Journal La Multiapp

Selection of the Best Lean Construction Techniques for the Execution Stage

Satriyo Agus Adi Prastyo, I Putu Artama Wiguna — Wed, 04 Feb 2026 00:00:00 +0700

This study aims to identify and prioritize critical success factors and lean construction techniques that influence lean implementation during the execution stage of construction projects in Indonesia. A quantitative research approach was employed using expert judgment collected through a structured questionnaire. The Relative Importance Index was applied to rank critical success factors and assess the importance of selected lean construction techniques, while the Analytic Hierarchy Process was used to determine priority weights through pairwise comparisons. The results indicate that top management, financial management, task execution based on scope of work, and improvement tools are the most influential factors supporting successful lean execution. The analysis also shows that Just In Time, Last Planner System, Daily Huddle Meeting, and Building Information Modeling are all considered very important techniques during the execution stage. Further prioritization reveals that Daily Huddle Meeting has the highest priority, followed by Just In Time, while Building Information Modeling and Last Planner System function as supporting techniques. The findings highlight that effective lean execution is driven primarily by managerial commitment and daily coordination practices rather than technical tools alone. This study provides practical guidance for construction practitioners in selecting appropriate lean strategies to improve execution performance and reduce waste on site.

Adoption of Auto-replenishment as Digital Transformation Solution For Supply Chain Operation In Gas Station Using TAM-TOE Framework

Ferdian Adhika Kurniawan, Jonathan Nahum Marpaung — Wed, 04 Feb 2026 00:00:00 +0700

Indonesia’s downstream oil and gas sector faces persistent supply chain challenges due to manual processes and high demand variability. This study aims to investigate the determinants influencing the behavioral intention of Indonesian Gas Station (SPBU) operators and supervisors to adopt the Auto-Replenishment system. Data were collected from 419 valid respondents of gas station operators and supervisors who had completed the Digitalisasi SPBU e-learning. The model comprises nine latent constructs from Technology Organization Environment and Technology Acceptance Model Framework. Data were analyzed using Partial Least Squares–Structural Equation Modelling (PLS-SEM), and reveals that Top Management Support and Organizational Readiness are the strongest drivers, significantly enhancing both Perceived Usefulness and Perceived Ease of Use. Environmental factors, specifically Regulatory Support and Vendor Support, significantly impact Perceived Ease of Use. Technological Readiness influenced Perceived Usefulness but failed to significantly affect Perceived Ease of Use. In line with TAM, both Perceived Usefulness and Perceived Ease of Use have significant positive effects on Behavioral Intention, and mediation analysis confirms that these TAM constructs transmit a substantial portion of TOE effects to adoption intention.

An Analysis of the Effectiveness of the SI-PAL Application in Hospital Quota Management within the Medical Professional Study Program Using the PIECES Method

Edy Hidayat Arsan, Aries Muslim, Amri Sinuraya — Thu, 05 Feb 2026 00:00:00 +0700

This study aims to analyze the effectiveness of the SI-PAL Application in hospital quota management in the Medical Profession Study Program, with a focus on optimizing coordination between the Study Program and Komkordik and equalizing the distribution of student clinical rotations. The research method used a quantitative approach with an evaluative model based on the PIECES Framework, which includes the dimensions of Performance, Information, Economy, Control, Efficiency, and Service. The relationship between variables was tested using the Partial Least Square-Structural Equation Modeling (PLS-SEM) method, involving a sample of 202 respondents consisting of program directors, administrators, and students. The analysis results show that the SI-PAL application has a significant effect on the effectiveness of quota management, with a path coefficient value of 0.982 and a coefficient of determination ($R^2$) of 0.964. This finding indicates that SI-PAL is capable of improving response speed, data exchange reliability, and proportionality of rotation distribution, particularly through automatic plotting mechanisms and concurrency techniques. Evaluation across the six PIECES dimensions also demonstrated the system's superior performance, particularly in terms of information quality, operational efficiency, and user satisfaction.Overall, this study confirms that SI-PAL is an effective digital instrument in supporting clinical rotation management and improving the quality of institutional coordination. However, approximately 3.6% of the variability in quota management performance is influenced by external factors such as network stability, user readiness, and resistance to digital change, thus requiring mitigative strategies to improve overall performance.

Identification of Critical Factors Influencing Lean Construction Performance in Construction Projects

Devita Candraningtyas, I Putu Artama Wiguna — Thu, 05 Feb 2026 00:00:00 +0700

The Indonesian construction sector continues to face persistent problems related to inefficiency, material waste, schedule delays, and cost overruns, despite increasing interest in adopting Lean Construction principles. These challenges indicate that lean implementation has not been fully supported by well-defined organizational and managerial foundations. This study aims to identify and validate the indicators that form the Critical Success Factors influencing Lean Construction Performance in high-rise building projects in Indonesia. This research uses quantitative methods. The research population consists of construction professionals involved in high-rise building projects, including contractors, consultants, and project management teams. A total of 360 questionnaires were distributed, of which 155 responses were returned. After data screening, 125 valid responses were used for further analysis. Data analysis was conducted using Structural Equation Modeling with the Partial Least Squares method through SmartPLS 3.0, focusing on the evaluation of the measurement model. The results indicate that the proposed constructs are formed by valid and reliable indicators after eliminating indicators with outer loadings below 0.70. The validated indicators represent management commitment and involvement, human resource development, communication and collaboration, internal client focus, and planning techniques and continuous improvement. These findings provide empirical evidence on the key indicator structure of Critical Success Factors relevant to Lean Construction Performance.

Systematic Review of Hyperparameter Adjustment and Evaluation Metrics in Bert-Based Sentiment Analysis

Aris Rifki Setiya Bahari, Fandy Setyo Utomo, Berlilana Berlilana — Thu, 05 Feb 2026 00:00:00 +0700

The development of sentiment analysis towards Aspect-Based Sentiment Analysis (ABSA) has made significant progress thanks to deep learning technology, especially through the Bidirectional Encoder Representations from Transformers (BERT) architecture. Despite its increasing popularity, a comprehensive synthesis of global research patterns and optimal model configurations is still urgently needed. This study presents a Systematic Literature Review (SLR) combined with bibliometric analysis to examine BERT-based ABSA research indexed in Scopus. Using the PRISMA and VOSviewer frameworks for visualization, a total of 62 eligible articles up to mid-2025 were analyzed. The results of the study show a strong upward trend of publications with a peak in 2024, where China, India, and Indonesia emerged as the major contributors in this domain. Further, the review identified a critical technical standard for effective model training: the Adam optimizer was the most dominant choice, typically paired with a learning rate between 1e-5 to 2e-5 and a batch size of 16. Regarding performance evaluation, Accuracy and F1-Score are set as de facto standard metrics. These findings provide strategic guidance for researchers to optimize BERT implementation and identify future directions in more in-depth sentiment analysis tasks.

Performance of Removal Efficiency in the Main Wastewater Treatment Plant for Physical, Chemical (Fe, Zn, Bod), and Biological Parameters at the Steam Power Plant (Pltu) Paiton Units 3, 7 & 8

Totok Widianto, Zainal Arifin, Erwan Yulianto — Thu, 05 Feb 2026 00:00:00 +0700

Wastewater treatment in Wastewater Treatment Plant (WWTP) plays a vital role in protecting environmental quality. This study aims to evaluate the treatment efficiency of various pollutant parameters such as Total Suspended Solids (TSS), Iron (Fe), Zinc (Zn), Biochemical Oxygen Demand (BOD), and Total Coliform, based on data collected during the period 2023-2024. The results of the analysis showed fluctuations in pollutant levels in the influent and effluent of WWTP, with TSS removal efficiency reaching 95.83% in August 2023, and Total Coliform showing the highest efficiency of 99.88% in the same month. Although most parameters showed significant reductions, there were months where the removal efficiency decreased, even showing negative values. These findings indicate anomalies in the treatment process that need to be addressed, especially in certain parameters. The analysis of variance (ANOVA) conducted showed no significant difference between pollutant levels, indicating consistency in the treatment results carried out by the WWTP. The importance of regular monitoring and evaluation of the wastewater treatment system to ensure that the wastewater from the WWTP process that is discharged does not pollute the environment. Efforts to improve treatment technology and implement better waste management practices are needed to achieve environmental sustainability effectively.

Leaf Type Recognition System Using Image Processing Method Using Convolutional Neural Network Algorithm

Evan Kolauw, Alfiansyah Hasibuan, Sondy C Kumajas — Thu, 05 Feb 2026 00:00:00 +0700

A digital image-based leaf recognition system is one of the modern solutions in the fields of botany and agriculture to identify plants automatically. This study developed a leaf recognition system using image processing methods and Convolutional Neural Network (CNN) algorithms. CNN was chosen because of its ability to independently extract features through convolution layers, thus capturing important visual patterns such as shape, edges, textures, and leaf veins without requiring manual feature engineering processes. The research dataset consists of a collection of leaf images from several types of plants obtained through direct photo-taking and public dataset sources. Each image goes through a pre-processing stage, including cropping, resizing, image quality enhancement, and pixel normalization to ensure data consistency before entering the training stage. The CNN model is designed with several convolutional layers, pooling, activation functions, and fully connected layers to produce optimal classification performance. Model training is carried out by dividing training and testing data, as well as augmentation techniques to increase image variation. System performance is evaluated using accuracy, precision, recall, and confusion matrix. The test results show that the CNN model is able to recognize leaf types with a high level of accuracy and is stable under various test conditions, including variations in lighting and shooting angles. Overall, this study proves that CNN is an effective and reliable approach in building an automatic leaf recognition system. This system has the potential to be applied in the fields of precision agriculture, mobile application-based plant identification, and botanical research that require speed and accuracy in plant classification.

Multivariate Water Stress Index Model for Predicting Water Vulnerability

Thu, 05 Feb 2026 00:00:00 +0700

Sabu Raijua Regency is a semi-arid island region with limited water resources and increasing water demand pressure, thus potentially experiencing high water vulnerability. This study aims to develop a multivariate Water Stress Index (WSI) model to analyze the dominant factors causing water stress and predict the level of water vulnerability in the medium to long term on Sabu Island. The study uses a predictive quantitative approach by combining multivariate regression analysis and spatial analysis based on Geographic Information Systems (GIS). The WSI value for the base year 2023 and the projection for 2033 were calculated using the Water to Availability (WTA) method which has been validated through the reliable discharge from the FJ Mock simulation results and field data. Eight independent variables were analyzed, including population density, water demand, dry spell and wet spell conditions, water availability, and clean water service coverage. The analysis results show that water demand (X3) is the most dominant factor in increasing the WSI value, while water availability (X7) plays a significant role in reducing water stress. Seasonal climate variables and clean water service coverage show relatively weak statistical effects. Spatial analysis identified West Sabu District as the area with the highest and persistent water stress, while Central Sabu and Sabu Liae Districts were at medium vulnerability levels, and Hawu Mehara and East Sabu were relatively lower.

Assessment and Prioritization of Contractor Internal Schedule Delay Risks in Phnom Penh High-Rise Building Projects Using Relative Importance Index Method

Res Haitin, I Putu Artama Wiguna — Thu, 05 Feb 2026 00:00:00 +0700

The rapid expansion of high-rise building projects in Phnom Penh has been accompanied by persistent schedule delays that significantly impact project performance. While external factors are often emphasized, contractor internal risks represent controllable factors requiring systematic assessment and prioritization. This study aims to identify and prioritize contractor internal delay risk factors in high-rise building projects in Phnom Penh through quantitative analysis of expert perceptions. A mixed-methods approach was employed, combining qualitative data collection through structured questionnaires with quantitative analysis using the Relative Importance Index (RII). Data were collected from 97 construction professionals involved in high-rise projects (exceeding 10 floors) in Phnom Penh during 2020–2025. Seventeen internal risk factors were assessed across probability (RII-P) and impact (RII-I) dimensions. Analysis identified three critical risks with Risk Scores ≥ 0.60: Poor Planning and Scheduling (RII-P: 0.81, RII-I: 0.83, Risk Score: 0.68), Cash Flow Problems (RII-P: 0.76, RII-I: 0.83, Risk Score: 0.64), and Ineffective Monitoring and Control (RII-P: 0.79, RII-I: 0.79, Risk Score: 0.62). Fourteen additional risks were classified as Medium Priority (Risk Scores: 0.46–0.56). Managerial and financial factors dominate internal delay risks in Phnom Penh's high-rise construction, suggesting contractors should prioritize planning systems, financial management, and monitoring controls to improve schedule performance. These findings have implications for similar developing construction markets, particularly in Indonesia.

Performance Evaluation of Drainage Systems in Coastal Areas Using Storm Water Management Model: Case Study

Tandya Afilda Milad, Mahendra Andiek Maulana, Yang Ratri Savitri — Thu, 05 Feb 2026 00:00:00 +0700

This study analyzes the performance of the urban drainage system in the Sringin Watershed, Semarang City, in response to rainfall runoff and evaluates the effectiveness of pump operation for flood mitigation. The assessment was carried out using a hydrologic and hydraulic modeling approach based on the Storm Water Management Model, supported by rainfall frequency analysis and spatial data processing. Design storms with ten‑year and twenty‑five‑year return periods were developed and applied to simulate system behavior under scenarios without pumps and with pump capacities of ten cubic meters per second and twenty‑five cubic meters per second. The results show that both return periods produce similar hydrologic responses, with peak inflow discharges ranging from fifty‑two to fifty‑five cubic meters per second and maximum storage volumes without pumping reaching forty‑six to forty‑seven percent of total capacity. The operation of a ten cubic meter per second pump effectively maintains water levels below the full‑storage elevation, while a twenty‑five cubic meter per second pump primarily accelerates drainage without significantly improving peak‑level control. These findings indicate that the ten‑year return period combined with a ten cubic meter per second pump provides the most efficient and practical design basis for current conditions, whereas the twenty‑five‑year return period is more suitable for evaluating extreme scenarios and long‑term resilience needs.

Geological Investigation and Treatment of the Cabean Dam Foundation

Muhammad Perwira Rachman, Sri Sangkawati, Ignatius Sriyana, Yudi Kurniawan — Fri, 06 Feb 2026 00:00:00 +0700

Embankment dams are critical infrastructure requiring comprehensive geological and geotechnical investigation to ensure long-term structural integrity and prevent catastrophic failure. This study presents the results of two-stage Multichannel Analysis of Surface Waves (MASW) investigations conducted at Cabean Dam core zone to characterize shear wave velocity (Vs) profiles and identify weak zones susceptible to seepage and slope instability. The first stage (2022) involved 17 survey lines while the second stage (2025) comprised 11 additional lines targeting critical areas. Shear wave velocity values ranged from 78–756 m/s, indicating significant material heterogeneity. Low Vs anomalies (78–200 m/s) were identified in the upper 0–7 meter depth at survey lines L-01, L-07, L-08, and L-09, correlating with residual soil having high permeability values up to 3.06×10⁻⁴ m/s and Lugeon values reaching 25.34 Lu. Integration of MASW data with borehole logs confirmed material classifications ranging from Site Class E (soft soil) at shallow depths to Site Class B (stiff soil) at greater depths. The identified weak zones pose significant risks for seepage, internal erosion (piping), and slope instability, requiring remedial measures including recompaction, optimized internal drainage design, and enhanced instrumentation monitoring. This research contributes methodological advancement in applying MASW as an integral component of staged investigation protocols for embankment dam projects, particularly for monitoring and refining geological-geotechnical conditions during construction phase. Results serve as a reference for practitioners and researchers in geotechnical dam engineering for improving safety and reliability of embankment dam infrastructure in Indonesia.

Causal Structure of Toll Road Tariff Determination in Public Private Partnership Projects: A System Dynamics Approach

Anggito Adji Wicaksono, Farida Rachmawati — Fri, 06 Feb 2026 00:00:00 +0700

The Solo Yogyakarta Yogyakarta International Airport Toll Road is a national strategic infrastructure project in Indonesia developed under a public private cooperation scheme using a build operate transfer arrangement. In this scheme, toll tariffs function as the primary mechanism for investment recovery, making tariff determination a critical factor influencing traffic demand and project feasibility. This study aims to develop a system dynamics based model to explain the structural relationships governing toll tariff determination in toll road projects implemented through public private cooperation. The research applies a quantitative modeling approach using system dynamics, following stages of problem formulation, development of a dynamic hypothesis, and construction of a causal loop diagram. Key variables are identified through literature review, expert judgment, and secondary data analysis, including toll tariffs, average daily traffic, user affordability, operational costs, and financial performance indicators. The analysis focuses on identifying cause effect relationships and feedback structures that influence traffic demand and revenue behavior. The results show that toll tariff determination is governed by interacting reinforcing and balancing feedback loops, with traffic volume emerging as a central variable linking tariff policies to revenue and financial performance. Higher tariff levels may increase unit revenue while simultaneously reducing traffic demand due to affordability constraints, whereas economic growth and service improvements support reinforcing demand effects. This study concludes that toll tariff determination should consider dynamic interactions among demand, affordability, and financial variables rather than relying solely on static cost-based approaches.

Mode Choice Analysis between Executive Rail and Sleeper Bus on the Surabaya – Jakarta Corridor Using Binary Logit Regression

Slamet Wahyudi, Hera Widyastuti — Fri, 06 Feb 2026 00:00:00 +0700

Surabaya and Jakarta are two major cities in Indonesia that serve as national centers of business, economic activity, and government administration, resulting in high intercity mobility between them. The operation of the Trans‑Java Toll Road has accelerated the growth of sleeper bus services, while executive rail remains a preferred mode due to its stable comfort and reliability. This study aims to identify passenger characteristics, analyze the probability of mode choice based on factors such as fare affordability, travel time, comfort, calmness, and safety, and propose service improvements for both modes. A revealed preference survey was conducted with two hundred respondents who had already selected either executive rail or sleeper bus for their Surabaya–Jakarta trip. The data were analyzed using binary logistic regression to model the probability of choosing each mode. The results show that most passengers belong to the choice‑user category at 65.5 percent and are predominantly private‑sector employees at 52.5 percent. The key determinant for selecting executive rail is the importance placed on calmness and comfort, yielding a predicted probability of 78.52 percent. In contrast, the dominant factor influencing sleeper bus selection is safety consideration, with a predicted probability of 96.52 percent. These findings indicate that executive rail attracts passengers seeking a quiet and comfortable travel environment, while sleeper buses appeal to travelers prioritizing safety and time efficiency supported by toll‑road connectivity. The study concludes that service improvements should focus on enhancing comfort and safety to strengthen the attractiveness of both premium land transport modes along the Surabaya–Jakarta corridor.

Implementation of Automated Attendance System Using Fingerprint with Wemos D1 R32 and WhatsApp Notifications

Bambang Permadi, Erik Iman Heri Ujianto — Fri, 06 Feb 2026 00:00:00 +0700

The manual attendance system often leads to inefficiencies, such as input errors, delayed data recap, and slow delivery of information to parents. This study aims to design and implement an automated attendance system based on the Internet of Things (IoT) using the AS608 fingerprint sensor and Wemos D1 R32 microcontroller integrated with attendance notifications via WhatsApp. The research method includes the stages of design, implementation, and testing using the black box approach and system performance evaluation. The results show that the system functions properly, successfully reads fingerprints, displays attendance information on the LCD, and sends WhatsApp notifications with an average response time of under 3 seconds. Black box testing confirmed that all main features, such as login, registration, fingerprint verification, and data export, work as expected. The sensor test results indicate a very high success rate under normal finger conditions but decreased accuracy when the finger is dusty, wet, or wrinkled. Overall, this system improves the efficiency, accuracy, and transparency of the student attendance process and is ready for implementation in a school environment.

Analysis of Spun Pile Bearing Capacity Based on SPT Data Using the Luciano Decourt and Tomlinson Methods

Rizwan Aziez Setyawan, Hafiz Hamdani, Heni Pujiastuti, Ahmad Zarkasi — Mon, 09 Feb 2026 00:00:00 +0700

Spun pile foundations were used in the construction of the Mayor's Office Building in Jempong Village, Mataram City, West Nusa Tenggara Province, as elements that transfer structural loads to more stable soil. This study aims to analyze the bearing capacity of spun pile foundations manually to obtain the ultimate bearing capacity and allowable bearing capacity values by comparing the Luciano Decourt and Tomlinson methods based on SPT data. Through this analysis, it is hoped that the most efficient and appropriate method for application in the soil conditions in the area can be determined. Based on the calculation results, the Luciano Decourt method produced a bearing capacity and allowable bearing capacity of 137,344 tons and 54,938 tons, respectively, while the Tomlinson method produced a bearing capacity and allowable bearing capacity of 89,489 tons and 35,795 tons, respectively. It can be concluded that the most efficient and appropriate method to use in pile foundation planning for the soil conditions in the area is the Tomlinson method because it produces a more conservative bearing capacity. The use of this conservative bearing capacity can help avoid the risk of foundation failure.

Technology Readiness and Acceptance as Predictors of Bim Adoption Intention: A Tram Study an a Project Management Consultancy

Harrie Awan Setya Pamunkkaz, Tri Joko Wahyu Adi — Tue, 10 Feb 2026 00:00:00 +0700

Building Information Modeling (BIM) is a critical digital innovation for improving coordination and decision-making in construction project management; however, its adoption among project management consultancies in Indonesia remains limited. This study examines how technology readiness and technology acceptance influence BIM adoption intention in a pre-adoption consultancy context using the Technology Readiness and Acceptance Model (TRAM). A confirmatory survey was conducted at a state-owned project management consultancy involving 85 respondents from management and project site teams. Data were analyzed using the Relative Importance Index (RII) and Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results show that BIM adoption intention is mainly driven by perceived usefulness (β = 0.531, p < 0.001), followed by perceived ease of use (β = 0.263, p = 0.037). The TRAM model explains 55.7% of the variance in adoption intention (R² = 0.557). Innovativeness plays an important enabling role by positively influencing perceived usefulness (β = 0.387, p = 0.002) and perceived ease of use (β = 0.481, p = 0.001), while optimism and insecurity do not show significant effects. Descriptive analysis further indicates that BIM is highly valued for its long-term organizational benefits (RII = 0.925) and early problem detection capability (RII = 0.915). No significant perceptual differences are found between management and project site teams. These findings suggest that BIM adoption intention in project management consultancies is primarily shaped by performance-related and usability perceptions, underscoring the importance of targeted training, infrastructure readiness, and organizational support to translate intention into effective implementation.

Sentiment Analysis of the Song Lost - Bring Me the Horizon Based on Reviews on YouTube Using the SVM Algorithm

Efraim Moningkey, Wanki Dwi Warsun Sombo — Tue, 10 Feb 2026 00:00:00 +0700

Bring Me The Horizon's song "Lost" received a wide response on YouTube, evident in the thousands of comments containing a variety of responses ranging from support and criticism to neutral opinions. The rapid development of social media has made it easier for people to freely share their views and experiences on musical works without being bound by space and time. YouTube, as one of the largest video-sharing platforms, plays a crucial role in documenting public perception of the song. This study was conducted to analyze listener sentiment towards the song "Lost" based on YouTube comments using the Support Vector Machine (SVM) algorithm and the Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction technique. Comments were collected through web scraping from the song's official video, then processed through case folding, punctuation removal, tokenizing, stopword removal, and stemming to produce clean and uniform data. Term weights were calculated using TF-IDF and then used to label positive, negative, and neutral sentiments. The SVM model was built from training data and tested with test data to evaluate its performance using accuracy, precision, recall, and f1-score metrics so that classification quality could be assessed comprehensively. Based on the test results, the SVM algorithm was able to classify listener comments with 94% accuracy, with a distribution of negative sentiment of 207 comments, neutral comments of 1,280, and positive comments of 732. These findings demonstrate the effectiveness of SVM in analyzing the sentiment of song comments on social media and provide a more comprehensive picture of the public's view of Bring Me the Horizon's song "Lost.".

Predicting ZNERGY Athletes' Swimming Times Using Random Forest Based on Lifestyle Physiological Exercise

Tue, 10 Feb 2026 00:00:00 +0700

This research was conducted to fill the gap in previous research in swimming performance prediction that rarely combines daily lifestyle aspects with training and physiological data, especially in the context of local swimming clubs in Indonesia that still rely on conventional approaches. The purpose of this research is to develop a prediction model for 50-meter freestyle swimming times using a holistic Random Forest Regressor algorithm. The method applied is a data-oriented methodology for prediction with longitudinal primary data from 10 ZNERGY Aquatic Swimming Club adolescent athletes (81 observations) collected through coach logbooks for training variables and time targets, and digital questionnaires for parents for physiological and lifestyle variables, including a specially developed Nutrition Compliance Index (IKG). The data was processed with Python (Scikit-Learn and Pandas), including pre-processing, lagged feature engineering, and model evaluation with an 80:20 split. The results show the model has high accuracy with an R² of 0.9591, MAE of 1.511 seconds, and RMSE of 2.254 seconds; The most important variable was the previous test time (45.66%), followed by training variables (33.67%), while lifestyle variables contributed little. The implication of this study is the availability of an evidence-based decision support system for coaches to design training programs objectively, optimize athlete progress, and prevent overtraining through a holistic approach that integrates multidimensional data.

Feasibility Analysis of the 40 MWp Tembesi Floating Solar Power Plant Development Project

Muhammad Ariansyah Putra, Eri Prabowo — Tue, 10 Feb 2026 00:00:00 +0700

This study aims to analyze the feasibility of the Tembesi Floating Solar Power Center (PLTS) construction project with a capacity of 40 MWp in the Batam - Bintan electricity system. The financial feasibility includes calculation of investment costs, operation and maintenance costs, and cost benefit analysis using indicators such as Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period. The operational feasibility analysis includes alternatives to achieving goals, assessment of how to achieve goals, scope of work, and conclusions of the operational feasibility study. The results showed that the construction of a floating solar power plant in Tembesi Reservoir is technically feasible. The proposed design and technology are capable optimal performance of the floating solar PV system, ensuring effective and efficient operations. Financial analysis, including consideration of sensitivities and assumptions, shows that the project is financially viable. The project has an internal rate of return (IRR) that meets the investment feasibility requirements. The rates are 8.82 cUSD/kWh if the power is evacuated through 150 kV lines, and 7.96 cUSD/kWh if evacuated through 20 kV lines. Based on the analysis, the construction of 40 MWp Tembesi Floating Solar Power Plant electricity system is declared feasible to be implemented.