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Dwi Sulisworo
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Journal of Novel Engineering Science and Technology
Published by Indonesian Institute of Science and Technology Research
ISSN : 29618916     EISSN : 29618738     DOI : https://doi.org/10.56741/jnest.v1i02
Core Subject : Science, Social, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Environmental Science Mechanical Engineering
Journal of Novel Engineering Science and Technology is a multi-disciplinary international open-access journal dedicated to natural science, technology, and engineering, as well as its derived applications in various fields. JNEST publishes high-quality original research articles and reviews in all of the disciplines mentioned above. All papers submitted will go through a rapid peer-review process to ensure their quality. Submissions must contain original research and contributions to their field. The manuscript must adhere to the author’s guidelines and have never been published before. All accepted manuscripts will be indexed in DOAJ, EBSCO, and Google Scholar. The indexation in SINTA, Scopus, and WoS will be provided in the future to provide maximum exposure to the articles.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 82 Documents
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Seismic Performance of FRP-Retrofitted RC Building Using Pushover Analysis Nabil Mochammad Yusuf; Ari Wibowo; Retno Anggraini
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1452

Abstract

The seismic evaluation of existing bank office buildings is critical for ensuring post-earthquake operational continuity. This study investigates a 14-story RC bank building in Kendari using the Nonlinear Static Procedure (NSP) per ASCE 41-17, targeting an Immediate Occupancy (IO) performance level. The initial analysis revealed a critical contradiction: while the structure's global performance appeared to meet the IO target, this assessment was found to be misleading. A detailed, element-based analysis identified a concealed local failure where a critical beam reached the Life Safety (LS) performance level, caused by a non-trivial Positive Moment at the support. A sequential Fiber Reinforced Polymer (FRP) retrofitting strategy was then implemented. The primary contribution of this study is the demonstration of the Failure Migration phenomenon. It was shown that a naive, 'single-point' retrofit (on LS-1) did not solve the problem but merely shifted the failure mode to the next weakest element (LS-2). This sequential retrofitting procedure proved necessary to track the migrating failure, which moved non-linearly between various floors, until all migrating vulnerabilities were eliminated. This finding proves that a sequential procedure is necessary to address Force Redistribution and achieve a true IO performance. The final minor global base shear increase (0.16%) was only a secondary benefit, confirming the objective was local vulnerability elimination, not a significant increase in global stiffness.
A Low-Cost Wearable System to Detect Fall and Non-Fall Activities for Elderly Individuals Muhammad Adib Syamlan; Ahmad Arifin; Josaphat Pramudijanto; Fauzan Arrofiqi; Muhammad Ariq Syamlan; Raihan Aria Muhamad Noor; Alif Syihabudin Fawwaz Suhartono
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i01.1534

Abstract

As the elderly population grows, the prevalence of age-related health conditions such as cardiovascular diseases, cognitive decline, and mobility impairment is also increased. Among these health conditions, falls are considered one of the greatest threats to elderly individuals. A low-cost wearable fall detection system is designed, with the purpose of monitoring and detecting their activities. Three master modules were constructed, with each consisting of an inertial sensor, a microcontroller, and a power supply circuit block. The data were collected using IMU MPU6050 and preprocessed using the MCU ESP32. Each master module is also supplied using a 3.7V 1S LiPo battery. 18 healthy subjects, consisting of 13 males and 5 females, agreed to volunteer for the experiments. They were instructed to do 8 different activities, including non-fall (stand still, sit-to-stand, walk, and sleep position) and fall events (forward fall, sideways fall, and backward fall). Overall, the system showed a good performance using the Multilayer Perceptron (MLP) algorithm with an accuracy of 95.3% across all activities. While misclassification happens between classes, our system is still able to distinguish between non-fall vs. fall events with 100% accuracy. Cost analysis was also conducted; the overall cost for the three master modules in our proposed system is $65.4. This is cheaper than commercial fall detection systems and other related research, and our proposed system can also be used continuously. The system will alert caregivers to the immediate attention of elderly individuals.
Optimization of hybrid-based Collaborative Filtering using Matrix Factorization, Feedforward Neural Network, and XGBoost Filimantaptius Gulo; Ronsen Purba; Muhammad Fermi Pasha
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.1356

Abstract

Collaborative filtering recommendation systems are widely used in digital applications; however, they still face challenges such as cold-start and first-rater problems, as well as limited accuracy due to their inability to capture complex user–item relationships. This study proposes a hybrid recommendation model that integrates Matrix Factorization, MLP-based Feedforward Neural Network (MLP) and Extreme Gradient Boosting (XGBoost). Experiments were conducted on two real-world datasets, namely MovieLens (movies) and PT XYZ (hotels), to validate the effectiveness of the proposed approach. The results indicate that the hybrid model consistently outperforms baseline methods such as SGD-based Matrix factorization, Matrix factorization +MLP, and user/item-based Collaborative filtering. Specifically, the integration of nonlinear learning through MLP and feature enhancement via XGBoost significantly improves prediction accuracy while mitigating cold-start and first-rater issues. These findings suggest that hybrid machine learning–based approaches can advance the development of more adaptive, accurate, and personalized recommendation systems.
Evaluating Friction Dampers for Seismic Protection of Non-structural Elements in Hospitals Yusril Abdurrahman; Ari Wibowo; Achfas Zacoeb
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.1178

Abstract

This study evaluates the effectiveness of friction dampers in improving the seismic performance of non-structural components in hospital buildings. A five-story reinforced concrete hospital, intentionally modeled to exceed allowable drift limits, was analyzed using nonlinear time history analysis under three earthquake scenarios: BSE-1E (225-year), BSE-2E (975-year), and BSE-2N (2475-year), assuming soft soil conditions. Non-structural components were classified as drift- or acceleration-sensitive, with damage probabilities assessed using fragility curves and categorized into risk classes. Results show that friction dampers significantly reduced damage probability for acceleration-sensitive components up to 74% for cabinet contents under BSE-1E. However, drift-sensitive elements remained vulnerable, particularly in higher-intensity events, due to the building's flexible design and limited damper activation. While friction dampers improved global structural performance, their effectiveness declined with increasing seismic demand. These findings underscore the potential and limitations of friction dampers in retrofitting hospital buildings and highlight the need for careful damper sizing and consideration of alternative strategies to protect non-structural systems.
Data-Driven Marketing Management Competencies: Strategies for Performance Optimization in the Big Data Era Safitri Nurhidayati; Tamam Rosid; Rahmawati
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.1467

Abstract

The proliferation of big data technology has fundamentally changed the paradigm of marketing management, requiring a new competency framework for optimal performance outcomes. Despite extensive technological advances, significant gaps remain in understanding how data analytic competencies translate into measurable improvements in marketing performance. This study investigates the relationship between data-analytic-based marketing management competencies and performance optimization strategies in contemporary business environments, with particular emphasis on identifying critical competency dimensions and their impact on organizational marketing effectiveness. A mixed-methods approach was used, combining quantitative analysis of 847 marketing professionals from 156 Indonesian companies with qualitative interviews of senior marketing executives. Data collection employed validated instruments measuring analytic competencies, technology adoption, and performance metrics in Q2–Q4 2024. Findings revealed four critical competency dimensions: technical analytic proficiency (β=0.43, p<0.001), strategic data interpretation (β=0.38, p<0.001), cross-functional collaboration (β=0.32, p<0.01), and ethical data governance (β=0.28, p<0.01). Organizations with high analytic competency scores reported 34% higher marketing ROI and 28% greater efficiency in customer acquisition compared to low-competency counterparts. Data analytic competencies significantly influence marketing performance outcomes, with technical proficiency and strategic interpretation serving as primary drivers. This study provides empirical evidence supporting the adoption of competency-based frameworks in marketing management practice.
Optimizing Pelton Turbine Efficiency through Variable Flow Rates: An Experimental Study Rahmad Hidayat Boli; Rifaldo Pido; Wawan Rauf
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.1791

Abstract

This study experimentally investigates the efficiency characteristics of a laboratory-scale Pelton turbine under variable water flow rates and mechanical loading conditions. Five flow rates (0.04 m³/s, 0.035 m³/s, 0.029 m³/s, 0.024 m³/s, and 0.021 m³/s) were tested by measuring rotational speed, torque, shaft power, and turbine efficiency. The results reveal a nonlinear relationship between load and efficiency for all tested flow rates, with maximum efficiency occurring at intermediate loading conditions. The highest efficiency of 52.727% was obtained at a flow rate of 0.04 m³/s and a load of 12 kg. Were associated with reduced efficiency, which is commonly linked to decreased jet momentum. These findings provide experimental insight into Pelton turbine performance under non-design operating conditions at laboratory scale.
Cosmetic Packaging Quality Analysis Using the Six Sigma and House of Quality Methods at PT XYZ Roland Y.H. Silitonga; Marla Setiawati; Christabel Jovanka
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.1197

Abstract

Product quality is a critical factor in the success of manufacturing industries, particularly in the cosmetic packaging sector, where packaging serves both protective and marketing functions. PT XYZ experienced a defect rate of 4.09% in cosmetic packaging products, resulting in increased production costs and reduced customer satisfaction. This study aims to reduce product defects by applying the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology integrated with Failure Mode and Effects Analysis (FMEA) and the House of Quality (HoQ). DMAIC was employed to identify defect sources, measure process performance, analyze root causes, implement improvements, and establish process control. FMEA was used to evaluate potential failure modes and prioritize corrective actions based on Risk Priority Numbers, while HoQ translated improvement priorities into practical technical actions. Data were collected through direct observation and interviews with relevant personnel at PT XYZ. The implementation of the proposed improvements reduced overall defects by 2,321.43 defects per million opportunities (DPMO), equivalent to 2.32%, and increased the process sigma level by 0.21. Specifically, the “Dirty Glass/Grepes” defect decreased by 11,616.07 DPMO (1.16%) with a sigma improvement of 0.58, whereas the “Scratch” defect declined by 5,660.71 DPMO (0.57%) with a sigma improvement of 0.25. Root cause analysis revealed that Dirty Glass/Grepes defects were primarily associated with human and method factors, while Scratch defects were related to human, equipment, and method factors. Seven improvement alternatives were identified, with two successfully implemented: adding a blower stage during final assembly and inspecting the cover pull-force testing instrument.
Open Banking API and Employee Competency: Driving Remittance Performance and Customer Growth in Islamic Banking Arina Al-Haque; Andreas Hadiyono
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.2015

Abstract

This paper analyzes the impact of employee competency and business performance on the effectiveness of remittance services, with Open Banking API implementation acting as a supporting technological enabler. The study focuses on an Islamic bank undergoing digital transformation. A quantitative approach was employed using questionnaire data from 40 remittance employees, analyzed through regression methods, and complemented by a before-and-after system performance evaluation based on operational data from 2022 to 2024. The measurement instruments demonstrated excellent reliability (Cronbach’s α > 0.96) and validity (p < 0.05). The statistical results indicate that employee competency has a significant positive effect on remittance business performance.  Concurrently, the implementation of the Open Banking API significantly reduced average transaction processing time by 80% (from 10 seconds to 2 seconds), which directly contributed to a 52.9% growth in active partners. Grounded in the Resource Based View (RBV) theory, these findings highlight that orchestrating complex API integrations requires parallel human resource readiness to maximize organizational performance and active partner growth.
Integrated Prediction Model for Normal and Recycled Aggregate Concrete Strength Using Ensemble Learning Techniques Sujiat; Eko wahyu Abryandoko; Ocha Silvia Kencana; Nayla Farikha Zahra
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.2092

Abstract

Recycled aggregate concrete (RAC) is a sustainable alternative construction material to reduce natural resource exploitation and manage construction and demolition waste. However, predicting the mechanical performance of RAC remains a challenge due to the high variability of recycled aggregate properties. The purpose of this study is to develop a machine learning model to predict the compressive strength of recycled aggregate-based concrete and compare its performance with normal concrete. The dataset used consists of 2165 samples (1600 normal concrete and 565 recycled aggregate concrete) collected from various scientific publications. Three tree-based machine learning algorithms (Random Forest, XGBoost, and LightGBM) were implemented and optimized using RandomizedSearchCV with 5-fold cross-validation. The results showed that LightGBM provided the best performance with R² = 0.92, MAE = 2.45 MPa, and RMSE = 3.52 MPa on the test set. This model is able to predict the compressive strength of normal concrete (R² = 0.92) and recycled aggregate concrete (R² = 0.91) with almost the same accuracy, indicating strong generalization. Feature importance analysis revealed that curing age, cement content, and water content are the most important factors in compressive strength prediction, while for RAC, recycled aggregate water absorption (WRCA) also makes a significant contribution. Error analysis shows that residuals are random and normally distributed without systematic bias. This model can reliably predict concrete compressive strength in the range of 20-60 MPa with an average error of ±3-4 MPa and can be integrated into mix proportioning design software to improve the efficiency of the design process and support the use of sustainable construction materials.
From the Laboratory to the Market: A Review of the Research Commercialisation Ecosystem in Nigeria Oluremi Nurudeen Olaleye; Abdulrahman Babajide Ogunji; Ahmed Adebowale Adedeji; Mutiat Adetayo Omotayo; Johnson Oshiobugie Momoh
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.2401

Abstract

Nigeria’s 200-plus universities generate substantial research across agriculture, healthcare, and engineering, yet output rarely translates into commercial products. This policy and literature review synthesises evidence from 47 government documents, peer-reviewed articles, and international reports to diagnose the research commercialisation landscape through National Innovation Systems and Triple Helix lenses. Thematic analysis reveals chronic underfunding (0.22% of GDP on R&D), weak university–industry linkages, absence of professional Technology Transfer Offices, and regulatory ambiguity as systemic barriers. Comparative analysis of South Korea, Finland, Malaysia, Rwanda, and Kenya identifies transferable institutional mechanisms. The review proposes a hybrid model combining strategic state direction in priority sectors with bottom-up entrepreneurial support, offering Nigeria-specific implementation pathways including phased R&D investment, sector-focused intermediary agencies, and diaspora engagement mechanisms. Limitations and critiques of commercialisation-first approaches are discussed. Strategic recommendations target policymakers, university administrators, and development partners.

Page 8 of 9 | Total Record : 82

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