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Jurnal Fourier
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : -     EISSN : -     DOI : -
Core Subject : Economy, Science, Education,
Computer Science & IT Economics, Econometrics & Finance Education Mathematics
FOURIER adalah Jurnal Ilmiah bidang yang memadukan dan mengembangkan ilmu Matematika dan pembelajarannya yang diintegrasikan dan interkoneksikan dengan nilai-nilai keislaman terbit sejak tahun 2012 dengan frekuensi terbit 2 kali dalam setahun yang dengan bahasa utama (Bahasa Indonesia dan Bahasa Inggris) yang proses reviewernya sesuai dengan disiplin ilmunya (Analisis, Aljabar, Matematika Terapan, Statistika, dan Pendidikan Matematika).
Arjuna Subject : -
Articles 3 Documents
 1 
Search results for , issue "vol. 15 no. 1 (2026)" : 3 Documents clear
Pengaruh Hiperparameter dan Variabel Eksogen pada Prediksi Multi-Langkah Kecepatan Angin menggunakan LightGBM Fitriyati, Nina; Liebenlito, Muhaza; Tsabitah, Salma Hasna
Jurnal Fourier Vol. 15 No. 1 (2026)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2025.151.1-16

Abstract

Penelitian ini menganalisis pengaruh konfigurasi hiperparameter dan variabel eksogen terhadap prediksi kecepatan angin multi-langkah menggunakan algoritma LightGBM dengan strategi Recursive dan Direct. Hasil penelitian menunjukkan bahwa panjang horizon prediksi merupakan salah satu faktor utama yang mempengaruhi tingkat kesalahan prediksi, dimana nilai kesalahan meningkat secara konsisten dari horizon 1 langkah hingga 30 langkah. Penambahan variabel eksogen pada model berupa suhu minimum dan kelembaban relatif terbukti mampu meningkatkan kinerja model pada seluruh horizon, dengan dampak yang lebih besar pada horizon menengah dan panjang. Selain itu, tuning hiperparameter menunjukkan pengaruh yang bergantung pada horizon, dimana tuning memberikan manfaat yang lebih jelas pada prediksi jangka panjang. Secara keseluruhan, hasil penelitian ini menunjukkan bahwa kinerja prediksi kecepatan angin tidak hanya dipengaruhi oleh pemilihan algoritma, tetapi juga dipengaruhi oleh kombinasi strategi prediksi, variabel eksogen, konfigurasi hiperparameter, dan panjang horizon prediksi. Kata Kunci: horizon, konfigurasi hiperparameter, pendekatan Direct, pendekatan Recursive. Abstract This study analyzes the effect of hyperparameter configuration and exogenous variables on multi-step wind speed prediction using the LightGBM algorithm with Recursive and Direct strategies. The results show that the length of the prediction horizon is one of the main factors affecting the level of prediction error, with the error increasing consistently from 1 step to 30 steps. The addition of exogenous variables to the model, such as minimum temperature and relative humidity, has been shown to improve model performance across all horizons, with a greater impact on medium- and long-horizon forecasts. In addition, hyperparameter tuning exhibits a horizon-dependent effect, with greater benefits for long-term predictions. Overall, the results of this study indicate that wind speed prediction performance is influenced not only by the choice of algorithm but also by the combination of prediction strategy, exogenous variables, hyperparameter configuration, and prediction horizon length. Keywords: horizon, kyperparameter configuration, Direct approach, Recursive approach.
Peramalan Jumlah Penumpang Kereta Api Nasional dengan Model Hybrid ARIMAX-XGBoost Menggunakan Dummy Intervensi Rafi Kamindra; Dadan Dasari; Fitriani Agustina
Jurnal Fourier Vol. 15 No. 1 (2026)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2026.151.45-53

Abstract

Abstrak Keberadaan guncangan struktural (structural break) pada data deret waktu menyebabkan metode peramalan linear konvensional memiliki keterbatasan dalam menghasilkan akurasi yang tinggi. Keterbatasan ini sangat terlihat pada data mobilitas penumpang kereta api nasional yang merekam fluktuasi ekstrem dan perubahan level secara permanen akibat krisis makro di masa lalu, seperti pandemi COVID-19. Penelitian ini bertujuan membangun model peramalan yang adaptif menggunakan pendekatan Hybrid yang menggabungkan model statistik linear Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) dan algoritma Machine Learning non-linear eXtreme Gradient Boosting (XGBoost). Metodologi dilakukan dalam dua tahap, yaitu estimasi komponen linear dan dampak kebijakan menggunakan ARIMAX dengan empat variabel dummy intervensi, dilanjutkan dengan pemodelan sisaan (residual) yang mengandung pola non-linear menggunakan XGBoost. Hasil analisis menunjukkan model linear terbaik adalah ARIMAX(0,1,3) dengan residu yang terbukti tidak berdistribusi normal, menjustifikasi penggunaan XGBoost pada tahap kedua. Evaluasi kinerja pada data uji (out-of-sample) membuktikan bahwa model Hybrid ARIMAX-XGBoost lebih unggul dibandingkan model ARIMAX tunggal, dengan penurunan nilai RMSE sebesar 2,76% dan perbaikan akurasi MAPE sebesar 2,81%, sehingga pendekatan ini lebih efektif digunakan untuk perencanaan operasional transportasi di era pemulihan pasca-pandemi. Abstract The existence of structural breaks in time series data causes conventional linear forecasting methods to have limitations in producing high accuracy. This limitation is highly evident in the national train passenger mobility data, which records extreme fluctuations and permanent level shifts due to past macro crises, such as the COVID-19 pandemic. This study aims to build an adaptive forecasting model using a Hybrid approach combining the linear statistical model Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) and the non-linear Machine Learning algorithm eXtreme Gradient Boosting (XGBoost). The methodology was conducted in two stages, estimating linear components and policy impacts using ARIMAX with four intervention dummy variables, followed by modeling the residuals containing non-linear patterns using XGBoost. The analysis results indicated that the best linear model was ARIMAX(0,1,3) with residuals proven to be non-normally distributed, justifying the use of XGBoost in the second stage. Performance evaluation on testing data (out-of-sample) demonstrated that the Hybrid ARIMAX-XGBoost model outperformed the single ARIMAX model, with a 2.76% decrease in RMSE and a 2.81% improvement in MAPE accuracy, proving that this approach is more effective for transportation operational planning in the post-pandemic recovery era.
Prime Graph Over Semidihedral Group and Its Connectivity Indices Riyanto Riyanto; Arif Munandar
Jurnal Fourier Vol. 15 No. 1 (2026)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2026.151.54-62

Abstract

This study investigates the structural properties and topological connectivity indices of the prime graph associated with the semidihedral group of order 2 raised to the power of n, whose vertex set consists of all elements of the group. Two distinct vertices are adjacent precisely when the greatest common divisor of their orders is a prime number. Since the identity element has order one, it is not adjacent to any other vertex and therefore forms an isolated vertex. Consequently, the prime graph is disconnected and consists of the isolated identity element together with a non-trivial connected component containing all remaining group elements. The vertex set is partitioned into three disjoint subsets consisting of the identity element, elements of order two, and elements of order greater than two. Based on this partition, we characterize the degree sequence, diameter, radius, and clique number of the graph. Furthermore, exact closed-form analytical formulas are obtained for several fundamental degree-based and distance-based topological connectivity indices, including the First Zagreb index, Wiener index, Hyper-Wiener index, Harary index, and Forgotten index.

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