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All Journal The Indonesian Journal of Business Administration Jurnas Nasional Teknologi dan Sistem Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika Techne : Jurnal Ilmiah Elektroteknika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) ProBisnis : Jurnal Manajemen Jurnal Minfo Polgan (JMP) Community: Jurnal Pengabdian Pada Masyarakat
Adhitya Nugraha
Sistem Informasi Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Peningkatan Akurasi Deteksi Dini Penyakit Parkinson melalui Pendekatan Ensemble Learning dan Seleksi Fitur Optimal Wulandari, Kang Andini; Nugraha, Adhitya; Luthfiarta, Ardytha; Nisa, Laila Rahmatin
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27788

Abstract

Early detection of Parkinson's disease (PD) is essential to enhance patient quality of life through timely intervention. This research aims to develop a predictive model using an ensemble learning approach and optimal feature selection. This experimental study employs three machine learning algorithms: random forest, XGBoost, and extra trees, optimized through hyperparameter tuning, feature selection techniques, and Kernel Principal Component Analysis (KPCA) for dimensionality reduction. The study utilizes the UCI Machine Learning Parkinson Dataset, which consists of 80 samples and 44 acoustic features extracted from patients' voices as they sustain the vowel sound "/a/" for five seconds. Results show that XGBoost achieved the highest accuracy at 88.93% after tuning and KPCA, followed by extra trees with 86.15%, and random forest with 85.47%. The application of KPCA successfully reduced data dimensionality without sacrificing accuracy, thereby improving modeling efficiency. These findings suggest that voice data holds significant potential for early PD detection and that selecting appropriate algorithms and dimensionality reduction techniques is crucial for optimizing data-driven diagnostic models.
Co-Authors Abu Salam Amanda, Nabila Rizka Ardytha Luthfiarta Ariel Immanuel Prayogo Astuti, Yani Parti Cahya, Leno Dwi Dheo Rimbano Dwi Larso Egia Rosi Subhiyakto, Egia Rosi Erick Irawadi Alwi, Erick Irawadi Erwin Yudi Hidayat Fahrezi, Sahrul Fahrezi Fahrezi, Sahrul Yudha Fahri Firdausillah Farandi, Muhammad Naufal Erza Fauzyah, Zahrah Asri Nur Firmansyah, Gustian Angga Gaffar, Andi Widya Mufila Ganiswari, Syuhra Putri Gustian, Dinda Aulia Hafiizhudin, Lutfi Azis Harun Al Azies Huda, Alam Muhammad Idayati, Irma Ika Novita Dewi Imam Muttaqin, Almas Najiib Joshua Calvin Kurniawan Krisna, Julius Immanuel Theo Kurniawan, Joshua Calvin Md. Mahadi Hasan, Md. Mahadi Miftahussilmi, Ray Ikhsandri Mumtaz, Najma Amira Muttaqin, Almas Najiib Imam Nisa, Laila Rahmatin Novanto , Fandy Novanto , The, Fandy Octaviani, Dhita Aulia Paramita, Cinantya Prayogo , Ariel Immanuel Primadya, Nauval Dwi Rachmad Hidayatullah, Rachmad Rumita, Dea Salsabila, Pramesya Mutia Setiawan, Dicky Sri Winarno Sundari Sundari Syarifah, Ulima Muna The, Fandy Novanto Wulandari, Kang Andini Wulandari, Kang, Andini Zarifa, Yasmine