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All Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknik Informatika (JUTIF)
Nisa, Laila Rahmatin
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Computer Science & IT Education

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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.
A TOPIC-BASED APPROACH FOR RECOMMENDING UNDERGRADUATE THESIS SUPERVISOR USING LDA WITH COSINE SIMILARITY Nisa, Laila Rahmatin; Luthfiarta, Ardytha; Nugraha, Adhitya; Hasan, Md. Mahadi; Wulandari, Kang, Andini; Huda, Alam Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4061

Abstract

The thesis is one of the critical factors in determining student graduation. While working on the thesis, students will be guided by a lecturer who has the role and responsibility to ensure that students can prepare the thesis well so that the thesis is ready to be tested and is of good quality. Therefore, selecting a supervisor with the same expertise as the thesis topic is essential in determining students' success in completing their thesis. So far, the selection of thesis supervisors at Dian Nuswantoro University still needs to be done manually by students, so the lack of information about the supervisor can hinder students in determining the supervisor. This study aims to model the topic of lecturer research publications taken from the ResearchGate and Google Scholar platforms so that it is easier for students to choose a thesis supervisor whose research topic is relevant to the student's thesis using the Latent Dirichlet Allocation method. The LDA method will mark each word in the topic in a semi-random distribution. It will calculate the probability of the topic in the dataset and the likelihood of the word against the topic for each iteration. The results of LDA modeling present six main topics of lecturer research with the highest coherence score of 0.764, and then the resulting topics and thesis titles will be compared using cosine similarity. Students can use The highest cosine value as a reference when determining the right thesis topic. Thus, the supervisor selection process will be more focused and in accordance with the student's research interests.
Co-Authors Adhitya Nugraha Ardytha Luthfiarta Huda, Alam Muhammad Md. Mahadi Hasan, Md. Mahadi Wulandari, Kang Andini Wulandari, Kang, Andini