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All Journal VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Mangiri, Nalto Batty
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

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Pemodelan dan Prediksi Pola Musiman Menggunakan Holt-Winters Pangruruk, Thesya Atarezcha; Mangiri, Nalto Batty; Rombeallo, Esra; Nurmayanti, Wiwit Pura
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm391

Abstract

Samarinda City, with its tropical climate, experiences significant variations in rainfall throughout the year. This instability has the potential to cause impacts such as flooding, disruptions in the agricultural sector, and damage to infrastructure. This study aims to analyze and forecast the seasonal rainfall patterns in Samarinda City by applying the Holt Winters Exponential Smoothing method based on a multiplicative model. Monthly rainfall data were analyzed to identify stationarity properties in both mean and variance. The results indicate that the data are stationary in the mean but not in the variance, thus justifying the use of the Holt-Winters Multiplicative Exponential Smoothing model. Parameter estimation yielded alpha , beta , and gamma values of 1 each, with a MAPE of 50%, indicating a moderate level of accuracy. Despite the relatively high error rate, the model remains effective in illustrating seasonal patterns, which can be useful for preliminary water resource management planning in the region
Penerapan Metode Kuadratik untuk Peramalan Banyaknya Penduduk Miskin di Sulawesi Selatan Tahun 2008-2025 Mangiri, Nalto Batty; Aidid, Muhammad Kasim; Ikhwana, Nur
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm436

Abstract

Masalah kemiskinan adalah masalah yang kompleks dan bersifat multidimensional atau saling berkaitan antara berbagai aspek diantaranya yaitu aspek sosial, ekonomi, dan budaya, serta aspek lainnya. Banyaknya penduduk miskin di Indonesia adalah 23,85 juta pada Maret 2025. Provinsi Sulawesi Selatan pada Maret 2025, terdapat kurang lebih 698,13 ribu penduduk miskin. Sebagai langkah pencegahan meningkatnya angka kemiskinan perlu dilakukan peramalan banyaknya penduduk miskin sehingga pemerintah dapat melakukan perencanaan kebijakan. Data yang digunakan pada penelitian ini adalah data tahun 2008-2025 yang bersumber dari Badan Pusat Statistik Provinsi Sulawesi Selatan. Penelitian ini menggunakan Analisis Trend Nonlinear khususnya Metode Kuadratik untuk melakukan peramalan banyaknya penduduk miskin. Metode ini cocok digunakan untuk data 10 periode atau lebih. Metode Kuadratik memiliki nilai R-Square sebesar 80,24% dan MAPE sebesar 3,28%. Hasil Peramalan selama 6 tahun menunjukkan banyaknya penduduk miskin di Provinsi Sulawesi Selatan mengalami peningkatan.
Trend, Cyclical, and Forecasting Analysis of Indonesia’s Monthly Inflation Using the Hodrick–Prescott Filter and ARIMA Ikhwana, Nur; Syalsabila, Annisa; Mangiri, Nalto Batty
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

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Abstract

This study aims to analyze the structure of inflation and forecast monthly inflation in Indonesia using a time series approach. The method used is the Hodrick–Prescott Filter to decompose data into trend and cycle components, and the ARIMA model to forecast inflation. The data used is monthly inflation data for the period 2010–2025. The decomposition results show that inflation has a relatively stable long-term trend with short-term fluctuations reflecting the presence of economic shocks. Based on model identification, the best model is ARIMA(2,0,1)(1,0,1)[12] which is able to capture past influences, seasonal components, and short-term shocks. The evaluation results show that the model meets the white noise assumption and is suitable for use in forecasting. The forecasting results show that inflation tends to be stable with a moderate increasing tendency, although uncertainty increases over longer periods. This study shows that the combination of structural analysis and time series modeling provides a more comprehensive understanding of inflation dynamics and produces relevant predictions to support decision making.
Co-Authors Aidid, Muhammad Kasim Esra Rombeallo Ikhwana, Nur Nurmayanti, Wiwit Pura Pangruruk , Thesya Atarezcha Pangruruk Syalsabila, Annisa