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Covid-19 Data Analysis in Tarakan with Poisson Regression and Spatial Poisson Process Sofro, A'yunin; Wardani, Ika Nurwanitantya; Khikmah, Khusnia Nurul
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 4 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i4.19653

COVID-19 entered Indonesia in March 2020 and included North Kalimantan Province, Tarakan. COVID-19 cases have outspread in Tarakan. The cause of the outspread and the patterns were not known yet. One relevant approach was to use Generalized Linear Models. The two methods are Poisson Regression and Stochastic with Spatial Poisson Process. The variables used were rainfall, population density, and temperature in each village in Tarakan. The Poisson Regression analysis founds that only one factor affected temperature. Then, the results were refined with the Spatial Poisson Process, where in addition to the influencing factors also, the distribution patterns are obtained. The analysis showed that the pattern of case distribution was included in the non-homogeneous Poisson process criteria. Then the model of the case density intensity was obtained using regression. From the model, it was known that the covariate variables significantly influence rainfall and temperature. Compared with general Poisson regression analysis, the results showed that only the average temperature variables had a significant effect. Thus, a better method was used, namely the Spatial Poisson Process. It was also shown by the two models' AIC values, where the AIC value of the Spatial Poisson Process model was smaller than the Poisson Regression.

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

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.

POSITIVE CONFIRMED PREDICTION OF COVID-19 IN EAST JAVA USING COUNT TIME SERIES BASED DOUBLE POISSON INAR(p) PROCESS Sofro, A'yunin; Subiantoro, Framitha Septian; Khikmah, Khusnia Nurul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp879-888

In December 2019, there was a virus outbreak caused by a virus disease with a relatively high spread in Indonesia, one of which was in East Java Province. It is proven by the number of new cases on January 15, 2021, in East Java, reaching 12818 cases. This is why researchers predict the number of positive cases of COVID-19 in East Java so that the Government can anticipate an increase in the number of COVID-19 patients. This study uses data on the addition of positive COVID-19 cases in East Java from May 16, 2020, to January 24, 2021. Because the count time series data shows overdispersion, predictions are made by modeling the COVID-19 data using the INAR( ). development model, namely Double Poisson INAR( ). Several tests were carried out with data from the Double Poisson distribution, and then the ACF and PACF plots were analyzed to find the order of INARDP. After obtaining the order, the model can be constructed and estimated using MLE. Then, the prediction of adding COVID-19 cases in East Java on January 25, 2021, obtained 949 cases with an estimated error of 13.73 percent. So, the model show that the accuracy of the forecasted value with actual value is 86.17 percent.

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

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

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

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

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

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

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

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.

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