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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT INOVTEK Polbeng - Seri Informatika Jurnal Pengembangan Riset dan Observasi Teknik Informatika SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Cahaya Mandalika Bulletin of Computer Science Research
Wahyu, Sawali
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Journal : Jurnal Pengembangan Riset dan Observasi Teknik Informatika

 1 
Perbandingan Algoritma Regresi Linear Sederhana dan Regresi Polinomial Dalam Prediksi Jumlah Penumpang Kereta Api Di Wilayah Jabodetabek Rohman, Ubaedillah; Sunupurwa Asri, Jefry; Ariessanti, Hani Dewi; Wahyu, Sawali
Pengembangan Riset dan Observasi Teknik Informatika Vol 12 No 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/protekinfo.v12i1.9842

Abstract

Intisari— Di era digital saat ini pertumbuhan data semakin mengalami peningkatan yang signifikan sehingga memerlukan solusi untuk analisis data yang efisien dan efektif. Machine learning menjadi solusi dikarenakan teknologi ini banyak digunakan di berbagai sektor. Diantara teknik machine learning algoritma regresi linear sederhana dan regresi polinomial berperan dalam melakukan analisis prediksi. Tujuan dari penelitian ini adalah membandingkan kinerja algoritma regresi linear sederhana dan regresi polinomial dalam prediksi jumlah penumpang kereta api di wilayah jabodetabek. Hasil penelitian akan menentukan manakah algoritma yang lebih akurat dalam memprediksi. Dari hasil penelitian regresi polinomial order 3 mendapatkan hasil akurasi tertinggi dengan R-Square 0.8983, MAPE 0.0704, dan RMSE 1446.6222 dibandingkan dengan regresi linear sederhana dengan R-Square 0.7943, MAPE 0.1013, dan RMSE 1713.8127. Kata kunci— Perbandingan, Regresi linear sederhana, Regresi polinomial, Prediksi, Jumlah penumpang kereta   Abstract— In today's digital era, data growth is increasing significantly, requiring solutions for efficient and effective data analysis. Machine learning is the solution because this technology is widely used in various sectors. Among the machine learning techniques, simple linear regression algorithms and polynomial regression play a role in conducting predictive analysis. The purpose of this study is to compare the performance of simple linear regression algorithms and polynomial regression in predicting the number of train passengers in the Jabodetabek area. The results of the study will determine which algorithm is more accurate in predicting. From the results of the study, polynomial regression order 3 obtained the highest accuracy results with R-Square 0.8983, MAPE 0.0704, and RMSE 1446.6222 compared to simple linear regression with R-Square 0.7943, MAPE 0.1013, and RMSE 1713.8127. Keywords— Comparison, Simple linear regression, Polynomial regression, Prediction, Number of train passengers.    
Co-Authors Apriana, Delia Gotama, Jeskel Fornardi Handaya, Ken Ken Hani Dewi Ariessanti Irwanto, Rikky Febi Jefry Sunupurwa Asri Kheng, Tek Pangestu, Danu Faisal Putri, Risanty Amelia Rohman, Ubaedillah Ruslan, Fisabilia Adipati Samditya Putra, Nafiza Savero, Adriel Sulthon, Rifat Wibowo, Sidik Harto Yulhendri Yulhendri