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All Journal The Indonesian Journal of Business Administration Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal 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) Advance Sustainable Science, Engineering and Technology (ASSET) ProBisnis : Jurnal Manajemen Jurnal Minfo Polgan (JMP) Community: Jurnal Pengabdian Pada Masyarakat
Adhitya Nugraha
Sistem Informasi Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Humanities Chemical Engineering, Chemistry & Bioengineering 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 Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

 1 
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.
DiabTrack: Sistem Prediksi Dini Diabetes Melitus Tipe 2 berbasis Web menggunakan Algoritma K-Nearest Neighbors Pangestu, Aditya Gilang; Winarno, Sri; Nugraha, Adhitya; Muttaqin, Almas Najiib Imam
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29691

Abstract

Type 2 diabetes mellitus is a chronic disease that is often not detected early enough, increasing the risk of serious complications. Based on this, early detection of this disease is very important to reduce its negative impact. This research aims to develop the DiabTrack system, a web-based prediction system using the K-Nearest Neighbors (KNN) algorithm. This type of research is development research using the Rapid Application Development (RAD) model, including the requirements planning, design workshop, and implementation stages. The dataset used comes from Kaggle, containing 53,000 samples and 8 features. The model is trained using the KNN algorithm and the SMOTE technique to balance the data. Evaluation results show that the KNN model achieves an accuracy of 99.17%, a recall of 100%, and an F1-score of 94%, making it the chosen algorithm for the DiabTrack website. Additionally, Black Box testing results indicate that all features in the DiabTrack system function as expected, helping the public monitor their health conditions while serving as an initial analysis tool for medical professionals.
Co-Authors Abu Salam Amanda, Nabila Rizka Ardytha Luthfiarta Ariel Immanuel Prayogo Astuti, Yani Parti Cahya, Leno Dwi Dheo Rimbano Dwi Larso Dzaki, Muhammad Hafizh 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 Mulyana, Yudha Mumtaz, Najma Amira Muttaqin, Almas Najiib Imam Nisa, Laila Rahmatin Novandian, Yohanes Deny Novanto , Fandy Novanto , The, Fandy Octaviani, Dhita Aulia Pangestu, Aditya Gilang Paramita, Cinantya Prayogo , Ariel Immanuel Primadya, Nauval Dwi Rachmad Hidayatullah, Rachmad Riyanto, Azizu Ahmad Rozaki Rumita, Dea Salsabila, Pramesya Mutia Setiawan, Dicky setiawan, nabila putri Sri Winarno Sundari Sundari Syarifah, Ulima Muna The, Fandy Novanto Wulandari, Kang Andini Wulandari, Kang, Andini Zarifa, Yasmine