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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Simetris Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Teknologi Terpadu Building of Informatics, Technology and Science Technologia: Jurnal Ilmiah Journal of Computer Networks, Architecture and High Performance Computing Jurnal Media Informatika Indonesia Journal of Midwifery Sciences Innovative: Journal Of Social Science Research Jurnal PETISI (Pendidikan Teknologi Informasi)
Nurlaelasari, Euis
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice Nursing Public Health Social Sciences Other

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Pemodelan Prediksi Ekspor Kopi Indonesia Berbasis Algoritma Machine learning Novita, Hilda Yulia; Rohana, Tatang; Nurlaelasari, Euis; Awal, Elsa Elvira
Jurnal Media Informatika Vol. 6 No. 6 (2025): Edisi Desember 2025
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i6.7097

Abstract

Penelitian ini bertujuan untuk membangun model prediksi ekspor kopi di Indonesia dengan menggunakan tiga algoritma machine learning, yaitu regresi inier, neural networks, dan gradient boosting. Data yang digunakan berasal dari data historis ekspor kopi Indonesia. Penelitian dilakukan melalui tahapan pra-pemrosesan data, pemodelan, dan evaluasi kinerja masing-masing algoritma. Hasil penelitian menunjukkan bahwa ketiga algoritma mampu memprediksi ekspor kopi dengan performa yang cukup baik. Algoritma Linear Regression memberikan hasil terbaik dengan nilai mean squared error (MSE) sebesar 0.0000867, mean absolute error (MAE) sebesar 0.00766, dan skor R² sebesar 95%. neural networks menghasilkan MSE sebesar 0.000171, MAE sebesar 0.01196, dan skor R² sebesar 91%. Sementara itu, gradient boosting menunjukkan performa terendah dengan MSE sebesar 0.01918 dan skor R² sebesar 74%. Temuan ini menunjukkan bahwa pendekatan machine learning dapat digunakan sebagai alat bantu dalam memprediksi tren ekspor komoditas secara akurat.
Air quality prediction using boosting-based machine learning models for sustainable environment Fauzi, Ahmad; Maharina, Maharina; Indra, Jamaludin; Ratna Juwita, Ayu; Hananto, Agustia; Nurlaelasari, Euis
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp515-523

Abstract

High levels of air pollution are extremely harmful to humans and the environment. They increase the risk of respiratory infections and lung cancer, especially among vulnerable populations. Therefore, developing effective pollution control measures is crucial for mitigating these negative impacts. We need to implement effective methods to predict and manage air quality for the sake of public health and a healthier environment. In recent years, machine learning (ML) methods have been increasingly utilized in air quality prediction due to their ability to analyze datasets and identify complex patterns. However, the reliability and accuracy of air quality prediction models remain a challenge. This study proposes a boosting-based ML model for predicting air quality. We implemented three stages in the proposed method. In the first stage, we conducted data preprocessing and analysis to eliminate noise, remove redundant data, and encode categorical features. In the second stage, we predicted air quality categories by leveraging 25 ML models, dividing them into three distinct categories. The results show that the extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and adaptive boosting (AdaBoost) models outperform the others in air quality prediction, achieving an accuracy of 99%. Finally, we compared these three models using 10-fold cross validation to ensure they generalize well in last stage.
Co-Authors Amansyah, Ilham Awal, Elsa Elvira Bagus Setiawan Baihaqi, Kiki Ahmad Fauzi Ahmad Muda Fauzi, Rifqi Arul Hananto, Agustia Hanny Hikmayanti Handayani Indra, Jamaludin Juwita, Ayu Ratna Laurentzia, Rini Beatrix Lenggana, U. Tresna Maharina, Maharina Meriyana, Putri Munzi, Gugy Guztaman Novita, Hilda Yulia Nurdin, Cherry Januar Nurlaila, Diah Rahmat Rahmat Ratna Juwita, Ayu rizky pratama, adi Rohana, Tatang Supriyadi Supriyadi Tri Vicika, Vikha Wahiddin, Deden