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All Journal Jurnal CoreIT JISKa (Jurnal Informatika Sunan Kalijaga) Innovation in Research of Informatics (INNOVATICS) Journal of Soft Computing Exploration Journal of Information System Exploration and Research
Dullah, Ahmad Ubai
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Journal : Journal of Information System Exploration and Research

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The Asthma Classification Using an Adaptive Boosting Model with SVM-SMOTE Sampling Dullah, Ahmad Ubai; Utami, Putri; Unjung, Jumanto
Journal of Information System Exploration and Research Vol. 3 No. 1 (2025): January 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i1.486

Abstract

Asthma is a disease that affects the human respiratory tract, characterized by inflammation and narrowing of the respiratory tract such as wheezing, coughing, and shortness of breath. The causes of asthma can come from genetics, lifestyle, and a bad environment. Diagnosis made to asthma patients is very influential on the severity and treatment carried out. However, the diagnosis process may not be able to precisely determine asthma patients because the diagnosis is influenced by the classification of asthma based on the symptoms that appear. Therefore, this study proposes an asthma disease classification model that is optimized using a sampling method to balance the data. The proposed classification model uses the Adaptive Boosting algorithm with a sampling technique using SVM-SMOTE to help balance the data. The results obtained from the experiment achieved an accuracy of 98.60%. This result shows that the proposed model is more accurate and optimal in performing classification when compared to previous research.
Guava Disease Classification Using EfficientNet and Genetic Algorithm-Optimized XGBoost Darmawan, Aditya Yoga; Al Qohar, Bagus; Dullah, Ahmad Ubai; Ishak, Muhamad Izaidi
Journal of Information System Exploration and Research Vol. 3 No. 2 (2025): July 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i2.593

Abstract

Guava is an evergreen plant in the Myrtaceae family, is renowned for its adaptability and noteworthy nutritional benefits. However, guava production has experienced a substantial decline in recent years due to various diseases affecting the fruit. Farmers typically employ manual inspection to identify these diseases, a method that is time-consuming, labor-intensive, and susceptible to errors. This underscores the necessity for an automated classification model capable of accurately diagnosing guava fruit diseases. While numerous machine learning and deep learning models have been developed for agricultural disease detection, research on combining deep transfer learning as a feature extractor with machine learning classifiers remains relatively limited. Addressing this research gap, the proposed model integrates the strengths of both approaches, achieving an impressive accuracy of 98.62%, surpassing the performance reported in previous studies. This encouraging outcome underscores the potential of hybrid models in enhancing guava fruit disease classification, paving the way for more efficient and scalable agricultural management solutions.
Co-Authors Al Qohar, Bagus Apsari, Fitri Noor Darmawan, Aditya Yoga Ishak, Muhamad Izaidi Jumanto Jumanto, Jumanto Jumanto Unjung Much Aziz Muslim Ningsih, Maylinna Rahayu Pertiwi, Dwika Ananda Agustina Putri Utami, Putri Qohar, Bagus Al Wibowo, Kevyn Aalifian Hernanda