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All Journal Jurnal Kesehatan Andalas Civil Engineering Science and Technology (CEST)
Putri Anindita
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Civil Engineering, Building, Construction & Architecture Engineering Health Professions Mechanical Engineering

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Hubungan Aktivitas Fisik Harian dengan Gangguan Menstruasi pada Mahasiswa Fakultas Kedokteran Universitas Andalas Putri Anindita; Eryati Darwin; Afriwardi Afriwardi
Jurnal Kesehatan Andalas Vol 5, No 3 (2016)
Publisher : Fakultas Kedokteran, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jka.v5i3.570

Abstract

 AbstrakGangguan menstruasi dapat menimbulkan stres dan menurunkan kualitas hidup wanita. Gambaran menstruasi seseorang dapat memperlihatkan keadaan fungsi reproduksi seseorang dan risiko mengalami berbagai penyakit. Aktivitas fisik diperkirakan sebagai salah satu cara untuk mengurangi terjadinya gangguan menstruasi tersebut. Tujuan penelitian ini adalah menentukan hubungan antara aktivitas fisik harian dan gangguan menstruasi. Desain penelitian  menggunakan cross sectional study dengan jumlah subjek 90 mahasiswi Fakultas Kedokteran Universitas Andalas Angkatan 2011-2013. Data didapatkan dari kuisioner yang diisi langsung oleh masing-masing responden yang kemudian dianalisis denga uji chi-square. Hasil penelitian mendapatkan gangguan menstruasi terjadi pada 73,3% mahasiswi dengan gangguan yang paling sering terjadi yaitu dysmenorrhea sebanyak 63,3%. Sebagian besar mahasiswi tersebut memiliki aktivitas fisik harian yang cukup menurut rekomendasi WHO yaitu sebanyak 60%. Berdasarkan uji chi-square, tidak ditemukan adanya hubungan antara aktivitas fisik harian dan gangguan menstruasi (p= 0,846). Kesimpulan ialah tidak terdapat hubungan yang bermakna antara aktivitas fisik harian dan gangguan menstruasi pada mahasiswi Fakultas Kedokteran Universitas Andalas.Kata kunci: aktivitas fisik, gangguan menstruasi, mahasiswi FK AbstractMenstrual disorder is often cause stress and decrease the life quality of a woman. Menstrual pattern can describe the condition of reproduction function and risk of having several disease. Physical Activity is considered as one of the way to reduce menstrual disorder. The objective of this study was to determine the association between daily physical activity and menstrual disorder.This  study  used cross sectional design on 90 female medical student of Andalas University Class of 2011-2013 as the sample. The data from self reported questionnaire that was given to the students is analyzed using chi-square.The results show that menstrual disorder is occured in 73,3% of the female medical student and the most frequent disorder is dysmenorrhea 63,3%. Most of the students are physically active correspond to the recommendation of WHO about 60%. It is inferred that there is no association between daily physical activity and menstrual disorder (p= 0,846). The conclusion is daily physical activity and menstrual disorder among female medical students in Andalas University have no significant association.Keywords: physical activity, menstrual disorder, female medical student
AI-Driven Digital Twin for Predictive Maintenance in Urban Infrastructure: Enhancing Structural Resilience and Sustainability Diana, Dita; Anindita, Putri; Mukti, Anggi Angga
Civil Engineering Science and Technology Vol. 1 No. 1 (2025): March | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/3c72e647

Abstract

The increasing complexity of urban infrastructure necessitates more efficient and proactive maintenance strategies. Traditional maintenance approaches often rely on reactive measures, leading to increased costs, unplanned downtime, and potential structural failures. The emergence of Artificial Intelligence (AI)-driven Digital Twin technology offers a promising solution by enabling predictive maintenance through real-time monitoring and advanced analytics. This study aimed to evaluate the effectiveness of AI-driven Digital Twin systems in enhancing predictive maintenance for urban infrastructure. A qualitative case study methodology was employed, analyzing multiple infrastructure projects that integrated Digital Twin technology. Data were collected from project reports, real-time sensor outputs, and expert interviews. The predictive capabilities of machine learning models, including Decision Trees, Support Vector Machines (SVM), and Deep Learning networks, were assessed based on their precision, recall, and F1-score. The results demonstrated that Deep Learning models achieved the highest fault detection accuracy, with an F1-score of 92.5%, outperforming other models. The adoption of Digital Twin systems resulted in a 30% reduction in maintenance costs and a 40% decrease in infrastructure downtime. Additionally, AI-driven predictive maintenance improved fault detection efficiency, reducing the average detection time from 15 days to 3 days. These findings highlight the potential of AI-enhanced Digital Twins in optimizing urban infrastructure resilience, cost efficiency, and sustainability. This study underscores the importance of integrating AI and Digital Twin technologies in predictive maintenance strategies. Future research should focus on addressing implementation challenges, including data security, interoperability, and computational costs, to facilitate broader adoption in smart city development
Co-Authors Afriwardi Afriwardi Diana, Dita Eryati Darwin Mukti, Anggi Angga