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International Journal of Science and Environment
Published by CV. Inara
ISSN : -     EISSN : 28090551     DOI : https://doi.org/10.51601/ijse.v2i4
Core Subject : Science, Education, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Mathematics Physics
International Journal of Science and Environment (IJSE) is to provide a research medium and an important reference for the advancement and dissemination of research results that support high-level research in the fields of Science and Environment . Original theoretical work and application-based studies, which contributes to a better understanding all fields of Science and Environment. The aim and scope of the journal Chemistry, Chemical Analysis, Physical Chemistry, Physics, Biology, Ecology, Biodiversity, Zoology, Biochemistry, Mathematics, Environmental Science, Agriculture, Environment, Forestry.
Arjuna Subject : Biokimia, Genetika dan Biologi Molekuler - Biokimia, Genetika dan Biologi Molekuler (Lain-Lain)
Articles 622 Documents
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Developing an Architectural Spatial Integration Framework for Sustainable Waste Management Education Facilities Arya Sagara Dwi Indrawanto; Luhur Sapto Pamungkas; Endah Tisnawati
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.718

Abstract

The growing adoption of Waste-to-Energy facilities requires an architectural approach capable of integrating industrial operations, environmental education, and sustainable waste management within a coherent spatial system. However, existing studies primarily emphasize technological optimization, industrial building performance, or educational environments independently, leaving limited attention to architectural spatial integration as a design foundation. This study aims to develop an Architectural Spatial Integration Framework to support the design of Sustainable Waste Management Education Facilities. A Design-Based Research approach was employed through the synthesis of scientific literature, policy reviews, architectural precedent studies, and comparative analysis to identify transferable spatial design principles. The proposed framework consists of four integrated components, namely Spatial Organization Integration, Functional Integration, Environmental Integration, and Architectural Integration, which are translated into architectural design parameters including zoning, circulation, spatial hierarchy, building form, environmental systems, visual connectivity, and educational spaces. The framework is subsequently implemented through a conceptual architectural model demonstrating how industrial operations, visitor experiences, environmental learning, and sustainable building strategies can be integrated into a coherent spatial configuration. The findings establish Architectural Spatial Integration as the principal mechanism linking operational efficiency, environmental performance, and educational functions within Waste-to-Energy facilities. This study contributes a transferable architectural framework that advances the discourse on sustainable industrial architecture while providing practical guidance for the planning and design of future Sustainable Waste Management Education Facilities supporting circular economy principles and sustainable urban development.
Analysis of Changes In Biofloc Wastewater Quality Following Eco-Enzyme Application Nor Inayah; Noor Arida Fauzana; Fatmawati; Pahmi Ansyari
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.726

Abstract

The biofloc system improves water- and feed-use efficiency but generates wastewater containing suspended solids and nitrogenous compounds that require treatment before reuse. This study evaluated changes in biofloc wastewater quality after the addition of eco-enzyme at concentrations of 0% (control), 5%, 10%, 15%, 20%, and 25% (v/v). The wastewater and eco-enzyme were homogenized, incubated for 24 h, and analyzed for pH, dissolved oxygen (DO), ammonia (NH₃), total suspended solids (TSS), total nitrogen, total phosphorus, total potassium, and the C/N ratio. Data from two measurement replicates were analyzed using one-way analysis of variance followed by Fisher's least significant difference (LSD) test at the 5% significance level. Eco-enzyme had a highly significant effect on pH (p < 0.001), a significant effect on DO (p = 0.012), and a highly significant effect on NH₃ (p < 0.001). The pH increased from 2.760 in the control to 3.145 at the 25% dose, whereas DO decreased from 2.650 to 2.500 mg/L and NH₃ increased from 0.500 to 2.000 mg/L. TSS, total nitrogen, and total phosphorus did not differ significantly among treatments (p > 0.05). Total potassium increased from 0.0015% to 0.0705% (p < 0.001), while the C/N ratio increased from 0.460 to 6.310 (p = 0.004). These findings indicate that, after 24 h of contact, eco-enzyme functions more as a carbon source and a mineralization bioactivator than as a stand-alone treatment capable of directly producing water suitable for aquaculture. The treated water still requires neutralization, aeration, ammonia control, and a longer maturation period before reuse.
Navigating the Knowledge Network of New Venture and Small Business: Bibliometric Insights and Forward-Looking Perspectives Idzani Muttaqin; Budi Setiadi; Noorlaily Maulida; Ayu Niken Faizati; Rizka Zulfikar
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.730

Abstract

The purpose of this research was to navigate the intricate knowledge network that underpins new ventures and small businesses through bibliometric analysis. The primary data source using the Scopus database, 123 peer-reviewed documents including journal articles, conference papers, and book chapters which published in period 2003-2025 were systematically selected following the PRISMA framework to ensure transparency and eliminate bias. The research applies the Biblioshiny interface for R to conduct multidimensional analyses covering source impact, document influence, keyword trends, and conceptual structures. The findings reveal an increasingly dynamic knowledge network characterized by the dominance of themes such as entrepreneurship, innovation, small firm strategy, and internationalization. The co-occurrence network uncovers interrelated conceptual clusters, while the thematic evolution map traces a shift from foundational entrepreneurship themes to more contemporary concerns like digital transformation and ecosystem resilience. Highly cited papers were clustered in influential journals such as Small Business Economics and the International Journal of Entrepreneurial Behaviour and Research, underscoring the interdisciplinary depth of the field. The study offers both theoretical and practical implications. For academics, it identifies underexplored yet promising areas such as absorptive capacity and entrepreneurial ecosystems, while practitioners may leverage insights to strengthen knowledge-sharing strategies and innovation capacity.
Effect of Bagasse Mixture On Mechanical Properties and Physical Properties Using Polyester and Vinylester Mixture Matrix Gusti Nur Alamsyah; Rahmat Fajrul
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.735

Abstract

This study aims to analyze the effect of the variation of bagasse fiber mixture on the mechanical properties and microstructure of composites using a mixture matrix of polyester and vinyl ester. The research method used is an experimental method by making composite specimens through a hand lay-up process. The variation in the fiber content of the bagasse used was 0%, 5%, 10%, 15%, and 25%, while the composition of the polyester and vinyl lester matrix was maintained at a ratio of 70:30. Material characterization was carried out through tensile testing to obtain Tensile Strength (TS), Tensile Strength at Break (TSb), Yield Strength (Sy), Yield Strain (Ey), and modulus of elasticity, as well as fracture morphology observation using Scanning Electron Microscope (SEM). The results showed that the increase in bagasse fiber content led to a decrease in TS, Sy, and EY values, with the highest TS value obtained in PV0 specimens of 32.62 MPa and the lowest in PV25 of 6.47 MPa. The 5% fiber variation provides the best TSb value and modulus of elasticity, of 3.37 MPa and 25.12 MPa, respectively. The results of SEM observations show that the addition of fiber up to 5% still produces a fairly good fiber-matrix bond, while at 25% fiber content there is porosity and uneven distribution of fibers that reduce the mechanical properties of composites.
The Effect of Gurdi Process Prameter and Fiber Variation On The Delamination of Banana Stem Fiber Composites Supendi Supendi; Bambang Dwi Haripriadi; Siti Umira
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.739

Abstract

Delamination is one of the main defects that often occur in the process of curving composite materials and can degrade the quality of the hole as well as the integrity of the joint structure. Banana stem fiber-reinforced thermoplastic composites have the potential to be an environmentally friendly material, but they are still susceptible to delamination due to the influence of the processing process parameters and variations in fiber composition. This study aims to analyze the influence of drill bit diameter, feed rate, drill bit tip angle, fiber arrangement, and cooling medium on delamination at the entrance and exit sides of the banana stem fiber composite hole. The research method used was an experiment with the design of the Taguchi Orthogonal Array L27, while the data analysis was carried out using Signal-to-Noise Ratio (S/N Ratio) with characteristics of Smaller-The-Better and Analysis of Variance (ANOVA) at a constant spindle rotation of 500 rpm. The results showed that the addition of banana stem fiber was able to increase the tensile strength of composites by 61.7% compared to pure thermoplastics. ANOVA analysis showed that on the inlet side delamination, the drill bit diameter, feeding motion, and cooling medium had a significant effect, while on the exit side, the drill bit diameter and drill bit tip angle were the most dominant parameters. Overall, the drill bit diameter is the factor that has the greatest influence on minimizing delamination so that it is the main parameter that needs to be optimized to improve the quality of the hole from the drill in the thermoplastic composite reinforced with banana stem fiber.
The Landscape of Ai-Driven Interactive Storytelling In Adolescent Mental Health: A Scoping Review On Nursing and Communication Perspectives Muhammad Faiz Alfarizi; Ilham Fauzi Agiliansyah; Talitha Nabilah
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.740

Abstract

Background: The post-pandemic surge in adolescent mental health disorders calls for innovative and decentralized digital intervention breakthroughs. Artificial Intelligence (AI) technology, through interactive storytelling models and generative chatbots, is now being integrated to provide a safe narrative space for adolescents. However, the computational-linguistic effectiveness and clinical-ethical boundaries of these interventions still require comprehensive mapping. Objective: This study aims to map the landscape, effectiveness, linguistic barriers, and ethical implications of using AI-driven interactive storytelling in adolescent mental health management by integrating psychiatric nursing and digital communication perspectives. Methods: This study used a scoping review design based on the PRISMA 2020 guidelines. Literature was systematically searched across three reputable electronic databases: Scopus, ScienceDirect, and CINAHL. The data selection process used the PCC (Population, Concept, Context) framework strategy. From a total of 146 articles identified in the initial search, 18 final original studies met the inclusion criteria and were extracted for narrative analysis. Results: Synthesis from a digital communication lens shows that adolescents use AI's anonymous venting features to freely express emotional distress. Nevertheless, Natural Language Processing (NLP) still has significant limitations in understanding the dynamics of cyber-slang and crisis metaphors (such as algospeak), which can trigger contextual failures in algorithmic responses. From a psychiatric nursing lens, interactive chatbots have proven effective as instruments for large-scale digital triage and Psychological First Aid. However, the application of this technology must comply with the principle of non-maleficence, in which AI systems must have strict operational limits to detect critical indicators of acute crisis (self-harm and suicidal ideation) and automatically redirect them to professional clinical mental health nurses. Conclusion: AI-driven interactive storytelling holds great potential as a complement to decentralized adolescent mental health services, but its optimization strongly depends on improving the AI's linguistic accuracy toward local adolescent language as well as strengthening automatic referral systems to clinical nursing staff to guarantee patient safety.
Formulation of Strategic Priorities for Smart Factory Implementation Through Importance–Performance Map Analysis (IPMA) in a SEM-PLS-Based TAM–UTAUT Integration Model Sulistyawati; Feby Artwodini Muqtadiroh
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.741

Abstract

The success of Smart Factory implementation is determined not only by satisfying statistical significance requirements between constructs, but also by an organization's ability to set well-targeted improvement priorities. Many technology acceptance studies stop at the hypothesis-testing stage without translating those findings into actionable managerial direction. This study aims to formulate strategic priorities for improving the acceptance and use of the Smart Factory 4.0 system in railway manufacturing using Importance–Performance Map Analysis (IPMA) built upon an integrated Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) model. Data were collected through a structured questionnaire administered to 60 active users on the Bogie Fabrication and Finishing lines, then analyzed using Partial Least Squares-based Structural Equation Modeling (SEM-PLS) as the basis for calculating the importance value (total effect) and performance value (rescaled mean score) of each construct with respect to Actual Use. The IPMA results show that Facilitating Conditions occupies Quadrant I (high importance, suboptimal performance), making it the top priority for improvement, while Behavioral Intention and Perceived Usefulness fall into Quadrant II (high importance, high performance) and thus need to be maintained as key strengths. Perceived Ease of Use and Social Influence contribute indirectly through the mediation of Behavioral Intention, while demographic variables (age, experience, and gender) show low levels of importance and/or performance and are therefore not the focus of short-term intervention. Based on this mapping, the study formulates five tiered strategic recommendations that can be directly adopted by management, covering network infrastructure strengthening, provision of production-environment-resistant devices, rapid technical assistance mechanisms, microlearning-based training, and the use of social influence through digital change agents. The main contribution of this study lies in providing a data-based diagnostic instrument that guides measurable organizational resource allocation, rather than merely confirming causal relationships between constructs.
The Effect of Carbon Emission on Company Performance with Financial Slack as a Moderation Variable in Mining and Energy Sector Companies in Indonesia (2021-2025) Laili Faizatussalamah; Indah Fajarini Sri Wahyuningrum
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.742

Abstract

This study examines the effect of Carbon Emission Scope 1 and Carbon Emission Scope 2 on company performance and investigates the moderating role of Financial Slack. This study employed a quantitative approach using secondary data from the annual reports and sustainability reports of mining and energy companies listed in Indonesia during the 2021–2025 period. The sample was selected using purposive sampling, resulting in 42 companies (210 firm-year observations). Panel data regression analysis was conducted using the Fixed Effect Model (FEM). The results indicate that Carbon Emission Scope 1 has a positive and significant effect on company performance, while Carbon Emission Scope 2 has a negative and significant effect. Furthermore, Financial Slack significantly moderates the relationship between carbon emissions and company performance by weakening the effect of Carbon Emission Scope 1 and strengthening the effect of Carbon Emission Scope 2. These findings suggest that the impact of carbon emissions on company performance varies according to the type of emissions and that financial flexibility plays an important role in supporting sustainable corporate performance.
The Relationship between Team Performance Pressure and Unethical Pro-Team Behavior: The Mediating Role of Collective Moral Disengagement Nila Sari Puspita Ningsi Latif; Etik Darul Muslikah; Eben Ezer Nainggolan
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.743

Abstract

The increasing reliance of organizations on team-based work systems has intensified the pressure placed on team members to achieve, maintain, or improve collective performance targets. While such pressure is often assumed to enhance productivity, it may also create conditions under which team members are willing to violate ethical norms to protect or benefit their team, a phenomenon labeled unethical pro-team behavior (UPTB). This study examined the relationship between team performance pressure (TPP) and UPTB, as well as the mediating role of collective moral disengagement (CMD) in that relationship. A quantitative correlational design with a cross-sectional survey was employed, involving 357 employees working in teams across public and private organizations in East Java, Indonesia, selected through quota sampling. Data were collected using modified Likert-type instruments adapted from established scales and analyzed using PROCESS Macro Model 4 with 5,000 bootstrap resamples. Results indicated that TPP had a significant positive total effect on UPTB (B = 0.612, p < 0.001), supporting Hypothesis 1. Mediation analysis showed that TPP significantly predicted CMD (B = 0.562, p = 0.003), and CMD significantly predicted UPTB after controlling for TPP (B = 0.487, p < 0.001). The indirect effect of TPP on UPTB through CMD was significant (effect = 0.274, 95% CI [0.090, 0.450]), while the direct effect remained significant (B = 0.338, p < 0.001), indicating partial mediation and supporting Hypothesis 2. These findings extend Social Cognitive Theory and Moral Disengagement Theory to the team level, demonstrating that performance pressure shapes unethical behavior both directly and through collectively shared moral rationalization. Practical implications for performance management, team leadership, and human resource interventions are discussed.
The Implementation of Support Vector Machine and Naïve Bayes Algorithm to Predict Diabetes Joshua Roy Danna Lacanlale; Vitri Tundjungsari
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.744

Abstract

The significant increase in diabetes mellitus cases within the community demands a technology-based solution that can provide accurate, efficient, and reliable predictions. This study aims to evaluate the impact of various data preprocessing schemes on the performance of the Gaussian Naive Bayes (GNB) and Support Vector Machine (SVM) algorithms in classifying diabetes risk. The dataset used in this research was sourced from the UCI Machine Learning Repository and consists of 520 records with 16 symptom features and 1 target label. The preprocessing stages include handling missing values, encoding categorical features, normalizing numerical data using StandardScaler, balancing the dataset with the Synthetic Minority Over-sampling Technique (SMOTE), and feature selection using the SelectKBest method. A total of nine preprocessing scheme combinations were tested for each algorithm. The experimental results show that for the GNB model, the best performance was achieved using the combination of StandardScaler, SMOTE, and SelectKBest (k=5), reaching an accuracy of 94.53%, precision 98.36%, recall 90.91%, and f1-score 94.49%. Meanwhile, for the SVM model, the highest performance was obtained through the combination of StandardScaler and RBF kernel hyperparameter tuning, achieving an accuracy of 99.04%, precision 99.05%, recall 99.04%, and f1-score 99.03%. The evaluation was conducted using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and learning curve visualization. These findings highlight the critical role of proper preprocessing in enhancing predictive model performance. This study is expected to serve as a reference for developing early detection systems for diabetes based on machine learning.

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