ANALISIS MODEL KLASIFIKASI DENGAN OPTIMASI PARTICLE SWARM OPTIMIZATION DALAM KLASIFIKASI STATUS GIZI ANAK
Abstract
Abstract: Nutritional status represents a state of equilibrium in the form of specific variables or the manifestation of nutrition in the form of specific variables. Optimal nutritional status is achieved when there is a balance between nutrient intake and nutrient requirements. Malnutrition not only affects an individual's physical health but also negatively impacts their overall development and well-being. However, data warehouses related to child nutritional status in Asahan Regency from various community health centers (Puskesmas) have not been optimally utilized to generate valuable information. Therefore, the purpose of this study was to analyze child nutrition datasets using a machine learning-based classification model that can predict children's nutritional status early. The classification model was chosen because of its ability to group data or objects based on specific classes, making it suitable for future data prediction. Based on the results, it can be concluded that optimizing the toddler nutritional status classification model using the Particle Swarm Optimization (PSO) method and cross-validation was able to identify the best model, namely the Decision Tree, with an accuracy of 37.50%. Although the overall accuracy of all models was still relatively low. This indicates the need for further data processing, such as data balancing, selecting more relevant features, and further parameter tuning to improve classification performance.
Keywords: Classification; Particle_Swarm Optimization; Cross Validation, Nutritional Status
Abstrak: Status gizi adalah representasi dari keadaan keseimbangan dalam bentuk variabel tertentu atau manifestasi nutrisi dalam bentuk variabel tertentu, di mana status gizi optimal dicapai ketika terjadi keseimbangan antara asupan dan kebutuhan zat gizi. Gizi buruk tidak hanya memengaruhi kesehatan fisik individu, tetapi juga berdampak negatif pada perkembangan dan kesejahteraan mereka secara keseluruhan. Akan tetapi, gudang data yang terkait status gizi anak di Kabupaten Asahan dari berbagai puskesmas belum dimanfaatkan dengan optimal untuk menghasilkan informasi yang berharga. Oleh karena itu, tujuan dari penelitian ini adalah untuk menganalisis dataset gizi anak menggunakan model klasifikasi berbasis machine learning yang dapat memprediksi status gizi anak secara dini. Model klasifikasi dipilih karena kemampuannya dalam mengelompokkan data atau objek berdasarkan kelas tertentu, sehingga cocok untuk prediksi data di masa depan. Berdasarkan hasil penelitian, dapat disimpulkan bahwa optimasi model klasifikasi status gizi balita menggunakan metode Particle Swarm Optimization (PSO) dan cross validation mampu mengidentifikasi model terbaik, yaitu Decision Tree dengan akurasi 37,50%, meskipun secara umum tingkat akurasi seluruh model masih relatif rendah. Hal ini menunjukkan perlunya pengolahan data lebih lanjut, seperti balancing data, pemilihan fitur yang lebih relevan, maupun tuning parameter lanjutan untuk meningkatkan performa klasifikasi.
Kata kunci: Klasifikasi; Particle Swarm Optimization; Cross Validation, Status Gizi
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DOI: https://doi.org/10.54314/jssr.v8i4.4247
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