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Equivalent: Jurnal Ilmiah Sosial Teknik
Published by Politeknik Siber Cerdika Internasional
ISSN : 27750329     EISSN : 27750833     DOI : 10.59261
Core Subject : Economy, Social, Engineering,
Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences
Equivalent: Jurnal Ilmiah Sosial Teknik provides a means for ongoing discussion of relevant issues including the focus and space of the journal which can be examined empirically. This journal publishes research articles covering all aspects of Civil Engineering, Environmental Engineering, computer Engineering, Industrial Engineering, Mechanical Engineering, Electrical Engineering, Social and Political Sciences, Education, Economics, Management, Sociology, Religion, Law that belong to the social and engineering context.
Arjuna Subject : Ilmu Sosial - Ilmu Sosial (Lain-Lain)
Articles 289 Documents
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Key Success Factors Affecting the Cost Performance of Value Engineering-Based Green Retrofitting on Rivers Using Envision Guidelines Nela Devila; Albert Eddy Husin
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 4 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i4.402

Abstract

Background: Rivers in Indonesia face serious challenges related to water quality and river management due to weak infrastructure governance, highlighting the need for cost-effective river restoration. Objective: This study aims to analyze the key success factors influencing the cost performance of Value Engineering (VE)-based green river retrofits guided by Envision v3. Method: The study employed a quantitative survey method involving 68 respondents, including project managers, field engineers, supervisors, consultants, and Watershed Authority personnel. Data were analyzed using multiple linear regression in IBM SPSS Statistics Version 25. Three independent variables (river characteristics (X1), Envision guidelines (X2), and Value Engineering practices (X3)) were examined for their effects on cost performance (Y). All constructs demonstrated strong reliability and validity. Results: The results confirmed that X1, X2, and X3 significantly and jointly influenced cost performance (F= 9.422; p < .001), yielding the regression equation: Y = 4.398 + 0.046(X1) + 0.045(X2) + 0.135(X3). Value Engineering exerted the strongest influence, as indicated by the highest regression coefficient (0.135). The ten most influential success factors identified through the mean rank analysis were predominantly associated with the Value Engineering dimension, particularly cost savings identification, functional clarity, structured planning, and top management commitment. Conclusion: The results demonstrate that disciplined Value Engineering practices can significantly improve the cost performance of green river retrofit projects.
Statistical Analysis of Factors Influencing Green Cost Performance in Indonesian Toll Road Projects Based on the Greenroads Framework Abdullah Ahmad Badawi; Albert Eddy Husin
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 4 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i4.404

Abstract

Background: Road transport contributes approximately 69% of global inland transport greenhouse gas (GHG) emissions, making compliance with green infrastructure standards increasingly critical. However, no evidence-based model of green cost performance has yet been developed for Indonesian toll road projects. Objective: This study examined the statistical factors influencing green cost performance in Indonesian toll road projects implementing the Greenroads sustainability framework. Method: A quantitative survey of 80 toll road industry professionals investigated the effects of three independent variables Key Success Factors (X1), Greenroads implementation (X2), and Value Engineering application (X3) on Green Cost Performance (Y). Data were analyzed using IBM SPSS Statistics Version 21 through reliability (Cronbach’s alpha), validity (Pearson correlation), normality (Kolmogorov–Smirnov), and multiple linear regression analyses. All 93 sub-indicators were found to be valid and reliable. Results: The Durbin–Watson statistic (DW = 2.153) indicated no autocorrelation, while tolerance values > 0.10 and variance inflation factor (VIF) values < 10.0 confirmed the absence of multicollinearity. The regression equation, Y = 3.591 + 0.135X1 + 0.125X2 + 0.160X3, identified Value Engineering (X3) as the strongest predictor (t = 7.490), followed by Greenroads implementation (X2; t = 6.898) and Key Success Factors (X1; t = 5.394). The overall model was statistically significant (F = 47.329, p < .05), with R² = 0.734, indicating that the three independent variables jointly explained 73.4% of the variance in green cost performance. Conclusion: The findings provide a measurable and replicable decision-making framework for cost-sustainable infrastructure development, with direct implications for policies promoting mandatory green financing.
Feasibility of Odoo 17 ERP Implementation for Human Resource Management in a Small Indonesian Educational Institution Ihza Fajrur Rachman Hasani; Ni Wayan Trisnawaty; Teguh Raharjo
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 4 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i4.416

Abstract

Background: Small educational institutions often rely on manual human resource management (HRM) despite having limited staff and increasing data demands. Objective: This study evaluates the feasibility of Odoo 17 as an Enterprise Resource Planning (ERP) system for HRM at Edu_Inst, a small Indonesian K–9 educational institution with two HR staff members serving approximately 150 employees. Methods: A single-case feasibility study was conducted using semi-structured interviews, observation, and document analysis, combining fit-gap analysis, technical assessment, cost-benefit analysis, and a risk matrix. The economic analysis applied an opportunity-cost approach, valuing staff time freed through automation rather than direct cash savings. Results: Odoo 17 supports most HRM requirements, particularly employee data management, recruitment, attendance and leave management, reporting, and workflow automation, while partial fits remain in payroll localization, tax and BPJS reporting, attendance integration, grievance handling, and employee self-service access. The Software-as-a-Service (SaaS) deployment is technically feasible, given adequate devices and redundant connectivity. Economically, the case is conditional: only the 100% full-displacement scenario yields a positive net benefit (IDR 571,500/year), whereas the 30%, 50%, and 70% scenarios do not cover the annual subscription cost. This scenario represents an optimistic upper bound rather than an expected outcome. External training results in an impractical 41.4-year payback period, whereas internal training shortens the payback period to approximately 7.4 years. Conclusion: Odoo 17 is operationally and technically feasible, but its economic justification depends on internalizing training costs, achieving substantial time savings, and recognizing non-financial benefits such as data quality, process reliability, and information governance.
FDTD-Based CU/GO D-Shaped Fiber SPR Sensor for Detecting Refractive Index Changes in NaCl Solutions Nindira Amelia Keysa; Wiwis Sasmitaninghidayah; Wildan Panji Tresna
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 4 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i4.417

Abstract

Background: Surface Plasmon Resonance (SPR) is an optical sensing method with high sensitivity to refractive index changes near a metal surface. Integrating SPR with D-shaped optical fibers provides compact dimensions, flexibility, resistance to electromagnetic interference, and real-time sensing capabilities. Sensor performance is affected by the plasmonic material, metal thickness, and incorporation of two-dimensional materials such as graphene oxide (GO). Objective: This study analyzes the characteristics of a D-shaped fiber-optic SPR sensor with a double-layer Cu/GO structure for detecting refractive index changes in NaCl solutions using the Finite-Difference Time-Domain (FDTD) method in Ansys Lumerical. Methods: The study compared Ag and Cu as plasmonic materials, evaluated the effect of GO addition, optimized the Cu thickness from 30–60 nm with a fixed GO thickness of 5 nm, and optimized the GO thickness from 0.5–30 nm. Sensor performance was evaluated based on the resonance wavelength, sensitivity, full width at half maximum (FWHM), and figure of merit (FOM). Results: Cu produced better resonance characteristics than Ag in the investigated configuration. Addition of GO induced a redshift in the resonance wavelength and enhanced the sensor response to refractive index changes. Variations in Cu and GO thickness demonstrated that sensor performance depends on their combined configuration. Optimization identified a configuration of 45 nm Cu and 5 nm GO provided a favorable balance among sensitivity, resonance sharpness, FWHM, and FOM. Conclusion:  Optimized Cu/GO configuration shows potential as a basic structure for D-shaped fiber-optic SPR sensors with improved sensitivity for refractive index detection and future biosensing applications.
Optimizing Renewable Energy Utilization in the Manufacturing Industry in Indonesia Moden Purba; Zaenal Arifin
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 4 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i4.418

Abstract

Background: Indonesia's manufacturing sector contributes substantially to global energy demand and greenhouse gas emissions, yet renewable energy accounts for only about 15% of industrial energy use. Although relevant policies are in place, food and beverage manufacturers continue to encounter technical, financial, and institutional barriers to integrating clean energy. Objective: This study aims to examine the associations of technical, economic, policy, and energy management factors with the optimization of renewable energy utilization in Indonesia's food and beverage manufacturing industry. Methods: This study employed a quantitative approach using multiple linear regression analysis. Data were collected through questionnaires distributed to 102 stakeholders in the food and beverage manufacturing industry who were selected using purposive sampling. The data were analyzed using validity and reliability tests, t-tests, an F-test, and the coefficient of determination (R²). Results: The results indicate that, collectively, technical, economic, policy, and energy management factors are significantly associated with renewable energy optimization (F = 577.237, p < 0.05). Individually, technical factors (t = 4.592, p < 0.05) and policy factors (t = 4.653, p < 0.05) show positive and significant associations, whereas economic and energy management factors do not show significant associations. The R² value of 0.958 suggests that the model explains a substantial proportion of the variance, although this finding should be interpreted with caution given the characteristics of the sample. Conclusion: The optimization of renewable energy utilization in the manufacturing industry is more strongly associated with technological readiness and policy support than with economic and energy management factors.
Hybrid Threat Detection Using Wazuh, DBSCAN, Random Forest, and MISP Threat Intelligence Miko Dewi Hatmanti; Rojali Rojali
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 4 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i4.419

Abstract

Background: Cyber-attacks continue to grow in volume and sophistication, and small-to-medium organizations are increasingly exposed because of limited budgets and security personnel. Open-source Security Information and Event Management (SIEM) platforms such as Wazuh offer a low-cost alternative to commercial solutions, yet their reliance on static, rule-based correlation logic limits their ability to detect previously unseen or evolving threats and tends to generate a high rate of false positives. Objective: This study evaluates a layered Wazuh pipeline in which DBSCAN supplies density-context features to a Random Forest classifier and MISP provides post-classification IOC enrichment within a controlled AIT-ADS laboratory setting. Methods: The experimental evaluation is conducted using the AIT-ADS dataset, which consists of 423,499 alerts. A within-class blocked temporal split produced 338,799 train set and 84,700 test set. Four strictly causal temporal features were combined with ten row-local features; DBSCAN was fitted only on the train set (ε = 0.466428, min_samples = 5) and added four density-context features, yielding 18 classifier inputs before being cross-checked against MISP indicators of compromise. Results: Experimental results show DBSCAN+Random Forest achieved macro-F1 = 0.2976, balanced accuracy = 0.3747, and MCC = 0.4940. Its binary matrix changed from TP = 84,266, TN = 63, FP = 241, FN = 130 to TP = 84,284, TN = 63, FP = 241, FN = 112 after MISP, a 13.85% FN reduction with no induced FP. XGBoost FN decreased from 72 to 54 (25.00%), but FP increased from 270 to 272. Conclusion: The proposed hybrid pipeline demonstrates that machine-learning-based alert classification can reduce false positives in Wazuh while preserving high threat detection sensitivity. However, the results also show that performance remains uneven across minority attack classes, and the benefits of DBSCAN and MISP depend on data characteristics and IOC coverage. Overall, Random Forest provides a more suitable operational trade-off for reducing alert burden in the evaluated scenario.
Customer-Based Green Brand Equity: The Mediating Role of Psychological Ownership of Green Products among IKEA Family Members in Indonesia Mazaya Izzatur Rahim; Ratih Hurriyati; Puspo Dewi Dirgantari
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 4 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i4.423

Abstract

Background: This study advances Cue Utilization Theory and Psychological Ownership Theory by establishing psychological ownership as a pivotal mediating mechanism through which digital green product cues generate green brand value within digitally engaged loyalty communities. Objective: This study investigates how digital green product cues shape customer-based green brand equity via the psychological ownership of green products among IKEA Family Indonesia members. Methods: This study employed a quantitative survey design, collecting data from 348 purposively sampled IKEA Family Indonesia members. CB-SEM was applied to examine both direct and indirect pathways among the research variables. Results: Findings reveal that digital green product cues meaningfully strengthen psychological ownership of green products, which in turn elevates customer-based green brand equity. Moreover, psychological ownership of green products serves as a significant positive mediator in the pathway from digital green product cues to customer-based green brand equity. Conclusion: These results confirm that communicating sustainability credentials via digital channels both directly enhances green brand perception and cultivates a sense of product belonging that reinforces customer-based green brand equity.
Natural Language Query Hybrid Architecture for Cross-Sectoral Analytics Dashboards on Public Statistical Data Yusuf Fadlila Rahcman; Puput Suryaningtyas; Masbahah Masbahah; Nur Azizul Haqimi; Ahmad Faisal Sani
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 4 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i4.430

Abstract

Background: Public statistical data are essential for evidence-based policymaking, yet the complexity of cross-sectoral indicators often limits accessibility for nontechnical users. This challenge is particularly relevant in East Java, where regional development disparities require integrated analytical tools. Objective: This study aims to develop a cross-sectoral analytics dashboard integrating Natural Language Query (NLQ), Apriori association-rule mining, and a supporting local large language model (LLM). Methods: The system was developed using the SDLC Waterfall model and utilized more than 50 Statistics Indonesia (BPS) indicators covering 38 districts/cities in East Java from 2016 to 2026. A deterministic, rule- and regular-expression (regex)-based Text-to-SQL mechanism processed natural-language queries, while Apriori identified cross-sectoral association patterns. A local LLM (Ollama, llama3.2:1b) generated supporting narratives. Evaluation included black-box testing, 50-query intent classification, association-rule metrics, and an exploratory LLM assessment. Results: The NLQ module achieved 96.00% accuracy (48/50 queries). Apriori generated 55,480 high-level rules with an average confidence of 0.909 and lift of 15.44. The exploratory LLM evaluation achieved a composite score of 4.35/5.00, a 2.5% hallucination rate, a 26.07-second P95 latency, and a 100% fallback success rate. Conclusion: The hybrid architecture effectively supports accessible statistical-data retrieval and cross-sectoral pattern discovery, while the LLM remains a supporting narrative component that requires further formal validation.
Intelligent Remaining Useful Life Prediction of Power Transformers Using Machine Learning Fanni Mudarris Kurniawan; Dyah Lestari Widaningrum
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 4 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i4.440

Abstract

Background: Power transformers play an integral role in power generation and transmission. Transformer failures can lead to service interruptions, reduce system reliability, and incur significant failure-related costs. Efficient transformer maintenance requires accurate condition assessment and effective maintenance decision-making. Objective: The purpose of this paper is to develop a machine-learning-based decision-support framework for power transformer maintenance that integrates health index estimation, Remaining Useful Life (RUL) prediction, and Life Cycle Cost (LCC) evaluation. Methods: The framework draws on multimodal monitoring data from 166 oil-immersed power transformers collected over a ten-year period. Six machine learning models (XGBoost, Random Forest, Support Vector Regression, Convolutional Neural Networks, Ridge Regression, and K-Nearest Neighbors) were evaluated using various feature sets. Results: Among the evaluated models, XGBoost achieved the best performance under the basic feature configuration, with an RMSE of 0.285 years, an MAE of 0.199 years, and an R² of 0.956. The robustness evaluation showed that the model retained satisfactory predictive performance under moderate feature reduction. Based on the Life Cycle Cost assumptions applied in this study, the predictive maintenance scenario was estimated to reduce the total cost of the transformer fleet by approximately 47%. Conclusion: The proposed approach combines accurate RUL prediction with technical and economic analyses to provide a practical decision-making tool for transformer asset management.

Page 29 of 29 | Total Record : 289

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