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All Journal INTI Nusa Mandiri JOMLAI: Journal of Machine Learning and Artificial Intelligence
Arfany Dhimas Muftareza
Sekolah Tinggi Meteorologi Klimatologi dan Geofisika
Computer Science & IT Engineering

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Comparative Evaluation of MLR and SVM Algorithms for DKI Jakarta Air Quality Prediction Arfany Dhimas Muftareza
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.5369

Abstract

This research explores the application of Machine Learning using Multiple Linear Regression (MLR) and Support Vector Machine (SVM) algorithms to predict air quality categories in Jakarta based on key pollutant parameters, such as PM10, PM2.5, NO2, CO, SO2, and O3. The dataset used comes from ISPU data measured from five Air quality monitoring stations in DKI Jakarta Province in 2021. The research process includes data collection, data cleaning, model implementation using the scikit-learn library, and model performance evaluation using Accuracy, R-Squared, RMSE, and MAE metrics. The results of model performance evaluation show that SVM performs better than MLR, as evidenced by higher accuracy value (91.78% vs. 90.41%), higher R-squared value (69.63% vs. 64.56%), lower RMSE value (0.2867 vs. 0.3097), and lower MAE value (0.0822 vs. 0.0959), indicating that the error in SVM model is smaller than MLR. This study proves the effectiveness of machine learning-based models in providing accurate air quality category predictions, although there are still challenges in predicting the “Good” category that require further development, such as balancing data and advanced feature engineering to improve the prediction accuracy of all categories.
ANALISIS SENTIMEN INFORMASI GEMPA BUMI BMKG PADA APLIKASI X MENGGUNAKAN SVM DAN RANDOM FOREST Arfany Dhimas Muftareza; Reza Okta Pratama; I Dewa Gede Loka Maheswara; Giarno; Agustina Rachmawardani
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8550

Abstract

This study analyzes public sentiment towards the dissemination of earthquake information by BMKG through the X application with modeling using the Support Vector Machine (SVM) and Random Forest (RF) algorithms. Data were collected from 2,711 tweets mentioning @infoBMKG using tweet-harvest, then processed through the stages of case folding, cleaning, tokenization, slang normalization, stopword removal, and stemming. Automatic sentiment labeling was performed using a hybrid approach of VADER and InSet Lexicon. Feature representation used TF-IDF (Term Frequency–Inverse Document Frequency) with 1,000 features and data distribution 80% train and 20% validation. The results show that RF achieved an accuracy of 81.92% and SVM 81.17%, with almost identical Macro F1 (RF: 0.7445; SVM: 0.7443). Neutral sentiment indicates informative tweets without emotional content (61.75%), negative sentiment represents the public's emotional response that is not solely intended as a form of negative assessment of BMKG (24.71%), and positive sentiment is an expression of appreciation, gratitude, and hope for the delivery of information (13.54%). SVM excels in cross-validation stability (std ±0.0574) and negative sentiment recall (0.71), making it more suitable for real-time disaster communication monitoring.
Co-Authors Agustina Rachmawardani Arno, Giarno I Dewa Gede Loka Maheswara Reza Okta Pratama