Mortalesel Grito, Sely Novita Sari, Andrea Sumarah Asih

Identifikasi Penggunaan Material Untuk Hunian Modular Sementara Menggunakan Metode Statistik Ciri Orde Pertama

Introduction

Identifikasi penggunaan material untuk hunian modular sementara menggunakan metode statistik ciri orde pertama. Identifikasi material optimal untuk hunian modular sementara pascabencana di Indonesia menggunakan statistik. Analisis mencakup tiang, dinding, dan pondasi untuk solusi konstruksi darurat.

Abstract

Indonesia sering mengalami bencana alam yang merusak infrastruktur, termasuk hunian. Korban bencana memerlukan hunian sementara yang aman dan nyaman, di mana hunian modular menjadi solusi karena fleksibel, cepat dipasang, dan dapat dioptimalkan menjadi semi permanen. Pemilihan material untuk hunian modular harus ringan, mudah didapat, dan tahan cuaca. Tujuan penelitian ini adalah untuk mengidentifikasi penggunaan material yang optimal untuk hunian modular sementara dengan menggunakan metode statistik ciri orde pertama. Data diperoleh dari 91 responden melalui pengamatan terhadap proyek hunian modular. Data tersebut dianalisis secara statistik untuk mengevaluasi rata-rata, simpangan baku, serta mengidentifikasi potensi anomali dalam penggunaan material pada struktur tiang penyangga, dinding, dan pondasi. Analisis material pada hunian modular sementara menunjukkan variasi signifikan. Material TP_BR pada tiang penyangga digunakan lebih dominan (rata-rata 2.83) dibandingkan TP_P (1.87), yang bisa menjadi anomali jika perbedaan melebihi deviasi standar. Pada dinding, AD_GG digunakan lebih sering (2.8) dibandingkan AD_PE (2.24) dan AD_S (2.45), yang juga bisa dianggap anomali jika tidak konsisten. Pada pondasi, SP_BT memiliki rata-rata 2.55, lebih tinggi dari SP_A (2.22), yang mungkin mencerminkan anomali

Review

This study addresses a highly pertinent issue in Indonesia, a region frequently impacted by natural disasters, specifically the urgent need for robust and comfortable temporary modular housing solutions. The research aims to identify optimal materials for such dwellings using first-order statistical features, a practical approach to an important challenge. The flexibility, rapid deployment, and potential for semi-permanent optimization of modular homes underscore the significance of this work. By focusing on material selection criteria such as lightness, availability, and weather resistance, the study positions itself to offer valuable insights for disaster response and resilient community building efforts. Methodologically, the study employs a statistical analysis derived from observations of existing modular housing projects, which then translates into data from 91 respondents. While the abstract mentions "pengamatan terhadap proyek hunian modular" leading to "data diperoleh dari 91 responden," clarity is needed on whether these respondents are project managers, users, or other stakeholders, and how their input directly relates to the observational data. The core analysis focuses on mean, standard deviation, and identifying "potential anomalies" in material usage across support pillars, walls, and foundations. The findings highlight significant variations, with TP_BR dominating support pillars (mean 2.83 vs. TP_P 1.87), AD_GG leading in walls (2.8 vs. AD_PE 2.24 and AD_S 2.45), and SP_BT being more prevalent in foundations (2.55 vs. SP_A 2.22). The interpretation of these differences as "anomalies" based on exceeding standard deviation warrants further discussion within the full paper to clarify whether these represent suboptimal choices, interesting deviations, or simply statistically significant preferences without an inherent negative connotation. Furthermore, defining the material codes (TP_BR, TP_P, AD_GG, etc.) would greatly enhance readability and understanding for a broader audience. The findings, once fully elaborated and contextualized in the complete manuscript, hold significant implications for practitioners and policymakers involved in disaster relief and housing provision. The identification of dominant material choices, even if termed "anomalies," provides a data-driven starting point for evaluating current practices and informing future material specifications. To strengthen the paper, the authors should delve deeper into the *reasons* behind these material preferences and statistical differences, exploring factors such as cost-effectiveness, local availability, ease of installation, and actual performance metrics in the full paper. Additionally, a discussion on the limitations of relying solely on first-order statistics for "optimality" and potential avenues for multi-criteria decision analysis or life-cycle assessment would add substantial value. This research presents a solid foundational analysis, but the full paper should expand on the "why" and "so what" of these observed material usage patterns to truly guide optimal selection.

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