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Performance Analysis of Resampling Techniques for Overcoming Data Imbalance in Multiclass Classification Anggit Larasati; Sugiyarto Surono; Aris Thobirin; Deshinta Arrova Dewi
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.25270

Abstract

In the digital era, the development of modern technology has brought significant transformation to the medical world. The main objective of this research is to identify the performance of deep learning models in classifying kidney disease. By integrating the Convolutional Neural Network model, the performance of the classification process can be analyzed effectively and efficiently. However, data imbalance dramatically affects the performance evaluation of a model, requiring data resampling techniques. This research applies two resampling techniques, bootstrap-based random oversampling and random undersampling, to training data and adds data augmentation to increase image variations to prevent model overfitting. The architecture uses MobileNetV2, which compares hyperparameter fine-tuning in three optimizers. This research shows that the performance of MobileNetV2, which implements the bootstrap-based random oversampling technique, has the highest accuracy compared to random undersampling and no resampling methods. The oversampling technique with the RMSprop optimizer produced the highest accuracy, namely 95%. With precision, recall, and F-1 score, respectively, 0.93, 0.95, 0.94. The accuracy of oversampling with the Adam and Nadam optimizer is 94%. So, the contribution of this research is by applying bootstrap-based oversampling techniques and adding data augmentation to produce good model performance to be used to classify medical images.
Fake News in Hate Speech Containing Ethnicities, Religions, Races and Intergroup (SARA) on Indonesian Social Media: A Forensic Linguistics Study Agus Syahid; Sutarman Sutarman; Ana Rahmatyar; Dita Meldina; Deshinta Arrova Dewi
Anglophile Journal Vol. 6 No. 1 (2026): Anglophile Journal
Publisher : CV. Creative Tugu Pena

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51278/anglophile.v6i1.2526

Abstract

This study investigates the dissemination of fake news embedded in hate speech containing ethnicity, religion, race, and intergroup (SARA) issues on Indonesian social media from a forensic linguistic perspective. Drawing on speech act theory, the study aims to identify the linguistic forms used in the dissemination of fake news and examine their legal implications. A qualitative method with a forensic linguistic approach was employed. The data consisted of linguistic evidence extracted from eight court cases adjudicated between 2018 and 2020 and obtained from final and legally binding district court decisions available through the Supreme Court Decision Directory of the Republic of Indonesia. Data were analyzed using Searle’s speech act framework, focusing on assertive speech acts. The findings reveal two dominant forms of assertive speech acts in the dissemination of fake news, namely assertive accusations (or slander) and assertive lies. These speech acts were used to promote hate speech, blasphemy, incitement of hatred, and the humiliation or denigration of particular groups based on SARA identities. The study further demonstrates that the dissemination of fake news containing SARA-related hate speech carries significant legal consequences, as perpetrators may be prosecuted under Article 28(2) in conjunction with Article 45A(2) of Law No. 19 of 2016 concerning Electronic Information and Transactions (ITE). The findings contribute to the development of forensic linguistic scholarship by highlighting the relationship between language, misinformation, hate speech, and legal accountability in digital communication.
Governance Capacity and STCW Compliance in Maritime Education: A PCA–FASTCLUS Approach Antonius Edy Kristiyono; Ariyono Setiawan; Iie suwondo; Warju Warju; Deshinta Arrova Dewi
Jurnal Ilmiah Manajemen Kesatuan Vol. 14 No. 4 (2026): JIMKES Edisi Juli 2026
Publisher : LPPM Institut Bisnis dan Informatika Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jimkes.v14i4.5307

Abstract

Maritime accidents remain influenced by human and institutional factors despite strengthened global regulations. However, variations in Standards of Training, Certification, and Watchkeeping for Seafarers (STCW) compliance across maritime education institutions indicate that compliance alone does not fully explain safety readiness. This study examines how governance capacity shapes STCW compliance as an intermediate institutional capability. Using survey data from 109 maritime education institutions in Indonesia, this study applies Principal Component Analysis (PCA), multiple regression, ΔR² analysis, and FASTCLUS clustering to capture structural priorities and institutional heterogeneity. The results show that resource availability (β ≈ 0.31) and implementer disposition (β ≈ 0.27) are the most influential factors, jointly accounting for over 59% of the explained variance. Despite similar compliance levels, institutions exhibit different governance configurations, indicating latent heterogeneity. These findings suggest that compliance should be interpreted as a capacity-driven process rather than a procedural endpoint. The study contributes by integrating multivariate and configurational approaches to reframe compliance as an institutional capability mechanism and provides policy implications for shifting from compliance enforcement to capacity development in maritime education systems.
Legal Challenges to the Implementation of Meritocracy in Organizational Governance Dimas Ferry Anuraga; Suparto Wijoyo; Juansih; Deshinta Arrova Dewi
Nusantara: Journal of Law Studies Vol. 5 No. 2 (2026): Nusantara: Journal of Law Studies
Publisher : PT. Islamic Research Publiser

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66325/nusantaralaw.v5i2.265

Abstract

Meritocracy is widely regarded as a fundamental principle of organizational governance because it promotes fairness, transparency, and competence in recruitment, promotion, and leadership selection. However, its implementation remains legally complex because merit-based systems must be reconciled with equality principles, labor rights, affirmative action, constitutional provisions, and institutional accountability. This study addresses the problem of fragmented understanding of the legal challenges surrounding meritocratic governance across different organizational and regulatory contexts. It asks: What are the principal legal challenges in implementing merit-based recruitment, promotion, and leadership selection across institutional and jurisdictional contexts? The study employs qualitative documentary analysis of legal, regulatory, institutional, and scholarly sources addressing meritocratic recruitment, promotion, and leadership selection across diverse governance contexts. The findings identify five interconnected challenges: balancing merit with equality and anti-discrimination requirements; reconciling meritocracy with affirmative action; ensuring transparency and accountability in personnel decisions; limiting political and institutional interference; and addressing legal risks associated with artificial intelligence in recruitment and personnel assessment. The synthesis further demonstrates that meritocracy is not a legally neutral or universally applicable principle, but is shaped by the interaction between merit criteria, equality obligations, institutional structures, and jurisdiction-specific regulations. Academically, this study contributes an integrative legal-governance perspective that conceptualizes meritocracy as a conditional institutional principle, providing a foundation for comparative research and the development of legally responsive merit-based governance systems.