Klasifikasi Data Penyakit Demam Berdarah Dengue (DBD) Melalui Algoritma Decision Tree Dengan RapidMiner
DOI:
https://doi.org/10.35473/jamastika.v5i1.4571Abstract
Demam Berdarah Dengue (DBD) merupakan permasalahan kesehatan masyarakat yang hingga kini terus mendapat perhatian serius di Indonesia. Proses diagnosis untuk DBD biasanya dilakukan melalui pemeriksaan laboratorium dan evaluasi klinis, Akan tetapi, pendekatan konvensional ini cenderung membutuhkan waktu yang relatif panjang serta sangat bergantung pada analisis profesional kesehatan. Oleh karena itu, diperlukan suatu pendekatan berbasis teknologi yang mampu mempercepat sekaligus mempermudah proses klasifikasi secara lebih akurat. Penelitian ini bertujuan untuk mengidentifikasi tingkat keparahan penyakit Demam Berdarah Dengue (DBD) dengan menerapkan algoritma Decision Tree yang dijalankan melalui aplikasi RapidMiner. Data yang digunakan merupakan algoritma sekunder yang di ambil dari internet,berisi 200 catatan pasien dengan delapan artibut medis, yaitu suhu tubuh, jumlah trombosit, jumlah leukosit, tekanan darah, ruam kulit, sakit kepala, nyeri otot, dan muntah. Langkah-langkah dalam penelitian mencakup pengumpulan data, preprocessing, penerapan algoritma Decision Tree, serta evaluasi model dengan memanfaatkan confusion matrix dengan metode cross-validation. Temuan dari studi ini memperlihatkan bahwa model Decision Tree mencapai tingkat ketepatan akurasi mencapai 85%, precision 84%, recall 83%, dan F1-score 83%, dengan trombosit dan leukosit sebagai variabel yang paling dominan dalam proses klasiifikasi. Dengan merujuk pada hasil yang diperoleh, studi ini menyimpulkan bahwasanya algoritma Decision Tree cukup efektif dalam mengklasifikasikan tingkat keparahan penyakit DBD dan dapat di gunakan sebagai dasar untuk mengembangkan system pendukung keputusan di bidang kesehatan.
Kata Kunci: Demam Berdarah Dengue, Data Mining, Decision Tree,RapidMIner, Klasifikasi
Dengue Hemorrhagic Fever (DHF) is a public health problem that continues to receive serious attention in Indonesia. The diagnosis process for DHF is usually carried out through laboratory examinations and clinical evaluations. However, this conventional approach tends to take a relatively long time and relies heavily on the analysis of health professionals. Therefore, a technology-based approach is needed that can accelerate and simplify the classification process more accurately. This study aims to identify the severity of Dengue Hemorrhagic Fever (DHF) by applying the Decision Tree algorithm run through the RapidMiner application. The data used is a secondary algorithm taken from the internet, containing 200 patient records with eight medical attributes, namely body temperature, platelet count, leukocyte count, blood pressure, skin rash, headache, muscle pain, and vomiting. The steps in the study include data collection, preprocessing, application of the Decision Tree algorithm, and model evaluation using the confusion matrix with the cross-validation method. The findings of this study show that the Decision Tree model achieved an accuracy rate of 85%, precision of 84%, recall of 83%, and an F1-score of 83%, with platelets and leukocytes as the most dominant variables in the classification process. Based on these results, this study concludes that the Decision Tree algorithm is quite effective in classifying the severity of dengue fever and can be used as a basis for developing decision support systems in the healthcare sector.
Keywords: Dengue Hemorrhagic Fever, Data Mining, Decision Tree, RapidMiner, Classification
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