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All Journal Journal of Applied Civil Engineering and Infrastructure Technology (JACEIT) Breakthroughs Information Technology
Maulana, Miftah
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Transportation

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Analisis Kerusakan Permukaan Jalan Berdasarkan Penilaian Dengan Metode SDI Dan IRI Maulana, Miftah; Rulhendri; Chayati, Nurul
Journal of Applied Civil Engineering and Infrastructure Technology Vol 4 No 2 (2023): Desember 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jaceit.v4i2.566

Abstract

Transportation infrastructure in the form of roads holds an important role in mass mobility. Sustainable usage combined with the increase in vehicle volume can lead to road damage, which eventually affects its construction condition and has implications for quality, comfort, safety, and traffic flow. Therefore, periodic analysis of road surface damage is crucial to determine whether the road is in good condition or requires repair and maintenance. Jalan Raya Ciherang in Bogor Regency is categorized as a busy district road, with a length of 2 km, serving as a secondary local road. The Surface Distress Index (SDI) and International Roughness Index (IRI) methods were used to assess the road surface condition. By comparing visual assessments and using the Roadroid application, the results were more effective in determining the level of damage and finding appropriate actions to solve the problem. The analysis showed that the average percentage of road damage using the SDI and IRI methods, the highest percentage of SDI values indicates a good condition at 50%, while the highest percentage of IRI values indicates a combination of good and fair conditions, both at 37.50%. Referring to the data on determining the type of road condition management from Bina Marga, 2011, it was necessary to perform routine maintenance. According to information from Kebina-Margaan, and Dinas Pekerjaan Umum 2007 regarding road service conditions, Jalan Raya Ciherang falls into the category of Mantap service condition.
Mitigating Bias in AI: A Review of Sources, Impacts, and Strategies Maulana, Miftah
Breakthroughs Information Technology Vol 1 No 1 (2025)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2025.1(1)-02

Abstract

Objective: This research examines trends, approaches, and application contexts of bias mitigation strategies in artificial intelligence (AI) systems. The primary focus is on how biases emerge in different sectors and how mitigation practices are developed to address equity and ethical challenges in AI development. Research Design & Methods: This research uses a Systematic Literature Review (SLR) approach with source selection and literature analysis from trusted databases such as IEEE Xplore, Scopus, SpringerLink, and ACM Digital Library. This study reviewed literature between 2018 and 2024 to ensure the relevance and novelty of findings in the context of bias mitigation in AI systems.Findings: The study results show that bias mitigation strategies have evolved from a narrow technical approach to a comprehensive system lifecycle-based approach. Notable innovations include the application of data-centric AI, fairness-aware algorithms, targeted data augmentation techniques, post-processing, bias auditing, and explainable AI. These approaches have been applied in various sectors. Implications & Recommendations: Effective bias mitigation demands a shift from a technical focus to a collaborative and multidisciplinary approach. System developers must embed fairness principles from the design stage, while regulators should promote transparency and accountability through strong policies. Systematic evaluation, cross-disciplinary collaboration, and public engagement are key for AI systems to be accepted as fair and responsible. Contribution & Value Added: This research provides a structured synthesis of bias mitigation approaches and demonstrates how they can be applied in real-world contexts. By offering practical guidance towards adaptive and integrated mitigation practices, this study contributes to strengthening ethical AI discourse
Co-Authors Chayati, Nurul Rulhendri Rulhendri