Jurnal Riset Ilmu Teknik

Strengthening Institutions and Governance in the Palm Oil Sector for Smallholder Empowerment: A Soft-Structural-Analytical Systems Model

Valentina Febi Gurning — 2025-09-30

This study formulates a model for strengthening the institutions of independent oil palm farmers in Kalimantan through a systemic and participatory approach. The weaknesses of independent oil palm farmers' institutions remain a major obstacle to increasing productivity, competitiveness, and the sustainability of farming businesses. This research method integrates Soft Systems Methodology (SSM), Interpretative Structural Modelling (ISM), and Analytical Hierarchy Process (AHP) to identify key elements, map structural relationships between variables, and determine institutional strengthening strategy priorities. The SSM results show that the root causes of institutional problems include low member participation, weak coordination between parties, and limited access to capital and information. The ISM analysis reveals that government support and farmers' willingness to change are the main driving elements, while professional human resource development and access to capital serve as connecting elements. Meanwhile, the AHP results show that developing partnerships among stakeholders (0.529) is the highest strategic priority, followed by improving organizational management (0.211) and enhancing training and mentoring (0.156). These findings emphasize the importance of synergy between the government, financial institutions, farmer associations, and palm oil companies in building strong, adaptive, and sustainable farmer institutions. Theoretically, this research contributes to the development of an integrated systems approach in strengthening agricultural institutions in Indonesia, particularly in the smallholder palm oil sub-sector.

Hybrid Fuzzy–Eckenrode Model for Quantitative Evaluation of Fermented Cocoa Bean Quality in Ivory Coast

Koffi Ahua René — 2025-09-30

This study aims to develop a quantitative decision-support model for assessing the quality of fermented cocoa beans in Ivory Coast, addressing the persistent inconsistency in cocoa bean quality that undermines international competitiveness. Despite being the world’s largest cocoa producer, Ivory Coast experiences frequent quality downgrades due to weak fermentation control and limited data-driven evaluation frameworks. A descriptive–analytical quantitative approach was employed, integrating the Eckenrode and Fuzzy Eckenrode methods to transform expert judgment into measurable weights for key cocoa quality attributes—namely fat content, acidity (pH), moisture, aroma, and bitterness. Field data were collected from three major cocoa-producing regions (Yamoussoukro, Daloa, and San-Pédro) through expert evaluations and sensory analyses. The hybrid fuzzy logic model was used to enhance precision in ranking attribute importance under uncertain assessments. Results revealed that fat content (0.103), pH acidity (0.092–0.106), and moisture content (0.078–0.089) are the most influential quality determinants. Cocoa Type C exhibited the most balanced fermentation and post-harvest consistency, achieving a total attribute dispersion (ΣB = 0.139) that aligns with premium-grade standards. The fuzzy hybrid model successfully reduced subjective variability and improved ranking stability compared to classical MCDM methods. This study pioneers the application of Fuzzy Eckenrode in cocoa quality evaluation within the African agroindustrial context. By linking physicochemical and sensory parameters through quantitative weighting, it provides a replicable decision-support tool that bridges traditional post-harvest practices with data-driven quality management for sustainable cocoa competitiveness in Ivory Coast.

Analysis of Prestress Loss and Structural Performance of Box-Type Prestressed Concrete Girders on the Cakung Flyover

Aldo Dion Selvistre — 2025-09-30

The increasing traffic congestion in East Jakarta, particularly at the Cakung intersection, prompted the construction of the Cakung Flyover, designed with a prestressed concrete box-girder system. However, field observations revealed several serviceability issues, including hairline cracks (0.10–0.28 mm), vertical deflections of 18–25 mm, and a 30.5 % reduction in effective prestress compared with theoretical design values. This research aimed to analyze prestress losses and their structural implications through an integrated analytical–empirical approach combining field measurements, theoretical modeling, and validation using SAP2000 and MATLAB simulations. The results showed that the box-girder section (A = 5.65 m², Wa = 5.43 m³, Wb = 2.76 m³) exhibited satisfactory flexural rigidity but experienced frictional losses at tendon angular deviations, particularly near anchorage zones. The tropical environment—average temperature 33 °C and relative humidity 85–90 %—accelerated creep, shrinkage, and relaxation in the 7-wire low-relaxation strands. Consequently, measured prestress losses were 1.3–1.6 times higher than those predicted by standard codes. This study confirms that tropical humidity significantly amplifies prestress degradation and highlights the need for climate-specific calibration of SNI 2847:2019 coefficients. The research contributes a calibrated correlation between tendon eccentricity (eₛ ≈ 1.195 m), deflection, and stress relaxation, enabling more accurate prediction and control of structural performance. The proposed framework provides practical guidelines for tendon configuration, prestress monitoring, and maintenance strategies for prestressed concrete flyovers in humid tropical regions such as Jakarta.

A Hybrid Exponential Smoothing–DRP– Framework for Sustainable Distribution Optimization in a Traditional Food Rengginang

Qurratu Ainun — 2025-09-30

Purpose: Distribution Requirement Planning (DRP), and route optimization using the Saving Matrix method. The goal is to enhance inventory accuracy, minimize logistics costs, and improve delivery efficiency under fluctuating market demand. Methodology: A quantitative–descriptive analysis was conducted using primary data (field observation and interviews) and secondary data (production and sales records). Weekly demand forecasting was performed using the Exponential Smoothing method with α = 0.9, evaluated through Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The DRP model was used to determine gross and net requirements, while the Saving Matrix method was applied to design optimal delivery routes. Findings: The Exponential Smoothing model achieved a high predictive accuracy (MAPE = 10.33%), showing reliable short-term forecasting for MSMEs. DRP implementation with a one-week lead time and a 168-unit safety stock successfully balanced production capacity and customer demand. Integration of DRP and Saving Matrix resulted in approximately 30% reduction in total logistics cost and significant improvement in stock availability. Compared to 17 related studies (2021–2024), this hybrid model demonstrated superior efficiency and cost stability within traditional food industries. Practical Implications: The results provide MSMEs with a data-driven framework to synchronize production, inventory, and distribution planning, reducing decision-making errors and improving operational sustainability. The proposed model can serve as a replicable reference for traditional food SMEs facing fluctuating demand conditions. Value: The novelty of this study lies in combining Exponential Smoothing forecasting, DRP scheduling, and Saving Matrix routing into a unified optimization framework—rarely applied in Indonesia’s traditional food sectors. This integrative method strengthens both academic insight and managerial practice in MSME supply chain efficiency.

Sustainable Quality Transformation In Agro-Industrial Manufacturing: A Six Sigma–Kaizen Model For Thailand’s Crude Palm Oil Sector

Saswattecha Nantakrit Yodpijit — 2025-09-30

This study enhances the process capability and sustainability of Thailand’s crude palm oil (CPO) production through the integration of Six Sigma DMAIC and Kaizen frameworks. Although Thailand ranks as the world’s third-largest palm oil producer, its CPO frequently fails to meet international standards due to excessive Free Fatty Acid (FFA) and moisture content. To address these persistent inefficiencies, a quantitative–descriptive approach was applied to a Krabi Province mill using 30 days of operational data. The DMAIC methodology systematically identified root causes—unstable sterilization temperature, pressing delays, and inadequate vacuum maintenance—while Kaizen interventions reinforced operator discipline and preventive control. Post-intervention analysis revealed a 19% reduction in FFA, a 36% decrease in moisture, and a 53% reduction in defect rate, accompanied by a rise in process capability index (Cp) from 0.78 to 1.24 and an improvement in sigma level from 4.09 to 4.5. Benchmarking confirmed that Thailand’s performance now closely approaches Malaysia’s automation-led systems while surpassing Indonesia in consistency and energy efficiency. The findings demonstrate that sustainable process improvement can be achieved through data-driven analysis and human-centered Kaizen culture, even in semi-automated contexts. This hybrid model supports compliance with RSPO, ISO 22000, and Bio-Circular-Green (BCG) frameworks while reducing operational waste and cost. The originality of this research lies in establishing an empirically validated Six Sigma–Kaizen hybrid applicable to ASEAN agro-industries, bridging the gap between technological advancement and behavioral quality systems toward globally competitive CPO production.