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All Journal Abjadia, International Journal of Education ARRUS Journal of Mathematics and Applied Science VARIANSI: Journal of Statistics and Its Application on Teaching and Research Eksponensial
Annisa Syalsabila
Universitas Negeri Makassar
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Physics Other

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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.
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.
Pengelompokan Tren Kunjungan Wisatawan Mancanegara ke Indonesia Menggunakan Metode Hierarchical Clustering Ward Linkage Hardianti Hafid; Annisa Syalsabila
EKSPONENSIAL Vol. 17 No. 1 (2026): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/cgfhj284

Abstract

This study aims to cluster the trends of international tourist arrivals to Indonesia based on entry points using the Hierarchical Clustering Ward Linkage method. The research employs secondary data on monthly tourist arrivals by entry points from 2008 to 2024, obtained from BPS. Data analysis was conducted through preprocessing, distance measurement using Euclidean distance, clustering with Ward’s method, validation using the Dunn Index, and visualization through dendrograms. The results show that the optimal cluster structure is obtained when the data are grouped into four clusters, with the highest Dunn Index value of 3.201. The clustering reveals distinct patterns: Ngurah Rai International Airport and Soekarno-Hatta International Airport form separate clusters due to their dominant role in international arrivals, while Batam Port constitutes a single cluster driven by cross-border tourism with Singapore. Other entry points are grouped into a homogeneous cluster with relatively smaller volumes of arrivals. These findings highlight the importance of segmented tourism development strategies. Entry points with high tourist volumes require enhanced international capacity and services, while cross-border hubs such as Batam necessitate strengthened regional cooperation. The results provide valuable insights for policymakers in formulating more targeted and effective tourism strategies to enhance Indonesia’s global competitiveness.
Identifying Motivational Factors in Nahwu Courses Using Principal Component Analysis (Pca): A Study of Arabic Language Education Students Annisa Raina Khairani; Shofil Fikri; Iklil Syaqifah; Iqbal Juliansyah; Annisa Syalsabila
Abjadia : International Journal of Education Vol 11, No 1 (2026): Abjadia
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/abj.v11i1.36003

Abstract

The challenges faced by students in studying Nahwu, an essential component of the Arabic language, often relate to the complexity of its rules, making it feel difficult and diminishing learning motivation. This study aims to analyze the factors influencing students' learning motivation in Nahwu courses at the university level, offering solutions through a deeper understanding of intrinsic motivation, extrinsic motivation, and amotivation. The research employed a descriptive quantitative method with factor analysis using Principal Component Analysis (PCA). Data were collected over a one-month period via questionnaires from 46 fifth-semester students (cohort 2020) in the Arabic Language Education Program at UIN Datokarama Palu. The motivation indicators employed in this study were based on the Academic Motivation Scale (AMS) developed by Robert J. Vallerand, which comprises seven distinct indicators. The findings reveal two main factors affecting learning motivation: the Self-Regulation Factor, which is associated with positive motivation, and the Helplessness Factor, reflecting feelings of inability. These findings are expected to provide insights for educators in creating effective teaching methods to enhance students' motivation and learning outcomes in Nahwu courses.
Co-Authors Agung Triutomo Annisa Raina Khairani Hardianti Hafid Iklil Syaqifah Iqbal Juliansyah Lalu Ramzy Rahmanda Nalto Batty Mangiri Nur Ikhwana Shofil Fikri