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All Journal BIOMA : Jurnal Biologi Makassar Jurnal EMT KITA Journal Of Nursing Practice RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Jurnal Informasi dan Teknologi Administraus Jurnal Ilmu Administrasi dan Manajemen BJRM (Bongaya Journal of Research in Management) JENIUS: Jurnal Terapan Teknik Industri Multica Science and Technology Formosa Journal of Multidisciplinary Research (FJMR) Formosa Journal of Social Sciences (FJSS) Jurnal Ilmu Komputer Dan Informatika Edu Society: Jurnal Pendidikan, Ilmu Sosial dan Pengabdian Kepada Masyarakat Indonesian Journal of Islamic Theology and Philosophy JUSTER: Jurnal Sains dan Terapan Ash-Shahabah: Jurnal Pendidikan dan Studi Islam JAMAS: Jurnal Abdi Masyarakat Semar : Jurnal Sosial dan Pengabdian Masyarakat The Prosecutor Law Review Jurnal Malikussaleh Mengabdi MEDIA KESEHATAN GIGI : POLITEKNIK KESEHATAN MAKASSAR JADMENT: Journal of Administration and Development Jurnal Pengabdian Masyarakat Wadah Publikasi Cendekia PESHUM Business Research and Administration Innovation (BRAIN) RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi Public Resource Innovation Management and Excellence (PRIME)
Bakhtiar
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Journal : Multica Science and Technology

 1 
Determining the Quality of Earthquake Resistant House Buildings Using Simple Additive Weighting (Saw) and Technique For Order Of Preference By Similarity To Ideal Solution (Topsis) Burhanuddin Burhanuddin; Bakhtiar; Emi Maulani; Edi Yusuf
Multica Science and Technology Vol 4 No 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.850

Abstract

This research aims to evaluate the quality of house buildings using the Simple Additive Weighting (SAW) model and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This research uses six main criteria to assess building quality: Material strength, structural design, foundation, construction technology, construction quality, construction costs. With these six variables in determining the evaluation of house building materials. Determining the final ranking of these alternatives is based on their proximity to the ideal solution. Type A House, Type B House, Type C House, Type D House, Type E House and the weight value for each house C1 = 0.2; C2 = 0.1 ; C3 = 0.15 ; C = 0.20 ; C = 0.15; C = 0.2%. The ranking results for Type A Houses were 0.871, Type B Houses 0.874, Type C Houses 0.813, Type D Houses 0.976 and Type E Houses 0.959. The largest value is in Type D House 0.976 so the alternative Type D House 0.976 is the alternative chosen as the best alternative. Meanwhile, the ranking results for the topsis model for Type A Houses are 0.5423, Type B Houses are 0.5302, Type C Houses are 0.2709, Type D Houses are 0.8515 and Type E Houses are 0.959. The largest value is for Type D House 0.976 so that the Type E House alternative 0.8227 is the alternative chosen as the best alternative for Type E house. The research results show that the combination of the SAW and TOPSIS methods is effective in providing a comprehensive and objective evaluation of the quality of earthquake resistant house buildings. . The results of this research can be applied practically in the construction industry to improve the quality of earthquake-resistant house buildings, helping make more accurate and objective decisions.
Implementation of Ward AHC for Material Clustering Based on Mechanical Parameters Yusuf, Edy; Bakhtiar; Syukriah; Burhanuddin; Riyadhul Fajri
Multica Science and Technology Vol 4 No 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.977

Abstract

This study aims to implement the Ward Agglomerative Hierarchical Clustering (Ward AHC) algorithm to classify materials based on mechanical parameters, including tensile strength (Su), yield strength (Sy), elastic modulus (E), shear modulus (G), Poisson's ratio (μ), and density (ρ). The clustering results reveal that the data is divided into three main groups with the following distributions: Cluster 1 (321 data points), Cluster 2 (403 data points), and Cluster 3 (828 data points). Each cluster exhibits unique characteristics: Cluster 1 is dominated by materials with low Su and Sy values, moderate E and G values, and light ρ. Cluster 2 features materials with very high E values, while Su, Sy, and G values vary. Cluster 3 is characterized by moderate Su values, low Sy values, high E and G values, and light ρ. An evaluation using the Silhouette Score yielded a value of 0.492, indicating that the clustering quality is reasonably good, though there is evidence that some data points may lie near the boundaries between clusters.
Determining the Quality of Earthquake Resistant House Buildings Using Simple Additive Weighting (Saw) and Technique For Order Of Preference By Similarity To Ideal Solution (Topsis) Burhanuddin Burhanuddin; Bakhtiar; Emi Maulani; Edi Yusuf
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 4 No. 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.850

Abstract

This research aims to evaluate the quality of house buildings using the Simple Additive Weighting (SAW) model and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This research uses six main criteria to assess building quality: Material strength, structural design, foundation, construction technology, construction quality, construction costs. With these six variables in determining the evaluation of house building materials. Determining the final ranking of these alternatives is based on their proximity to the ideal solution. Type A House, Type B House, Type C House, Type D House, Type E House and the weight value for each house C1 = 0.2; C2 = 0.1 ; C3 = 0.15 ; C = 0.20 ; C = 0.15; C = 0.2%. The ranking results for Type A Houses were 0.871, Type B Houses 0.874, Type C Houses 0.813, Type D Houses 0.976 and Type E Houses 0.959. The largest value is in Type D House 0.976 so the alternative Type D House 0.976 is the alternative chosen as the best alternative. Meanwhile, the ranking results for the topsis model for Type A Houses are 0.5423, Type B Houses are 0.5302, Type C Houses are 0.2709, Type D Houses are 0.8515 and Type E Houses are 0.959. The largest value is for Type D House 0.976 so that the Type E House alternative 0.8227 is the alternative chosen as the best alternative for Type E house. The research results show that the combination of the SAW and TOPSIS methods is effective in providing a comprehensive and objective evaluation of the quality of earthquake resistant house buildings. . The results of this research can be applied practically in the construction industry to improve the quality of earthquake-resistant house buildings, helping make more accurate and objective decisions.
Implementation of Ward AHC for Material Clustering Based on Mechanical Parameters Yusuf, Edy; Bakhtiar; Syukriah; Burhanuddin; Riyadhul Fajri
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 4 No. 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.977

Abstract

This study aims to implement the Ward Agglomerative Hierarchical Clustering (Ward AHC) algorithm to classify materials based on mechanical parameters, including tensile strength (Su), yield strength (Sy), elastic modulus (E), shear modulus (G), Poisson's ratio (?), and density (?). The clustering results reveal that the data is divided into three main groups with the following distributions: Cluster 1 (321 data points), Cluster 2 (403 data points), and Cluster 3 (828 data points). Each cluster exhibits unique characteristics: Cluster 1 is dominated by materials with low Su and Sy values, moderate E and G values, and light ?. Cluster 2 features materials with very high E values, while Su, Sy, and G values vary. Cluster 3 is characterized by moderate Su values, low Sy values, high E and G values, and light ?. An evaluation using the Silhouette Score yielded a value of 0.492, indicating that the clustering quality is reasonably good, though there is evidence that some data points may lie near the boundaries between clusters.
Co-Authors A. Kiay Demak, Nini Ade Hermawan Ahmadin Akbar, Akbar Velayati Amri Andar, Muh. Andi Mustopa Anjelika, Fara Ari Putra Rachman Aris Baharuddin Arma Arman Arman, Arma Budihantho, Moh Heru Bulkis, Siti Burhanuddin Burhanuddin Burhanuddin Dian Ekawati Sari Donny Dharmawan Durya, Ngurah Pandji Mertha Agung Edi Yusuf Adiman Elas Fitriyani Emi Maulani Erniwaty Ezwarsyah Faisal Faisal Faisal Fatimah Fauzah Nur Aksa FAUZI Feldiansyah A Akili Fitriana Herlambang Hermawan*, Ade Heru Budhihanto Imam Suyitno Irawanto Ishak Kenre Jamaluddin Acmad Kholik Jamidi, Jamidi Jatmiko, Ludfie Jatmiko Setyo Poerwoko Jumiarsih Purnama Ketut Listiani Khairul Anshar Lemi Iryani Lisdawati M. Jamili Marsal Muh. Luthfi Siraj Muh. Nur Yamin Muhamad Syaikhu Muhammad Jamili Muhammad Saleh Muhammad Tahir Saenong Natasya Shofia Az-Zahra Nia Umilizah Novita Shamin Nur Hijriyati Syam Pebriana, Olvia Putri Purba, Mariana Putri Nur Sabrina Natasya Putu Novi Andini Ramli, Muh. Ramli Rapika, Nor Renaldi Rifdan Riska Marsya Riyadhul Fajri Riyadhul Fajri Riyanti Rizal S, Muh. Rizqi, Maulidha Camelia Romi Asmara Sainuddin Sri Meutia SRI WULANDARI Suci Lestari Sulaeman Sumiati Syahribulan Syukriah Syukriah Syukriah, Syukriah Tasalim, Rian Teuku Multazam Yusuf, Edy Zikrina Nur Insyani Masri Zulmiardi, Zulmiardi