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At the research location at PT. Ansaf Inti Resource, there are several mining stages such as exploration. In the exploration itself, drilling and logging activities are carried out. However, if not careful in calculating coal resource estimates, there can be potential discrepancies between the estimated coal resources based on the results of drilling data identification and well logging data with the actual results. Exploration activities are activities that are often carried out by companies to determine reserves in an area. Without carrying out these activities, companies will have difficulty estimating the profits to be obtained. To obtain the data needed to carry out exploration activities such as calculating the quality and quantity of coal, it is necessary to carry out drilling such as the full coring method and also carry out logging activities to complete the data and also as comparative data from core drilling. Coal Resources are part of a coal deposit in the form of a certain quantity and have reasonable prospects that allow for economic consideration. Coal resources are divided according to the level of geological confidence into inferred, indicated, and measured categories. Therefore, in this study, the author will calculate coal resources from exploration data collection such as coring data, cutting data, and well logging data as well as coal quality data and process it in Minescape 5.7 software so that it can obtain an estimate of coal resources for each seam at the research location.

Blasting is a critical method for overburden removal in open-pit coal mines, where fragmentation quality directly impacts loading efficiency and operational costs. This study aims to analyze the actual fragmentation resulting from overburden blasting at Pit 4 Middle of PT. Victor Dua Tiga Mega, Central Kalimantan, to predict fragmentation using the Kuz-Ram model, and to evaluate the conformity of both results against the company standard (boulder size ≤144.6 cm or ≥50 cm for analysis). The research employed a quantitative comparative method. Primary data included blasting geometry and photographs of muck piles from 10 blasting events, which were analyzed using WipFrag software to obtain actual fragmentation distribution. Secondary data comprised rock characteristics and explosive properties for Kuz-Ram prediction input. The results showed significant variation in actual boulder percentage (≥50 cm), ranging from 6.19% to 32.91% with an average of 16.05% (medium category). Statistical analysis revealed a very weak negative correlation (r = -0.21) between powder factor (PF) and boulder percentage, indicating that PF is not the dominant factor within the consistent application range (0.21-0.23 kg/bcm). Comparison with Kuz-Ram predictions showed that the model consistently over-predicted coarse material, with an average difference of +25.21%, suggesting the need for rock factor (A) recalibration. It is concluded that the blasting results are inconsistent, strongly influenced by uncontrollable factors such as geological conditions. Recommendations include geometri evaluation, particularly burden and spacing, and calibration of the Kuz-Ram model for more accurate future predictions.

Low productivity and high sick leave in the weaving division of PT XYZ in Yogyakarta are caused by excessive physical and mental workload on Shuttle Loom Unit 2 operators. The productivity of the weaving division of PT XYZ was recorded at only 61.96% (target 75%) with sick leave of 4.17%, indicating operator fatigue. This study measured physical workload using the Cardiovascular Load (CVL) method based on heart rate and mental workload using NASA-TLX, and analyzed the correlation with age and length of service. The results showed a moderate physical workload category (40% light CVL <30, 60% moderate 30-60) at a temperature of 30.5°C, and high-very high mental workload (45% high 50-79, 55% very high ≥80, average EF 79.3) due to strict quality targets (0.5% defects). There is a correlation between CVL and NASA-TLX with age. Recommendations include reducing the daily production target from 100 to 85-88 yards, optimizing ergonomics, and training to reduce EF to 65 and defects to 0.3, to increase effective productivity.

Landslides in the Giritengah Catchment Area are influenced by several factors, including geological conditions, rainfall intensity, geomorphology, soil characteristics, and inappropriate land use practices, all of which affect regional spatial planning and environmental stability. This study presents a literature review that analyzes landslide vulnerability, evaluates the impact of land use changes, and proposes integrated Soil and Water Conservation Techniques (SWCT) to support sustainable land management. The analysis applies Geographic Information System methods using thematic map overlays such as rainfall distribution, slope gradient, geological structure, and land use patterns. The results show that areas categorized as having high landslide vulnerability cover 44.02% or approximately 158.69 hectares of the catchment area, while areas with very low vulnerability account for only 0.12% or about 0.79 hectares. Land use conversion, particularly mixed dryland agriculture, has increased landslide risk by reducing slope stability and increasing surface runoff. To address this issue, conservation strategies are recommended, including vegetative measures such as greening 38.51 hectares in settlement areas and implementing agroforestry systems across 218.48 hectares. In addition, structural measures such as three dam retainers and twenty gully plugs are proposed in both protected and cultivation zones to support disaster mitigation and align with regional spatial planning policies.

Conflicts in transmigration areas are generally multidimensional and influenced by social, economic, land, and institutional factors. This study aims to identify the forms and distribution of conflicts in three districts of the transmigration area, namely Momi Waren District, Ransiki District, and Oransbari District, as well as to formulate a smart system-based conflict resolution approach through the use of spatial data, local institutions, and local wisdom-based settlement practices. Based on field mapping, four main categories of conflict were identified: 1) Land conflicts occur throughout the transmigration sites in the form of claims to transmigration land that has not been handed over to transmigrants because the compensation price is below normal. In addition, there is no ATR BPN office in South Manokwari Regency, one of whose functions is community empowerment and conflict resolution. 2) Economic conflicts occur because transmigrants are registered and recorded in the population registry, making it easy for them to access capital. Several economic activities in agriculture and transportation services are dominated by transmigrants, causing economic jealousy. 3) Social conflicts occur when the distribution of social assistance is uneven and the excessive use of illegally sold alcoholic beverages causes social unrest. 4) Institutional conflicts occur when civil servants, police, and military personnel are recruited, and not all indigenous Papuans who are nominated can be accommodated, requiring the involvement of tribal councils to formulate recommendations for recruitment that prioritize indigenous Papuans. The root causes of the conflict were analyzed using a root cause analysis approach that covered unclear land boundaries, unequal economic access, weak coordination between institutions, and low social trust due to differences in interests between groups. This study utilizes best practices from the Tribal Council, the South Manokwari Regency Transmigration and Manpower Office, the Religious Harmony Forum, and the Social Services Office as the basis for developing smart maps for an early warning system for conflicts. The results of the study formulate a Smart Conflict Resolution System framework consisting of three main components: (1) participatory spatial mapping of conflicts and key actors, (2) integration of institutional databases and social-customary mediation channels, and (3) design of smart maps as a mitigation and decision-making tool in transmigration areas. This system is expected to strengthen collaborative governance, prevent conflict escalation, and realize inclusive and sustainable management of transmigration areas

The digital transformation of health services through the Mobile JKN application was introduced by BPJS Kesehatan to provide easier access for the public in obtaining information, managing membership administration, and receiving health services more quickly and efficiently. This study aims to measure the readiness level of patients at Abdul Manap Regional Hospital, Jambi City, in adopting the Mobile JKN application using the HOT-Fit method, which covers three main components: Human, Organization, and Technology. Data were collected from 360 respondents through questionnaires and analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM). The results indicate that technology factors—including system quality, information quality, and service quality— along with organizational support have a significant effect on system use and user satisfaction, which in turn positively influence the net benefits. The outer loading values of all indicators exceeded 0.7, with Composite Reliability above 0.8 and AVE above 0.6, confirming that the research instruments are valid and reliable. Overall, patients at Abdul Manap Hospital are categorized as ready to adopt Mobile JKN, although improvements in digital literacy and stronger organizational support are still required to optimize its utilization.

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.

This study presents ReflectAI, a web-based system designed to automate the creation of teaching materials tailored to students' learning styles using behavior data from a Learning Management System (LMS). Student digital activity data—such as logins, material access, forum participation, assignment submission, and quiz results—are extracted and processed using a Hierarchical Clustering algorithm to categorize students into three learning styles: visual, auditory, and kinesthetic. Based on the clustering results, the system automatically generates personalized learning modules using generative AI (ChatGPT API), aligned with each student's learning preferences. Employing a data-driven system development approach, the system was tested with data from 230 students in a mathematics course. The results show diverse learning style distributions and relevant, tailored content generation. ReflectAI is designed to reduce teachers’ administrative workload and enhance personalized and adaptive learning. This system contributes to educational transformation through deep, data-driven technology integration.

Since the announcement of the policy plan for a name transfer system in the sale of used mobile phones, the issue has attracted widespread public attention and discussion. People have expressed their opinions on social media platforms, particularly TikTok. This study aims to classify the sentiment of TikTok users using Naive Bayes and Support Vector Machine (SVM) algorithms. The data were collected through a comment scraping technique on related content.The research stages include text preprocessing, sentiment labeling into positive, negative, and neutral categories, and feature extraction using TF-IDF. The classification process employs Naive Bayes and Support Vector Machine algorithms, which are then evaluated based on accuracy, precision, recall, and F1-score. The results of this study indicate that both methods are capable of classifying sentiment effectively. However, the Support Vector Machine method is superior to the Naive Bayes method with an accuracy rate of 99.57% compared to 94.30%. This study is expected to help the government understand public responses to the planned policy of the used mobile phone name transfer system.

The increasing demand for housing in tropical regions requires building materials that are fast to apply, environmentally friendly, and resilient to extreme climate conditions as well as disaster risks. Conventional interlocking bricks are often chosen for their ease of construction, yet they still face challenges such as moisture and early cracking. This study proposes the innovation of the Hybrid Living Green Brick, a combination of lightweight bricks made from rice husk ash and fly ash waste (FRCB) with a biological layer of cyanobacteria. FRCB improves compressive strength by approximately 30% with the addition of 5% rice husk ash, achieving 65 kg/cm², thereby meeting Class 50 requirements (≥50 kg/cm²) according to SNI-15-2094-2000. The incorporation of 3% cyanobacteria provides an additional though not significant strength improvement, while still within the Class 50 category. It also reduces brick weight by 4.3%, with further optimization potential through cyanobacteria integration, and lowers carbon emissions from the firing process. Cyanobacteria induce the formation of CaCO₃ layers that seal pores, reduce water absorption by an average of 10%, and provide self-healing properties for microcracks. Preliminary observations indicate that FRCB offers stable mechanical performance, while biological activity was observed on the 7th day with the formation of pale-white mineral layers continuing until the 28th day. This hybrid innovation shows potential to support sustainable and disaster-resilient tropical construction by combining the mechanical strength of waste-based materials with the biological durability of cyanobacteria against extreme climates. Despite challenges related to moisture control and production standardization, the Hybrid Living Green Brick concept opens new pathways for developing environmentally friendly construction materials that are more adaptive to disaster-prone tropical conditions.

MMP is a metal manufacturing company engaged in casting, forging, and machining processes with a three-shift work system, including night shifts that may increase mental and physical workload due to disruptions in workers’ biological rhythms. This study aims to analyze the mental and physical workload of die casting machine operators during the night shift and to propose improvement measures using the Fault Tree Analysis (FTA) method. The study involved 23 operators, with mental workload assessed using NASA-TLX and physical workload measured using CVL. The results indicate that mental workload falls into high to very high categories, with WWL values ranging from 46.6 to 97.3, where 12 operators experienced very high mental workload. The dominant contributing dimensions were effort, physical demand, and temporal demand. Meanwhile, physical workload ranged from 19.48% to 36.36% CVL, with most operators not experiencing fatigue. Although physical workload remains within acceptable limits, the high mental workload indicates the need for improvements. FTA analysis identified key contributing factors, including work methods, work systems, ergonomics, machine conditions, and the work environment. Proposed improvements include job rotation, improvements in work methods and task distribution, adjustments to the work system, enhanced machine maintenance, and ergonomics-based workplace improvements.

This study was conducted to analyze the causes of occupational accidents and to identify potential hazards in the material cutting process at PT Kanaan Global Indonesia using the Job Safety Analysis (JSA) approach. The high number of occupational accidents, totaling 15 cases during the period of December 2024–May 2025, indicates the existence of gaps in occupational safety control in this area. Data were collected through direct observation, interviews with workers, and the collection of historical data from the Occupational Health and Safety (OHS) unit. Risk analysis was carried out using a risk matrix to determine likelihood and severity values, followed by JSA for each work stage. The results identified 25 potential hazards, consisting of 4 risks in the extreme category, 9 in the high category, 7 in the medium category, and 5 in the low category. High-risk hazards were predominantly found in roll cutting and cutting press machines, which are characterized by mechanical hazards. Control recommendations focus on the implementation of engineering controls such as the installation of guarding systems and safety light curtains, improvements in safe work procedures, and increased discipline in the use of personal protective equipment (PPE). These findings demonstrate that the JSA method provides a comprehensive overview of risk sources and serves as an effective basis for formulating occupational accident control strategies within the company.

This study aims to identify and minimize waste in the process of procuring Food & Beverage raw materials at Hotel PXY Jasa Bali using the Lean Inventory Management approach. Observations over one month showed that the total procurement cost reached 47% of the total revenue, exceeding the ideal limit of 35%. One of the main causes is the inefficient and lack of synchronization between divisions in the Rhapsody platform, which causes waste in the form of defects, delays, over-processing, and transportation. This study uses the Value Stream Mapping (VSM) method to map actual conditions and Process Activity Mapping (PAM) to identify the type of activity based on the waste category. In addition, root cause analysis is carried out using the Root Cause Analysis (RCA) approach. The mapping results showed that out of the total lead time of 598.44 minutes, only 278.49 minutes or 46.54% were value-added activities. This finding indicates that the procurement process is not yet efficient and needs optimization. Several recommendations were given to minimize waste, such as clearer division of tasks, improved communication with vendors, and the procurement of supporting facilities to reduce physical movement between divisions. The implementation of this lean service is expected to be able to increase the efficiency of the logistics process and reduce the company's overall operational costs.

The construction sector is an industry with a high level of work accident risk due to its dynamic and complex work characteristics. This study aims to examine the application of the Occupational Health and Safety (OHS) System and evaluate its effectiveness in mitigating risks at the Health Facility Building Construction Project (Project X) in Fakfak Regency. The research method used is a descriptive-analytical quantitative approach with purposive sampling of 25 respondents, including executors, supervisors, and field workers. Risk analysis was conducted using the Failure Modes and Effects Analysis (FMEA) method by measuring Severity, Occurrence, and Detection parameters to produce a Risk Priority Number (RPN). The results show that RPN values range from 52.35 to 452.30. The highest risk was found in the variable of limited safety signage in hazardous locations (RPN 452.30), which falls into the very high category. Additionally, 10 high-risk variables and 9 medium-risk variables were identified, dominated by technical, operational, and management factors, such as the use of heavy equipment by uncertified operators and weak implementation of Standard Operating Procedures (SOP) and OHS audits. This study concludes that the application of OHS in Project X is still reactive and requires strengthening risk-based safety management systems as well as improving workforce competence to achieve zero accident conditions.

The development of artificial intelligence technology is a great opportunity for the fashion industry, especially in designers based on personalization and consumer needs. This study aims to examine Midjourney's AI technology in the design personalization process by integrating solid data and consumer style preferences. This research is expected to support the concept of mass customization in the fashion industry and increase the relevance of design to user character. This research uses a mixed method method by combining quantitative data and qualitative data. The research stages include body data collection and style preferences, prompt formulation, data-driven prompt formulation, design generation using Midjourney, design validation by experts and consumers, and integrated data analysis.The results showed that the majority of the designs produced were considered feasible in terms of construction (83%) and in accordance with the character of the consumer's body (75%). The modest and minimalist style categories received the highest personalization scores. The qualitative findings reinforce the quantitative results, showing that consumers feel the fit of the style and proportions of the design with the character of their bodies.The study concludes that Midjourney's AI integration in the fashion design process is able to effectively support design personalization, although it still requires the role of designers in technical refinement. This approach has the potential to be an innovative solution in the development of data-driven fashion design.

Wood consumption in Indonesia, which is expected to reach more than 64.84 million m³ by 2025, is putting enormous pressure on forests, as evidenced by the reduction in forest area through the clearing of 96,230 hectares of forest in 2023. To reduce dependence on wood as a building material, alternative materials with comparable physical and mechanical properties are needed. Bamboo, especially laminated bamboo, was chosen because it has high tensile strength, a short harvest time, and abundant availability. This study examines the behavior of hollow section laminated bamboo beam-column connections with a glue-in-rod-bracket system to determine the ductility of the connection under unidirectional (static) loads. An experimental method was used with the independent variables of diameter and number of bolts, while the dependent variables included the moment-rotation of the connection, stiffness, strength, and ductility. The results show that the average ductility values for 4 and 6 thread rods D6, D8, and D10 mostly meet the SNI 1729 (≥1.25) and AISC 360 (rotation >0.03 rad) standards, with classifications ranging from "partially ductile" to "fully elastic." However, the 10-diameter 6-thread rod only achieved a ductility of 1.2, thus failing to meet the SNI standard. Based on the Handbook of Structural Steel Connection Design and Details, all connections fall into the non-seismic category because their ductility values are less than 3. These findings confirm the potential of laminated bamboo as an environmentally friendly construction material, while also providing technical guidelines for the design of non-seismic beam-column connections.

The increasing volume of online hotel reviews provides valuable insights into customer perceptions but poses challenges for manual analysis due to its unstructured nature. This study aims to compare the performance of Recurrent Neural Network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) in hotel review sentiment analysis. A total of 20,491 TripAdvisor hotel reviews were classified into three sentiment categories: negative, neutral, and positive. The research methodology includes text preprocessing, stratified data splitting, class imbalance handling using Random Over-Sampling, tokenization, and supervised model training. Model performance was evaluated using a confusion matrix and classification metrics. The results indicate that BERT outperforms RNN, achieving an accuracy of 80.54%, while RNN reached 62.21%. BERT demonstrated superior capability in capturing contextual and semantic information in hotel reviews. These findings suggest that transformer-based models are more effective for sentiment analysis of complex textual data in the hospitality domain and can support data-driven service improvement strategies.    

The development of information technology has driven digital transformation in various sectors, including the food and beverage (F&B) industry. However, many small to medium-scale F&B businesses still rely on manual ordering systems, resulting in long queues, order recording errors, limited menu information, and suboptimal user experience. This study aims to design the user interface (UI) and user experience (UX) of a web-based Smart Ordering System that provides convenience, efficiency, and comfort in the food ordering process. The research method used is the Design Thinking approach, which includes empathize, define, ideate, prototype, and testing stages. The design process involves user needs analysis, user flow development, wireframe creation, and high-fidelity prototype development using Figma. Usability testing is conducted using the Single Ease Question (SEQ) method to evaluate ease of use and user satisfaction. The results indicate that the proposed UI/UX design provides a clear ordering flow, intuitive interface, and easy-to-understand user experience. Based on the SEQ results, most users experienced no difficulty in using the system, indicating that the design meets usability criteria with a very good category and is suitable for implementation in the F&B industry.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

The skill mismatch between university graduates and technology industry requirements remains a significant challenge in Indonesia. PT Vinix Seven Aurum requires an assessment tool to objectively identify the initial competencies of MBKM and internship program participants. This research aims to design a web-based self-assessment platform that helps students measure their skill gaps against industry standards through radar chart visualization and personalized learning recommendations. The UI/UX design applies the Design Thinking method with empathize, define, ideate, prototype, and test phases, utilizing Figma for wireframe and high-fidelity prototype development. Data collection was conducted through observation, interviews, literature studies, and usability testing with 10 respondents. The results demonstrate good usability with a 100% completion rate across all features. The VINIX Skill Radar platform provides five assessment categories, a 1-10 rating scale system, radar chart visualization, gap analysis, and learning recommendations. This system enhances students' self-awareness of their competencies and supports effective mapping of training program participants' capabilities.