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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Minfo Polgan (JMP)
Dahri Yani Hakim Tanjung
Universitas Satya Terra Bhinneka
Computer Science & IT Library & Information Science Mathematics Social Sciences

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ENHANCING MACHINE LEARNING ALGORITHM PERFORMANCE FOR PCOS DIAGNOSIS USING SMOTENC ON IMBALANCED DATA Rofiqoh Dewi; Ratna Sri hayati; Alfa Saleh; Dahri Yani Hakim Tanjung; Abwabul Jinan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 1 (2025): JITK Issue August2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i1.6676

Abstract

Polycystic Ovarian Syndrome (PCOS) is one of the most frequently occurring endocrine disorders in women of reproductive age, distinguished by disruptions in hormonal regulation that can impact menstrual cycles, fertility, and physical appearance. Despite its high prevalence, PCOS is often diagnosed late and inaccurately, leading to inappropriate treatment and long-term health issues for patients. Machine learning can serve as an effective solution to enhance the accuracy of PCOS diagnosis. However, one of the primary challenges encountered is the class imbalance in the dataset, where the number of positive case data (PCOS) is often significantly lower than the negative case data. This imbalance can result in a biased model that is less effective in predicting the actual condition of patients. In this study, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTENC) method is recommended to address the issue of imbalanced data, thereby improving the performance and accuracy of the machine learning model employed. The evaluation matrix test results clearly demonstrate that the accuracy of each machine learning model improved after applying the SMOTENC method. Specifically, the accuracy of the K-Nearest Neighbors (KNN) algorithm increased from 81.6% to 89.8%, the Support Vector Machine (SVM) algorithm from 90.6% to 92.5%, the Naive Bayes algorithm from 70% to 82.3%, and the C4.5 algorithm from 99.6% to 99.7%. This research provides a substantial contribution to advancing the development of diagnostic methods thatare both more precise and efficient.
Analisis Kinerja Algoritma Klasifikasi terhadap Dataset Penerimaan Pegawai Outsourcing Firman Syahputra; Dahri Yani Hakim Tanjung; Wiwi Verina; Ok.Muhammad Ihsan; Rofiqoh Dewi; Andi Sanjaya
Jurnal Minfo Polgan Vol. 14 No. 1 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i1.14729

Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja tiga algoritma klasifikasi, yaitu Decision Tree, Naive Bayes, dan K-Nearest Neighbors (K-NN), dalam memprediksi kelayakan calon pegawai outsourcing berdasarkan data historis. Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Dari hasil pengujian yang telah dilakukan dimana nilai akurasi dari algoritma C4.5 yang dihasilkan adalah 75,00%. Nilai akurasi ini lebih besar daripada model algoritma klasifikasi K-NN sebesar 73,00% dan naïve bayes sebesar 64,00%, namun nilai performa algoritma KNN ini memiliki keunggulan nilai performa akurasi dibandingkan dengan Naïve bayes. Hasil analisis menunjukkan bahwa algoritma Decision Tree memiliki kinerja terbaik dibandingkan dua algoritma lainnya, baik dari sisi akurasi maupun keseimbangan antara presisi dan recall. Hal ini menunjukkan bahwa model Decision Tree cukup efektif dalam menangani data campuran dan menghasilkan prediksi yang andal dalam konteks klasifikasi calon pegawai outsourcing. Dengan hasil ini, diharapkan model klasifikasi berbasis Decision Tree dapat diterapkan dalam sistem pendukung keputusan untuk meningkatkan efisiensi dan akurasi dalam proses rekrutmen tenaga kerja outsourcing. Penelitian ini diharapkan dapat menjadi acuan untuk penelitian lebih lanjut di bidang penerapan data mining dalam manajemen sumber daya manusia.
Co-Authors Abwabul Jinan Alfa Saleh Andi Sanjaya Firman Syahputra Ok.Muhammad Ihsan Ratna Sri hayati Rofiqoh Dewi Wiwi Verina