Garuda - Garba Rujukan Digital

Budiman, Ramdani

Universitas Banten Jaya

Author-ID : 1237590

Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Materials Science & Nanotechnology Physics Veterinary Other

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Design And Develop a Website Application For Waste Retribution Payment Budiman, Ramdani; Hidayanti, Nur; Agustiani, Widia
Journal Collabits Vol 1, No 1 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i1.25425

Dinas Lingkungan Hidup Kabupaten Serang carries out regional government affairs in the environmental sector in accordance with the Regent's vision, mission and programs outlined in the Regional Medium Term Development Plan (RPJMD). The problem that occurs in the waste levy payment process is that errors often occur in the data collection service for waste levy payments, then in the process of collecting data and recording payment reports, also in the process of recor`xding payments it can still be said to be less efficient because the levy officer has to write the data on proof of payment and wait for it to be recorded. on paper by the treasurer and then summarized in Microsoft Office Excel. The method used in this research is CodeIgniter (CI) for the information system being developed. The system design used in this research uses the waterfall method so that it is easier to develop and uses the Unified Modeling Language (UML) for visual system modeling. The results achieved after implementing this application made it easier for admins to overcome difficulties in inputting waste levy data which had previously been carried out at the Serang district environmental service. The conclusion obtained is that by using this waste levy application, it can support improvements in work so that the data input process is more effective and efficient. Waste levy data becomes more structured and well documented.

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

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.

Laptop Price Prediction Based on Specifications: A Comparison of Random Forest and Linear Regression Bagas Pratama Putra; Ilham Miftahali Mahfuzh; Agus Fahrizal Kurniawan; Ramdani Budiman
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

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.

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