Delays in the completion of construction projects remain a major problem, particularly in the construction and rehabilitation of educational buildings, which require precision in terms of time, cost and quality. One effort to address this problem is accelerating the project duration through the crashing method. Therefore, this study aims to analyze the acceleration of construction project duration through the addition of workforce using the crashing method in the rehabilitation project of SD Negeri 2 Murtajih, Pamekasan Regency. The research method used is a quantitative descriptive method with a case study approach, using secondary data in the form of a time schedule, budget plan (RAB), S curve, and project progress reports. The analysis was conducted by determining normal workforce productivity, crashing productivity, normal duration, crash duration, and the percentage of time acceleration due to the addition of workforce. The results showed that adding workers significantly accelerated the implementation duration. For instance, in the brick wall and tile floor installation, which had a normal duration of 10 days, adding 15 workers resulted in a crash duration of 7 days with an acceleration of 33%, while adding 17 workers resulted in a crash duration of 6 days with an acceleration of 41%. In conclusion, the crashing method proved effective in accelerating the project duration, despite impacting increased workforce costs. Thus, optimal acceleration planning is required to achieve a balance between time savings and additional costs for more efficient and economical project implementation.

This study analyzes power losses due to load imbalance and the calculation of losses in distribution transformers at the Merauke City Customer Service Unit (ULP). The results show that most distribution transformers on the Angkasa, Bampel, Parako, Kompi, and Ermasu feeders experienced load imbalances exceeding the 20% tolerance limit according to SPLN D3.002-1:2014 standards, contributing to increased neutral currents and power losses, particularly in transformers with heavy loads and uneven phase distribution. After phase current balancing using ETAB 19, significant reductions in neutral current and losses were observed, with transformer efficiency increasing by an average of 2-5% per unit. A feeder-based, data-driven balancing strategy, involving the redistribution of load from dominant phases to deficit phases along with regular monitoring, proved effective in improving distribution system efficiency, extending transformer lifespan, and enhancing voltage quality for customers. These findings provide an operational basis for PLN ULP Merauke to prioritize distribution network improvements, maintain a minimum bus voltage profile of 93–95%, and reduce operational costs through regular load balancing.

Construction activities for water pipeline projects in industrial facilities present various occupational and operational hazards that may disrupt project performance if not properly controlled. This study aims to analyze the occupational risks associated with the water pipeline construction project of PT CATL Karawang using the Hazard Identification, Risk Assessment, and Risk Control (HIRARC) method. The research was conducted through field observation, work breakdown structure analysis, and semi quantitative risk assessment based on likelihood and severity scoring. The results indicated ten (10) dominant hazards across five (5) major activities including trench excavation, pipe lowering, pipe joint installation, backfilling and compaction, pressure testing, and site restoration. Three (3) risks were categorized as high level, namely trench collapse, pipe dropping during lifting, and stuck by heavy equipment, while the remaining risks were classified as medium level. Appropriate risk controls are recommended following the hierarchy of control, emphasizing engineering controls, administrative procedures, and mandatory use of personal protective equipment. The study concludes that the overall risk category of the project is moderate, high and requires consistent monitoring and implementation of control measures to reduce the probability of incident and ensure safe and continuous project execution.

Searching for products online is often an inefficient and confusing process, especially when users do not know the exact name of the product or use terms that differ from the search system. Keyword-based searches tend to produce irrelevant results because the system only matches text literally without understanding the meaning. As users increasingly talk to digital devices, voice-based search technology has become a more natural and intuitive alternative. This research aims to develop a semantic-based voicebot supported by Natural Language Processing (NLP) to improve the effectiveness of product searches on e-commerce platforms. The designed system not only recognizes user speech but also understands the context, intent, and semantic meaning of the given commands. The research stages include collecting user voice data, training the Automatic Speech Recognition (ASR) model for voice-to-text conversion, and applying the semantic NLP model for interpreting the context of product searches. The testing was conducted using Indonesian voice commands in a simulated e-commerce scenario. The results showed that the system achieved an average Word Error Rate (WER) of 1.29%, indicating a high level of accuracy in recognizing speech and understanding user intent. The integration between ASR and semantic NLP proved capable of creating a more natural, responsive search experience that resembles the way humans think and communicate when interacting with online search systems.

Modern office buildings with glass envelopes often encounter challenges in energy efficiency, particularly due to excessive reliance on artificial lighting during daytime and intensive use of cooling systems. This condition is occurred at the PT Pertamina Patra Niaga - ITJ Jakarta office building. The installation of window films and curtain blinds has made interior spaces darker, preventing optimal utilization of natural daylight and consequently increasing electricity consumption. Such conditions not only reduce energy efficiency but also compromise visual comfort for occupants. This study aims to analyze the impact of retrofit shading and the double skin façade (DSF) on daylighting performance, visual comfort, and energy efficiency. The research employed an existing condition analysis using the Sefaira plug in SketchUp software and Sefaira web model - energyplus to simulate and evaluate the effects of retrofitting on the glass office building. The findings reveal that the integration of retrofit shading and DSF significantly improves natural daylighting quality 2% underlit, 42% well lit, and 44% overlit. Visual comfort was enhanced by lowering indoor illuminance levels of ASE from 87% to 43% lux and sDA from 100% to 98% lux, aligning with recommended standards. Furthermore, annual electricity consumption decreased substantially, from 269 to 130 kWh/m² per year. In conclusion, retrofit shading and DSF provide effective passive design strategies that enhance daylight utilization, improve occupant comfort, and support energy conservation. This study serves as a preliminary investigation for future research on integrating multiple passive design strategies in office building retrofits.

The dairy cattle sector in Indonesia significantly contributes to greenhouse gas (GHG) emissions, particularly methane (CH₄). This study aims to analyze the environmental impacts of biogas production using the Life Cycle Assessment (LCA) approach. Data was collected from several biogas projects in Central Java and Yogyakarta, then analysed using OpenLCA software with cradle-to-grave system boundaries. The analysis was conducted using the Life Cycle Assessment (LCA) approach to assess the environmental impact at each stage of the life cycle, from raw material collection and production processes to utilisation and final disposal. In addition, supporting data was collected through interviews with project managers, field observations, and literature studies to ensure the accuracy and completeness of the analysis results. The results indicate that anaerobic digestion and biogas combustion are the major contributors to emissions but also play a key role in reducing methane release by up to 60%. Optimization of digester management and utilization of digestate as organic fertilizer can further minimize additional impacts. Hence, biogas demonstrates substantial potential as a mitigation strategy to support net zero emissions in the dairy cattle sector.

This study aims to evaluate and compare the performance of two recommendation system approaches, namely Collaborative Filtering (CF) and Content-Based Filtering (CBF), in providing relevant product recommendations to users in an e-commerce context. The dataset used consists of 120 data including 90 relevant and recommended products (True Positive), 20 recommended but irrelevant products (False Positive), and 10 relevant but not recommended products (False Negative). Based on the calculation results, both methods show a precision value of 0.818 and a recall of 0.900. This means that approximately 81.8% of products recommended by the system are truly relevant, while 90% of the total relevant products are successfully recommended to users. The F1-score value obtained of 0.857 illustrates a good balance between the accuracy and completeness of the recommendations generated by the system. Furthermore, to measure the level of rating prediction error, the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics are used. The evaluation results show that the CF method has an MSE value of 0.0784 and an RMSE of 0.28, while the CBF method shows an MSE of 0.0961 and an RMSE of 0.31. The lower RMSE value of CF indicates that this method has better accuracy in predicting user preferences than CBF. Overall, both methods show good performance with a low error rate. However, CF proved slightly superior in providing recommendations that match user preferences, so it can be used as a basis for developing smarter and more personalized recommendation systems on e-commerce platforms.

Accurate forecasting of visitor numbers is essential in tourism management to ensure service quality and visitor satisfaction, especially during peak seasons such as holidays and weekends. This study addresses the lack of a predictive tool at PT Taman Impian Jaya Ancol (TIJA), a major recreational destination in Indonesia, by developing a forecasting model for visitor numbers. The research utilized monthly time series data of visitor numbers from January 2012 to December 2022. A Deep Learning approach was applied using the Recurrent Neural Network (RNN) architecture with the Long Short-Term Memory (LSTM) algorithm. The dataset was split with an 80:20 ratio for training and testing, normalized using the RobustScaler technique, and optimized with the ADAM optimizer. The model achieved a minimum Mean Squared Error (MSE) of 0.3095 and a prediction accuracy of 94.85%. These results indicate that the LSTM model can effectively predict visitor trends. The findings are expected to support TIJA and other tourism operators in preparing resources and facilities in advance, improving operational planning, and enhancing the overall visitor experience.

Higher education institutions are required to provide a conducive learning environment to produce excellent and competitive human resources. One of the keys supporting factors is the availability of adequate infrastructure, including accessible financial services within the campus. Institut Teknologi Sepuluh Nopember (ITS) responded to this need by constructing a banking building as part of its efforts to create an integrated digital campus ecosystem. However, the project faced significant delays from the original schedule, prompting the need for acceleration strategies that maintain both cost and quality. One such strategy is the Fast Track method, which allows construction activities to be carried out in parallel to shorten the project duration. This study aims to analyze the project’s time and cost using the Fast Track method, a project acceleration technique that enables activities to be performed simultaneously to reduce execution time. The research was conducted on the ITS Banking Building construction project, which has a contract value of Rp 11,871,000,000 and a planned duration of 120 calendar days. The analysis involved identifying critical activities using the Critical Path Method (CPM) and then rescheduling using the Fast Track approach to calculate the optimal time and cost. The results of the study show that the optimal project duration after applying the Fast Track method is 112 working days, which represents a 6.7% reduction from the original 120 days. The optimal cost after acceleration using the Fast Track method is Rp 9,696,512,763, representing a 9.3% reduction from the initial cost of Rp 10,694,683,195pa.