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All Journal Prosiding KOMMIT Jurnal Simetris Prosiding SNATIF Jurnal Transformatika JAIS (Journal of Applied Intelligent System) Journal of Information System Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Devotion: Journal of Research and Community Service Jurnal Pengabdian Literasi Digital Indonesia
Fikri Budiman
Universitas Muria Kudus
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Optimasi Klasifikasi Stunting Balita dengan Teknik Boosting pada Decision Tree Hastuti, Nanda Tri; Budiman, Fikri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27913

Abstract

Malnutrition in the growth of small children is known as stunting. Currently, nutrition is still a serious problem that needs to be addressed, especially the nutrition of children under five. Considering the target prevalence rate (14%) in 2024 and how dangerous stunting is in Indonesia, this stunting problem needs to be addressed. The purpose of this research is to optimize the decision tree algorithm in stunting classification using boosting technique optimization. The boosting techniques used are AdaBoost, XGBoost, and Gradient Boosting methods. The boosting technique was chosen because it can improve classifier performance by combining multiple models that are learned sequentially, resulting in more effective predictions. This research uses infant data from Kaggle, which has a total of 10,000 data points, 8 attributes, and 2 classes. Based on the results of this study, decision tree optimization using the XGBoost method achieved the best results with accuracy of 83.8%, precision of 82%, recall of 83.8%, and F1-score of 81.2%, which shows great potential in improving the classification of stunted infants. The boosting technique is the best choice compared to other techniques. Based on the results of this study, the boosting technique can accurately predict and demonstrate a high level of precision in handling stunting classification.
Peningkatan Akurasi Prediksi Curah Hujan menggunakan Gradient Boosting dan CatBoost dengan Pendekatan Voting Classifier Fudhlatina, Dina; Budiman, Fikri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.28988

Abstract

Accurate rainfall prediction is essential for agriculture, disaster mitigation, and water resource management, especially in the face of climate change impacts. This research aims to improve the accuracy of rainfall prediction using gradient boosting and CatBoost with a voting classifier approach. The data used in this study amounted to 1,461 based on weather data from BMKG Semarang City (2020-2023). The data was analyzed using the Gradient Boosting and CatBoost algorithms with a voting classifier framework. The input features include temperature (Tn, Tx, Tavg), humidity (RH_avg), rainfall (RR), length of irradiation (ss), wind speed (ff_x, ff_avg), and wind direction (ddd_x). The GridSearchCV technique was used for hyperparameter optimization. The model predicts based on rainfall intensity categories, namely no rain, light rain, moderate rain, heavy rain, and extreme rain. The results showed that the model with optimization and ensemble approach achieved 87.89% accuracy, 0.88 precision, 0.88 recall, 0.88 f1-score, and 0.8486 cohen's kappa. Meanwhile, gradient boosting and CatBoost individually produced 75.99% and 85.68% accuracy. With these data input features, the model is able to predict extreme rainfall categories that match the actual data. This research is an important contribution to the development of early weather warning systems, disaster mitigation, and climate management.
Co-Authors A.A. Ketut Agung Cahyawan W Abi Senoprabowo Agus Winarno, Agus Arief Soeleman, M Arifudin, Agus Asih Rohmani, Asih Awaludin, Yoga Mahendra Bao Sumantoro, Yeremia Joen Candra Irawan Edi Sugiarto Fahmi Amiq Fudhlatina, Dina Hastuti, Nanda Tri Kamarudin, Fatkhurridlo Pranoto Karis Widyatmoko Kurniawan, Defri Mahendra, Syafrie Naufal Moch Arief Soeleman, Moch Arief Mulyanto, Edy Muslih Muslih MY. Teguh Sulistyono Noor Hasyim Novi Hendriyanto, Novi Rahmawan P, Elkaf Rahmayani, Ririt Sheila Tina S Nurhendratno Sri Winarno Supriyono Asfawi Susanto, Al Birr Karim Yuventius Tyas Catur Pramudi Zaenal Arifin