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All Journal Buletin Sistem Informasi dan Teknologi Islam NEAR: Jurnal Pengabdian kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS)
Sitti Nurhaliza
Universitas Tadulako
Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Nursing Public Health Social Sciences Transportation Other

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Evaluasi Pelatihan Gamma AI bagi Peningkatan Kompetensi Guru SMA Negeri 3 Palu Sitti Nurhaliza; Rais Rais; Nur’eni Nur’eni; Asfar Asfar; Nur Rezky Safitriani
NEAR: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 1 (2025): NEAR
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/nr.v5i1.3327

Abstract

Perkembangan kecerdasan buatan (AI) memberikan peluang baru bagi guru untuk menyusun materi presentasi secara cepat dan menarik. Pelatihan pemanfaatan Gamma AI di SMA Negeri 3 Kota Palu bertujuan untuk meningkatkan kemampuan guru dalam membuat PowerPoint yang lebih informatif dan efisien. Penelitian ini menggunakan desain pretest–posttest one group untuk melihat perubahan pengetahuan sebelum dan sesudah pelatihan, dengan analisis Wilcoxon Signed Rank Test. Pelatihan meliputi pengenalan konsep dasar AI, demonstrasi fitur Gamma AI, serta praktik langsung pembuatan slide presentasi. Hasil penelitian menunjukkan adanya peningkatan pengetahuan yang signifikan setelah pelatihan (p = 0.002), yang menunjukkan bahwa guru mampu memahami dan mengoperasikan Gamma AI dengan lebih baik. Pelatihan ini juga mendorong guru untuk membentuk kelompok belajar mandiri dalam mengeksplorasi penggunaan Gamma AI lebih lanjut. Secara keseluruhan, pelatihan terbukti efektif dalam meningkatkan kompetensi guru dalam menyusun PowerPoint yang lebih menarik dan adaptif terhadap kebutuhan kelas
Analisis Trend dan Single Exponential Smoothing dalam Meramalkan Harga Komoditas Beras di Kota Makassar Arwini Arisandi; Ismail Gaffar; Retno Mayapada; Sitti Nurhaliza
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 6, No 2 (2025)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v6i2.2807

Abstract

Comparative Evaluation of Long Short-Term Memory and Support Vector Regression for Daily Rice Price Forecasting in Makassar Arwini Arisandi; Ismail Gaffar; Sitti Nurhaliza; Hardianti Hafid
Journal of Mathematics, Computations and Statistics Vol. 9 No. 3 (2026): Volume 09 Issue 03 (September 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/pphv6749

Abstract

Rice is a strategic food commodity that plays an important role in food security, household purchasing power, and regional economic stability. Fluctuations in daily rice prices can affect inflation, market behavior, and policy decisions related to food distribution and price stabilization. This study aims to comparatively evaluate the performance of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) models in forecasting daily rice prices, particularly for Premium Rice and Medium Rice. The study initially used 1,077 observations of daily rice price time-series data. Following preprocessing, including the construction of lag and moving-average features, 1,047 observations remained for modeling. Descriptive analysis showed that Premium Rice had a minimum price of IDR 11,000, a maximum price of IDR 16,250, a mean price of IDR 13,690.55, and a standard deviation of 1,300.812. Meanwhile, Medium Rice had a minimum price of IDR 9,800, a maximum price of IDR 14,760, a mean price of IDR 12,212.12, and a standard deviation of 1,334.867. The LSTM model was implemented using Python, whereas the SVR model was implemented using R. Both models were evaluated using the same chronological data partitions and testing period. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Both models demonstrated accurate forecasting performance, with MAPE values below 10%. Under the experimental configuration and common testing period used in this study, SVR achieved lower forecasting errors than LSTM for both rice categories. For Premium Rice, SVR produced an MAE of 61.18, an RMSE of 159.78, and a MAPE of 0.42%, whereas LSTM produced an MAE of 180.00, an RMSE of 293.00, and a MAPE of 1.18%. For Medium Rice, SVR produced an MAE of 67.03, an RMSE of 209.29, and a MAPE of 0.49%, compared with LSTM, which produced an MAE of 181.00, an RMSE of 284.00, and a MAPE of 1.32%. These findings indicate that SVR was more suitable for the data and feature configuration applied in this study.
Simulation Study of Survival Analysis Methods Using C-index and IBS-IPCW: Application to Exclusive Breastfeeding Cessation Sitti Nurhaliza; Morina A. Fathan; Muhammad Edy Rizal; Khairunnisaaa Khairunnisaaa; Rifat Muyassar; Dwi Indri Nur Anisa
Journal of Mathematics, Computations and Statistics Vol. 9 No. 3 (2026): Volume 09 Issue 03 (September 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/bh7q9p42

Abstract

Right-censored data remain a persistent challenge in survival analysis, particularly in exclusive breastfeeding studies, where premature cessation is common, yet appropriate predictive models are rarely validated, risking biased estimates that misinform interventions. This study compares three survival analysis models, namely Weibull Accelerated Failure Time (AFT), Cox Proportional Hazards (CPH), and Random Survival Forest (RSF), in addressing right-censored data. Performance was evaluated through simulations varying sample sizes, censoring proportions, and hazard function shapes, using the Concordance Index (C-index) and Integrated Brier Score (IBS) with Inverse Probability of Censoring Weighting (IPCW). The simulation results showed that all three models achieved good discriminative performance, with median C-index values above 0.65 across scenarios. The Weibull AFT model performed best when the simulated data conformed to its underlying distribution. Both the CPH and Weibull AFT models consistently outperformed the RSF model, particularly under scenarios characterised by constant or decreasing hazard functions. Analysis of variance (ANOVA) confirmed that model type, sample size, and censoring proportion significantly influenced model performance. The Weibull AFT model was subsequently applied to real-world data on the cessation of exclusive breastfeeding in Donggala Regency. The findings indicated that psychosocial factors, including resilience, self-efficacy, and spousal support, served as protective factors that prolonged the duration of exclusive breastfeeding. In contrast, maternal fatigue and certain childbirth conditions were associated with an increased risk of early breastfeeding cessation. Sociodemographic variables, including age, educational attainment, and employment status, were not statistically significant predictors of breastfeeding cessation.
Co-Authors Arwini Arisandi Asfar Asfar Dwi Indri Nur Anisa Hardianti Hafid Ismail Gaffar Khairunnisaaa Khairunnisaaa Morina A. Fathan Muhammad Edy Rizal Nur Rezky Safitriani Nur’eni Nur’eni Rais Rais Retno Mayapada Rifat Muyassar