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All Journal Lensa Ilmiah: Jurnal Manajemen dan Sumberdaya Journal Collabits
Putra, Bagas Pratama
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences

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Pengaruh Komunikasi dan Disiplin Kerja terhadap Kinerja Karyawan PT. Mackessen Indonesia Putra, Bagas Pratama; Sunarsih, Denok
Lensa Ilmiah: Jurnal Manajemen dan Sumberdaya Vol. 2 No. 1 (2023): Lensa Ilmiah: Jurnal Manajemen dan Sumberdaya
Publisher : ELRISPESWIL - Lembaga Riset dan Pengembangan Sumberdaya Wilayah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54371/jms.v2i1.265

Abstract

This study aims to analyze the effect of communication and work discipline on the performance of PT Mackessen Indonesia) employees. The research method used in this research is quantitative research. The sample used in this study is the result of the slovin technique, namely 61 respondents. Data analysis used the SPSS version 25 program. The analysis techniques used were data instrument testing, classical assumption testing, analysis testing and hypothesis testing. Testing the hypothesis partially on the communication variable (X1) obtained the value of tcount > ttable (2.716 > 1.671) and a significance value of 0.009 > 0.05. This means that communication (X1) has a significant effect on employee performance (Y). Statistical test of work discipline variable (X2) on employee performance obtained tcount > ttable (5,280 > 1.671) and a significant value of 0.000 <0.05. This means that work discipline (X2) has a significant effect on employee performance (Y). Simultaneous hypothesis testing obtained fcount > ftable (50,709 > 3.153) and a significance value of 0.000 <0.05. So it can be concluded that communication (X1) and work discipline (X2) have a significant effect simultaneously on employee performance (Y). The determination obtained by R Square is 0.111, meaning that the communication and work discipline variables together have an influence contribution of 88.9% on employee performance. While the remaining 11.1% is influenced by other factors not examined in this study.
Laptop Price Prediction Based on Specifications: A Comparison of Random Forest and Linear Regression Putra, Bagas Pratama; Mahfuzh, Ilham Miftahali; Kurniawan, Agus Fahrizal; Budiman, Ramdani
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37603

Abstract

This study investigates the prediction of laptop prices based on hardware specifications by comparing the performance of Linear Regression and Random Forest algorithms. The dataset consists of both numerical and categorical features, including brand, processor type, RAM capacity, storage configuration, screen size, and other relevant attributes that influence pricing. Data preprocessing was conducted through data cleaning, handling missing values, and transforming categorical variables using one-hot encoding. The dataset was then divided into training and testing sets with a 70:30 ratio to evaluate model generalization. Exploratory data analysis was performed using visualizations such as average price per brand, correlation heatmaps of numerical features, and scatter plots comparing actual and predicted prices. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²) on both training and testing data. The results indicate that the Random Forest model achieves higher predictive accuracy compared to Linear Regression, as it is more effective in capturing non-linear relationships and complex feature interactions. In contrast, Linear Regression tends to underperform due to its linear assumptions when applied to heterogeneous laptop specification data. These findings suggest that ensemble-based models are more suitable for laptop price prediction tasks involving diverse and non-linear feature patterns.
Co-Authors Budiman, Ramdani Kurniawan, Agus Fahrizal Mahfuzh, Ilham Miftahali Sunarsih, Denok