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Greenation International Journal of Engineering Science

GIJES
Website

Published by Greenation Research

ISSN : 2986089X EISSN : 29860326 DOI : https://doi.org/10.38035/gijes

Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Mechanical Engineering

Greenation International Journal of Engineering Science (GIJES) is a peer-reviewed journal managed and published by Greenation Research & Yayasan Global Resarch National. GIJES is published four times a year, in March, June, September, and December. GIJES provides a platform for academics, researchers, and practitioners to publish scientific articles. The scope of articles listed in this journal is related to various topics such as Engineering Education, Civil Engineering, Electrical Engineering, Informatics Engineering, Craft Engineering, Architectural Engineering, Industrial Engineering, Mechanical Engineering, Environmental and Safety Technology, Basic Chemical Engineering, and Applied Chemistry, Transportation Engineering, Geology & Mining Engineering, Marine & Naval Engineering, and other related engineering fields.

Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)

Articles 131 Documents

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Analysis of the Influence of User Distance and Transmission Speed on Data Communication Network Performance Reni Silviah
Greenation International Journal of Engineering Science Vol. 3 No. 2 (2025): (GIJES) Greenation International Journal of Engineering Science (June - August
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v3i2.549

Analysis of the influence of user distance and transmission speed on data communication network performance is a scientific article with the aim of analyzing whether network performance affects user distance and data communication transmission speed. Qualitative method with a phenomenological approach, The phenomenological approach emphasizes the description of the subject's life experience from the participant's own perspective (phenomenology as a social research method). The results of this article are: 1) User distance affects data communication network performance, 2) Transmission speed affects data communication network performance. Apart from these 2 exogenous variables that affect the endogenous variable of signal quality, there are many other factors including LAN networks, communication obstacles, connection stability.

Integrating Lean-Green Waste Reduction into Supply Chain Strategy: Evidence from the Indonesian Automotive Industry Roma Wibero
Greenation International Journal of Engineering Science Vol. 3 No. 2 (2025): (GIJES) Greenation International Journal of Engineering Science (June - August
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v3i2.553

The application of the Lean and Green Management System (LGMS) and Lean and Green Waste Reduction Technique (LGWRT) has become a focal point for companies aiming to enhance operational efficiency and environmental sustainability. This study examines the implementation of LGMS and LGWRT and their impact on Lean and Green Business Results (LGBR) in the Indonesian industrial sector. By integrating lean and green principles, LGMS assists companies in eliminating waste, minimizing resource usage, and reducing emissions. Simultaneously, LGWRT aims to decrease waste through environmentally friendly and efficient techniques, supporting productivity and cost reduction. Using a quantitative method based on survey data from manufacturing companies, the findings reveal that these approaches significantly improve green business outcomes, including enhanced energy efficiency, lower carbon emissions, and reduced operational costs. Additionally, LGMS and LGWRT enable companies to meet strict environmental regulations and improve their public image. The study concludes that the combination of LGMS and LGWRT positively impacts environmental performance and competitive advantage through improved profitability and process efficiency. These findings encourage more companies in Indonesia to adopt lean and green approaches for achieving optimal business sustainability.

Supply Chain Management Analysis Using the Supply Chain Operations Reference (SCOR) Method in Vehicle Accessories Manufacturing Companies Wildan Bagus Purnomo; Roma Wibero
Greenation International Journal of Engineering Science Vol. 3 No. 3 (2025): (GIJES) Greenation International Journal of Engineering Science (September - No
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v3i3.554

Vehicle accessories manufacturing company is company that produces various kinds of vehicle accessories products. The company does not have a complete supply chain measurement system. This results in not covering all the company's problems so that it is not known where things need improvement. Therefore, there needs to be a method that can analyze the overall performance of the company using the Supply Chain Operations Reference (SCOR) method. The measurement is carried out with several stages, namely identifying the SCOR matrix, verifying the Key Performance Indicator (KPI), weighting using the Analytical Hierarchy Process (AHP) method and calculating Snorm de boer normalization. Based on the research results, the supply chain performance value obtained is 78.48 with the good category. There are several performance indicators that are prioritized for improvement, namely. The accuracy of planning raw material requirements, the accuracy of raw material fulfillment, handling machine and production equipment damage, product quality after the delivery process and the number of complaints from consumers. By making improvements to these indicators, it is hoped that it can help in improving the supply chain performance of the company.

Behavioural Segmentation and Loyalty Determinants in Automotive Services: A Data-Driven Analysis Using k-Means and XGBoost Godspower Onyekachukwu Ekwueme; Harold Chukwuemeka Godwin; Chukwu Callistus Nkemjika; Ifeyinwa Faith Ogbodo
Greenation International Journal of Engineering Science Vol. 3 No. 3 (2025): (GIJES) Greenation International Journal of Engineering Science (September - No
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v3i3.555

Customer loyalty in Nigeria’s automotive service sector has become increasingly unstable due to digital competition, pricing inconsistencies, and evolving satisfaction dynamics. Traditional models often overlook the nonlinear relationships shaping loyalty behavior. Most prior research uses linear or descriptive approaches, limiting predictive accuracy and failing to capture behavioral heterogeneity in satisfaction–cost interactions. This constrains proactive customer retention strategies. The study aims to segment customer behavior and predict loyalty determinants using machine learning algorithms to enhance decision-making in automotive service management. Secondary data were obtained from the records department of Anaval Mechanic Workshop, Awka, spanning January to December 2023. The study employed k-Means clustering for behavioral segmentation and machine learning models, Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost), for loyalty prediction. The XGBoost model achieved the highest predictive accuracy (97.1%) and AUC (0.985). Customer satisfaction, total cost, and non-mechanic service expenses emerged as the strongest loyalty determinants. Machine learning effectively captured nonlinear satisfaction-cost-dynamics, outperforming traditional models. Integrating predictive analytics and cost-transparency frameworks can strengthen retention policies and inform fair-pricing regulations across Nigeria’s automotive service industry.

Screening and Ranking of Critical Process Parameters Influencing the Mechanical Properties of Plantain-Bamboo Reinforced High-Density Polyethylene Composites Obiora Jeremiah Obiafudo; Joseph Achebo; Kessington Obahiagbon; Frank. O. Uwoghiren; Callistus Nkemjika Chukwu
Greenation International Journal of Engineering Science Vol. 3 No. 3 (2025): (GIJES) Greenation International Journal of Engineering Science (September - No
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v3i3.629

Composites made of natural fibers reinforced polymer are constantly under consideration as the sustainable alternatives to synthetic reinforcements, but the fact that these materials are incredibly vulnerable to the processing conditions makes the problem of achieving a consistent mechanical performance one of the primary issues. Changes in fiber content, blending ratios and molding parameters tend to cause uncertainty in the strength and elasticity, making optimization challenging. This paper solves this problem by filtering and prioritizing the significant process parameters that affect the mechanical property of the plantain-bamboo reinforced high-density polyethylene (HDPE) composite. The experiment involved chemically modified plantain and bamboo fibers, which were processed by using NaOH, acetic acid, bleaching agents, and compatibilizers, and HDPE as a matrix. Fibers were washed, milled to (75 ?m), and mixed with HDPE in given volume ratios. The five most important process variables were considered, including the volume fraction of fibers, the ratio of bamboo fibers to other fiber types, the temperature at which it is molded, the clamping pressure, and the cure time. The compression molding was done under controlled conditions to generate composite samples. The response to each variable was analyzed in terms of influence on the yield stress, yield strain, and elasticity with the best results of fiber content and curing time. Five important variables were filtered; fiber volume fraction (VF), bamboo fiber ratio (BFR), molding temperature (T), clamping force (F), and curing time (t). The yield stress and strain decreased and became low at low fiber content, decreasing between (88 MPa) and (2.4) at (10 percent) VF and (26 MPa) and (0.5) at (50 percent). Pure plantain fiber was stronger, but the small bamboo fractions (03) enhanced elasticity. Increasing temperatures and forces at clamped end beyond (2400 N) minimized stress and strain. The best holding time was around (1823) minutes. In sum, reinforcement was best enhanced by low VF and sufficient curing period. The research provides a definite order of process importance and provides the basis on which the process could be optimized further and composite design could be enhanced.

Optimization of Injection Mold Settings and their Impact on the Final Plastic Product (Cup) Quality Thermal Performance Ogochukwu Chinedum Chukwunedum; Chike Pius Nwachukwu; Chukwuemeka Obumneme Umobi
Greenation International Journal of Engineering Science Vol. 3 No. 3 (2025): (GIJES) Greenation International Journal of Engineering Science (September - No
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v3i3.678

Injection molding requires careful optimization of production parameters to achieve high quality plastic products. This research investigates optimizing injection mold settings, namely: mold temperature, injection speed, injection pressure, and cooling time as input parameters and their impact on the final plastic cup quality of thermal performance as the output parameter. Thermal performance refers to the cup's ability to maintain the temperature of hot or cold liquids inside. Thermal performance can be a significant indicator of a plastic cup's quality, particularly influencing its ability to insulate and retain desired temperatures. The aim of this study is to develop an optimal mold setting combinations to improve key cup quality metrics, namely: tensile strength, thermal performance and weight. This study utilizes response surface methodology design of experiments (DOE) with multiple iterative test molding trials varying the selected parameters to achieve the optimized product quality. The molded cup samples are quantitatively evaluated for the target quality attributes. Analysis of variance statistical analysis determined the optimized parameter settings that maximize quality metrics. The results provide guidance for plastic cup manufacturers to optimize their molding operations. This research demonstrates that a systematic DOE approach allows molders to economically determine the ideal production settings for their specific molds and materials to improve quality control. The methods could be applied to other injection molded plastic products to reduce defects.

Application of Statistical Process Control (SPC) In Quality Control of Hard Disk Drive Products At PT Ennovi Integrated Engineering Services Batam Indriani Ayu Lestari; Hery Irwan
Greenation International Journal of Engineering Science Vol. 4 No. 3 (2026): (GIJES) Greenation International Journal of Engineering Science (September - No
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v4i3.1319

Quality control is an essential aspect of manufacturing operations to ensure that products consistently meet predetermined specifications and quality standards. In the hard disk drive manufacturing industry, maintaining process consistency is particularly important because production processes involve high levels of precision and strict quality requirements. This study aims to apply Statistical Process Control (SPC) to evaluate and control the quality of hard disk drive products at PT Ennovi Integrated Engineering Services Batam and to identify the factors contributing to product defects.This study employs a quantitative descriptive approach using Statistical Process Control integrated with selected tools from the Seven Quality Control Tools. The analytical tools consist of Check Sheet, Pareto Chart, Control Chart, and Fishbone Diagram, complemented by Process Capability Index (Cpk) analysis. The Check Sheet is used to systematically record and classify product defects. The Pareto Chart is then applied to determine the dominant defect types based on their frequency of occurrence. The Control Chart is used to evaluate whether the production process remains statistically stable over the observation period. The Fishbone Diagram is applied to identify potential root causes of the dominant defects based on factors related to Man, Machine, Method, Material, Measurement, and Environment. Furthermore, Cpk analysis is used to evaluate the capability of the production process to meet predetermined specification limits. The application of these analytical tools is expected to provide a comprehensive evaluation of product quality, process stability, and process capability. The findings are expected to support the identification of priority quality problems and provide an objective basis for developing corrective and preventive actions to reduce defects and improve the consistency of the hard disk drive production process.

Analysis of Defective Jeans Products at RWC Shop Using a U-Control Chart and FMEA Endang Elia; Intan Pratiwi
Greenation International Journal of Engineering Science Vol. 4 No. 3 (2026): (GIJES) Greenation International Journal of Engineering Science (September - No
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v4i3.1324

RWC Shop is a small and medium-sized enterprise producing jeans in Pekalongan Regency that continues to encounter product defects that are not detected during the early stages of production. Although the defect rate is relatively low, undetected defects can affect product quality, production efficiency, and business sustainability. This study addresses the limited application of integrated quality control using a u-control chart and Failure Mode and Effects Analysis to identify process control conditions and priority causes of defects. The study aims to evaluate defective product control and determine priority improvement actions. Production and defect data from November 2024 to November 2025 were analyzed using a u-control chart, while interview and assessment data were analyzed using a fishbone diagram and Failure Mode and Effects Analysis. The results show that defect proportions remained within the established control limits. Three major defects were identified: striped products, damaged zippers, and holes. The highest Risk Priority Number was 48, associated with low-quality materials and sewing operator negligence. Recommended improvements include raw-material inspection, supplier evaluation, improved work scheduling, additional workforce, and standardized operating procedures. The novelty lies in integrating statistical process control with systematic failure-risk prioritization for quality improvement in a small-scale jeans manufacturing enterprise.

Analysis of the Impact of Downtime Disruptions on SIMRS on the Operational Process of Outpatient Registration Services at AMC Hospital Cileunyi Nabilah Tsani Nurisyafa'ah; Diansyah
Greenation International Journal of Engineering Science Vol. 4 No. 3 (2026): (GIJES) Greenation International Journal of Engineering Science (September - No
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v4i3.1351

Downtime refers to the period during which a system's core services are unavailable or inaccessible for a certain period of time due to unexpected disruptions; specifically, the outpatient registration department at AMC Hospital still frequently experiences downtime issues. This study aims to determine the impact of downtime on operational processes in the outpatient registration department. The method used was a descriptive study with a qualitative approach, involving observations and interviews with 10 informants selected through purposive sampling based on inclusion and exclusion criteria. The results showed that the most frequent disruptions occurred between December 2025 and May 2026, with prolonged durations caused by server outages, slow BPJS bridging, and internet connectivity issues. These disruptions forced staff to switch to manual record-keeping, which led to long queues and increased the average registration time from approximately 3 minutes (under normal conditions) to approximately 35 minutes following the downtime. SIMRS downtime significantly reduced the efficiency of outpatient registration services. The hospital is advised to improve its IT infrastructure, increase the number of IT staff, and develop more structured standard operating procedures (SOP) for handling downtime.

Improving Machine Effectiveness Using the Overall Equipment Effectiveness (OEE) Method and Lean Manufacturing in the Automotive Components Industry Roma Wibero; Hernadewita
Greenation International Journal of Engineering Science Vol. 4 No. 3 (2026): (GIJES) Greenation International Journal of Engineering Science (September - No
Publisher : Greenation Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/gijes.v4i3.1398

Low machine effectiveness can reduce production efficiency through equipment failures, lengthy setup activities, speed losses, and product defects. Previous studies have generally examined machine effectiveness and lean manufacturing separately, leaving limited discussion of their integrated use to identify operational losses and formulate measurable process improvements. This study aims to evaluate machine effectiveness and identify production waste in an automotive manufacturing process to develop improvement strategies. A descriptive quantitative approach was employed using production, operating time, downtime, and defect data collected from January to December 2025. Machine effectiveness was evaluated based on availability, performance, and quality, while lean manufacturing analysis used value stream mapping, process activity mapping, seven wastes, and six major loss categories, supported by Pareto and cause-and-effect analyses. The results showed average availability of 67.39%, performance of 74.16%, quality of 73.42%, and overall effectiveness of 36.56%. Equipment failure was the largest loss at 38.26%, followed by setup and adjustment at 21.39%. The proposed improvements include preventive maintenance, autonomous maintenance, setup time reduction, standardized procedures, operator training, and improved material flow. The novelty lies in integrating machine effectiveness measurement with lean waste analysis to develop a future production state with projected effectiveness of 92.28%.

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Home Page

OAI Link

Editorial Team

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Reviewer

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Contact Name M. Rizky Mahaputra

Contact Email greenation.info@gmail.com

Phone +6281210467572

Journal Mail Official greenation.info@gmail.com

Editorial Address Jl. Kapten. A. Hasan, Telanaipura, Kota Jambi, Jambi 36361, Indonesia

Location Kota jambi, Jambi INDONESIA

Abstract

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Contact Name
M. Rizky Mahaputra

Contact Email
greenation.info@gmail.com

Phone
+6281210467572

Journal Mail Official
greenation.info@gmail.com

Editorial Address
Jl. Kapten. A. Hasan, Telanaipura, Kota Jambi, Jambi 36361, Indonesia

Location
Kota jambi,

Jambi

INDONESIA