Vol 8 No 4 (2025): November 2025
Artikel

ANALISIS MULTIVARIATE ADAPTIVE REGRESSION SPLINES DALAM PREDIKSI PENCAPAIAN TARGET PROFIT PERUSAHAAN DISTIBUTOR

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  • Suparmadi Suparmadi,
  • Rohminatin Rohminatin,
  • Rahmadita Rahmadita
Suparmadi Suparmadi
Universitas Royal
Rohminatin Rohminatin
Universitas Royal
Rahmadita Rahmadita
Universitas Royal

Diterbitkan 2025-12-11

Cara Mengutip

ANALISIS MULTIVARIATE ADAPTIVE REGRESSION SPLINES DALAM PREDIKSI PENCAPAIAN TARGET PROFIT PERUSAHAAN DISTIBUTOR. (2025). JOURNAL OF SCIENCE AND SOCIAL RESEARCH, 8(4), 5525– 5531. https://doi.org/10.54314/jssr.v8i4.4244

Abstrak

Abstract: An objective performance evaluation process that can describe the company's condition is through the achievement of targets from each factor that influences profit achievement. The many variables that influence this profit achievement require in-depth and systematic analysis to obtain complete, fast and accurate information. However, so far the Gerai Mustika distributor company has only focused on sales factors, and ignored other factors. Furthermore, the large amount of financial data that has been computerized so far at Gerai Mustika is only limited to weekly, monthly and annual reports, has not been optimally utilized to help management evaluate performance. The study aims to build a prediction model for achieving profit targets from company financial data using Multivariate Adaptive Regression Splines (MARS) based on machine learning. Based on the analysis results, the MARS model is proven to be superior to conventional multiple regression in predicting Gerai Mustika's profit, indicated by a higher R-squared value (0.756), as well as lower MAE and MAPE, so it is able to capture more complex data patterns and provide more accurate predictions; While multiple regression is still relevant for simple linear relationships, it is less than optimal in representing non-linear data variations.

 

Keywords: Prediction; MARS Method; Distributor; Profit; Machine Learning

 

Abstrak: Proses evaluasi kinerja yang objektif yang dapat menggambarkan konidisi perusahaan yaitu melalui pencapaian target-target dari seti ap faktor yang mempengaruhi pencapaian profit. Banyaknya veriabel yang mempengaruhi percapaian profit ini maka dibutuhkan analisa yang mendalam dan tersistem agar mendapatkan informasi yang utuh, cepat dan akurat. Akan tetapi, selama ini perusahan distributor Gerai Mustika hanya fokus pada faktor penjualan saja, dan mengabaikan factor yang lain. Selanjutnya, jumlah data keuangan yang besar yang telah terkomputerisasi selama ini di Gerai Mustika baru sebatas untuk laporan mingguan, bulanan dan tahunan saja, belum dimanfaatkan secara optimal untuk membantu manajemen mengevalausi kinerja. Penelitian bertujuan untuk membangun model prediksi pencapaian target profit dari data keuangan perusahaan menggunakan Multivariate Adaptive Regression Splines (MARS) berbasis machine learning. Berdasarkan hasil analisis, model MARS terbukti lebih unggul dibandingkan regresi berganda konvensional dalam memprediksi profit Gerai Mustika, ditunjukkan oleh nilai R-squared yang lebih tinggi (0,756), serta MAE dan MAPE yang lebih rendah, sehingga mampu menangkap pola data yang lebih kompleks dan memberikan prediksi yang lebih akurat; sementara regresi berganda masih relevan untuk hubungan linear sederhana, namun kurang optimal dalam merepresentasikan variasi data yang non-linear.

Kata kunci: Prediksi;Metode_MARS;Distributor;Profit;Machine_Learning

 

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