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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi International Journal of Advances in Intelligent Informatics AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Soulmath : Jurnal Edukasi Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan ComTech: Computer, Mathematics and Engineering Applications MATHunesa: Jurnal Ilmiah Matematika
Sofro, A'yunin
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Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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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.
Clustering Analysis of MAMA 2024 Song of the Year Nominees Based on Musical Elements and Popularity Indicators Harahap, Libelda Aldinaduma; Sofro, A'yunin
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.12860

Abstract

As K-pop continues to dominate global music charts, understanding the factors behind the success of songs has become increasingly essential. This study explores how musical elements and popularity indicators reveal patterns among topperforming songs. A total of 57 songs nominated for the 2024 Song of the Year category were grouped using hierarchical cluster analysis. The genre variable was consolidated into six broader categories and converted into numerical labels. All variables are normalized using the Min-Max normalization method before clustering. The data includes musical elements such as genre, tempo, danceability, energy, and happiness, as well as popularity indicators like YouTube views and Spotify streams. The analysis employs single, complete, and average linkage methods. Among these, the average linkage method yields the best results, with an agglomerative coefficient value of 0.8167. Seven distinct clusters are identified: Cluster 1 features R&B and hip-hop styles with varied energy and rhythms; Cluster 2, the largest group, includes high-energy pop, hip-hop, and dance-pop tracks that are popular on streaming platforms; Cluster 3 contains indie and experimental tracks; Cluster 4 emphasizes high-energy stage performances; Cluster 5 is an outlier with experimental traits; Cluster 6 highlights R&B and funk with global appeal; and Cluster 7 includes emotional OSTs and ballads with slower tempos. By combining musical elements and popularity indicators, this research uncovers patterns of success in K-pop songs. These findings offer actionable insights for artists, producers, and marketers, providing a datadriven reference for creating music that resonates with modern audience preferences.
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.
LOGISTIC AND PROBIT REGRESSION MODELING TO PREDICT THE OPPORTUNITIES OF DIABETES IN PROSPECTIVE ATHLETES Ariyanto, Danang; Sofro, A'yunin; Hanifah, A’idah Nur; Prihanto, Junaidi Budi; Maulana, Dimas Avian; Romadhonia, Riska Wahyu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1391-1402

Abstract

Diabetes is among the most prevalent chronic diseases globally, posing significant health risks to individuals. The identification of individuals at risk of developing these conditions is of paramount importance, particularly in high-stress and physically demanding activities such as athletic training. To find out the chances of a prospective athlete suffering from diabetes or not, models for binary data can be used, including logistic regression and probit models. The data used is primary data from prospective athletes in East Java, including prospective athletes from the State University of Surabaya and East Java Koni Athletes. This study aimed to develop an early prediction model for diabetes in prospective athletic candidates using a bivariate logistic and probit regression approach while considering the influence of socio-demographic and anthropometric factors. To selecting the best model between logistic regression and probit regression using Akaike’s Information Criterion (AIC) value, the smaller the AIC value gets means that the model is closer to the actual value or being the best model. Logistic regression has a smaller AIC value (129,85) than probit regression, this means that the logistic model is the best model. In this paper, an attempt is made to explore the use of logistic and probit regression to determine the factors which significantly influence the diabetes disease and we got that the logistic model as the best model because it has a smaller AIC value than the probit model. Based on the result of analysis and discussion, it can be concluded that there are two factors called mother’s job and finance which are influenced to the response variable, diabetes disease at significance level of 5%.
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.
STOCK PRICE PREDICTION AND SIMULATION USING GEOMETRIC BROWNIAN MOTION-KALMAN FILTER: A COMPARISON BETWEEN KALMAN FILTER ALGORITHMS Maulana, Dimas Avian; Sofro, A'yunin; Ariyanto, Danang; Romadhonia, Riska Wahyu; Oktaviarina, Affiati; Purnama, Mohammad Dian
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/barekengvol19iss1pp97-106

Abstract

Stocks have high-profit potential but also have high risk. Many people have ways to forecast stock prices. The Geometric Brownian Motion (GBM) method forecasts stock prices. The data used in this study are closing stock price data from July 1, 2021 to August 31, 2021 taken from Yahoo! Finance. The stocks used in this research are Bank Rakyat Indonesia (BBRI), Indofood Sukses Makmur (INDF), and Telkom Indonesia (TLKM). A strategy is carried out to improve prediction accuracy by utilising the Kalman Filter (KF). This research will compare the mean absolute percentage error (MAPE) value between GBM-KF, which was manually computed and computed using the Python library. As an example of this research, for BBRI stock, the high GBM MAPE value of 9.02% can be reduced to 3.52% with manually computed GBM-KF and 3.68% with Python library computed GBM-KF. Similarly, INDF and TLKM stocks are showing a significant reduction in MAPE values to deficient levels in some cases. The GBM-KF method employing manual computing may enhance the overall precision of stock price forecasting. Future research may enhance this study by using the GBM-KF model on alternative financial instruments, integrating supplementary market data, or evaluating its efficacy under extreme market conditions.
PREDIKSI KECEPATAN ANGIN EKSTREM DI KABUPATEN SIDOARJO MENGGUNAKAN BAYESIAN MARKOV CHAIN MONTE CARLO Putri, Safira Nuraini; Sofro, A'yunin
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

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

Abstract

Angin puting beliung merupakan salah satu bencana hidrometeorologi yang saat ini marak terjadi. Salah satu penyebabnya adalah cuaca ekstrem, yaitu angin kencang. Salah satu daerah rawan bencana angin puting beliung di Indonesia adalah Kabupaten Sidoarjo. Bencana angin puting beliung apabila dibiarkan akan menimbulkan dampak yang merugikan, baik secara fisik maupun non-fisik. Oleh karena itu, dibutuhkan prediksi kecepatan angin ekstrem yang dapat dijadikan sebagai acuan untuk pencegahan bencana ini. Kecepatan angin dapat dianalisis menggunakan Extreme Value Theory yang mengikuti Distribusi Generalized Extreme Value untuk mendapatkan return level pada periode pengembalian waktu 2025, 2026, dan 2027. Untuk mendapatkan return level tersebut, perlu dilakukan estimasi parameter distribusi GEV menggunakan pendekatan Bayesian Markov Chain Monte Carlo yang dalam hal ini adalah Metropolis-Hastings Algorithm. Data yang digunakan adalah kecepatan angin maksimum di Kabupaten Sidoarjo pada tahun 2014-2024. Hasil penelitian menunjukkan bahwa prediksi kecepatan angin ekstrem di Kabupaten Sidoarjo adalah 16.39035 m/s pada tahun 2025, 17.78805 m/s pada tahun 2026, dan 18.74473 m/s pada tahun 2027. Hasil penelitian ini dapat dijadikan sebagai acuan untuk melakukan pencegahan terhadap bencana angin puting beliung yang terjadi di Kabupaten Sidoarjo.
ANALISIS REGRESI MULTINOMIAL UNTUK PEMODELAN FAKTOR PENYEBAB KEKERASAN DALAM RUMAH TANGGA Novitasari, Eggi; Sofro, A'yunin
MATHunesa: Jurnal Ilmiah Matematika Vol. 11 No. 1 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v11n1.p25-34

Abstract

Domestic violence is an act committed against a person, which results in suffering and also physical, mental, sexual, and domestic neglect which includes the threat of committing acts, coercion, and unlawful deprivation of independence within the scope of the household. This study aims to analyze the model to determine the factors that influence domestic violence. Factors that are suspected to be influential are age, education level of the victim, place of occurrence, gender of the perpetrator, citizenship status, type of relationship between the perpetrator and the victim, family income, number of family dependents. The method used is the multinomial regression method, which is one of the data analysis methods that looks for the relationship between polychotomous response variables with a nominal scale. The data in this study used data obtained from the Office of Women and Children Empowerment in 2021. The results show that age and number of family dependents are factors that have a significant effect on domestic violence. Keywords: Domestic Violence, Multinomial Regression
AnalisisRegresi Binomial Negatif untuk Pemodelan Angka Positif Penyakit Kusta di Jawa Timur Dhahari, Nadiya Mushma; Sofro, A'yunin
MATHunesa: Jurnal Ilmiah Matematika Vol. 11 No. 3 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v11n3.p543-550

Abstract

Leprosy is a chronic disease caused by Mycobacterium leprae, which injures the peripheral nerves (sensory, motor and autonomic functions). Delayed treatment can result in permanent damage to the eyes, hands and feet. The purpose of this research is to identify the factors that influence the positive rate of leprosy in East Java. Factors that can influence include population density, the number of villages or sub-districts with health facilities, the percentage of people with health complaints, the percentage of households with adequate sanitation facilities, the percentage of poor people, the number of health workers, and the percentage who have health insurance. amount. Percentage of workers and those with health insurance. The method used is negative binomial regression method. This is one of the methods used to overcome data overdispersion in Poisson regression. The data for this study used data obtained from the Central Bureau of Statistics and Publication of the East Java Health Service in 2021. The results showed that population density, the percentage of people with health complaints, and the percentage of poor people were factors that influenced the significance of leprosy sufferers in East Java in 2021. Keywords: leprosy, Poisson regression, overdispersion, negative binomial regression.
Co-Authors AFFIATI OKTAVIARINA AGUNG LUKITO Aji, Raihan Puspita Al Akbar, Muhammad Mahdy Asri Maharani Auliya, Elok Rizqi Azzahro, Ika Aprilia Danang Ariyanto Dhahari, Nadiya Mushma Habibah, Sayyidah Ummi Habibulloh, Wildan Handitia, Nanda Reza Hanifah, A’idah Nur Harahap, Libelda Aldinaduma Ibnu Febry Kurniawan Iftah Camelia Dyah Apriliyanti Khikmah, Khusnia Nurul Kirana, Astika Eka Maulana, Dimas Avian Mega Teguh Budiarto Muhyidin, Kholil Mustafidah, Mutia Eva Novitasari, Eggi Nugrahanto, Zalfa Zaliana Oktaviarina, Affi Ombao, Hernando Prasetyo, Dimas Abi Prihanto, Junaidi Budi Purnama, Mohammad Dian Puspitasari, Riskyana Dewi I Putri, Safira Nuraini Rahajeng, Sita Riani, Rosalina Agista Romadhonia, Riska Wahyu Rooselyna Ekawati Siolimbona, Dedi Rahman Subiantoro, Framitha Septian Wardani, Ika Nurwanitantya YUSUF FUAD