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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) BAREKENG: Jurnal Ilmu Matematika dan Terapan Malaqbiq : Jurnal Pengabdian kepada Masyarakat.
Asrirawan, Asrirawan
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation

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Model Time Series untuk Prediksi Jumlah Kasus Infeksi Coronavirus (Covid-19) di Sulawesi Selatan Asrirawan, Asrirawan; Seppewali, Andi; Fitriyani, Nurul
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 8 No 2 (2020): Volume 8 Nomor 2
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v8i2.17427

Abstract

Since it was declared a pandemic outbreak, the COVID 19 virus has become one of the main focuses of countries in the world in efforts to prevent the spread of the virus, including Indonesia. The areas of greatest severity in Indonesia include Jakarta, East Java, West Java and South Sulawesi. South Sulawesi Province is recorded as the largest area exposed to the COVID 19 pandemic outside Java Island. Predicting the number of COVID 19 cases is an alternative in preventing the spread through making government policies based on predictive data. This article presents a predictive model for the number of COVID 19 cases based on the ARIMA, Holt Winters and Nonlinear Autoregressive Neural Network (NAR-NN) Model. The results of the analysis show that the ARIMA Model (1,1,1) has a better level of prediction accuracy than the HW and NAR-NN models based on the MAPE criteria. Meanwhile, for the RMSE, MAE and MPE criteria, the NAR-NN model is better than others.
IMPROVING ACCURACY OF PREDICTION INTERVALS OF HOUSEHOLD INCOME USING QUANTILE REGRESSION FOREST AND SELECTION OF EXPLANATORY VARIABLES Asrirawan, Asrirawan; Notodiputro, Khairil Anwar; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp1915-1926

Abstract

Quantile regression forest (QRF) is a non-parametric method for estimating the distribution function of response by using the random forest algorithm and constructing conditional quantile prediction intervals. However, if the explanatory factors (covariates) are highly correlated, the quantile regression forest's performance will decrease, resulting in low accuracy of prediction intervals for the outcome variable. The selection of explanatory variables in quantile regression forest is investigated and addressed in this paper, using several selection scenarios that consist of the full model, forward selection, LASSO, ridge regression, and random forest to improve the accuracy of household income data prediction. This data was obtained from National Labour Force Survey in 2021. The results indicate that the random forest method outperforms other methods for explanatory selection utilizing RMSE metrics. With regard to the criteria of average coverage value just above the 95% target and statistical test results, the RF-QRF and Forward-QRF methods outperform the QRF, LASSO-QRF, and Ridge-QRF methods for constructing prediction intervals.
Penerapan Analisis Piramida Penduduk untuk Pemberdayaan Sumber Daya Manusia di Desa Riso dalam Menghadapi Tantangan Ekonomika Biologi Kesehatan Masa Kini Wahyudi, Wahyu; Rahmah, Mufti Hatur; Salman, Salman; Asrirawan, Asrirawan; Nurhidayah, Nurhidayah; Badu, Muhammad Nasir; Novitasari, Eni
MALAQBIQ Vol. 3 No. 1 (2024): Malaqbiq: Jurnal Pengabdian Kepada Masyarakat
Publisher : Sekolah Tinggi Agama Islam Negeri Majene

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46870/jam.v3i1.937

Abstract

Ekonomika biologi kesehatan merupakan salah satu tantangan bagi masyarakat dalam meningkatkan kualitas hidupnya. Tujuan dari kegiatan ini adalah untuk menganalisis struktur demografis dan pemberdayaan sumber daya manusia (SDM) di Desa Riso dengan menggunakan piramida penduduk sebagai alat bantu visual. Pendekatan ini memberikan wawasan mendalam tentang distribusi usia dan jenis kelamin, yang kritikal dalam menghadapi tantangan ekonomika dan kesehatan yang berkembang. Melalui analisis ini, kami mengidentifikasi potensi dan tantangan yang dihadapi oleh penduduk desa dalam mengelola sumber daya lokal mereka secara berkelanjutan. Hasil penelitian ini menunjukkan bahwa dengan jumlah penduduk usia produktif yang dominan, Desa Riso memiliki kesempatan untuk memperkuat kapasitas lokal dalam menghadapi tantangan kesehatan dan ekonomi masa depan. Implikasi dari temuan ini adalah perlunya intervensi yang berfokus pada penguatan kapasitas dan pemberdayaan penduduk, khususnya dalam bidang pendidikan kesehatan dan keterampilan ekonomi, untuk memastikan kesejahteraan jangka panjang.
Co-Authors Bagus Sartono Muhammad Nasir Badu, Muhammad Nasir Novitasari, Eni Nurhidayah Nurhidayah Nurul Fitriyani Rahmah, Mufti Hatur Salman Salman Seppewali, Andi Wahyudi, Wahyu