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Mental health issues among university students have become a growing concern, driven by academic pressures, career uncertainties, and complex social transitions. However, a large proportion of students remains reluctant to seek professional psychological support due to social stigma and limited access to institutional counseling services. This study proposes the design and implementation of an Android-based chatbot application that integrates mood tracking and sentiment analysis to continuously monitor the emotional states of university students. The system employs a fine-tuned RoBERTa (Robustly Optimized BERT Pretraining Approach) model trained on the EmoContext conversational dataset (SemEval-2019 Task 3), comprising 30,160 labeled three-turn dialogue instances across four emotion classes: angry, happy, sad, and others. The model was fine-tuned for four epochs using the AdamW optimizer with a learning rate of 2e-5 and a maximum sequence length of 128 tokens. Evaluation on a held-out validation set of 6,032 samples yielded an overall accuracy of 88.28%, a macro-average F1-score of 0.87, and a weighted-average F1-score of 0.88. Per-class F1-scores were 0.89 (angry), 0.83 (happy), 0.91 (others), and 0.86 (sad). The classified emotion is transmitted in real time to the chatbot response logic, which generates empathetic replies and personalized relaxation recommendations based on the detected mood. Primary data collection through questionnaires and interviews with 62 and 19 university students respectively confirmed the need for accessible digital mental health support. The results demonstrate that RoBERTa-based fine-tuning on conversational data provides a reliable foundation for real-time emotion-aware mental health chatbot systems.

The increasing complexity of urban road networks demands intelligent navigation systems capable of determining optimal routes efficiently. This research implements the Dijkstra Shortest Path algorithm to optimize route search on a location navigation system in Medan City. The system models a road network as a weighted graph comprising 57 strategic locations and over 90 road connections, represented using adjacency list data structures. The Dijkstra algorithm, implemented in Python using the heapq module for priority queue management, achieves an optimal time complexity of O((V+E) log V). The system features five main functions: shortest route search, popular routes, location listing, dynamic location addition, and dynamic road connection addition. System testing using a case study from Kualanamu Airport to the University of North Sumatra (USU) yielded an optimal route of 16.5 km through 4 road segments. Results demonstrate that the system successfully determines the most efficient route, provides accurate distance and travel time information for multiple transport modes (motorcycle, car, walking), and presents step-by-step journey guidance. This research contributes as a practical reference for applying shortest path algorithms in urban areas and serves as a foundation for developing more complex navigation applications in the future.

This study aims to analyze the effectiveness of the e-SIDAK application as a tool for recording and evaluating the performance of civil servants at BKPSDM Belitung Regency. The research employs a descriptive qualitative approach with 12 purposively selected informants, including managers, administrators, and application users. Data were collected through semi-structured interviews, observations, and documentation, and analyzed thematically using NVivo 12 Pro based on the digital service effectiveness indicators proposed by Pedrosa et al., namely ease of use, simplicity, understandability, consistency, trustworthiness, availability, speed, and usefulness. The findings indicate that the e-SIDAK application fulfills most effectiveness indicators, particularly in terms of ease of use, process simplicity, clarity of information, interface consistency, and service usefulness. The application facilitates systematic work activity recording, simplifies reporting, improves employee discipline, and supports performance monitoring integrated with the employee performance allowance system. However, its effectiveness remains limited in terms of system reliability, service availability, and access speed due to server capacity constraints, network conditions, and the accumulation of data input at the end of reporting periods. In addition, the point-based evaluation system has not fully reflected task duration, complexity, and workload. This study recommends strengthening system infrastructure, developing user guidelines, regulating input timing, and improving the point-based evaluation system to enhance digital service effectiveness in local government institutions.

This study aims to analyze the diffusion of the SIMPEL-DUKCAPIL innovation in population administration services in Bengkalis Regency. The study employs a descriptive qualitative approach using Everett M. Rogers’ diffusion of innovation theory, which includes five attributes: relative advantage, compatibility, complexity, trialability, and observability. Data were collected through interviews, observation, documentation, and triangulation involving 19 informants consisting of officials from the Population and Civil Registration Office and service users. Data analysis was conducted using the Miles and Huberman interactive model, including data reduction, data display, and conclusion drawing. The results show that SIMPEL-DUKCAPIL provides relative advantages in improving time efficiency, cost savings, and service accessibility, particularly for communities in archipelagic areas. The data indicate 2,311 application users in 2025 with unequal distribution across sub-districts, along with 107 blank spot areas that hinder digital service access. The compatibility and observability dimensions are relatively strong, as the application aligns with service needs and its benefits are directly perceived by users. However, the adoption process remains suboptimal due to challenges in complexity and trialability, including low digital literacy, limited internet infrastructure, technical system issues, and user preference for face-to-face services. This study highlights that innovation diffusion in archipelagic regions is not solely determined by application quality, but also by infrastructure readiness, socialization strategies, and community digital capabilities.

Suspicious urban sound recognition is a critical component of intelligent public safety and urban monitoring systems, enabling the automated identification of anomalous acoustic events such as gunshots, sirens, and other security-sensitive sounds. However, existing deep learning approaches often struggle to simultaneously capture long-range temporal dependencies and global contextual relationships, particularly under noisy and acoustically complex urban conditions. This limitation can reduce reliability in safety-critical scenarios where missed detections carry significant risk. To address these challenges, this study proposes a Multi-Branch Bidirectional Long Short-Term Memory (BiLSTM) framework with Multi-Head Self-Attention (MHSA) for enhanced sequential and contextual feature modeling. Mel-frequency cepstral coefficients (MFCCs) are extracted from a curated subset of the UrbanSound8K dataset, comprising five suspicious sound classes, and used as input to the proposed architecture. The multi-branch design enables complementary temporal representations, while the self-attention mechanism provides lightweight contextual weighting of BiLSTM outputs. Experimental results demonstrate that the proposed model achieves a test accuracy of 95.59%, outperforming conventional Dense and LSTM-based baseline models under identical experimental settings. An ablation study further confirms the contribution of multi-branch integration and attention-based enhancement to overall performance. Class-wise evaluation reveals consistently high recall across all sound categories, particularly for safety-critical classes such as gunshots and sirens. These findings indicate that the proposed framework provides robust and reliable performance, making it suitable for real-time smart city surveillance and public safety applications.

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.

This study aims to analyze various research results related to the application of context-based mathematics learning in improving students' mathematical literacy. Mathematical literacy is an important skill that enables students to understand, use, and apply mathematical concepts and reasoning in solving various problems related to real life. This study uses a qualitative approach with a literature review method. Research data was obtained through a review of various scientific articles relevant to the research topic. The analysis process was carried out in several stages, namely identification of literature sources, selection of articles based on topic suitability, and synthesis of published research findings. The results of the study show that integrating everyday contexts into mathematics learning can improve students' conceptual understanding, mathematical reasoning abilities, and problem-solving skills. Learning that links mathematical material to real-life situations can also help students understand concepts more meaningfully and encourage active engagement in the learning process. The findings of this study provide an overview that the application of mathematics learning based on everyday life contexts can be an effective strategy in improving students' mathematical literacy. Therefore, teachers need to design learning that links mathematical concepts to real-life situations so that students' mathematical literacy skills can develop optimally.

This study aims to design and develop an Augmented Reality (AR)-based application for real-time data monitoring of an automatic weather station at PT. XYZ. The system was developed using a prototyping method, involving five key stages: sensor data acquisition via CBOX devices and the Modbus RTU protocol, data transmission through a Flask-based Web API, storage in a MySQL database, QR code-based target image creation using the Vuforia Engine, and a 3D visualization interface developed with Unity integrated with the Vuforia SDK on Android devices. The system successfully received and stored weather data—such as temperature, humidity, and wind speed—into the MySQL database. The AR application also displayed a stable 3D interface panel over QR code markers, providing real-time data updates through an HTTP-based mechanism. This research demonstrates the successful integration of SCADA, Flask, MySQL, and Unity, enhancing both the functionality and commercial appeal of PT. XYZ’s weather station products. Future research should focus on field testing, cloud network integration, and device compatibility.

This study aims to identify and analyze the application of Pancasila values in the development of Indonesian tourism as a foundation for ethics and character. The method used is library research, reviewing various relevant written sources, including books, journals, articles, and other academic publications. The findings indicate that globalization exerts both positive and negative influences on Indonesian tourism, while simultaneously opening opportunities for the advancement of more ethical and responsible tourism practices. The integration of Pancasila values within the tourism sector such as mutual cooperation, environmental stewardship, social justice, and respect for cultural diversity can foster sustainable tourism development that balances economic growth with moral responsibility. Furthermore, these values contribute to strengthening national identity, preserving local wisdom, and enhancing community participation in tourism activities. The study also highlights that tourism grounded in Pancasila principles can improve social welfare by empowering local communities and promoting inclusive development. In conclusion, the application of Pancasila principles in tourism development is essential for maintaining a balance between economic progress and socio-cultural preservation, while reinforcing national character and resilience in the face of globalization and rapid global change.

The premium healthcare industry in urban areas is experiencing increasing competition along with the increase in healthcare facilities and the increasing public demand for fast, comfortable, and quality medical services. In this context, Columbia Asia Pulomas Hospital is implementing an expansion strategy by increasing facility capacity, modernizing services, and adding a Center of Excellence (COE) as a service differentiation. This study aims to analyze customer retention strategies within the Balanced Scorecard (BSC) framework, focusing on the relationship between customer perspectives, internal processes, learning and growth, and their application to the financial perspective. The research method uses a document-based policy and strategy analysis approach, field findings, and a synthesis of Balanced Scorecard theory and patient experience.The analysis shows that customer retention during the expansion phase is influenced not only by clinical quality, but also by the assurance of doctor time in practice, speed of service, physical comfort, and digitization of queues and administration. The addition of a COE has been shown to increase the perception of service value and expand market share through service specialist differentiation. Within the BSC framework, the customer perspective serves as a leading indicator for achieving the financial perspective, where increased patient retention contributes to increased revenue, ROI growth, and long-term financial expectations. The research implications emphasize that strategies for strengthening human resources, modernizing internal processes, and service innovation are important foundations in ensuring successful hospital expansion and enhancing competitive advantage.

This community service program aimed to strengthen the capacity of economic institutions and community business groups in villages and urban areas within the OVOP program locations in East Flores Regency. The program responded to the growing need for adaptive business strategies in the digital era and the importance of developing local potential into competitive products. It was implemented through a participatory, educative, and applicative approach during September 8–12, 2025. The training methods included lectures, interactive discussions, case studies, hands-on practice, and simulations to ensure a balance between theoretical understanding and practical application. The findings indicate significant improvements in participants’ understanding of the OVOP concept, managerial competencies, financial administration, and technical skills in product processing, packaging, and labeling. In addition, participants demonstrated increased awareness and ability in applying digital marketing strategies to expand market reach. The program also fostered stronger collaboration networks among business groups, economic institutions, and local government stakeholders. High participant engagement and active involvement throughout the sessions reflect the effectiveness of the training design. Overall, this program contributes to enhancing local economic resilience and promoting sustainable, competitive, and community-based enterprises. The study highlights the importance of continuous mentoring, institutional strengthening, and policy support to sustain the long-term impact of capacity-building initiatives in rural and semi-urban areas.

Management psychology plays a crucial role in enhancing the effectiveness of educational organizational management, particularly in understanding individual behavior, work motivation, leadership, and communication within the educational environment. The dynamic development of educational institutions requires a managerial approach that focuses not only on administrative aspects but also on the psychological aspects of the human resources involved. Therefore, the study of management psychology in education is essential to understand how this approach can support improvements in educational quality. This study aims to review and synthesize various studies on the application of management psychology in educational contexts and identify emerging research trends. The research questions posed are: (1) What are the trends and focuses of research on management psychology in education? and (2) What is the role of management psychology in improving the effectiveness of educational institution management? This study is expected to provide insights into how management psychology can be applied to enhance managerial performance and quality in education.

The principle of the presumption of innocence is a fundamental principle in criminal law that aims to protect the rights of suspects from the beginning of the legal process. This principle is enshrined in the 1945 Constitution and regulated in Article 8 of Law Number 8 of 1981 concerning the Criminal Procedure Code (KUHAP), which states that a person cannot be considered guilty unless a court decision with permanent legal force declares them guilty. The application of this principle at every stage of examination (investigation, prosecution, and trial) is crucial to ensuring the protection of the rights of perpetrators (suspects or defendants) and preventing the abuse of power that may harm individuals who have not been proven guilty. This research focuses on examining the consequences of violations of this principle in investigations and the protection of suspects’ rights within the criminal justice system, along with efforts to prevent such violations. The method used is a literature review with a juridical-normative approach. The findings indicate frequent neglect of the presumption of innocence, leading to human rights violations. To prevent this, stricter supervision by external monitoring bodies, such as the National Commission on Human Rights, is necessary to safeguard the rights of suspects.

Health program planning is an essential process in improving public health status through a systematic and data-driven approach. This study aimed to analyze health program planning based on situational analysis and the problem solving cycle (PSC), as well as to identify the root causes of problems using the fishbone approach. The research employed a descriptive qualitative method supported by simple quantitative data. Data were collected through in-depth interviews, observations, and documentation involving purposively selected informants. The results of the situational analysis identified several major health problems, including stunting, low posyandu attendance, and limited maternal knowledge regarding nutrition. Priority setting using the USG method determined stunting as the main health issue. Furthermore, root cause analysis using a fishbone diagram revealed that stunting was influenced by various factors related to man, machine, method, material, environment, money, and management. The application of the problem solving cycle facilitated systematic intervention planning, including nutrition education, community empowerment, and child growth monitoring. Program implementation demonstrated improvements in maternal knowledge and posyandu attendance. In conclusion, health program planning based on situational analysis, fishbone analysis, and the problem solving cycle proved effective in producing targeted and sustainable interventions.

Health program planning is an essential process in improving public health status through a systematic and data-driven approach. This study aimed to analyze health program planning based on situational analysis and the problem solving cycle (PSC), as well as to identify the root causes of problems using the fishbone approach. The research employed a descriptive qualitative method supported by simple quantitative data. Data were collected through in-depth interviews, observations, and documentation involving purposively selected informants. The results of the situational analysis identified several major health problems, including stunting, low posyandu attendance, and limited maternal knowledge regarding nutrition. Priority setting using the USG method determined stunting as the main health issue. Furthermore, root cause analysis using a fishbone diagram revealed that stunting was influenced by various factors related to man, machine, method, material, environment, money, and management. The application of the problem solving cycle facilitated systematic intervention planning, including nutrition education, community empowerment, and child growth monitoring. Program implementation demonstrated improvements in maternal knowledge and posyandu attendance. In conclusion, health program planning based on situational analysis, fishbone analysis, and the problem solving cycle proved effective in producing targeted and sustainable interventions.

The distribution of social assistance in Indonesia faces challenges such as inaccurate recipient data, overlapping programs, and limitations of traditional data management systems that lead to inaccurate targeting of aid. This study proposes a social assistance distribution optimization system using the Greedy algorithm that assesses recipient priorities based on economic conditions, number of family members, location, and urgency of needs with certain weights to produce objective rankings. This system is implemented in a JavaScript-based web application without external frameworks, making it lightweight and easily accessible. Simulations with 20 prospective recipients and a quota of 10 slots and validation with a dataset of 10,000 entries show that the Greedy algorithm produces identical results to Dynamic Programming but is much faster (669 times faster). In terms of complexity, this algorithm has O(n log n) time and O(n) space, and meets the requirements of the Greedy Choice Property and Optimal Substructure, making it a practical and efficient solution for managing large-scale social assistance distribution in Indonesia.

Communication is an essential human right, yet a significant communication gap persists between individuals with sensory disabilities, specifically the deaf and speech-impaired, and the general public. While many technological solutions have been proposed to translate sign language, existing models primarily rely on heavy deep learning architectures such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN/LSTM). These models often demand high computational power, leading to latency and limiting real-time application on standard devices. This study proposes a lightweight, fast, and highly responsive sign language translation system specifically designed to recognize static alphabets (A-Z) and single-character air writing. The system utilizes MediaPipe for hand tracking, where feature extraction is intelligently processed by calculating the relative spatial coordinates of fingertips to the wrist, reducing dependency on raw camera coordinates. Classification is performed using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, prioritizing computational efficiency without sacrificing accuracy. To enhance user experience, the system introduces three key novelties: smart relative feature extraction, an anti-duplication hold system with a 1-second timer to prevent input spamming, and a non-blocking multithreaded audio execution (Daemon Thread) utilizing Google Text-to-Speech (gTTS), ensuring the webcam feed remains fluid during audio playback. Additionally, an alternative air-writing mode is integrated, utilizing geometric heuristics and PyTesseract OCR to read single drawn letters in the air. The results indicate that the proposed system operates swiftly and efficiently, bridging the communication barrier with a hardware-friendly approach.

Inventory management is a crucial aspect of business sustainability, particularly for Micro, Small, and Medium Enterprises (MSMEs) that often face limitations in data management and technological adoption. Selaras Muba Lestari is Incubation Center, which supports MSMEs in Musi Banyuasin Regency, encounters challenges in managing product inventory due to reliance on manual recording systems. This study aims to design and develop a web-based inventory application using the Laravel framework to improve the efficiency and accuracy of stock management. The research adopts the Software Development Life Cycle (SDLC) Waterfall model, which includes requirements analysis, system design, implementation, and testing. The results indicate that the developed system is capable of improving data accuracy, accelerating the recording process, and providing real-time inventory reports. Furthermore, this study reinforces previous findings that digitalizing inventory systems serves as a strategic solution to enhance the competitiveness of MSMEs.

This study aims to analyze the role of assessment in decision-making within guidance and counseling services. Assessment plays a crucial role in providing accurate, objective, and comprehensive data regarding students’ characteristics, needs, and problems. This research employs a qualitative approach using a literature review method by analyzing 25 relevant scholarly sources related to assessment, measurement, and counseling services. The findings indicate that measurement serves as the fundamental basis of assessment, which involves systematic processes to obtain quantitative data. The quality of assessment results is highly influenced by the reliability and validity of the instruments used. Reliable instruments ensure consistency, while valid instruments ensure accuracy in measuring intended variables. Furthermore, assessment functions as a basis for diagnosis, planning, implementation, and evaluation of guidance and counseling services. The study also reveals that the application of assessment in schools is not yet optimal due to several factors, including limited counselor competence and lack of effective use of instruments. In addition, the integration of technology and the use of both test and non-test instruments have been shown to improve the accuracy and effectiveness of assessment. In conclusion, systematic and well-implemented assessment significantly enhances the quality of decision-making in guidance and counseling services. Therefore, improving counselors’ competencies and optimizing the use of assessment instruments are essential to support more effective counseling practices

This study addresses the issue of suboptimal student academic performance, which is frequently linked to the selection of instructional strategies that do not actively engage learners. Learning outcomes represent a multidimensional construct involving knowledge acquisition, attitude development, and skill formation. Therefore, the application of an appropriate learning model is essential to support meaningful learning experiences. This research aims to investigate the impact of implementing a Problem Based Learning (PBL) model supported by audio-visual media on students’ academic achievement. A quantitative approach with a quasi-experimental design was employed. The study involved two groups: an experimental class receiving the PBL intervention and a control class experiencing conventional instruction. Data were collected through pretest and posttest assessments and analyzed using both descriptive and inferential statistical techniques. The findings reveal a statistically significant difference between the two groups, indicated by a significance value below 0.05. This confirms that integrating problem-based strategies with audio-visual support contributes positively to improving students’ learning outcomes. The results highlight the importance of student-centered learning environments in fostering deeper understanding and engagement.