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Hanif Amrulloh
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Journal of Multidisciplinary Applied Natural Science
Published by Pandawa Institute
ISSN : -     EISSN : 27743047     DOI : 10.47352/jmans
Core Subject : Science, Education, Agriculture, Social, Engineering,
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Energy Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics
Journal of Multidisciplinary Applied Natural Science (abbreviated as J. Multidiscip. Appl. Nat. Sci.) is a double-blind peer-reviewed journal for multidisciplinary research activity on natural sciences and their application on daily life. This journal aims to make significant contributions to applied research and knowledge across the globe through the publication of original, high-quality research articles in the following fields: 1) biology and environmental science 2) chemistry and material sciences 3) physical sciences and 4) mathematical sciences. The J. Multidiscip. Appl. Nat. Sci. is an open-access journal containing original research articles, review articles, and short communications in the areas related to applied natural science. The J. Multidiscip. Appl. Nat. Sci. publishes 2 issues in a year on January (first issue) and July (second issue). This journal has adopted a double-blind reviewing policy whereby both the referees and author(s) remain anonymous throughout the process.
Arjuna Subject : Umum - Umum
Articles 3 Documents
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Search results for , issue "Vol. 6 No. 2 (2026): Journal of Multidisciplinary Applied Natural Science" : 3 Documents clear
Spatial Clustering with Autocorrelation-Based Weighting for Regional Socio-Economic Pattern Analysis: A Case Study of East Java Fitriani, Rahma; Sumarminingsih, Eni; Amaliana, Luthfatul
Journal of Multidisciplinary Applied Natural Science Vol. 6 No. 2 (2026): Journal of Multidisciplinary Applied Natural Science
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.345

Abstract

Clustering, an unsupervised machine learning technique, categorizes objects into groups based on shared characteristics. When applied to spatial data, the assumption of independence is often violated due to similarities among adjacent regions—a phenomenon known as spatial autocorrelation. To address this, spatial clustering incorporates both non-spatial attributes (e.g., socio-economic indicators) and spatial attributes (e.g., geographic location), with spatial attributes weighted based on their influence in defining clusters. In regional economic development, creating clusters that are both spatially coherent and socio-economically homogeneous is critical for effective policy design. Strong interactions among neighboring regions can promote more integrated and balanced growth. This study proposes a spatial clustering framework that optimizes spatial attribute weighting according to the degree of spatial autocorrelation. A simulation study using 2023 data from East Java’s 38 regencies/municipalities determines optimal weights under varying spatial dependence levels. The results show that optimal spatial weights increase with the number of clusters and vary according to the strength of spatial autocorrelation. Applied to East Java, the method produced clusters with higher socio-economic homogeneity than official zones, though with reduced spatial contiguity. These findings highlight the importance of adaptive, autocorrelation-aware clustering to improve regional planning and support more evidence-based development strategies.
Multi-Objective Optimization of Solvent-Free Microwave Extraction vs. Microwave Hydrodiffusion and Gravity for Amomum compactum Essential Oil Variyana, Yeni; Rahma, Aulia Nabila; Mahfud, Mahfud
Journal of Multidisciplinary Applied Natural Science Vol. 6 No. 2 (2026): Journal of Multidisciplinary Applied Natural Science
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.346

Abstract

Amomum compactum essential oil, rich in 1,8-cineole (55–70%), is valued for its therapeutic and aromatic properties but suffers from low yield and thermal degradation in conventional extraction. This study compares and optimizes two green, solvent-free microwave-assisted methods—solvent-free microwave extraction (SFME) and microwave hydrodiffusion and gravity (MHG)—using Box–Behnken Design (BBD) under response surface methodology (RSM). Process variables included feed-to-distiller (F/D) ratio (0.10–0.20 g/mL), microwave power (150–450 W), and extraction time (60–90 min). SFME achieved the highest yield (5.25%) at 300 W, 0.15 g/mL, and 75 min, whereas MHG yielded 2.50% at 150 W, 0.10 g/mL, and 90 min, with superior 1,8-cineole recovery (59.65%) and linalool content (1.98%). Both methods reduced extraction time by 85–95% and energy use by over 90% compared with hydrodistillation, consuming only 0.004–0.006 kWh/g. SEM results confirmed extensive gland rupture (80–90%) and structural breakdown supporting enhanced mass transfer. These findings highlight SFME and MHG as sustainable, energy-efficient innovations aligning with SDGs 9, 12, and 13, advancing the circular bioeconomy and scalable green production of Amomum compactum essential oil.
Stomata Characterization of Native Dendrobium in Liwa Botanical Garden Wahyuningsih, Sri; Mahfut, Mahfut; Anbiya, Lulu
Journal of Multidisciplinary Applied Natural Science Vol. 6 No. 2 (2026): Journal of Multidisciplinary Applied Natural Science
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.347

Abstract

Dendrobium is one of the most commonly collected native orchid genera at the Liwa Botanical Garden, Lampung, Indonesia. In its conservation efforts, the identification of native orchid species through anatomical characterization is essential. The aim of this research is to identify the native Dendrobium species collection at the Liwa Botanical Garden based on stomatal anatomical characteristics and to confirm these results with previous morphological and molecular characterization. The research steps involved the collection of leaves from 19 Dendrobium accessions at the Liwa Botanical Garden, while the anatomical characterization was conducted by preparing paradermal sections to microscopically observe the stomata. The main anatomical characteristics observed included stomatal aperture width, stomatal length, stomatal width, number of stomata, stomatal density, and stomatal index. The results of the study show that, overall, the stomatal aperture width is 2.88 μm, stomatal length is 12.38 μm, stomatal width is 12.58 μm, stomatal density is 29 stomata/mm², and stomatal index is 0.061%. Phenetic analysis based on the dendrogram divided the different native Dendrobium samples into two clusters (A and B) with similarity indices of 1.60 and 0.90, and PCA values (0.170 and 0.044) were found to be greater than 0.02, indicating the contribution of each group. The PCA value of 0.170 reflects the influence of stomatal area, whereas 0.044 reflects the influence of stomatal aperture width, stomatal index, and stomatal density. The anatomical characterization results show a correlation with the identification outcomes based on morphological and molecular characteristics. Furthermore, these findings can serve as a recommendation for the identification of native orchid species and provide a basis for the conservation efforts of native Dendrobium at the Liwa Botanical Garden, Indonesia.

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