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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi International Journal of Advances in Intelligent Informatics Komatika: Jurnal Pengabdian Kepada Masyarakat
Riska Wahyu Romadhonia
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.
Rolling-Origin Evaluation of Lag-Based Regularized Regression Models for Indonesian Inflation Forecasting Rehan Risqi Saputra; Atik Wintarti; Riska Wahyu Romadhonia
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): 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/cauchy.v11i1.41964

Abstract

Inflation forecasting studies often rely on external macroeconomic predictors or complex machine learning models, while the predictive value and limitations of the internal lag structure of Indonesian inflation remain less explicitly examined. This study evaluates Indonesian inflation forecasting within a univariate lag-based framework using monthly inflation data from January 2010 to December 2025. Lagged inflation values are used to construct regression predictors, and OLS, Ridge, LASSO, and Elastic Net are evaluated through rolling-origin forecasting with an expanding window. To strengthen the time-series basis of the analysis, this study also conducts stationarity diagnostics, ACF and PACF analysis, seasonal diagnostics, lag-specification robustness checks, and comparisons with standard forecasting benchmarks, including Naive, Seasonal Naive, AR selected by AIC, and ARIMA selected by AIC. The ADF test produces a p-value of 0.608360, indicating weak evidence of stationarity in level form. Among the regularised regression models, Ridge produces the lowest descriptive forecast errors, with RMSE of 0.405018 and MAE of 0.308967. However, Diebold–Mariano tests indicate that the differences among OLS, Ridge, LASSO, and Elastic Net are not statistically significant. Benchmark comparisons show that the Naive forecast achieves the lowest RMSE of 0.373819, while ARIMA selected by AIC achieves the lowest MAE of 0.279641 and MAPE of 16.560958. Robustness checks also show that the twelve-lag specification is competitive for OLS and Ridge, but it is not uniformly optimal across all models. These findings suggest that the main value of lag-based regularised regression lies in clarifying the limited but useful short-run predictive information contained in the internal temporal structure of Indonesian inflation, rather than in providing a statistically dominant or complete inflation forecasting model.
Co-Authors A'yunin Sofro Affi Oktaviarina AFFIATI OKTAVIARINA Asri Maharani Atik Wintarti Danang Ariyanto Ibnu Febry Kurniawan Junaidi Budi Prihanto Khusnia Nurul Khikmah Maulana, Dimas Avian Muhammad Mahdy Al Akbar Mutia Eva Mustafidah Rehan Risqi Saputra