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All Journal Desimal: Jurnal Matematika Jurnal Poster Pirata Syandana
Azkiya, Azkal
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Arts Civil Engineering, Building, Construction & Architecture Education Energy Mathematics Social Sciences Other

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Griya Kecantikan di Kota Semarang Azkiya, Azkal
Jurnal Poster Pirata Syandana PERIODE 160
Publisher : Architecture Department, Engineering Faculty, Universitas Diponegoro

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Abstract

Padatnya aktivitas dan gaya hidup serba cepat di kota metropolitan sering kali membuat masyarakat merasa jenuh dan kesulitan untuk menyeimbangkan waktu antara pekerjaan, istirahat, dan kebutuhan perawatan diri. Fasilitas perawatan kecantikan, seperti klinik kecantikan, salon, spa, dan toko retail kosmetik telah banyak tersebar di Kota Semarang, tetapi mayoritas layanan kecantikan yang ada hanya menyediakan satu jenis pelayanan. Oleh karena itu, perencanaan dan perancangan Griya Kecantikan di Kota Semarang menjadi solusi untuk menyediakan berbagai layanan kecantikan, termasuk klinik kecantikan, salon kecantikan, spa, dan toko retail kosmetik, serta fasilitas pendukung seperti restoran dan kafe dalam satu bangunan sehingga mempermudah masyarakat untuk memenuhi kebutuhan perawatan diri. Griya Kecantikan ini akan menerapkan aspek-aspek desain Arsitektur Biofilik yang dapat memenuhi kebutuhan manusia akan lingkungan alami. Hal ini dikarenakan alam mampu merangsang panca indra manusia sehingga dapat memantu proses relaksasi.
Performance evaluation of clustering algorithms for protein sequence data Ardaneswari, Gianinna; Aminah, Siti; Awang, Mohd Khalid; Laksmitara, Anindya; Azkiya, Azkal; Razi, Fakhrur; Joshua Situmeang, Jason Nimrod
Desimal: Jurnal Matematika Vol. 8 No. 3 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i3.202528462

Abstract

Protein sequence data analysis is a fundamental task in bioinformatics, supporting the exploration of biological variations and the identification of functional relationships among proteins. This study presents a performance analysis of four clustering algorithms, which include Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Agglomerative Hierarchical Clustering, and Spectral Clustering, applied to protein sequence datasets. Feature extraction was conducted using the Discere package in Python, generating 27 numerical attributes from protein sequences. The optimal number of clusters for BIRCH, Agglomerative, and Spectral Clustering was determined using the Elbow method, while DBSCAN parameters (MinPts, Eps) were tuned using the sorted k-distance plot. Clustering performance was assessed using the Silhouette Score. Among the algorithms, DBSCAN produced the highest silhouette score of 0.8105, whereas BIRCH achieved a strong balance between clustering quality, with a score of 0.7405, and computational efficiency. Agglomerative clustering provided moderate results with a score of 0.6779, while Spectral clustering yielded the lowest score of 0.6310 but demonstrated flexibility in capturing complex structures. These findings provide a benchmark comparison of clustering methods for protein sequence data, offering practical insights into algorithm selection based on data characteristics and performance trade-offs.
Co-Authors Ardaneswari, Gianinna Awang, Mohd Khalid Fakhrur Razi Joshua Situmeang, Jason Nimrod Laksmitara, Anindya Siti Aminah