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INDONESIA
Jurnal Mandiri IT
Published by Institute of Computer Science
ISSN : 23018984     EISSN : 28091884     DOI : https://doi.org/10.35335/mandiri
Core Subject : Science, Education,
Computer Science & IT Library & Information Science Mathematics
The Jurnal Mandiri IT is intended as a publication media to publish articles reporting the results of Computer Science and related research.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 202 Documents
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Swarm driven automatic feature selection and classification framework for parkinson voice data Prabukusumo, Muhammad Azhar; Saragih, Hondor; Manurung, Jonson
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.470

Abstract

Parkinson’s disease (PD) severely impairs motor and vocal functions, and early detection is crucial for effective intervention. Conventional diagnostic procedures remain subjective and time-consuming, highlighting the need for automated, data-driven approaches. This study aims to develop an intelligent and fully automated framework integrating Particle Swarm Optimization (PSO)–based feature selection with ensemble machine learning classifiers for PD detection using voice data. The proposed Swarm-Driven Automatic Feature Selection and Classification Framework (SAFSCF) automates data preprocessing, adaptive feature optimization, and classification within a unified pipeline. The framework was evaluated on the Parkinson’s Speech Dataset comprising 743 numerical features. Baseline models achieved accuracies of 0.7738 (Logistic Regression), 0.8651 (Random Forest), and 0.8690 (Gradient Boosting). After PSO optimization, the feature set was reduced by nearly 50% to 382 attributes, achieving a test accuracy of 0.8421 slightly higher than the full-feature model (0.8355). Convergence plots confirmed that PSO effectively minimized the fitness function while maintaining high classification stability. Feature importance analysis revealed that the most discriminative attributes were derived from log energy, Teager Kaiser energy operators (TKEO), MFCCs, Shimmer, and entropy-based features biomarkers known to reflect Parkinsonian speech degradation. These findings demonstrate that the proposed framework enhances computational efficiency and interpretability, offering a reproducible and scalable solution for non-invasive, voice-based PD diagnosis.
Sentiment analysis of the 2024 election using the naïve bayes method using data x Zidan, Ahmad Halim Faizal; Handayani, Irma; Anggara, Afwan
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.471

Abstract

Text mining is a process for utilizing the vast amounts of data generated in today’s digital era. The rapid growth of social media usage has produced extensive textual data, one of which can be analyzed through sentiment analysis. This study uses the social media platform X to analyze public opinions regarding the 2024 Indonesian General Election. The analysis was conducted using 126 user comments as the dataset and 103 reviews as the testing data, which were then processed using the Naive Bayes method. Text mining with the Naive Bayes algorithm can be applied to examine public opinions and sentiments toward the 2024 election on X. The results of the analysis classify sentiments into positive, negative, and neutral categories.

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