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The optimization of road alignment has become increasingly important due to the continuous growth in traffic volume and the rising demand for efficient travel time. Road optimization considers several key factors, including traffic volume, population growth, and regional development, to ensure that transportation infrastructure remains functional and capable of meeting future mobility demands. The Malang–Batu corridor is characterized by mountainous terrain and hilly topography, requiring an appropriate geometric road design to determine the shortest feasible route while maintaining compliance with the Indonesian Bina Marga geometric design standards. This study developed a geometric road alignment model by applying Bina Marga specifications, in which the first horizontal curve was designed using the Spiral–Circle–Spiral (SCS) configuration, the second curve employed the Spiral–Spiral (SS) configuration, and the third curve utilized the Spiral–Circle–Spiral (SCS) configuration. The primary objective of this study was to determine the minimum achievable route length through variations in horizontal alignment parameters. The modeling results indicate that the shortest route length obtained was 1,101.17 m. The optimal horizontal alignment consisted of an SCS curve at Point A, an SS curve at Point B, and an SCS curve at Point C, whereas the vertical alignment followed the existing terrain conditions, incorporating both crest and sag vertical curves. These findings demonstrate that optimizing horizontal alignment parameters can effectively minimize route length while satisfying the geometric design requirements established by the Bina Marga standards.

Document-level sentiment analysis assigns a single polarity label to an entire review, often obscuring opinion diversity within multi-sentence submissions. This limitation is particularly evident in reviews of multi-service platforms, where users frequently express heterogeneous opinions toward different aspects of the platform in the same review. To address this challenge, this study proposes a sentence-level sentiment analysis framework for Indonesian Gojek app reviews collected from the Google Play Store. The proposed framework introduces a two-stage segmentation strategy that combines punctuation-aware rules with conjunction-aware splitting based on coordinating and adversative conjunctions (e.g., tapi [but], padahal [even though]) to identify opinion boundaries and decompose mixed-sentiment reviews into independently classifiable sentence units. A total of 14,730 raw reviews collected between May and July 2025 were subjected to data cleaning and quality filtering, resulting in 7,187 valid reviews that were further segmented into 14,187 sentence-level instances. Each instance was manually annotated by three annotators using a four-class labeling scheme consisting of app-positive, app-negative, app-neutral, and service categories. Sentiment-level inter-annotator agreement, computed on the subset of instances unanimously categorized as app-related by all three annotators (n = 4,384), achieved substantial agreement (Fleiss'  = 0.636). Hyperparameter optimization was conducted using Optuna with the Tree-structured Parzen Estimator (TPE) sampler across four experimental scenarios. The best performance was achieved by IndoBERTweet under Stratified K-Fold evaluation, attaining an accuracy of 0.751 and a macro F1-score of 0.729, outperforming all IndoBERT configurations. The results demonstrate the effectiveness of domain-adaptive pre-training on informal Indonesian text and highlight the value of conjunction-aware segmentation for preserving fine-grained opinion structures in mixed-sentiment reviews. These findings suggest that domain-aligned language representations provide a practical and effective solution for sentence-level sentiment analysis of Indonesian app reviews.

Gorontalo Province possesses significant mineral resource potential, particularly gold, silver, and copper, positioning the mining sector as a key driver of regional economic growth. However, a shortage of skilled local labor and the scarcity of vocational educational institutions in the mining field severely hamper human resource development in this sector. This study aims to design a Mining Polytechnic Campus in Gorontalo by applying sustainable architecture principles, encompassing energy efficiency, environmentally friendly materials, sound wastewater management, and user comfort. The research approach involves literature studies, field observations, interviews with relevant stakeholders, and quantitative data analysis regarding resource potential, the number of senior high school students, and educational space requirements. The design results emphasize site arrangement, building mass configuration, utility systems, and interior and exterior spaces that support academic, social, and community activities. The application of sustainable architecture principles is expected to create a campus that not only meets the needs of mining vocational education but also contributes to environmental conservation and sustainable regional development.

The increasing demand for electrical energy in military education facilities necessitates an efficient, reliable, and sustainable energy solution. This research aims to design a Solar Power Plant (PLTS) system to meet the street lighting needs in the Wangi-Wangi Complex of the Indonesian Naval Academy (AAL). The research method used is quantitative descriptive with an engineering design approach thru the stages of site survey, collection of solar energy potential data in the Surabaya area, calculation of electricity energy needs, calculation of solar panel capacity, calculation of battery capacity, and design of battery connection configuration. The research results show that the energy requirement for street lighting is 1,920 Wh/day, sourced from 8 units of 20 Watt LED lamps with an operating time of 12 hours per day. Based on the average solar radiation potential in Surabaya of 5 kWh/m²/day, the designed system requires 3 units of 200 Wp monocrystalline solar panels with a total area of approximately 4.89 m². For energy storage, 4 units of Yuasa N200 12 V 200 Ah batteries are used, configured in a series-parallel arrangement, capable of providing an effective energy of around 3,600 Wh with an estimated operating time of 22.5 hours. The research results indicate that the proposed solar power plant design is feasible to implement as an environmentally friendly, efficient alternative energy source that supports the green defense concept in the AAL environment.

Recent Internet of Things (IoT) intrusion detection studies have reported near-perfect benchmark performance for Distributed Denial of Service (DDoS) detection, yet limited attention has been given to understanding how different traffic representations contribute to the detection process under highly imbalanced traffic conditions. This study presents an ablation-driven analysis to investigate the contribution of statistical and temporal representations for large-scale IoT DDoS detection using the CICIoT2023 dataset. Three experimental scenarios are evaluated, including statistical representation, temporal sequence representation, and hybrid statistical–temporal representation. Temporal representations are learned using a one-dimensional Convolutional Neural Network (1D-CNN) with lag-based traffic sequences, while ensemble tree-based classifiers are employed for final classification and representation analysis. In addition, multiple ablation configurations are designed to evaluate the impact of temporal dependency modeling and feature engineering strategies on detection performance. Experimental results show that statistical traffic representations remain highly effective for DDoS detection on CICIoT2023, achieving 99.36% accuracy and 99.31% weighted F1-score in the statistical representation scenario. Feature importance analysis further indicates that engineered statistical features contribute substantially more to the classification process than CNN-based temporal representations. Although temporal modeling captures sequential traffic behavior, its contribution is relatively limited and mainly acts as a complementary representation. Furthermore, the hybrid configuration produces only marginal improvements over the statistical representation alone. These findings highlight the importance of representation-level analysis for understanding the actual contribution of statistical and temporal modeling in modern IoT intrusion detection systems beyond relying solely on benchmark accuracy.

 The rapid adoption of smartphones among Indonesian digital natives has increased reliance on biometric authentication systems. However, empirical evidence regarding the relationship between user satisfaction and security risk awareness remains limited, particularly in developing-country contexts. This study investigates the behavioral dynamics of biometric security perception among 266 respondents, consisting of 221 high school students and 45 university students in Indonesia. A Python-based computational pipeline incorporating Akaike Information Criterion (AIC) validation and 1,000-iteration stochastic bootstrapping was employed to evaluate nonlinear behavioral patterns using Polynomial Regression and Ordinary Least Squares (OLS) multivariate analysis. The results confirm the existence of a nonlinear Security Paradox. While the overall population demonstrates a positive quadratic trajectory, the university student group exhibits a concave-down parabolic relationship (a=−0.0460), indicating a decline in perceived utility beyond a specific security threshold. The identified behavioral breaking point occurs at X≈5.45 (95% CI: 2.99–20.77), suggesting that excessive security hardening may reduce perceived usability and increase cognitive friction. Furthermore, the ablation analysis reveals that security risk awareness (p<0.001) is the strongest predictor of user satisfaction, exceeding the influence of daily usage intensity. Segment-level analysis further demonstrates behavioral divergence between respondent groups. High school students exhibit relatively uniform satisfaction toward biometric systems, whereas university students display greater variability and more critical perceptions regarding authentication friction. These findings indicate that highly rigid security configurations may become less effective for users with higher digital literacy and risk awareness. This study contributes a computationally validated behavioral framework for understanding security–utility trade-offs and provides a conceptual foundation for developing adaptive, user-centric, and friction-aware biometric authentication systems.

Land conversion associated with renewable energy expansion generates profound socio-cultural transformations in agrarian communities. This study aims to analyze how wind power development reshapes agrarian identity, social capital configuration, and the meaning of land within rural society from an anthropology of development perspective. This research applies qualitative literature-based analysis supported by recent peer-reviewed scholarship on land use change, rural transformation, social capital, and political ecology. Conceptual synthesis integrates sustainable livelihood framework, identity negotiation theory, and energy landscape analysis to construct an interpretive analytical model. Findings indicate that agricultural land conversion produces deagrarianization, occupational shifts, and reconfiguration of social stratification. Land is redefined from a genealogical and productive space into infrastructure and investment asset. Social capital grounded in kinship networks, customary institutions, and local organizations functions as a resilience mechanism through risk redistribution, collective solidarity, and participatory negotiation. Energy landscapes restructure symbolic and material relations between community and territory, generating both hybrid identities and conflict dynamics. Inclusive governance determines whether renewable energy fosters adaptive transformation or deepens commodification and exclusion. Renewable energy transition in rural areas requires socio-cultural recognition beyond technical implementation. Integrating local identity, participatory governance, and community ownership strengthens just and sustainable transformation pathways.

This study examines the lived faith of Catholic migrants from East Nusa Tenggara (NTT) who have migrated to the Muslim-majority landscape of South Kalimantan, Indonesia. These migrants face a profound crisis of identity as they transition from a dominant religious environment to a marginalized minority status, necessitating a research objective that explores how their faith is reinterpreted amidst such socio-religious pressures. Employing a qualitative phenomenological-hermeneutical method, the research utilizes Paul Ricoeur’s threefold mimesis - prefiguration, configuration, and refiguration - as its primary interpretive framework. The findings reveal a significant narrative shift from an inherited “communal Catholic habitus” to a “refigured faith” characterized by personal agency and reflective commitment. This transformation is sustained through adaptive relational ethics, such as the sanctification of work and collaborative hospitality, which allow migrants to navigate their vulnerability. The study synthesizes these experiences to conclude that internal migration constitutes a vital locus theologicus, wherein the rupture of traditional religious structures does not erode faith but rather matures it into a more resilient, intentional, and relational existential orientation. Consequently, migration emerges as a transformative theological process that redefines the intersection of faith, culture, and minority existence in pluralistic societies.

Accurate classification of rainfall intensity patterns is important for early warning systems, hydrometeorological risk assessment, and water resource management. Surface rain gauges have limited spatial coverage, so this study uses NOAA NEXRAD Level II radar data from the KTLX station in 2023. K-Means clustering was applied to identify rainfall intensity patterns from 30 randomly selected days, with scans stratified into four daily time intervals. Seven features were extracted from each radar sweep, including reflectivity statistics, convective and stratiform ratios, and rainfall coverage. The data were normalized and balanced before clustering. The optimal cluster count was determined through a combined evaluation of the Elbow Method, Silhouette Score, and Davies-Bouldin Index, yielding K=5 as the most representative configuration. Evaluation results demonstrated a Silhouette Score of 0.3871 and a Davies-Bouldin Index of 0.8599, indicating moderate cluster cohesion that reflects the inherent overlapping nature of rainfall intensity transitions in radar reflectivity data. The clusters represent rainfall regimes from non-precipitating conditions to intense convective events. These results support the use of K-Means for automated rainfall pattern recognition and flood forecasting applications. 

Air transportation plays an important role in supporting mobility, tourism, and emergency activities such as medical evacuation and search and rescue (SAR). One of the essential supporting facilities for helicopter operations is a heliport, which must meet safety standards, including an adequate lighting system. This study aims to design an LED floodlight installation system for the Main Helipad of Fly Bali Heliport based on the international standard ICAO Annex 14 Volume II, while considering the corrosive coastal environmental conditions. The research method used is an engineering design approach with quantitative analysis of illumination requirements and current carrying capacity (CCC). Data were obtained through literature studies based on ICAO, FAA, and CAP 437 standards, as well as field observations. The design process includes determining the number and placement of floodlights, technical specifications, and electrical installation systems, including cable and protection selection. The results show that the configuration of four LED floodlight units is capable of producing a minimum illumination of 10 lux evenly across the TLOF and FATO areas in accordance with ICAO standards without causing glare. The use of Avlite AV-HL-FL floodlights with IP66 protection is suitable for coastal environments. The electrical installation system using NYY 2×2.5 mm² cables and a 2 Ampere MCB ensures system safety and reliability. Therefore, this design can enhance heliport operational safety and support optimal night operations.

Indonesia is in an earthquake-prone region, therefore, designing building constructions that can withstand seismic loads is crucial in civil engineering. Reinforced concrete shear walls are one of the vertical structural fundamentals that are effectively used in multi-story buildings to withstand lateral forces due to earthquake and wind loads. Structures that use shear walls can increase stiffness and reduce horizontal deviations (deflections) of buildings, which contribute to the stability and safety of structures based on the SNI 1726:2019 standard. This study aims to analyze the effect of shear wall configurations on deviation and torsion requirements in multi-story building planning. The study object is a 6-story reinforced concrete building model in a specific earthquake zone. The design and modeling were performed using structural analysis software, taking into account columns, beams, slabs, and shear walls. The analysis results show that optimal placement of shear walls at the building edges significantly reduces horizontal drift, torsion, and shear forces, and improves the structural performance level compared to structures without shear walls or those with less effective placement. Structures with shear walls have optimal stiffness in absorbing lateral forces, making them more resistant to damage from the planned earthquake.

The exponential growth of digital music catalogs on streaming platforms such as Spotify has made personalized recommendation systems crucial for enhancing user experience. This study develops a hybrid music recommendation system that addresses both warm-user and cold-user scenarios by combining Alternating Least Squares (ALS) collaborative filtering with content-based filtering (CBF) augmented by a popularity component. The dataset consists of 8,549,544 user-track interactions and a master file of 1,204,025 tracks with ten audio features. After preprocessing, users were segmented into 14,880 warm users and 723 cold users based on a five-interaction threshold. The ALS model was trained on the user-item implicit feedback matrix and tuned through grid search over factors, alpha, and regularization. CBF was implemented using cosine similarity on normalized audio features, while popularity scores were applied for new users with insufficient history. Evaluation used Precision@10, Recall@10, and NDCG@10. The final ALS configuration achieved NDCG@10 of 0.1116, representing a 30% improvement over baseline, while the hybrid CBF improved NDCG@10 for cold users from 0.0070 to 0.0201. Findings indicate that adaptive routing among ALS, CBF, and popularity reliably handles different user states, providing a practical foundation for production-grade music recommendation systems.

The limited use of interactive learning media has made it difficult for students to visualize the functions and assembly flow of computer components. This study aims to design and develop an Android-based educational game called “Assemble & Learn” as an interactive medium for computer assembly lessons, specifically for vocational high school students at SMK Negeri 1 Tanjung Jabung Barat. The development process follows the Multimedia Development Life Cycle (MDLC), which includes the stages of concept, design, material collection, assembly, testing, and distribution. The learning content covers several core competencies: KD 3.2/4.2 on computer assembly, KD 3.3/4.3 on assembly testing, KD 3.4/4.4 on BIOS configuration, and KD 3.5/4.5 on operating system installation, with a focus on KD 3.2 and KD 3.5. Research instruments consist of validation questionnaires for subject-matter experts, media experts, and student trials using the System Usability Scale (SUS). Validation results show that the educational game received an average score of 94% from media experts and 100% from subject-matter experts, both categorized as “Highly Feasible.” Meanwhile, student trials indicated strong acceptance, with an average SUS score of 85% (excellent usability). In conclusion, the “Assemble & Learn” educational game offers an innovative solution to boost learning motivation, simplify material visualization, and provide flexible practice opportunities, thereby supporting the achievement of computer assembly competencies in an optimal and effective way.

The shaft is a crucial component in mechanical systems because it serves to transfer power and rotational motion throughout the machine. This research aims to assess the structural strength and operational performance of shafts used in a tire shredding machine through numerical simulation methods in order to achieve a safe and efficient design. The study involved several stages, including the development of shaft geometry models, the determination of boundary conditions, load application, mesh generation, and stress analysis using the finite element method. Two shaft configurations were examined: a 59 mm diameter shaft made from AISI 1045 steel and a 49 mm diameter shaft manufactured from ASTM A36 steel. The simulation results indicate that the 59 mm shaft experiences a Von Mises stress of 8.9 × 10⁻⁵ MPa, with a maximum displacement of 0 mm and a safety factor of 15. Similarly, the 49 mm shaft shows a Von Mises stress of 8.4 × 10⁻⁵ MPa, no measurable displacement, and a safety factor of 15. These findings confirm that both shaft designs are capable of safely withstanding the applied working loads. In addition, cutting system tests revealed that a 24-tooth blade achieved an efficiency of 26.9%, while a 40-tooth blade reached only 22.3%, indicating that the 24-tooth configuration provides better performance.

The low thermal efficiency of Gas Power Plants (PLTG) due to exhaust gas heat loss drives the implementation of cogeneration at PLTGU Block II PT. PLN Indonesia Power UBP Semarang. This study analyzes the performance of the Gas Turbine Generator (GTG), combined cycle efficiency, and Exergy distribution using a 3-3-1 configuration. The research utilizes actual operational data from January 28, 2026, sampled at 10-minute intervals. Results indicate that cogeneration via a Heat Recovery Steam Generator (HRSG) significantly enhances plant efficiency. The GTG output ranged from 273–283 MW with an efficiency of 30.0–30.2%. Following combined cycle integration, system efficiency increased to 43.9–44.4%, a gain of approximately 14%, with a heat rate of 11,916–11,988 kJ/kWh. Exhaust heat of 665–713 MW was recovered to generate an additional 130 MW through the Steam Turbine Generator (STG). Exergy analysis reveals that the largest irreversibility occurs in the GTG combustion process (285 MW), followed by the HRSG (185 MW) and STG (49 MW).

The rapid development of information technology has transformed social media into a digital space that plays a crucial role in shaping the mindset, behavior, and lifestyle of young generations. TikTok is one of the most dominant platforms used by Generation Z, particularly students, due to its ability to present algorithm-based content that aligns with user interests. This condition has the potential to cause psychological stress in the form of Fear of Missing Out (FOMO), which reflects social anxiety due to the perception of being late in keeping up with trends, information, or experiences that hold a dominant position in the social landscape. This research initiative aims to examine the influence of TikTok usage on the lifestyle configurations of Generation Z students, with FOMO serving as a determinant that strengthens this relationship. This study is designed within the quantitative tradition with a positivist epistemological orientation. The study population includes 250 students from the Faculty of Computer Science at the National Pasim University in Bandung, with a sample size of 72 respondents derived using Slovin's formula. The analysis results indicate that TikTok usage and the level of FOMO have a positive and significant impact on changes in students' lifestyles. The regression coefficient estimates are in the positive direction, with a significance value exceeding the critical threshold of 0.05, indicating a unidirectional relationship between the variables. The model determination estimate, at around 57.5%, confirms that TikTok and FOMO are able to explain a significant portion of the variation in students' lifestyles, while the remainder is influenced by factors outside the scope of this study. This finding confirms that TikTok functions not only as a medium of entertainment but also as an agent shaping lifestyles influenced by social and psychological pressures. Therefore, strengthening digital literacy is crucial so that students can use social media more rationally and responsibly.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Political participation is a key indicator of local democratic quality. However, participation levels are shaped not only by structural factors but also by the configuration of social and psychological determinants underlying voting behavior. This study examines the configuration of social and psychological factors in political participation in Bangko District during the 2024 Merangin local election. A qualitative case study approach was employed. Informants were obtained using snowball sampling, including voters, community leaders, local election organizers, and campaign teams. Data were collected through in‑depth interviews, observation, and documentation, and analyzed using the interactive model of Miles, Huberman, and Saldaña. The findings reveal that participation is shaped by three key social factors family influence, neighborhood networks, and proximity to local political actors and three psychological factors political trust, perceived vote efficacy, and pragmatic motivation. The interaction of these dimensions produces a rational‑communal participation pattern, in which voting decisions are influenced by both benefit considerations and social pressure. These findings highlight that local political participation emerges from the interplay between social structure and individual psychological orientation. The study contributes to the literature on voting behavior in Indonesian local politics.

This study conceptually examines the influence of assurer type, assurance standards, and assurance level on the breadth of assurance statements in sustainability reports. Moving beyond prior literature that treats assurance as a binary variable (presence versus absence), this paper highlights disclosure breadth as a critical dimension of assurance quality and substance. Drawing on legitimacy theory and complemented by institutional theory, the study argues that the technical configuration of assurance shapes the quality of organizational legitimacy obtained by firms. Specifically, the type of assurer (public accounting firms versus non-accounting providers), the standards adopted (e.g., ISAE 3000 and/or AA1000AS), and the level of assurance (limited versus reasonable) influence the structure, systematic presentation, and comprehensiveness of assurance statements. Firms that engage reputable providers, apply globally institutionalized standards, and select reasonable assurance are more likely to issue broader and more detailed statements. In contrast, weaker institutional pressures may encourage symbolic assurance practices characterized by minimal disclosure. The study contributes theoretically by extending legitimacy theory to the technical dimensions of assurance and positioning disclosure breadth as a proxy for substantive legitimacy. Practically, it suggests that regulators and companies should emphasize transparency and comprehensiveness in assurance statements to enhance credibility and discourage symbolic sustainability reporting practices.