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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi International Journal of Advances in Intelligent Informatics Komatika: Jurnal Pengabdian Kepada Masyarakat
A'yunin Sofro
Universitas Negeri Surabaya
Humanities Computer Science & IT Education Mathematics Social Sciences

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Analisis Pelaksanaan Pelatihan Infografis Data Dengan Microsoft Excel Bagi Guru Kabupaten/Kota Pasuruan A'yunin Sofro; Riska Wahyu Romadhonia; Affiati Oktaviarina; Danang Ariyanto; Mutia Eva Mustafidah
Komatika: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2025): Mei 2025
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/komatika.v5i1.1078

Abstract

Dalam era teknologi yang berkembang pesat, pemanfaatan perangkat lunak seperti Microsoft Excel menjadi krusial, terutama di bidang pendidikan. Meski populer, sebagian besar guru di Kabupaten/ Kota Pasuruan belum sepenuhnya memaksimalkan Microsoft Excel untuk infografis data. Tim Pengabdian Kepada Masyarakat (PKM) mengusulkan kolaborasi dalam "Pelatihan Infografis data dengan Microsoft Excel Bagi Guru Kabupaten/Kota Pasuruan" tahun 2024, bertujuan meningkatkan pemahaman dan keterampilan guru sejalan dengan visi lembaga untuk meningkatkan mutu pendidikan. Pelatihan difokuskan pada pembuatan infografis data melalui fungsi Excel, meningkatkan keterampilan guru di Kabupaten/Kota Pasuruan dalam memanfaatkan perangkat lunak ini. Pelatihan ini telah dilaksanakan oleh tim secara luring pada hari Sabtu, 13 Juli 2024 bertempat di SMPN 1 Bangil. Pelatihan diikuti oleh 24 guru di beberapa SD di Pasuruan dan berjalan lancar. Pada saat pelatihan dilakukan penilaian dengan pretest dan posttest, berdasarkan hasil tes tersebut akan dilakukan pengujian menggunakan uji t, dengan hasil yang diperoleh adalah nilai thitung lebih besar dari ttabel dan terjadi kenaikan rata-rata nilai pretest (30,83) ke posttest (67,92) yang menunjukkan bahwa pelatihan yang dilakukan berhasil meningkatkan pemahaman dan kemampuan peserta, yang ditunjukkan oleh kenaikan nilai. Hasil survei evaluasi kegiatan menunjukkan bahwa secara keseluruhan, peserta memberikan penilaian yang sangat positif terhadap kegiatan PKM ini, dengan rata-rata poin keseluruhan sebesar 4.8. Hal tersebut menunjukkan bahwa materi dan penyampaian dalam kegiatan ini berhasil menarik minat dan memenuhi kebutuhan peserta, dengan mayoritas merasa antusias, termotivasi, dan mampu meningkatkan kemampuan mereka dalam membuat infografis data menggunakan Microsoft Excel.
K-means optimization with bat algorithm for predicting diabetes and hypertension risk in athletes’ comparison with machine learning A'yunin Sofro; Danang Ariyanto; Junaidi Budi Prihanto; Dimas Avian Maulana; Riska Wahyu Romadhonia; Asri Maharani; Affi Oktaviarina; Ibnu Febry Kurniawan; Khusnia Nurul Khikmah; Muhammad Mahdy Al Akbar
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.
Comparison of Extreme Value Logistic and Copula Approaches for Bivariate Extreme Value in Pekanbaru Fadilla Afsari; A'yunin Sofro
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.41081

Abstract

Global climate change has intensified extreme weather events in tropical regions, increasing heat-related risks in Pekanbaru City. This study aims to analyze the joint behavior of extreme maximum temperature and humidity using a multivariate extreme value framework. Daily data from 20142024 were processed using a seasonal block maxima approach, resulting in 24 seasonal extreme observations. The marginal distributions follow a Weibull-type Generalized Extreme Value (GEV) distribution, indicating bounded extreme values. Dependence analysis indicates a weak association, where the Joe copula converges to the independence case, while the Frank copula yields the lowest AIC value among the fitted copula models. However, the difference in AIC values is small and should be interpreted cautiously. Joint return period analysis indicates that joint exceedance of seasonal block maxima is relatively infrequent (approximately 100 years), whereas single-variable exceedances occur more frequently (approximately 5.2 years). These return period estimates are exploratory and subject to uncertainty due to the limited sample size. Overall, the results suggest a weak dependence structure between the variables, and the findings should be interpreted cautiously in the context of climate-related risk assessment.
Negative Binomial Modeling of District-Level Diarrhea Incidence in West Java Province Nurul Nabilah; A'yunin Sofro
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i2.42057

Abstract

This study investigated district-level diarrhea incidence in West Java Province during 2022–2024 and identified an appropriate count-data regression model for longitudinal epidemiological data. The response variable was the annual number of diarrhea cases, while explanatory variables included sanitation access, inpatient health centers, community health workers, nutritional indicators, drinking water facilities, and households implementing clean and healthy living behavior (PHBS). A population offset term was incorporated to account for differences in population exposure across districts/cities. Poisson Regression was initially fitted as a baseline model; however, severe overdispersion was detected. Therefore, Negative Binomial Regression and Generalized Linear Mixed Models (GLMM) were estimated and compared using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood values, and simulation-based residual diagnostics. The results showed that Negative Binomial Regression provided the best overall fit, yielding substantially lower AIC and BIC values than competing models. Residual diagnostics indicated no evidence of remaining overdispersion, zero inflation, or serious outliers. Sanitation access was significantly associated with lower diarrhea incidence rates, with a one-standard-deviation increase corresponding to an estimated 15.7% reduction in incidence. These findings highlight the importance of accounting for overdispersion and suggest that Negative Binomial Regression is an appropriate framework for modeling regional diarrhea incidence data.
Negative Binomial tv-DINGARCH Models for Monthly DHF Cases in Surabaya, Indonesia (2015-2025) Aminatuz Zahro; A'yunin Sofro
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i2.42667

Abstract

Count time series data are commonly encountered in epidemiological studies, such as monthly reports of Dengue Hemorrhagic Fever (DHF) cases. The INGARCH model has been widely used to handle temporal dependence and overdispersion in such data. However, the standard INGARCH model assumes constant dispersion, whereas in practice the dispersion level may vary over time due to epidemiological factors like seasonality or vector control policies. Accurately capturing this time-varying variability is essential for reliable disease surveillance. This study applies the tv-DINGARCH model to DHF cases in Surabaya from January 2015 to December 2025. The tv-DINGARCH(1,1,1,1) model with Negative Binomial distribution is used because it allows the dispersion parameter \(\phi_t\) to evolve dynamically. Parameter estimation is conducted using the Conditional Maximum Likelihood Estimation method implemented in RStudio. The results show that the tv-DINGARCH model yields AIC = 968.43 and BIC = 985.68, both smaller than those of the standard INGARCH model (AIC = 1012.18, BIC = 1023.68). The likelihood ratio test produces an LR statistic of 47.7560 with a p-value less than 0.000001, strongly rejecting constant dispersion. The stationarity condition is satisfied with \(\beta_2 + \alpha_2 + \beta_1 + \alpha_1 = 0.9990 1\). From a public health perspective, these findings indicate that the tv-DINGARCH model better captures the fluctuating dynamics of DHF transmission, supporting more effective surveillance and early warning systems.
Co-Authors Affi Oktaviarina AFFIATI OKTAVIARINA Aminatuz Zahro Asri Maharani Danang Ariyanto Fadilla Afsari Ibnu Febry Kurniawan Junaidi Budi Prihanto Khusnia Nurul Khikmah Maulana, Dimas Avian Muhammad Mahdy Al Akbar Mutia Eva Mustafidah Nurul Nabilah Riska Wahyu Romadhonia