<![CDATA[This study aims to address the problem of multicollinearity in a multiple regression model of the daily closing stock price of PT GoTo Gojek Tokopedia Tbk (GOTO) during the period from 2022 to early 2025. Multicollinearity occurs when independent variables are highly correlated, which can lead to inefficient and unreliable parameter estimates. GOTO’s stock price experienced high volatility following its Initial Public Offering (IPO) in April 2022, making it necessary to apply appropriate analytical approaches to identify factors influencing its price movements. The study uses the closing price as the dependent variable, with opening price, high price, low price, and trading volume as independent variables. The methods employed include multiple regression and several approaches to handle multicollinearity, namely variable elimination, Principal Component Analysis (PCA), Ridge Regression, LASSO Regression, and Partial Least Squares (PLS) Regression. The initial multiple regression model achieved an R² of 0.9990 and an RMSE of 2.88, but Variance Inflation Factor (VIF) analysis indicated severe multicollinearity. After applying the alternative methods, PLS Regression demonstrated the best performance, with an R² of 0.9990 and an RMSE of 0.0318. Therefore, it can be concluded that PLS Regression is a more stable and accurate method for addressing multicollinearity and improving the prediction of GOTO’s stock prices. Abstrak Penelitian ini bertujuan menangani masalah multikolinearitas dalam model regresi berganda terhadap harga saham penutupan harian PT GoTo Gojek Tokopedia Tbk (GOTO) selama periode 2022 hingga awal 2025. Multikolinearitas terjadi ketika variabel bebas saling berkorelasi kuat sehingga menyebabkan estimasi parameter menjadi tidak efisien dan kurang akurat. Harga saham GOTO mengalami volatilitas tinggi sejak IPO April 2022, sehingga diperlukan pendekatan analisis yang tepat untuk mengidentifikasi faktor-faktor yang memengaruhi pergerakan harga. Data penelitian menggunakan variabel Terakhir sebagai variabel dependen, serta Pembukaan, Tertinggi, Terendah, dan Volume sebagai variabel independen. Metode yang digunakan meliputi regresi berganda dan beberapa pendekatan penanganan multikolinearitas, yaitu penghapusan variabel, Principal Component Analysis (PCA), Ridge Regression, LASSO Regression, dan Partial Least Squares (PLS) Regression. Model awal menghasilkan R² sebesar 0,9990 dan RMSE 2,88, namun terindikasi multikolinearitas tinggi berdasarkan nilai VIF. Setelah penerapan metode alternatif, PLS Regression memberikan performa terbaik dengan R² = 0,9990 dan RMSE = 0,0318. Dengan demikian, PLS Regression dinilai paling stabil dan akurat dalam mengatasi multikolinearitas serta meningkatkan ketepatan prediksi harga saham GOTO.]]>
