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All Journal Jurnal Kybernan Integritas: Jurnal Antikorupsi Negara Hukum: Membangun Hukum untuk Keadilan dan Kesejahteraan ADLIYA: Jurnal Hukum dan Kemanusiaan Shar-E: Jurnal Kajian Ekonomi Hukum Syariah Indonesia Private Law Review Jurnal Alwatzikhoebillah : Kajian Islam, Pendidikan, Ekonomi, Humaniora Jurnal Politica Dinamika Masalah Politik Dalam Negeri dan Hubungan Internasional Jurnal Hukum dan Peradilan Jurnal Bawaslu Provinsi Kepulauan Riau Lex Publica Propeller The Prosecutor Law Review Jurnal Keadilan Pemilu Strategi Memperkuat Integritas Lembaga Peradilan Indonesia Judex Laguens CEFARS : JURNAL AGRIBISNIS DAN PENGEMBANGAN WILAYAH Jurnal Agribisnis dan Pengembangan Wilayah
Mustafa, Cecep
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Journal : Jurnal Keadilan Pemilu

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PENGUATAN INTEGRITAS PEMILU: PENDEKATAN INOVATIF MEMANFAATKAN MESIN PEMBELAJARAN UNTUK MENDETEKSI KECURANGAN Komalasari, Rita; Mustafa, Cecep
Jurnal Keadilan Pemilu Vol. 5 No. 1 (2024): Jurnal Keadilan Pemilu
Publisher : Bawaslu Provinsi Jawa Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55108/jkp.v5i1.434

Abstract

This study explores the development and implementation of machine learning (ML) models designed to detect patterns indicating electoral fraud. The aim of this study is to address existing gaps in the literature by integrating advanced ML algorithms, leveraging various data sources, developing real-time monitoring systems, ensuring model transparency, and addressing ethical considerations. The main argument is that ML provides a robust and adaptive approach to enhance the accuracy and efficiency of detecting electoral fraud. This research employs a comprehensive analysis using Systems Theory to integrate and optimize the various components involved in detecting electoral fraud. The study also develops real-time monitoring systems and incorporates methods for model interpretability and transparency. Ethical and practical challenges are addressed through thorough analysis and the provision of guidelines for responsible implementation. The study demonstrates that machine learning significantly improves the detection of electoral fraud by identifying complex and subtle patterns that may be overlooked by traditional methods. The integration of various data sources and real-time monitoring enhances the resilience and timeliness of fraud detection. Ensuring model transparency and addressing ethical considerations helps build trust and accountability in the electoral process. Overall, this research provides a comprehensive solution that enhances the accuracy, efficiency, and trust in detecting electoral fraud, thereby supporting the integrity of the democratic process.
Co-Authors Budi Suhariyanto, Budi Hasbi Hasan Komalasari , Rita Moch Ridwan, Moch RITA Rita Komalasari