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All Journal Jambura Journal of Mathematics TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol
MY, Hadyanti Utami
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mathematics

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Comparison of Seasonal ARIMA and Support Vector Machine Forecasting Method for International Arrival in Lombok MY, Hadyanti Utami; Setyowati, Silfiana Lis; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26478

Abstract

Seasonal Autoregressive Integrated Moving Average is a statistical model designed to analyze and forecast data with that shows seasonal patterns and trends. Support Vector Machine (SVM) is a machine learning-based technique that can be used to forecast time series data. SVM uses the kernel tricks to overcome non-linearity problems, whereas The SARIMA model is well-suited for data that exhibit seasonal fluctuations that repeat over time. Lombok International Airport is the main gateway to West Nusa Tenggara and has become a symbol of tourism growth in the region. Time series analysis is a very useful tool in determining patterns and forecasting the number of international arrivals at Lombok International Airport within a certain period. This study aims to compare the SARIMA model and SVM which can read non-linear patterns in the number of international arrivals at Lombok International Airport. After obtaining the SARIMA and SVM models, the two models are evaluated using test data based on the smallest RMSE value. The SVM model with a linear kernel trick provides the smallest RMSE when compared to SARIMA with SVM RMSE is 238,655. While the best model in Seasonal ARIMA is SARIMA (3,1,0)(1,0,0)12, the forecasting results show SARIMA is better in the forecasting process for the next 10 months.
Studi Komparatif Metode Boosting Dalam Pengklasifikasian Penerima Bantuan Program Keluarga Harapan (PKH) Amatullah, Fida Fariha; MY, Hadyanti Utami; Rizqi, Tasya Anisah; Wahyuni, Silvia Tri; Sartono, Bagus; Firdawanti, Aulia Rizki
TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol Vol 11, No 3 (2025): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v11n3.315-326

Abstract

Ensemble Learning adalah paradigma pembelajaran mesin dimana beberapa model (biasanya disebut "weak learners") dilatih untuk memecahkan masalah yang sama dan digabungkan untuk mendapatkan hasil yang lebih baik. Salah satu model Ensemble, yaitu model boosting. Beberapa metode boosting yang digunakan dalam penelitian ini, yaitu Gradient Boosting Machines (GBM), Extreme Gradient Boosting Machine (XGBM), Light Gradient Boosting Machine (LGBM), dan CatBoost. Penelitian ini akan mengklasifikasikan Rumah Tangga (RT) yang menerima bantuan Program Keluarga Harapan (PKH). Pengklasifikasian PKH sangat penting dilakukan, karena saat ini pemberian PKH belum optimal dan masih banyak yang tidak tepat sasaran. Hasil penelitian menunjukkan bahwa metode LGBM menunjukkan performa terbaik ketika jumlah data latih berukuran besar, yaitu 90% dengan akurasi sebesar 67,97%, sedangkan untuk data latih kecil yaitu 60:40, LGBM memiliki performa yang kurang baik, dengan nilai balanced accuracy terendah dibandingkan metode boosting lainnya, yaitu sebesar 54,43%. Keunggulan LGBM ini disebabkan karena kemampuannya dalam mengelola data besar dan kompleks yang sesuai dengan karakteristik data sosial ekonomi rumah tangga penerima PKH. Dua fitur yang memiliki peran penting untuk pengklasifikasian PKH dalam model terbaik yaitu LGBM adalah faktor ekonomi dan jumlah anggota rumah tangga. Ensemble Learning is a machine learning paradigm in which multiple models (commonly referred to as "weak learners") are trained to solve the same problem and combined to achieve better results. One of the Ensemble models is the boosting model. Several boosting methods used in this study include Gradient Boosting Machines (GBM), Extreme Gradient Boosting Machine (XGBM), Light Gradient Boosting Machine (LGBM), and CatBoost. This study aims to classify households (RT) that receive assistance from the Program Keluarga Harapan (PKH). The classification of PKH recipients is crucial because the distribution of PKH aid has not been optimal, with many cases of misallocation. The results of the study indicate that the LGBM method demonstrates the best performance when the latih dataset is large (90%), achieving an accuracy of 67.97%. However, when the latih dataset is small (60:40), LGBM performs poorly, recording the lowest balanced accuracy among the boosting methods, at 54.43%. The superiority of LGBM is attributed to its ability to handle large and complex data, which aligns with the socio-economic characteristics of PKH recipient households. Two key features that play a significant role in PKH classification using the best-performing model, LGBM, are economic factors and the number of household members.
Co-Authors Amatullah, Fida Fariha Aulia Rizki Firdawanti Bagus Sartono Mualifah, Laily Nissa Atul Rizqi, Tasya Anisah Setyowati, Silfiana Lis Wahyuni, Silvia Tri Yenni Angraini