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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Informatika dan Multimedia
Fauzia Anis Sekar Ningrum
Universitas Amikom Yogyakarta
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Materials Science & Nanotechnology Physics

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Reduksi Dimensionalitas pada Klasifikasi Kualitas Air Sungai Menggunakan Algoritma Genetika dan Seleksi Fitur Berbasis Korelasi Yudha Riwanto; Fauzia Anis Sekar Ningrum
Jurnal Penelitian Pendidikan IPA Vol 11 No 9 (2025): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i9.11863

Abstract

Water quality monitoring is a crucial element in data-driven environmental management. This study aims to identify the most important parameters in river water quality classification through feature selection and machine learning approaches. Eleven physicochemical parameters were used as initial features, and two selection methods were applied: Genetic Algorithm (GA) and Spearman Rank Correlation (RS). Classification was performed using Radial Basis Function Support Vector Machine (RBF-SVM), with performance evaluation based on accuracy, F1 score, and recall. GA testing results identified influential parameters (pH, DHL, DO, BOD, COD, TSS, NO₂⁻-N), achieving an accuracy of 96.67% and an F1 score of 0.82. RS generated seven different features with an accuracy of 90.00% and an F1 score of 0.67. Both methods revealed five consistently significant features (DHL, BOD, COD, TSS, NO₂⁻-N), which are the influential features. The model without feature selection, despite producing high accuracy (93.33%), only achieved an F1 score of 0.48, indicating poor recognition of the minority class. These findings confirm that feature selection improves classification efficiency and capability. In conclusion, GA-based feature selection provides the most effective subset for water quality classification and supports the development of intelligent and cost-effective monitoring systems suitable for sensor-based field applications.
KU Band Proximity-Coupled Supply Based Microstrip Array Antenna for Microwave Imaging Applications Fauzia Anis Sekar Ningrum; Yudha Riwanto
Jurnal Penelitian Pendidikan IPA Vol 11 No 9 (2025): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i9.11991

Abstract

This research focuses on the design and simulation of a 4x1 microstrip array antenna with a proximity-coupled supply technique for Ku frequency band applications, especially in microwave imaging. The antenna is designed to operate in the frequency range 12 - 16 GHz, with a resonance frequency of 14 GHz, using a Duroid 5880 substrate which has a thickness of 3.15 mm and a relative permittivity of 2.2. Array configuration and proximity-coupled techniques are applied to improve impedance matching as well as expand bandwidth. Evaluation through simulation includes important parameters such as return loss, gain, and radiation patterns. The simulation results show a return loss of -26.46 dB at a frequency of 14 GHz, which shows high transmission efficiency with minimal reflections. The radiation patterns in the azimuthal and elevation planes show consistent directivity, with stable gain throughout the frequency range. These results confirm that the designed microstrip array antenna is suitable for microwave imaging applications in the Ku band. The antenna design in this research produces high efficiency, directional radiation, and minimal signal loss, so it is able to support accurate and detailed imaging.
Prediksi Keberhasilan Pengobatan dan Identifikasi Faktor Klinis Penting pada Kanker Tiroid Berdiferensiasi Menggunakan Kolmogorov-Arnold Networks dan SHAP: Prediction of Treatment Success and Identification of Important Clinical Factors in Differentiated Thyroid Cancer Using Kolmogorov-Arnold Networks and SHAP Muhammad Ainul Fikri; Ajie Kusuma Wardhana; Fauzia Anis Sekar Ningrum; Inggrid Yanuar Risca Pratiwi; Yudha Riwanto; Raditya Arief Pratama
Jurnal Informatika dan Multimedia Vol. 18 No. 1 (2026): Jurnal Informatika dan Multimedia
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jtim.v18i1.9909

Abstract

Kanker tiroid berdiferensiasi memerlukan evaluasi respons terapi yang akurat untuk menentukan strategi penanganan pasien tingkat lanjut. Penelitian ini bertujuan mengembangkan model prediksi keberhasilan pengobatan kanker tiroid berdiferensiasi menggunakan Kolmogorov-Arnold Networks (KAN) yang diintegrasikan dengan metode SHapley Additive exPlanations (SHAP). Integrasi ini bertujuan menghasilkan sistem prediktif yang tidak hanya akurat tetapi juga memiliki interpretabilitas intrinsik yang transparan bagi tenaga medis. Data klinis retrospektif sebanyak 383 pasien dengan 17 fitur dievaluasi menggunakan pemodelan KAN dengan optimasi pemangkasan (pruning) jaringan pembobot adaptif. Interpretasi kontribusi fitur dianalisis secara post-hoc menggunakan algoritma SHAP KernelExplainer. Hasil pengujian membuktikan bahwa model KAN mencapai performa yang sangat kompetitif dengan akurasi 97,40%, precision 97,87%, recall 97,40%, F1-score 97,47%, dan ROC-AUC 99,75%. Model ini mencatatkan tingkat sensitivitas 100% dalam memprediksi kelas keberhasilan terapi tanpa adanya kesalahan klasifikasi. Analisis SHAP mengungkap bahwa fitur Response (evaluasi respons terapi) memberikan kontribusi paling dominan terhadap hasil prediksi, diikuti oleh variabel Risk (stratifikasi risiko), Age (usia), dan M (status metastasis). Sebagai alat pendukung keputusan klinis, KAN secara efektif menyeleksi fitur otomatis melalui mekanisme sparsity pada spline-nya dan memberikan penjelasan yang komprehensif bersama metode SHAP. Sebagai saran pengembangan ke depan, penelitian selanjutnya dapat memperdalam analisis korelasi matematis antara representasi spline KAN dengan nilai distribusi SHAP, serta memperluas pengujian model menggunakan dataset multisenter dengan skala yang lebih besar.
Co-Authors Ajie Kusuma Wardhana Inggrid Yanuar Risca Pratiwi Muhammad Ainul Fikri Raditya Arief Pratama Yudha Riwanto