MAHARDIKA, SANDY (2025) KLASIFIKASI KELULUSAN UJI EMISI KENDARAAN DENGAN RANDOM FOREST, LOGISTIC REGRESSION, DAN SVM. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Vehicle emission testing is crucial for mitigating urban air pollution. Predicting test outcomes offers valuable insights for proactive vehicle maintenance and assists regulators in crafting more targeted environmental policies. This study investigates the predictive capabilities of machine learning models for classifying vehicle emission test results. Utilize a historical dataset comprising key features such as vehicle make, fuel type, manufacturing year, and the definitive pass/fail status of the tests. Our research evaluates three prominent machine learning algorithms: Random Forest, Logistic Regression, and Support Vector Machine (SVM). The data undergoes rigorous preprocessing and is then strategically split into training and testing sets to ensure objective model evaluation. Model performance will be comprehensively assessed using standard classification metrics, including accuracy, precision, recall, F1-score, and the Area Under the ROC Curve (AUC). This study aims to identify the algorithm that delivers the best predictive performance for emission test outcomes. The findings are expected to contribute to the machine learning literature in environmental applications and lay foundational groundwork for developing intelligent systems. Such systems could enable faster, more efficient prediction of vehicle emission compliance, ultimately supporting global efforts towards cleaner air and sustainable environmental management. Keywords: Random Forest, Logistic Regression, SVM. Uji emisi kendaraan sangat penting untuk mitigasi polusi udara perkotaan. Memprediksi hasil uji dapat memberikan wawasan berharga bagi pemilik kendaraan untuk perawatan proaktif dan membantu regulator dalam menyusun kebijakan yang lebih terarah. Penelitian ini bertujuan untuk menyelidiki kemampuan prediktif model machine learning dalam mengklasifikasikan hasil uji emisi kendaraan. Saya menggunakan dataset historis yang mencakup fitur-fitur penting seperti merek kendaraan, jenis bahan bakar, tahun pembuatan, dan status kelulusan uji. Penelitian ini mengevaluasi tiga algoritma machine learning terkemuka: Random Forest, Logistic Regression, dan Support Vector Machine (SVM). Data melalui pra-pemrosesan menyeluruh dan kemudian dibagi menjadi set pelatihan dan pengujian untuk memastikan evaluasi model yang objektif. Kinerja model akan dinilai secara komprehensif menggunakan metrik klasifikasi standar seperti akurasi, presisi, recall, F1-score, dan Area di Bawah Kurva ROC (AUC). Studi ini berupaya mengidentifikasi algoritma yang memberikan kinerja prediktif terbaik untuk hasil uji emisi. Temuan diharapkan dapat berkontribusi pada literatur machine learning di bidang aplikasi lingkungan dan meletakkan dasar bagi pengembangan sistem cerdas. Sistem semacam itu dapat memungkinkan prediksi kepatuhan emisi kendaraan yang lebih cepat dan efisien, pada akhirnya mendukung upaya global menuju udara yang lebih bersih dan pengelolaan lingkungan yang berkelanjutan. Kata Kunci: Random Forest, Logistic Regression, SVM.
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 25 084 |
| NIM/NIDN Creators: | 41521010037 |
| Uncontrolled Keywords: | Random Forest, Logistic Regression, SVM. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 300 Social Science/Ilmu-ilmu Sosial > 380 Commerce, Communications, Transportation (Perdagangan, Komunikasi, Transportasi) > 388 Ground Transportation/Transportasi Jalan Raya > 388.3 Vehicular Transportation/Transportasi kendaraan |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | khalimah |
| Date Deposited: | 31 Jul 2025 09:30 |
| Last Modified: | 31 Jul 2025 09:30 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/96434 |
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