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All Journal ARRUS Journal of Mathematics and Applied Science VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Lalu Ramzy Rahmanda
Universitas Negeri Makassar
Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Physics

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Trend, Cycle, and Forecasting Analysis of Monthly Inflation in Indonesia Using the Hodrick–Prescott Filter and ARIMA Nur Ikhwana; Annisa Syalsabila; Nalto Batty Mangiri; Lalu Ramzy Rahmanda
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm526

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.
Factors Associated with Monocyte and SuPAR Levels in Pulmonary Tuberculosis Patients: A Longitudinal Study Lalu Ramzy Rahmanda; Agung Tri Utomo; Nur Ikhwana; Annisa Syalsabila
ARRUS Journal of Mathematics and Applied Science Vol. 6 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4851

Abstract

Tuberculosis in Indonesia had the second highest TB burden globally, after India. 92% of the estimated cases were confirmed to be pulmonary tuberculosis. However, controlling pulmonary tuberculosis relies heavily on accurate diagnosis, appropriate treatment, effective monitoring, and evaluation. This study aims to model and investigate the most significant factor affecting monocyte and suPAR levels as a biomarker based on observation time, body mass index, and erythrocyte sedimentation rate. This study was a longitudinal study. 60 patients were involved every two weeks over 13 periods. Path analysis with the generalized linear mixed model (GLMM) approach and comparing three estimation methods to investigate the longitudinal relationship between variables and compare the best structure for modeling the relationship. The best model for describing the relationship between observation time, body mass index, and erythrocyte sedimentation rate on monocyte and suPAR levels is GLMM with unstructured covariance (R2 = 0.977 and AIC = 0.052). A significant positive correlation between monocyte and suPAR levels further validates suPAR as a robust biomarker for monitoring treatment response. The study concludes that effective clinical management of pulmonary tuberculosis requires an integrated strategy that combines OAT with regular monitoring of BMI, ESR, and monocyte levels to optimize patient recovery.
Forecasting Monthly Red Chili Prices in South Sulawesi Using Prophet Model with Time Series Cross-Validation Nur Ikhwana; Agung Triutomo; Annisa Syalsabila; Lalu Ramzy Rahmanda
ARRUS Journal of Mathematics and Applied Science Vol. 6 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4893

Abstract

This study examines monthly red chili price movements in South Sulawesi using the Prophet forecasting model. Daily price data from the National Strategic Food Price Information Center (PIHPS) covering January 2020 to May 2026 were aggregated into 77 monthly observations. Missing values were handled using linear interpolation and Last Observation Carried Forward (LOCF) before modeling. The Prophet model using for forecasting and time series cross-validation was used as a validation method. The model performance was evaluated by Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The results indicate that the model produced an average RMSE = 11,024.68 IDR/kg, MAE = 9,139.04 IDR/kg and MAPE = 23.92%, suggests an acceptable forecasting performance for a highly volatile agricultural commodity. Results pattern demonstrate an annual seasonality on red chili prices, usually lowest price occurs in September and highest one in March of next year. The 12 months projections also foresee a maximum price of 72,597 IDR/kg in March 2027. These results show that the Prophet model can capture trend and seasonality, especially in predicting red chili prices of South Sulawesi.
Co-Authors Agung Tri Utomo Agung Triutomo Annisa Syalsabila Annisa Syalsabila Nalto Batty Mangiri Nur Ikhwana Nur Ikhwana