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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan SMART: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS)
Abdullah, Adib Roisilmi
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Analysis and Optimization of Rainfall Prediction in Makassar City Using Artificial Neural Networks Based on Data Augmentation, Regularization, and Bayesian Optimization Abdullah, Adib Roisilmi; Sadik, Kusman; Suhaeni, Cici; Saleh, Agus Muhammad
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8304

Abstract

This study develops a robust and efficient rainfall prediction model using an Artificial Neural Network (ANN), significantly enhanced through integrated data augmentation, regularization, and Bayesian optimization techniques. We utilized a dataset of 118 monthly rainfall records from Makassar City, spanning 2014–2022, sourced from the Meteorological, Climatological, and Geophysical Agency (BMKG). To effectively capture inherent temporal patterns, lag features (specifically lag-1, lag-3, and lag-6 rainfall values) were meticulously constructed as input variables. Subsequently, Min-Max normalization was applied across all features, ensuring input consistency and optimizing the ANN's learning process. An initial manual grid search identified the most effective baseline ANN architecture, featuring four hidden layers ([128, 32, 16, 64] neurons), a tanh activation function, and a learning rate of 0.01. While the baseline ANN model achieved a commendable initial performance with an RMSE of 0.1608, comprehensive experiments revealed the superior benefits of a fully integrated approach. This advanced model, which synergistically combined data augmentation (to address data limitations and enhance generalization), regularization (to mitigate overfitting), and Bayesian optimization (for efficient hyperparameter tuning), demonstrated significantly improved generalization capabilities and enhanced model stability. This integrated model yielded an RMSE of 0.1861, an MSE of 0.0346, and an MAE of 0.1359. These compelling findings unequivocally underscore that integrated optimization strategies are crucial for developing more robust and reliable ANN-based rainfall prediction models, particularly for critical applications in climate-based time series forecasting.
APPLICATION OF BACKPROPAGATION FOR FORECASTING OPEN UNEMPLOYMENT IN MAKASSAR CITY Syam, Rahmat; Sidjara, Sahlan; Abdullah, Adib Roisilmi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2359-2376

Abstract

Based on data from the Statistics Bureau of South Sulawesi Province, the open unemployment rate in Makassar City has remained consistently high over the past ten years, averaging 11.41%. This highlights a persistent labor market issue and positions Makassar as the leading contributor to the open unemployment rate in the province. To support effective policymaking and early intervention strategies, it is essential to forecast future unemployment trends based on historical data. Therefore, this study aims to forecast the open unemployment rate in Makassar City over the next five years using a machine learning approach. Among the available forecasting methods, the Backpropagation Artificial Neural Network (ANN) was selected due to its proven ability to model complex, non-linear relationships often found in socio-economic data. ANN is particularly effective in handling temporal dynamics without assuming linearity or stationarity, unlike traditional statistical models. In this study, the forecasting process involved data normalization, scenario-based data partitioning, ANN architecture design, and model training and testing. The model with the best performance consisted of 11 neurons in the input layer, 55 neurons in the hidden layer, and 1 neuron in the output layer, using 80% of the data for training and 20% for testing. This configuration yielded a forecasting accuracy of 91.896%, with a MAPE of 8.131% and an MSE of 0.003. The denormalized results forecast a steady decline in the open unemployment rate from 9.078% in 2023 to 7.248% in 2027, indicating a positive trend in employment. Nevertheless, it is important to acknowledge the limitations of forecasting models and the potential influence of external factors that may affect actual outcomes.
Program Rumah Cerdas Kesehatan sebagai Upaya Peningkatan Literasi Kesehatan Masyarakat Desa Mallongi-Longi Hidayah, Nurul; Assagaf, Said Fachry; Abdullah, Adib Roisilmi; Iqbal, Muhammad; Herman, Herman
SMART: Jurnal Pengabdian Kepada Masyarakat Vol 4, No 1 (2024): April
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/smart.v4i1.61519

Abstract

Literasi kesehatan adalah pemahaman dan penerapan informasi tentang perawatan kesehatan yang dibutuhkan dalam mengambil keputusan. Definisi ini menunjukkan bahwa peningkatan literasi kesehatan mengarah pada pemahaman yang lebih baik tentang keputusan sehari-hari yang berkaitan dengan Kesehatan. hasil observasi yang dilakukan oleh tim PPK Ormawa HMJ Matematika FMIPA UNM, terdapat beberapa permasalahan yang diperoleh salah satunya yaitu kurangnya pengetahuan warga desa Mallongi Longi terkait kesehatan. Terkhusus anak-anak yang berusia 7 hingga 15 tahun. Warga Desa Mallongi Longi masih belum mengetahui dan kurang paham mengenai hal-hal dasar yang berkaitan dengan kesehatan, seperti cara mencuci tangan yang baik dan benar, cara menggosok gigi dengan benar, dan hal-hal yang berkaitan dengan Kesehatan lainnya. Begitupun masyarakat desa yang berusia 20 tahun keatas pun masih sangat minim pengetahuan mengenai Kesehatan sehingga tim PPK Ormawa HMJ Matematika FMIPA UNM membuat salah satu program, yaitu “Program Rumah Cerdas Kesehatan” yang dimana terdapat 5 program utama yakni festival kesehatan, kelas kesehatan anak, edukasi kesehatan, fun day, dan poster kesehatan. Program ini mampu menjawab kebutuhan masyarakat terkait dengan perluasan wawasan kesehatan. Kata Kunci: Rumah Cerdas, Literasi, Kesehatan, Masyarakat.
Co-Authors Assagaf, Said Fachry Kusman Sadik Muhammad Iqbal NURUL HIDAYAH Rahmat Syam Saleh, Agus Muhammad Sidjara, Sahlan Suhaeni, Cici Syamsiar, Syamsiar