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This study examines students’ perceptions of the use of online platforms for English listening practice. The research employs a mixed-methods approach involving 150 undergraduate students through questionnaires and semi-structured interviews. The findings indicate that the majority of students hold positive attitudes toward the use of online platforms, primarily due to time flexibility, diverse content, and the ability to learn independently. Nevertheless, several challenges were identified, including internet connectivity issues that disrupt the learning experience, a lack of personalized feedback, and difficulties in maintaining learning motivation. Features such as adjustable playback speed, the availability of transcripts, and interactive exercises received high levels of satisfaction. Overall, the study concludes that although online platforms have great potential to enhance English listening skills, their effectiveness largely depends on proper implementation, adequate technical infrastructure, and active support from educators. Therefore, this study recommends improvements in technical aspects and the development of more effective learning methods to achieve more optimal outcomes.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

This study analyzes the effectiveness of using BNI Bank’s QRIS in improving transaction convenience and efficiency at the canteen of Universitas 17 Agustus 1945 (UNTAG) Surabaya. The research was conducted by a team led by Ida Ayu Nuh Kartini, S.E., M.M., together with students of the Management Study Program, during November–December 2025. The rapid development of digital banking has encouraged the adoption of QRIS as a standardized non-cash payment system regulated by Bank Indonesia. This study aims to examine the level of QRIS BNI utilization, assess ease of use (application access and QR code scanning), and evaluate transaction efficiency, including time savings, queue reduction, and error minimization. A descriptive quantitative approach was employed using a survey method with a five-point Likert scale questionnaire distributed to 36 respondents consisting of students, lecturers, and administrative staff who had used QRIS at the canteen. Primary data were analyzed descriptively using averages and percentages, supported by relevant literature. The results show that QRIS is perceived as highly effective, with scores above 90% for ease of use (95%), transaction speed (92.78%), reduction of change-related issues (97.78%), support for digitalization (97.22%), and security (91.11%). The main limitation identified is dependence on internet connectivity (85%). Overall, respondents strongly support the full implementation of non-cash payment systems at the campus canteen.

This study examines the implementation of the Coretax system in preparing electronic withholding tax slips (e-Bupot) and submitting Monthly Income Tax Article 21 (PPh 21) Returns at PT RSA. Coretax, launched by the Directorate General of Taxes (DGT) and officially implemented on January 1, 2025, is part of the government’s effort to modernize tax administration and improve taxpayer compliance. The research uses a qualitative descriptive method with a case study approach. Data were obtained through interviews with personnel responsible for PPh 21 administration, direct observation of e-Bupot preparation and monthly tax return filing through Coretax, and documentation review of tax reports and withholding slips. The analysis compares PT RSA’s practices with applicable tax regulations and identifies technical challenges during system use. The results show that PT RSA has complied with prevailing regulations, including the application of the Average Effective Rate (TER) and the utilization of Coretax for tax administration. However, technical constraints remain, particularly unstable internet connections and slow Coretax server performance during peak reporting periods, leading to data entry delays and upload failures. Therefore, improvements in system stability and performance are needed to support timely reporting and optimal tax compliance.

Effective communication is a key competency for medical professionals, but traditional classroom-based methods in Indonesia face challenges such as limited practice, geographical barriers, and insufficient exposure to diverse patient scenarios. Mobile-assisted learning (MAL) offers a promising solution to enhance medical communication training outside traditional settings. This study investigates the effectiveness of MAL interventions in developing communication skills among Indonesian medical students and healthcare professionals. A mixed-methods approach was used, involving a quasi-experimental design with pre- and post-assessments of communication competence among 180 participants from three Indonesian medical schools. The MAL intervention included a mobile app with video demonstrations, interactive case scenarios, peer feedback, and microlearning modules. Quantitative data showed a significant improvement in communication competence (mean increase of 23.4%, p<0.001), with notable gains in information gathering (28%), relationship building (26%), and patient education (21%). The mobile platform saw high engagement (average of 4.3 sessions/week) and 87% module completion. Qualitative data revealed increased confidence in consultations, improved cultural sensitivity, and better time management. Challenges included inconsistent internet access, varying digital literacy, and resistance from traditional educators. MAL shows potential for improving medical communication in Indonesia, offering flexible, accessible training. Successful implementation requires addressing infrastructure issues, integrating MAL into existing curricula, and training faculty. This study adds to the growing evidence supporting technology-enhanced medical education in resource-limited settings.

This study examines the implementation of RoRo ship services in Indonesia, highlighting the implementation of RoRo ships and the current national logistics framework potential strategies, particularly in the 3T region. This research employs a qualitative descriptive methodology that includes a literature review. It utilizes secondary data from previous studies, comprising books and original scientific reports published in articles or journals. The findings indicated 1) The use of RoRo ships has gone through progressive developments, like the government's initiative on “Sea Toll” designed to support the logistics sector, particularly in 3T regions. 2) Enhancing the logistics framework through the integration of National Logistic EcosIstem (NLE) alongside the Internet of Things (IoT) to increase logistics efficiency that is rapid, effective, and clear. Nevertheless, these initiatives faces challenges. Consequently. The proposed strategies focus on improving growth center development through logistics integration (shipping, port management, and intermodal) and implementing digitalization in logistics document processing.

This community service activity aims to improve digital literacy among children and adolescents in Telang Sari Village through basic computer training and networking introduction. The main problem faced by the community is the low level of technological literacy caused by limited facilities and lack of access to structured computer education. This program was implemented during the Thematic Community Service Program (KKNT) by applying participatory and practical training methods. The activities included introduction to computer hardware and software, basic typing skills, document creation, internet literacy, and simple LAN configuration practices for junior high school students. The results show a significant improvement in participants’ understanding and confidence in using computers and basic networking concepts. Children and adolescents who previously had minimal exposure to technology were able to operate computers, create simple documents, and understand responsible internet usage. The program also contributed to increasing students’ motivation to learn technology and preparing them to face digital challenges. Overall, this activity demonstrates that basic computer training with hands-on practice can effectively enhance digital literacy in rural communities.

ARP spoofing attacks are a serious threat to network security, particularly in vulnerable Internet of Things (IoT) environments. This final project aims to detect ARP spoofing attacks on IoT net-works using a combination of Random Forest (RF) and Robust PCA methods. RF is chosen for its classification capabilities and handling of non-linear data, while Robust PCA is used for di-mensionality reduction and handling outliers in the data. The dataset used is "MITMArpSpoof-ing.pcap.csv," which contains network traffic data. The data is processed by performing prepro-cessing, feature scaling, and converting labels to binary (0 for benign, 1 for ARP spoofing). Subsequently, Robust PCA is applied to reduce data dimensions, and then the data is trained using the RF model. The test results show that the RF model with Robust PCA achieves an accu-racy of 96.02% in detecting ARP spoofing attacks. This method has proven effective in identify-ing and classifying ARP spoofing attacks on IoT networks.

The massive adoption of Internet of Things (IoT) devices is expanding the cyberattacks surface, particularly by the Mirai botnet, which exploits the dynamic characteristics of data traffic. This research proposes a Mirai detection approach based on a Recurrent Neural Network (RNN) optimized using Bayesian Optimization to improve prediction accuracy on sequential data. Unlike previous studies, this research utilizes the latest CIC IoT-DIAD 2024 dataset and applies probabilistic optimization to the hyperparameter space, including RNN units, dropout, and learning rate. The experiment was conducted on 201,021 valid data points, with dimensionality reduction using PCA as the optimal point to represent essential features without redundancy. The results show a significant increase in accuracy from 97.95% to 99.69%, accompanied by an 84% decrease in False Negatives, an 86% decrease in False Positives, and an AUC value of 0.9999. These findings confirm that integrating RNN and Bayesian Optimization not only improves numerical performance but also strengthens the reliability of the intrusion detection system for modern IoT ecosystems with controlled computational loads.

The rapid development of the Internet of Vehicles (IoV) has introduced new security challenges, particularly in protecting Controller Area Network (CAN Bus) communications from cyberattacks such as Denial of Service (DoS) and spoofing attacks. This study proposes the implementation of the Extreme Gradient Boosting (XGBoost) algorithm combined with Information Gain feature selection to improve intrusion detection performance in IoV environments. The CICIoV2024 dataset, which represents both benign and malicious traffic, is used as the primary data source. The research process includes data integration, preprocessing, feature selection, data splitting, and model training using a 5-fold cross-validation approach. Experimental results demonstrate that the proposed model achieves outstanding performance, with accuracy, precision, recall, and F1-score exceeding 99.99%, and an Area Under Curve (AUC) value approaching 1.00. Furthermore, Information Gain successfully identifies the most influential CAN payload features, enhancing model efficiency without sacrificing accuracy. These findings confirm that the combination of Information Gain and XGBoost is highly effective for developing a fast, accurate, and efficient intrusion detection system in IoV networks.

Rapid developments in the era of digital transformation, which refer to the emergence of business and technology innovations based on artificial intelligence, big data, and the Internet of Things (IoT), have great potential for strategic sustainability for businesses in the digital age. Efforts to transform digital business models as a global competitive advantage and provide outputs that can be oriented towards future predictions. Digital business models refer to strategic designs for creating platform networks that are implemented through relationships with consumers and cross-sector collaboration. Challenges and opportunities for development between transformation and innovation are necessary in order to create and capture competitive value and provide added value in the digital economy era. The use of bibliometric analysis in research provides direction in understanding the perspectives and issues that require further research, opens up space for exploring publication trends, and identifies the mapping of key concepts that form the basis of main ideas, thereby providing a more structured understanding and developing new research opportunities, especially in the field of digital business models. Bibliometric analysis aims to gain an in-depth understanding of research using the R studio application as a tool for processing data trends over time and VOSviewer as a knowledge map visualization tool. The research was conducted to provide an understanding of current and future developments in a dynamic environment.

This study aims to analyze the effectiveness of the Village Information System in Delima Village as one of the village government's efforts to improve the quality of public services. The study used qualitative methods with descriptive research and data collection techniques such as observation, interviews, and documentation. The results of the study indicate that the implementation of the Village Information System in Delima Village has not been running effectively. Although this system has been used for administrative activities such as issuing certificates, managing population data, and disseminating village information, its implementation still faces various obstacles. Some of these inhibiting factors include limited human resources who have not fully mastered technology, lack of training for village officials, and unstable internet connections throughout the village. As a result, services through the Village Information System are not optimal and have not yet fully benefited the community. Therefore, it is necessary to increase the capacity of village officials and provide infrastructure support so that the implementation of the Village Information System in Delima Village can run more effectively in the future.

The implementation of industrial engineering technologies, such as automation, the Internet of Things (IoT), artificial intelligence (AI), and lean manufacturing, has significantly transformed human resource (HR) capabilities in the production sector, particularly in response to the Industry 4.0 paradigm. This study aims to examine the relatively low level of technology adoption in Indonesia, estimated at only 6–20% of manufacturing companies, and its impact on the development of HR competencies. The analysis focuses on changes in technical skill requirements, including digital literacy, data analytics, and technology-based decision-making, as well as the shift in job roles from manual tasks to more strategic functions. This research employs a qualitative descriptive approach grounded in sociotechnical systems theory and the strategic alignment model. The findings indicate that existing skill gaps can be addressed through continuous upskilling and reskilling programs, supported by strengthened triple helix collaboration among government, industry, and educational institutions. The implementation of these strategies has been shown to increase productivity by approximately 30–72% and enhance the competitiveness of the national production sector in the global industrial landscape.  

This study examines the administrative problems in managing the Education Data Sistem (Dapodik) as a key instrument for strengthening the institutional capacity of Early Childhood Education (PAUD) in West Sumatra. Using a literature review approach, data were collected from various sources, including reports from the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek), BAN PAUD and PNF, and previous studies. The findings reveal that although Dapodik reporting in West Sumatra has reached 97.8%, several challenges persist, such as limited internet access, low operator competence, insufficient technical supervision, and a lack of institutional awareness of data accuracy. These issues have led to delays in accreditation, misallocation of operational assistance funds (BOP), and ineffective educational planning. Therefore, innovative solutions such as Dapodik gamification, digital inter-institutional mentoring, and operator certification are recommended to improve data management effectiveness and sustainably strengthen PAUD institutional governance.

The JKN Mobile application is an electronic-based service innovation introduced by BPJS Health to facilitate JKN participants in accessing healthcare services and managing their membership information. This study aims to explore the experiences and perspectives of outpatient patients regarding the effectiveness of the JKN Mobile online registration system at Baubau City Regional General Hospital. A qualitative phenomenological approach was employed, with informants selected through purposive sampling. Data were collected to capture the meanings of patients’ experiences in using the application. The findings show that most patients are satisfied with the practicality, speed, and flexibility of the online registration system. However, technical problems, particularly unstable internet connectivity, remain a major challenge. Furthermore, the online registration system has contributed to improved service efficiency and patient satisfaction. These findings indicate the importance of continuous evaluation and improvement of the JKN Mobile online registration system to enhance service quality and health information system development at Baubau City Regional General Hospital.

Penelitian ini bertujuan untuk mengembangkan sistem telemetri guna memantau kinerja panel surya pada beberapa lokasi dengan menggunakan komunikasi jarak jauh dan platform Internet of Things (IoT). Metode pemantauan konvensional memiliki keterbatasan dalam menyediakan data secara real-time pada area yang luas, sehingga evaluasi kinerja jarak jauh menjadi kurang efisien. Untuk mengatasi permasalahan tersebut, dirancang sebuah sistem pemantauan menggunakan mikrokontroler ESP32, sensor INA219 untuk mengukur tegangan dan arus, modul GPS Neo-M8 untuk identifikasi lokasi, modul Real-Time Clock (RTC) DS3231 untuk pencatatan waktu, serta modul LoRa RA-02 sebagai media komunikasi nirkabel. Setiap node pengirim dilengkapi dengan modul MicroSD untuk menyimpan data pengukuran secara lokal. Data hasil pengukuran dikirimkan melalui LoRa ke unit penerima dan ditampilkan secara real-time pada platform Thinger.io. Hasil kalibrasi menunjukkan bahwa sensor INA219 memiliki rata-rata galat pengukuran arus sebesar 0,71% dan galat pengukuran tegangan sebesar 0,1%. Pengujian GPS menunjukkan koordinat lokasi yang stabil dengan tingkat akurasi sekitar ±3 hingga ±8 meter. Seluruh data pengukuran berhasil dikirim, disimpan, dan ditampilkan tanpa kehilangan data yang signifikan. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu menyediakan pemantauan parameter panel surya secara jarak jauh yang andal dan efisien dalam kondisi lapangan.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

Digital transformation has emerged as a major catalyst for reform in contemporary public administration, reshaping how governments design, deliver, and evaluate public services. This literature review synthesizes key findings from international studies to map the dynamics of technological innovation and bureaucratic adaptation in the era of digital government. The results demonstrate that technologies such as artificial intelligence, blockchain, cloud computing, and the Internet of Things accelerate administrative processes, enhance accuracy, reduce service costs, and strengthen transparency and accountability. However, the review also emphasizes that technological advancement alone is insufficient; the success of digital transformation depends on the capacity of public institutions to reorganize work structures, build digital competencies, and shift bureaucratic culture toward more adaptive and collaborative practices. Furthermore, digital participation platforms have expanded opportunities for citizen engagement, yet persistent digital divides—driven by socio-demographic disparities and unequal access to infrastructure—pose significant challenges to inclusive participation. The literature also reveals recurring barriers related to infrastructure readiness, cybersecurity, resistance to change, and limited digital literacy among public employees. Cross-country evidence from Turkey, Singapore, Italy, Iran, and the UAE shows similar transformation patterns, highlighting bureaucratic adaptation as a mediating factor between technological innovation and governance outcomes. Overall, this review offers an integrated conceptual understanding of digital transformation in public services and underscores the need for holistic strategies that combine technological investment, organizational reform, and inclusive governance to ensure sustainable and equitable digitalization.

Suhu dan kelembaban merupakan parameter lingkungan yang harus dijaga pada ruang kubikel untuk memastikan peralatan distribusi listrik tetap bekerja secara optimal. Pada multi-kubikel, perbedaan fungsi dan beban menyebabkan karakteristik suhu dan kelembaban pada tiap ruang kubikel tidak sama, sehingga pemantauan secara manual menjadi kurang efektif dan efisien. Penelitian ini bertujuan untuk merancang dan membangun prototype sistem monitoring dan kontrol suhu-kelembaban pada multi-kubikel berbasis Internet of Things (IoT) yang terdiri dari tiga buah kubikel. Sistem ini menggunakan ESP8266 sebagai mikrokontroler utama dan sensor DHT20 sebagai sensor suhu dan kelembaban yang masing-masing dipasang pada kubikel dengan kondisi lingkungan berbeda. Sistem dilengkapi dengan aktuator kipas dan lampu, serta notifikasi real-time melalui LCD dan Telegram. Meskipun kontrol dan monitoring dilakukan secara terpisah pada tiap kubikel, notifikasi kondisi seluruh kubikel terintegrasi pada satu kanal Telegram yang sama. Pengujian kinerja sistem dengan memberikan variasi suhu dan kelembaban yang berbeda untuk tiap kubikel. Kubikel 1 diberi kondisi normal (suhu 35°C-40°C dan kelembaban 50%-70%), kubikel 2 diberi kondisi overheat (suhu di atas 40°C), sedangkan kubikel 3 diberi kondisi overhumidity (kelembaban > 70%). Hasil pengujian menunjukkan sistem mampu melakukan kontrol suhu dan kelembaban dalam ruang multi-kubikel serta mengirimkan notifikasi melalui Telegram dengan tingkat keberhasilan 100% dan rata-rata delay 5,6 detik.

The increasing global energy demand drives the search for efficient and sustainable renewable energy solutions. Solar panels have become one of the most widely used technologies; however, their efficiency remains limited when installed in a static position. This research aims to analyze the performance of a single-axis auto tracking system on a 10WP solar panel integrated with the Internet of Things (IoT) for real-time monitoring, specifically in powering a portable powerbank. The research method employed was a quantitative experimental design with three testing scenarios: powerbank charging using an auto-tracking solar panel, a static solar panel, and conventional household electricity as a comparison. Charging data were collected via an IoT system integrated with the Blynk application in real-time. The results indicate that the auto-tracking system increased charging efficiency by around 10%, compared to only 6% with a static panel in one hour. This performance is nearly equal to household electricity charging, which reached approximately 10–11%. The study concludes that the single-axis IoT-based auto-tracking system significantly enhances the performance of small-scale solar panels and holds strong potential for portable energy solutions in remote areas.