Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Kultivasi Desimal: Jurnal Matematika Jurnal Poster Pirata Syandana
Azkiya, Azkal
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Arts Civil Engineering, Building, Construction & Architecture Education Energy Mathematics Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Griya Kecantikan di Kota Semarang Azkiya, Azkal
Jurnal Poster Pirata Syandana PERIODE 160
Publisher : Architecture Department, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Enhancing tea productivity through integrated fertilization and pruning management: A review Anjarsari, Intan Ratna Dewi; Murgayanti, Murgayanti; Kadapi, Muhamad; Mubarok, Syariful; Umiyati, Uum; Nadya, Amanda; Salsabilah, Salsabilah; Azmi, Alifatul; Ramadhan, Afifah Putri; Husniah, Trifani Hana; Rosita, Rosita; Azkiya, Azkal
Kultivasi Vol 25, No 2 (2026)
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/kultivasi.v25i2.69997

Abstract

Tea productivity is strongly influenced by agronomic management practices, particularly fertilization and pruning, which play critical roles in regulating plant growth, physiological performance, and shoot regeneration. This review aims to synthesize current scientific evidence on how integrated fertilization and pruning management can sustainably enhance tea productivity. A narrative review approach was employed by collecting and analyzing peer-reviewed articles published between 2015 and 2025 from reputable databases, including Google Scholar and major international journals. The review highlights that balanced fertilization strategies, combining inorganic, organic, and biofertilizers, significantly improve nutrient availability, photosynthetic efficiency, and shoot yield. Pruning, when properly timed and executed, stimulates vegetative growth, maintains canopy structure, and optimizes the distribution of assimilates. Several studies demonstrated that microbial-based biofertilizers enabled a 25% reduction in inorganic fertilizer inputs without compromising tea shoot yield, while optimum pruning cycles of four to five years-maintained productivity and canopy renewal. In addition, the proportion of pecco shoots was reported to decline from approximately 70% in the first year after pruning to 19% in the fourth year, emphasizing the importance of proper pruning management.  Importantly, the integration of fertilization and pruning practices produces synergistic effects, resulting in enhanced shoot density, improved yield stability, and long-term sustainability of the plantation. This review emphasizes that integrated nutrient and canopy management is essential to address productivity decline and environmental challenges in tea plantations, providing a scientific basis for developing climate-resilient and resource-efficient tea cultivation systems.
Co-Authors Ardaneswari, Gianinna Awang, Mohd Khalid Azmi, Alifatul Fakhrur Razi Husniah, Trifani Hana Intan Ratna Dewi Anjarsari Joshua Situmeang, Jason Nimrod Laksmitara, Anindya Muhamad Kadapi Murgayanti Murgayanti Nadya, Amanda Ramadhan, Afifah Putri Rosita Rosita Salsabilah, Salsabilah Siti Aminah Syariful Mubarok Uum Umiyati