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All Journal Building of Informatics, Technology and Science Perkara: Jurnal Ilmu Hukum Dan Politik
Ramadhan, Naufal Rizky
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Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Social Sciences

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Prediksi Periode Fosil Trilobita Menggunakan XGBoost dengan Seleksi Fitur Geologi–Geospasial dan Hyperparameter Tuning Ramadhan, Naufal Rizky; Pramudya, Elkaf Rahmawan
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8862

Abstract

This study investigates the application of the Extreme Gradient Boosting (XGBoost) algorithm to predict the age period of trilobite fossils based on geological and geospatial data. The challenges addressed in this research include the high complexity of paleontological data, the presence of missing values, and class imbalance in the target variable time_period, which can negatively affect predictive performance. The objective of this study is to develop an accurate and robust fossil age prediction model through systematic data preprocessing, feature selection, and model optimization. The dataset used in this research was obtained from Kaggle and consists of the attributes longitude, latitude, lithology, environment, and collection_type as the main features. The research workflow includes data cleaning, missing value imputation, categorical feature encoding, data splitting using stratified train–test split, and class imbalance handling through a class weight adjustment approach. The XGBoost model was trained on the training dataset and further optimized using RandomizedSearchCV to obtain the optimal hyperparameter configuration. Evaluation results on the testing dataset show that the tuned XGBoost model achieved an accuracy of 95%, precision of 90%, recall of 93%, and an F1-score of 91%, outperforming the model without hyperparameter tuning. These results demonstrate that the integration of geological–geospatial feature selection and hyperparameter tuning in XGBoost is effective in improving the performance of trilobite fossil age period prediction. The results of this study are expected to serve as a computational support approach in paleontology to assist fossil period determination in a more objective, efficient, and data-driven manner.
Dominasi Platform Digital dan Tantangan Penegakan Hukum terhadap Praktik Anti Kompetisi dalam Ekonomi Berbasis Data Mahendra, Ardiansyah Putra; Ramadhan, Naufal Rizky
Perkara : Jurnal Ilmu Hukum dan Politik Vol. 4 No. 1 (2026): Maret 2025: Perkara Jurnal Ilmu Hukum dan Politik
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/7taqfb57

Abstract

The rapid growth of the data-driven economy has intensified the dominance of digital platforms, creating challenges for market competition and legal enforcement. Large technology companies increasingly control market access and user data, leading to structural imbalances and potential anti-competitive behavior. This study aims to develop a comprehensive measurement of platform dominance through the Platform Dominance Legal Index (PDLI), integrating market share and data control to better capture digital market power. The research adopts a normative juridical approach combined with quantitative analysis using secondary data from academic literature, industry reports, and regulatory documents. Market concentration is measured by the Herfindahl–Hirschman Index (HHI), while PDLI assesses dominance using economic and data-related indicators. The results show a highly concentrated market, with an HHI value of 2530, indicating strong oligopolistic conditions. Google recorded the highest PDLI score of 35.88, followed by Amazon at 18.48 and Meta at 15.30, indicating clear differences in dominance levels. The findings also indicate that platforms with strong data control maintain significant dominance even with a lower market share. Practices such as self-preferencing, predatory pricing, and lock-in effects are associated with higher levels of dominance. The study provides a structured framework for assessing digital platform dominance and supports regulators in developing more adaptive competition policies.
Co-Authors Elkaf Rahmawan Pramudya Mahendra, Ardiansyah Putra