional approach. The proposed methods are binary logistic regression, binary logistic regression with the addition of random effects, highly randomized tree, and support vector classification. The binary logistic regression model is better than the binary logistic regression model with random effects, random trees, and support vector classification because the accuracy, sensitivity, specificity, and F1-score value (68.5%, 69%, 68%, and 68.8%) is highest than the others. Other results showed that the waist circumference variable, the father's occupation variable, and the salary variable significantly affected hypertension at the 5% significance level.
Co-Authors A'yunin Sofro A'yunin Sofro AFFIATI OKTAVIARINA AGUNG LUKITO Al Akbar, Muhammad Mahdy Asri Maharani Assyifa Lala Pratiwi Hamid Auliya, Elok Rizqi Bagus Sartono Budi Susetyo Danang Ariyanto Dito, Gerry Alfa Erfiani Erfiani Fatimah Fatimah Fitrianto, Anwar Fulazzaky, Tahira Habibulloh, Wildan Ibnu Febry Kurniawan Ika Nurwanitantya Wardani Indahwati Kusman Sadik Maulana, Dimas Avian Mukaromah, Muizzatul Mustafidah, Mutia Eva Oktaviarina, Affi Prihanto, Junaidi Budi Riani, Rosalina Agista Romadhonia, Riska Wahyu Sofro, A'yunin Sofro, A'yunin Subiantoro, Framitha Septian Wardani, Ika Nurwanitantya YUSUF FUAD