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Project-Based Learning (PjBL) is a learning method that emphasizes students' active involvement in completing real-world projects relevant to nursing education. Evaluating the implementation of PjBL is essential to assess its application at the Faculty of Nursing, Universitas Riau. This study aimed to analyze the implementation of the PjBL method among nursing students at Universitas Riau. The study employed a descriptive evaluative quantitative design with a cross-sectional approach. A total of 123 fourth-semester students were selected using a purposive sampling technique. The research instrument was a closed-ended questionnaire consisting of 25 Likert-scale items that had been validated through expert judgment and demonstrated good reliability with a Cronbach’s alpha coefficient of 0.80. The instrument covered the dimensions of project planning, collaboration and communication, motivation and creativity, critical thinking and independence, understanding and application of learning materials, interest and satisfaction, as well as lecturer assessment and feedback. Data were analyzed using univariate analysis with frequency and percentage distributions. Most respondents were 20 years old (55.3%), female (87.0%), and had a Semester Grade Point Average (SGPA) and Cumulative Grade Point Average (CGPA) above 3.50. The implementation of PjBL was categorized as high (63.4%), moderate (35.8%), and low (0.8%). Overall, the implementation of PjBL was considered optimal and effectively supported students’ active engagement, collaboration, and understanding of learning materials.

Project-Based Learning (PjBL) is a learning method that emphasizes students' active involvement in completing real-world projects relevant to nursing education. Evaluating the implementation of PjBL is essential to assess its application at the Faculty of Nursing, Universitas Riau. This study aimed to analyze the implementation of the PjBL method among nursing students at Universitas Riau. The study employed a descriptive evaluative quantitative design with a cross-sectional approach. A total of 123 fourth-semester students were selected using a purposive sampling technique. The research instrument was a closed-ended questionnaire consisting of 25 Likert-scale items that had been validated through expert judgment and demonstrated good reliability with a Cronbach’s alpha coefficient of 0.80. The instrument covered the dimensions of project planning, collaboration and communication, motivation and creativity, critical thinking and independence, understanding and application of learning materials, interest and satisfaction, as well as lecturer assessment and feedback. Data were analyzed using univariate analysis with frequency and percentage distributions. Most respondents were 20 years old (55.3%), female (87.0%), and had a Semester Grade Point Average (SGPA) and Cumulative Grade Point Average (CGPA) above 3.50. The implementation of PjBL was categorized as high (63.4%), moderate (35.8%), and low (0.8%). Overall, the implementation of PjBL was considered optimal and effectively supported students’ active engagement, collaboration, and understanding of learning materials.

This study aims to analyze the influence of digital experience and customer experience on passenger loyalty on the Progo Train route, Pasar Senen–Lempuyangan. The increasing digitalization of public transportation services through the Access by KAI application has highlighted the importance of examining the quality of digital experience and customer experience in maintaining passenger loyalty. The study employed a quantitative approach with a causal associative design. Data were collected from 150 respondents through a questionnaire with a five-point Likert scale using a purposive sampling technique. Data analysis was performed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 software (Ringle et al., 2024). The results of the outer model evaluation showed that all indicators had outer loadings > 0.70, Average Variance Extracted (AVE) > 0.50, Composite Reliability (CR) and Cronbach's alpha > 0.70, thus all constructs were declared valid and reliable. The inner model test shows that digital experience has a positive and significant effect on passenger loyalty (β = 0.192; T = 2.237; p = 0.025). Customer experience also has a positive and significant effect on passenger loyalty (β = 0.243; T = 3.268; p = 0.001). The coefficient of determination (R²) of 0.133 indicates that both variables explain 13.3% of the variance in passenger loyalty. The customer experience variable has a more dominant influence than digital experience on Progo Train passenger loyalty.

Abstract. This research aims to analyze the effectiveness and reliability of production machines in the process of making public street lighting poles (PJU) at PT. XYZ The main problem faced by the company is high machine downtime so that production targets are not achieved. Therefore, a method is needed that is able to measure machine effectiveness as a whole and identify the main causes of production losses. The method applied includes Overall Equipment Effectiveness (OEE) to measure machine effectiveness based on three components, namely availability, performance and quality. The Total Productive Maintenance (TPM) approach is used to identify factors causing low effectiveness through Six Big Losses analysis. Apart from that, Mean Time To Repair (MTTR) and Mean Time Between Failure (MTBF) are calculated to determine the level of machine reliability. The data used includes machine working hours, downtime, operating time, production quantities, defective products, as well as machine damage and repair data. The analysis results are expected to show the level of machine effectiveness and identify the dominant factors causing downtime. Based on these results, improvement proposals are prepared to reduce downtime, increase machine reliability and improve production productivity Keywords: Overall Equipment Effectiveness (OEE), Total Productive Maintenance (TPM), Six Big Losses, Downtime, Efektivitas   Abstrak. Penelitian ini bertujuan untuk menganalisis efektivitas dan keandalan mesin produksi pada proses pembuatan tiang penerangan jalan umum (PJU) di PT. XYZ Permasalahan utama yang dihadapi perusahaan adalah downtime mesin yang tinggi sehingga target produksi tidak tercapai. Oleh karena itu, diperlukan metode yang mampu mengukur efektivitas mesin secara menyeluruh dan mengidentifikasi penyebab utama kerugian produksi. Metode yang diterapkan meliputi Overall Equipment Effectiveness (OEE) untuk mengukur efektivitas mesin berdasarkan tiga komponen, yaitu availability, performance, dan quality. Pendekatan Total Productive Maintenance (TPM) digunakan untuk mengidentifikasi faktor penyebab rendahnya efektivitas melalui analisis Six Big Losses. Selain itu, dilakukan perhitungan Mean Time To Repair (MTTR) dan Mean Time Between Failure (MTBF) untuk mengetahui tingkat keandalan mesin. Data yang digunakan mencakup jam kerja mesin, downtime, waktu operasi, jumlah produksi, produk cacat, serta data kerusakan dan perbaikan mesin. Hasil analisis diharapkan dapat menunjukkan tingkat efektivitas mesin dan mengidentifikasi faktor dominan penyebab downtime. Berdasarkan hasil tersebut, disusun usulan perbaikan untuk mengurangi downtime, meningkatkan keandalan mesin, dan memperbaiki produktivitas produksi Kata kunci: Overall Equipment Effectiveness (OEE), Total Productive Maintenance (TPM), Six Big Losses, Downtime Mesin, Efektivitas Mesin

Modern food supply chains, particularly those involving essential commodities like rice, often suffer from major challenges such as product fraud, inefficient record-keeping, and a lack of consumer trust. Traditional centralized systems are prone to data tampering, limited transparency, and poor traceability, making it difficult to verify the authenticity and origin of goods. To address these issues, our research introduces TraceRoot, a blockchain-based traceability framework designed to enhance transparency, accountability, and trust in agricultural supply chains.TraceRoot leverages the immutability and decentralization of blockchain technology to maintain a secure, distributed ledger that records every transaction and movement of goods across the supply chain. Each stakeholder including farmers, distributors, retailers, and consumers has role-based access to authenticated data through a user-friendly interface. The framework integrates smart contracts to automate transactions and digital signatures to verify the integrity of the data being uploaded, minimizing the risk of human error or manipulation

This study analyzes the effect of logistics digitalization systems and personnel competence on the distribution of personnel uniforms (kaporlap) at Kodiklatal. The study was motivated by the fact that the distribution system had not yet operated optimally despite the implementation of digital logistics, while delays in updating the Sistem Informasi Entitas Samapta (SIMBENSAM), limited documentation of device identities, and unequal digital skills among personnel still constrained distribution performance. A quantitative approach with explanatory design was applied. The population consisted of 3,099 personnel and the sample comprised 97 respondents selected using Slovin's formula. Data were collected through Likert-scale questionnaires and internal documentation, then analyzed using validity and reliability tests, classical assumption tests, descriptive analysis, and simple and multiple linear regression. The results show that the logistics digitalization system has a positive and significant effect on kaporlap distribution (t = 24.607; p < 0.05), personnel competence also has a positive and significant effect (t = 37.972; p < 0.05), and both variables simultaneously affect distribution performance (F = 788.285; p < 0.05) with an R square of 0.944. These findings confirm that digital systems and human competence must be strengthened in an integrated manner to support efficient, accurate, and timely distribution in a military education environment.

Hot material dumping in the chemical industry is an important process stage that affects occupational safety, process stability, and product quality reliability. In manual processes, dumping activities may cause variation because they are influenced by operator condition, pouring angle, work speed, and exposure to hot materials. This study aims to analyze the consistency of the hot material dumping process and formulate a B2B strategy based on process reliability. The research method uses a descriptive-quantitative case study approach with data from 20 batches, analyzed through three indicators: dumping time, pouring flow stability, and material temperature change or ΔT. The data were primary data obtained directly through field observation, production-process documentation, and recording of process parameters; journal literature was used only as the theoretical and interpretive basis. The results show that the automated process provides more consistent performance than the manual process. The average dumping time decreased from 49.14 seconds to 30.18 seconds, the coefficient of variation for dumping time decreased from 12.44% to 1.36%, the pouring flow stability score increased from 2.55 to 4.70, and the average material ΔT decreased from 15.26°C to 8.16°C. These findings indicate that automation can improve process regularity, reduce variation between batches, and maintain material conditions in a more controlled manner. This process consistency can serve as the basis for a B2B strategy based on process reliability, emphasizing process reliability, production safety, quality consistency, and data-based evidence as key values in building industrial customer trust.

The development of digital services by BPJS Ketenagakerjaan through the JMO (Jamsostek Mobile) application has triggered a surge in large-scale and unstructured user reviews on the Google Play Store, thereby complicating manual analysis and conventional sentiment analysis in accurately identifying specific issues. This research aims to implement the Aspect-Based Sentiment Analysis (ABSA) method to granularly evaluate JMO application reviews based on specific aspects, while simultaneously addressing class imbalance and computational efficiency issues. The proposed method combines the pretrained IndoBERT model as a contextual feature extractor, the SMOTE technique to balance the training data, and an artificial neural network (Neural Network) as the classification layer without performing full fine-tuning. The dataset used consists of 90,268 unique reviews categorized into five main aspects through keyword matching, namely General Satisfaction/Complaints, Performance & Stability, Service & Support, Feature Quality, and UI/UX, with initial lexicon-based labeling using the InSet Lexicon. The research results indicate that the proposed model successfully achieves highly optimal performance with an accuracy rate of 91.81% and a weighted F1-score of 92%. Furthermore, the implementation of SMOTE proved effective in enhancing model reliability on the minority class (negative sentiment), achieving an F1-score of 89%. The implications of this research contribute an accurate and efficient aspect-based sentiment analysis framework for developers, and serve as a strategic evaluation tool for BPJS Ketenagakerjaan in mapping specific user complaints to accelerate continuous improvements in the performance, stability, and service quality of the JMO application.

Misconceptions on the topic of waves are a systemic problem in high school physics learning that requires a comprehensive diagnostic instrument. Conventional diagnostic instruments up to the four-tier generation have limitations in revealing the conceptual structure behind students' thinking errors. This study aims to develop a five-tier multi-representation diagnostic instrument on the topic of waves using the 4D model (Define, Design, Develop, Disseminate). The main innovation of this instrument lies in the integration of multi-representations (verbal, pictures, graphics, mathematical) in Tier 1 and the addition of a paraphrase tier (Tier 5) that asks students to rewrite their understanding in their own words. The development process includes needs analysis, designing a grid of 16 questions, validation by two experts, and a limited trial on 34 grade XI students of SMAN 13 Kota Jambi. The results of expert validation showed an average percentage of 91.25% (Very Good) from both validators. Content validity was met with a percentage of False Positive (FP) of 6.80% and False Negative (FN) of 4.41%, both below the 10% threshold. Construct validity was confirmed through a significant Product Moment correlation (r = 0.342–0.348; sig. < 0.05) and factor analysis with six significant factors (eigenvalue > 1). The instrument's reliability was high, with a Cronbach's Alpha of 0.726. This instrument is expected to help physics teachers diagnose students' misconceptions more precisely and thoroughly on the topic of waves.

This study aims to analyze the influence of social support and soft skills on the work readiness of the 2022 cohort students in Tasikmalaya City. Work readiness is an essential aspect that students must possess to face increasingly competitive labor market demands. This research employed a quantitative approach using a survey method. Data were collected through an online questionnaire distributed to 110 respondents selected from a population of 150 students using the Slovin formula with a 5% margin of error and a simple random sampling technique. Data analysis was conducted using multiple linear regression with the assistance of IBM SPSS Statistics 25. Prior to hypothesis testing, validity and reliability tests were performed to ensure the quality of the research instruments. In addition, classical assumption tests, including normality, multicollinearity, heteroscedasticity, autocorrelation, and linearity tests, were conducted to verify the suitability of the regression model. The findings indicate that both social support and soft skills have a positive and significant effect on students’ work readiness. Support from family, peers, and the academic environment enhances students’ confidence in preparing for employment. Furthermore, communication skills, teamwork, problem-solving abilities, and responsibility as components of soft skills strengthen students’ readiness to enter the professional workforce. These findings highlight the importance of developing soft skills and strengthening social support to improve students’ work readiness.

This study investigates the performance of Internet of Things (IoT)-based monitoring systems using a mobile hotspot and IoT sensors for temperature and humidity data transmission. The research is based on the IoT concept, which enables electronic devices to communicate and exchange data through internet networks without direct human intervention. System performance was evaluated using standard Quality of Service (QoS) parameters, including throughput, packet loss, delay, and jitter. The experimental setup utilized a NodeMCU ESP32 microcontroller and a DHT22 sensor, with measurements conducted at various transmission distances through wireless communication media. The objective was to determine the reliability of hotspot connectivity and sensor communication in supporting IoT applications. The results indicate that the optimal performance was achieved at a distance of 20 meters using a 40-lambda variation. Furthermore, the communication signal between the ESP32 device and the mobile hotspot remained detectable up to a maximum distance of 32 meters. These findings demonstrate the effectiveness of the proposed IoT system for environmental monitoring applications within specific transmission ranges.

This study was motivated by the lack of standardized and objective assessment instruments for the teaching of the Syofyani Minang Payung Dance at the junior high school level, resulting in an assessment process that remains largely subjective and fails to measure psychomotor, affective, and cultural aspects in a balanced manner. This study aims to design and test the validity and reliability of a performance assessment rubric for the Syofyani Minang Payung Dance in cultural arts education at SMP Negeri 1 Bukittinggi. The research method used is a mixed-methods approach with a sequential explanatory design. The research subjects consisted of three dance instructors serving as expert judges, one cultural arts teacher, and 33 junior high school students. The research instrument was a performance-based assessment rubric covering five competency indicators: basic movement techniques; alignment with musical rhythm and dynamics; expression and character interpretation; mastery of payung props and movement safety; and accuracy of floor patterns and group synchronization. Quantitative data analysis was conducted using IBM SPSS Statistics through the Corrected Item-Total Correlation validity test and Cronbach’s Alpha reliability test, while qualitative data was analyzed using descriptive-interpretive methods. The research results show that all indicators have validity scores above 0.30 and are therefore considered valid, and the Cronbach’s Alpha reliability score is above 0.70, indicating good internal consistency of the instrument. Furthermore, the interview results indicate that the rubric is considered relevant, clear, and aligned with the learning characteristics of Syofyani’s Minang Payung Dance at the junior high school level. Consequently, the developed assessment rubric is deemed suitable for use as an objective, standardized, and contextually appropriate assessment instrument for dance education rooted in local culture.

This study aims to determine the effect of the deep learning learning approach on fourth-grade elementary school students’ understanding of IPAS concepts, particularly on the topic of pollination in perfect flowers. This research employed a quantitative approach with a quasi-experimental design using a pre-test and post-test control group design. The population consisted of all fourth-grade students of SDN 4 Muara Dua, totaling 58 students, who were divided into experimental and control classes through total sampling techniques. Data collection techniques included tests, observations, and documentation. The research instruments were tested for validity and reliability before being implemented in the study. The results revealed that the implementation of the deep learning approach had a positive effect on students’ understanding of IPAS concepts. This was indicated by the higher post-test scores achieved by students in the experimental class compared to those in the control class. In addition to improving conceptual understanding, the deep learning approach also enhanced students’ critical thinking skills, activeness, and participation during the learning process. Therefore, the deep learning learning approach can be considered an effective alternative strategy to improve elementary school students’ understanding of IPAS concepts.

The quick advancement of digital technology has drastically changed the social and religious life of Indonesian teenagers. The purpose of this study is to investigate how pupils at Al Muslim Junior High School's daily worship practices, self-control, and fear of missing out (FoMO) affect the principles of Islamic Religious Education (PAI). The study used a quantitative methodology with a causal and correlational design. All students in grades VII, VIII, and IX made up the study population for the 2025–2026 school year. Using the Slovin formula, a proportionate stratified sample of 171 students with a 5% margin of error was chosen. A five-point Likert scale questionnaire was used to gather data. The Pearson Product-Moment correlation (r > 0.30) was used to evaluate validity, while Cronbach's Alpha (α > 0.70) was used to test reliability. Multiple linear regression using SPSS version 26 was used for quantitative analysis, beginning with traditional assumption tests for heteroscedasticity (Glejser), multicollinearity (VIF), and normality (Kolmogorov-Smirnov). According to the analysis results, self-control had a substantial, favorable impact on the practice of PAI values, but FoMO had no significant influence (β = -0.034, p = 0.530).

This study examines the role of extension agents in supporting the success of beef cattle farmer groups in Tabongo District, Gorontalo Regency. The success of farmer groups is influenced not only by livestock potential, but also by the effectiveness of extension services, institutional capacity, and farmers’ willingness to adopt improved farming practices. Using an explanatory approach, the study collected primary data from 50 members of beef cattle farmer groups across nine villages through a Likert-scale questionnaire. Data were analyzed using descriptive statistics, validity and reliability tests, classical assumption tests, and multiple linear regression. The descriptive findings show that extension agents performed well as educators, facilitators, motivators, dynamizers, and catalysts. However, the catalyst role was excluded from the regression model because its indicators were invalid. Partially, only the motivator role had a significant effect on group success, with a coefficient of 1.683, t-value of 4.758, and significance value of 0.000. The educator, facilitator, and dynamizer roles showed positive but insignificant effects. Simultaneously, the roles of extension agents significantly influenced group success, with an F-value of 9.474 and significance value of 0.000. The R Square value of 0.457 indicates that extension agents explained 45.7% of group success.

This study was motivated by the low basic multiplication skills of third-grade elementary school students, particularly in conceptual understanding, calculation accuracy, and problem-solving abilities. The objective of this study was to determine the effect of the Jarimatika method combined with the Problem Based Learning (PBL) model on students’ basic multiplication skills. This study employed a quantitative approach using a one-group pretest-posttest design. The participants were 30 third-grade students of SD Negeri Sukanegara. Data were collected through pretest and posttest instruments that had been tested for validity and reliability. The results showed a significant improvement in students’ multiplication skills after the treatment, as indicated by the increase in the mean score from 55.17 in the pretest to 80.00 in the posttest. The paired sample t-test analysis revealed a significant difference between pretest and posttest scores (t = -10.951, p = 0.000 < 0.05). The findings indicate that the implementation of the Jarimatika method combined with the Problem Based Learning (PBL) model effectively improves students’ basic multiplication skills and supports more active and meaningful mathematics learning.

An appropriate strategy has become a crucial factor for Micro, Small, and Medium Enterprises (MSMEs) in maintaining customer loyalty. This study focuses on analyzing the implementation of marketing communication strategies adopted by Nasi Goreng Barokah Jaya, an MSME located in Cikampek, to retain its loyal customers. This study employed a descriptive qualitative approach based on the 7P Marketing Mix theory developed by Kotler and Keller (2016). Data were collected through in-depth interviews, field observations, and documentation involving the business owner, employees, and loyal customers. The data were subsequently analyzed using the Miles and Huberman interactive model, while data validity was ensured through triangulation. The findings reveal that direct interaction, responsive and friendly service, word-of-mouth promotional activities, and personalized approaches serve as the main pillars in building customer loyalty. Customer loyalty is significantly influenced by product quality, price affordability, service reliability, and the emotional bond established between the business and its customers. These findings confirm that simple yet consistently implemented marketing communication strategies are effective in maintaining a loyal customer base in the culinary MSME sector.

This study was conducted based on the crucial role of diesel generator engines as the primary source of electrical power on board ships, making their operational reliability essential. One of the factors influencing generator engine performance is lubricating oil temperature. Excessive lubricating oil temperature may reduce lubrication effectiveness, increase friction between moving components, and lower engine efficiency. Therefore, this research aimed to identify the causes of high lubricating oil temperature in diesel generator engines and determine appropriate corrective actions. The study employed a descriptive quantitative approach. Data were collected through direct observation, interviews, and documentation during sea practice aboard MV. CL FLANDERS from 4 July 2024 to 5 July 2025. The data were analyzed using simple linear regression to examine the relationship between generator load and lubricating oil temperature. The findings revealed that generator load had a significant effect on the increase in lubricating oil temperature. Under normal operating conditions, the lubricating oil temperature ranged from 60°C to 72°C, while under abnormal conditions it increased to between 68°C and 81°C. The abnormal rise in temperature was mainly caused by cooling system problems, particularly a dirty LO cooler, scale deposits on the sea water pump impeller, and blockage in the cooling capillary pipes. The regression equation under normal conditions was Y = 45 + 0.30X, whereas under abnormal conditions it was Y = 53.5 + 0.30X. Elevated lubricating oil temperature resulted in lower oil viscosity, higher fuel consumption, and reduced diesel generator engine performance. Therefore, regular maintenance of the LO cooler, sea water pump, and continuous temperature monitoring are necessary to maintain optimum engine performance.

Government procurement plays a strategic role in supporting effective, efficient, transparent, and accountable public administration. To promote procurement reform, the government has implemented an electronic procurement system through the Electronic Procurement Service (LPSE). The implementation of the Electronic Procurement System (SPSE) in Pematang Siantar City aims to improve procurement quality, reduce irregularities, and accelerate the delivery of goods and services. However, several challenges persist, including limited human resources capable of operating the system, technical disruptions, lack of outreach to local vendors, and weak coordination between the Procurement Unit (UKPBJ) and related agencies. This study aims to analyze the effectiveness of SPSE implementation in Pematang Siantar City. The research uses a descriptive qualitative approach. Data were collected through interviews with UKPBJ officials and procurement officers from several local government units, direct observation of procurement activities via SPSE, and documentation review. Data analysis employed Duncan’s effectiveness indicators: goal attainment, integration, and adaptation. The results show that SPSE implementation has not been fully effective. In terms of goal attainment, procurement processes follow established procedures, but delays still occur due to system disruptions and limited user competence. In terms of integration, coordination between UKPBJ and technical units remains suboptimal, affecting communication and oversight. Regarding adaptation, although the information technology infrastructure is adequate, human resource capacity is still limited. Therefore, improving human resources, strengthening coordination, and enhancing system reliability are necessary to achieve optimal effectiveness.

Public Wi-Fi services (Wifi_STAKat) at the State Catholic College of Pontianak continue to face technical issues, such as network speed and connection stability, as well as non-technical challenges, including the responsiveness of administrators to user complaints. This study aims to analyze user satisfaction based on the five Servqual dimensions and to map improvement priorities using the Importance–Performance Analysis (IPA) method. The relationship between Servqual and IPA is explained by mapping GAP values (perception–expectation) into the four IPA quadrants to determine the urgency level of service improvements. A 4-point Likert scale was used to avoid neutral responses and strengthen the clarity of respondents’ perceptions. The results show that all dimensions have negative GAP scores, particularly responsiveness and reliability, which are directly related to technical indicators (speed, stability, coverage) and non-technical factors (responsiveness to complaints, ease of access. The study recommends integrating an IT-based monitoring system and increasing network capacity to improve service quality.