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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Sirok Bastra
Angelica, Cheryl
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Humanities Computer Science & IT Engineering Languange, Linguistic, Communication & Media

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REPRESENTASI KELUARGA TIONGKOK MODERN DALAM FILM YOUTH《谁的青春不带伤》 Angelica, Cheryl; selvia, selvia
Sirok Bastra Vol 11, No 2 (2023): Sirok Bastra
Publisher : Kantor Bahasa Provinsi Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37671/sb.v11i2.467

Abstract

Salah satu hasil dari karya sastra, yaitu cerita dalam film. Selain menjadi media hiburan, film juga dapat merepresentasikan sesuatu. Dalam kehidupan sosial, ada dua jenis keluarga, yaitu keluarga tradisional dan keluarga modern. Salah satu film yang merepresentasikan keluarga Tiongkok, yaitu film Youth, diproduksi pada tahun 2021. Film ini menyoroti kisah kehidupan keluarga modern di Tiongkok sekitar tahun 2013–2015. Penelitian ini bertujuan untuk mengetahui representasi keluarga Tiongkok modern. Teori yang digunakan adalah teori representasi dan konsep Wu Lun (五伦). Metode yang digunakan untuk penelitian ini ialah deskriptif kualitatif. Sumber data pada penelitian ini adalah film Youth. Teknik pengumpulan data menggunakan teknik simak dan catat dengan teknik analisis data berupa reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan dengan adanya emansipasi wanita, kebebasan ekonomi, kesempatan kerja yang luas, membuat masyarakat sulit mempertahankan kebijakan-kebijakan keluarga tradisional yang telah mengakar. Adanya perkembangan zaman dan sistem perubahan sosial masyarakat membuat konsep-konsep tradisional Tiongkok sulit diterapkan di keluarga Tiongkok modern.
Elevating fraud detection: machine learning models with computational intelligence optimization Angelica, Cheryl; Charleen, Charleen; Wibowo, Antoni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4273-4280

Abstract

The amount of crimes committed online has undoubtedly increased as more people use the internet for e-commerce and other financial transactions. Machine learning algorithms have been created to detect payment fraud in online purchasing in order to address the issue. This study performs a thorough comparative examination of different metaheuristic optimizations as hyperparameter tuning methods; these are particle swarm optimization (PSO) and genetic algorithm (GA). They are used to optimize the receiver operating characteristic (ROC) area under the curve (AUC) of the three machine learning algorithms, namely X-gradient boost, random forest classifier, and light gradient boost machine. Since the study's data are unbalanced, the determined metrics were ROC AUC. PSO offers consistent conditions for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations which are different from GA, a consistent condition for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations. The findings indicate that random forest classifier provided the highest ROC AUC value both before and after the hyperparameter tuning process, with a score of 88.69% attained while utilizing PSO. 
Co-Authors Antoni Wibowo Charleen, Charleen Selvia Selvia