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All Journal SAINSMAT Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam
Isnaini, Mardatunnisa
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Mathematics Medicine & Pharmacology Physics

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Analisis Dampak Covid-19 Terhadap Tingkat Inflasi di Indonesia Aswi; Zulhijrah; Isnaini, Mardatunnisa; Sri Sulastri
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 11 No 2 (2023): VOLUME 11 NO 2 TAHUN 2023
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v11i2.40304

Abstract

Penyebaran corona virus diseases 19 (Covid-19) telah meluas ke seluruh penjuru dunia dan membawa dampak terhadap pendidikan, pariwisata maupun ekonomi. Inflasi adalah salah satu dampak pandemi COVID-19 terhadap kondisi makro Indonesia. Penelitian tentang dampak Covid-19 terhadap tingkat inflasi di Indonesia telah dilakukan, tetapi hasilnya tidak konsisten. Analisis yang mengaitkan antara Covid-19 dan tingkat inflasi di Indonesia menggunakan model Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) belum dilakukan. Studi ini bertujuan untuk mendapatkan model ARIMAX terbaik dan menentukan apakah terdapat hubungan antara kasus Covid-19 dengan tingkat inflasi di Indonesia. Data inflasi bulanan dan data rata-rata Covid-19 bulanan (Maret 2020-September 2022) di Indonesia digunakan pada studi ini. Data Inflasi dituliskan sebagai variabel Zt yakni variabel independen yang diperoleh dari Badan Pusat Statistik dan data Covid-19 sebagai peubah Xt yang merupakan variabel independen yang diperoleh dari situs resmi Kementerian Kesehatan Republik Indonesia. Berdasarkan hasil analisis data, disimpulkan bahwa model ARIMA terbaik adalah ARIMA (0, 1, [6]). Dari hasil estimasi model ARIMAX diperoleh bahwa dampak Covid-19 terhadap inflasi di Indonesia tidak berpengaruh secara signifikan.
Time Series Intervention Analysis With Gradual Impact Function A Case Study Of Railway Passenger Volume In Java Island Zulhijrah, Zulhijrah; Isnaini, Mardatunnisa; Angraini, Yenni; Notodiputro, Khairil Anwar; Mualifah, Laily Nissa Atul
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142774742025

Abstract

Java Island has been significantly impacted by the COVID-19 pandemic, which started in March 2020. This study aims to analyze the impact of the pandemic on the volume of railway passengers’ volume with a time series approach using an interventional ARIMA model. The data used is the number of monthly passengers from 2015 to 2024. Initial modeling on data before the pandemic produced the best model, namely ARIMA (0,2,1). To measure the impact of the pandemic, a gradual step intervention function is used which represents the gradual effect of the event. The estimation results show that the ARIMA (0,2,1) model with a gradual step intervention function is able to provide more accurate forecasting results, with a MAPE value of 18.39%. This model effectively captures changes in mobility patterns due to the pandemic, especially in the post-intervention recovery phase. The findings make an important contribution to transportation policy evaluation and future strategic planningKeywords: Time Series, ARIMA  Intervention, Gradual Function, Railway 
Time Series Intervention Analysis With Gradual Impact Function A Case Study Of Railway Passenger Volume In Java Island Zulhijrah, Zulhijrah; Isnaini, Mardatunnisa; Angraini, Yenni; Notodiputro, Khairil Anwar; Mualifah, Laily Nissa Atul
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142774742025

Abstract

Java Island has been significantly impacted by the COVID-19 pandemic, which started in March 2020. This study aims to analyze the impact of the pandemic on the volume of railway passengers’ volume with a time series approach using an interventional ARIMA model. The data used is the number of monthly passengers from 2015 to 2024. Initial modeling on data before the pandemic produced the best model, namely ARIMA (0,2,1). To measure the impact of the pandemic, a gradual step intervention function is used which represents the gradual effect of the event. The estimation results show that the ARIMA (0,2,1) model with a gradual step intervention function is able to provide more accurate forecasting results, with a MAPE value of 18.39%. This model effectively captures changes in mobility patterns due to the pandemic, especially in the post-intervention recovery phase. The findings make an important contribution to transportation policy evaluation and future strategic planningKeywords: Time Series, ARIMA  Intervention, Gradual Function, Railway 
Evaluasi Perbandingan Model XGBoost, Random Forest, LightGBM, dan Artificial Neural Network dalam Klasifikasi Kerawanan Pangan Isnaini, Mardatunnisa; Gustiara, Dela; Muhadi, Rizqi Annafi; Shafa, Shalshabilla; Sartono, Bagus; Firdawanti, Aulia Rizki; Susetyo, Budi; Dito, Gerry Alfa
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.36227

Abstract

Food insecurity remains a serious household-level issue, particularly in densely populated regions such as West Java, highlighting the need for analytical approaches capable of accurately identifying vulnerable groups. Machine learning algorithms offer the potential to improve the accuracy and precision of food insecurity classification based on survey data. This study aims to compare the predictive performance and variable importance identification of four machine learning algorithms—Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)—in predicting household food insecurity status. The analysis employs SUSENAS 2023 data covering 26,012 households with 14 predictor variables, and food insecurity is classified using the Food Insecurity Experience Scale (FIES). Class imbalance is addressed using the Synthetic Minority Over-sampling Technique (SMOTE) within a 10-fold cross-validation framework. The results show that XGBoost achieves the highest accuracy of 71%, while Random Forest provides the best balanced accuracy under the SMOTE scenario. Moreover, all algorithms consistently identify the Wealth Index as the most influential predictor based on their respective Variable Importance measures, followed by variables related to water access and food assistance. Accordingly, XGBoost is recommended in terms of accuracy, whereas Random Forest demonstrates superior balanced accuracy and prediction stability.
Co-Authors AA Sudharmawan, AA Aswi, Aswi Aulia Rizki Firdawanti Bagus Sartono Budi Susetyo Dito, Gerry Alfa Gustiara, Dela Mualifah, Laily Nissa Atul Muhadi, Rizqi Annafi Shafa, Shalshabilla Yenni Angraini Zulhijrah Zulhijrah, Zulhijrah