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Human–Robot Interaction (HRI) systems increasingly rely on data-driven approaches to interpret multimodal sensory inputs and support natural interaction. However, purely neural-based HRI models often suffer from limited interpretability and insufficient context-aware decision-making, which can reduce user trust and adaptability in dynamic interaction scenarios. To address these limitations, this study proposes a hybrid neural–symbolic HRI framework that integrates multimodal neural perception with explicit symbolic reasoning for adaptive and interpretable robot behavior. The proposed system combines deep neural networks for processing visual, speech, and gesture inputs with a rule-based symbolic reasoning layer that models interaction context, user states, and behavioral constraints. A loosely coupled integration strategy enables neural outputs to be transformed into symbolic representations, allowing logical inference to guide action selection while preserving perceptual accuracy. The framework was evaluated through controlled HRI experiments comparing a neural-only baseline with the proposed hybrid configuration across multiple interaction scenarios. Experimental results demonstrate that the hybrid neural–symbolic system significantly improves interaction accuracy, contextual responsiveness, and user satisfaction, while achieving substantial gains in interpretability. These findings indicate that symbolic reasoning effectively complements neural perception by enhancing transparency and context-aware adaptation without compromising performance. The study concludes that hybrid neural–symbolic architectures provide a promising foundation for developing trustworthy, adaptive, and human-centered HRI systems.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

Benign Paroxysmal Positional Vertigo (BPPV) is the most common cause of peripheral vertigo, characterized by brief episodes of vertigo due to otoconia displacement. Although most previous studies have focused on intrinsic factors such as age, gender, osteoporosis, and metabolic disorders, evidence regarding the role of environmental factors, particularly occupational noise exposure, is limited. Chronic noise has the potential to affect vestibular function through both sensory and vascular mechanisms. This study aims to narratively review the effect of occupational noise exposure on the risk of BPPV by integrating clinical, epidemiological, and experimental findings. The method used is a literature-based narrative review of the PubMed, Scopus, Web of Science, and Google Scholar databases without year restrictions, using the keywords "BPPV", "occupational noise exposure", "vestibular dysfunction", "VEMP", and "otoconia displacement". The search results obtained 25 relevant articles linking BPPV to otolith, hormonal, vascular, lifestyle factors, and occupational noise exposure. The results indicate that chronic noise can cause sensory damage (otoconia and vestibular hair cells), vascular disorders (hypertension, cardiovascular disorders, and inner ear microvascular circulation disorders), and exacerbate lifestyle comorbidities (sedentary lifestyle, osteoporosis, hypertension, diabetes). The discussion confirms that these multifactorial mechanisms explain the susceptibility of industrial workers to BPPV despite normal hearing function. The conclusion of this study is that workplace noise exposure has been shown to play a significant role as a risk factor for BPPV, therefore, prevention strategies, vestibular health monitoring, and healthy lifestyle interventions need to be optimized in occupational health programs.

The City of Singkawang faces significant challenges in drinking water management, characterized by limited production capacity, high non-revenue water (NRW), and the absence of digital infrastructure such as AMR, LoRaWAN, and DMA, necessitating a structured development framework to support the transition toward a smart water system. This study formulates an IoT-based smart water roadmap aligned with the regional development plan (RPJMD) and the operational capacity of the local water utility through a performance gap analysis with benchmark cities (Surabaya and Balikpapan), capacity assessment using six objective parameters, and the use of secondary data from official reports and technical documents. The resulting roadmap comprises three sequential phases covering basic infrastructure reinforcement, network digitalization through IoT sensors and telemetry, and the implementation of DMA and SCADA to enable real-time monitoring and control. This approach provides a realistic and adaptive implementation framework for medium-sized cities with limited resources, strengthening NRW reduction efforts, improving service reliability, and supporting the integration of digital technologies in sustainable water utility management.

Inclusive education requires a comprehensive understanding of the characteristics and learning needs of students with special needs, particularly children with autism spectrum disorders. Assessment plays a crucial role as an initial step in identifying abilities, challenges, and educational needs to design appropriate learning interventions. This study aims to describe the results of developmental assessment of a child with autism in an inclusive education setting at SKH 01 Nurbayan Grade 5. This research employs a descriptive approach using assessment instruments that cover five developmental domains: social interaction, communication, behavior, emotional regulation, and sensory perception. The results reveal significant difficulties in social communication, including low social interest, delayed language development, limited nonverbal communication, and the presence of repetitive behaviors. Emotional regulation remains underdeveloped, and sensory processing issues are evident. These findings indicate that children with autism require individualized, structured, and needs-based educational interventions. Therefore, assessment serves as a fundamental basis for planning effective and sustainable inclusive learning programs.

This study aims to analyze the sensory quality, Escherichia coli microbial contamination, and lead (Pb) content in frozen squid (Loligo sp) produced by Queen Seafood in Pasuruan. The method used is descriptive qualitative with laboratory testing of whole frozen squid samples. The testing includes sensory evaluation (ice layer, appearance, aroma, and texture), microbiological testing to detect the presence of E. coli using the APM method, and lead (Pb) contamination testing using the Atomic Absorption Spectrophotometry (AAS) method. The results showed that all sensory quality parameters of frozen squid met the SNI 9192:2023 standard with an average score above the minimum limit. However, Escherichia coli contamination exceeded the maximum threshold set in the SNI, which is suspected to originate from unclean production water sources. The lead (Pb) content was recorded at 0.075 mg/kg, which is still within the safe limit according to the SNI (<1.0 mg/kg). These findings emphasize the importance of water quality monitoring in the production process and the need for additional washing and reprocessing instructions before consumption to ensure the food safety of frozen squid.

Acute pain is a complex phenomenon frequently experienced by post-surgical patients. If not properly managed, it can hinder the recovery process and increase the risk of chronic complications. This study aims to conduct an in-depth analysis of the concept of acute pain in surgical patients from a nursing perspective to improve the quality of care. The method used was a narrative literature review, analyzing various research articles and clinical protocols related to surgical pain management. The study findings indicate that acute post-surgical pain involves sensory and emotional dimensions influenced by the type of surgical procedure, individual pain threshold, and the effectiveness of pharmacological and non-pharmacological interventions. Furthermore, the role of nurses in conducting accurate pain assessments and patient education is a key factor in successful pain management. The implications of this study emphasize the importance of implementing integrated multimodality pain management protocols and improving nurses' competency in conducting intensive monitoring. Optimizing pain management is expected to accelerate patient mobilization, shorten hospital stays, and increase patient satisfaction with nursing services.

This study examines the teacher's strategy in guiding the memorization of prayer prayers in early childhood at RA Muslimat NU Banin Banat Manyar through a qualitative case study approach. The main focus is the application of the practice (repetition) and habituation method, which was observed for 6-8 weeks in 35 children in group AB (aged 4-6 years), including participant observation, in-depth interviews with 4 teachers and 5 parents, and analysis of RPP documentation and murojaah videos. The results show that the practice method is applied rhythmically daily (3x / day, 10-15 minutes), starting from simple pronunciations such as iftitah and ruku' with 20-30 repetitions per chain cycle, resulting in an average increase in memorization from 42% to 91%, with variations in singing and movements reducing boredom by 27%. Meanwhile, integrated habituation through congregational prayer routines (Dhuha, Zuhur simulation, Ashar), independent ablution, and home supervision, achieved 89% of children's independence in becoming mini imams after 21 days consistently, supported by verbal rewards and gender row rotation. The discussion confirmed alignment with Piaget's theory (preoperational stage) and Vygotsky's (ZPD scaffolding), where drills build sensory memory schemes while habituation forms permanent religious character ala Abdullah Nasih Ulwan. Supporting factors include parental collaboration and a conducive NU environment, overcoming the obstacle of low concentration. Practical implications recommend replicating this strategy in similar RAs to optimize the golden age of Islamic early childhood, with memorization retention of 8-10 basic prayer prayers.

Catfish farming is one of the fisheries areas with great potential in Indonesia. However, the problem of feed efficiency, use of probiotics, and air temperature control are still the main obstacles that affect the health and growth of fish. This study aims to design and implement an automatic system that can regulate the provision of feed and probiotics and control and control air temperature directly using the ESP32 microcontroller. This system has several important components, such as the ESP32 microcontroller as a control center, servo motors for feed and probiotic distribution, and DS18B20 temperature sensors to monitor air temperature. All components can be controlled and viewed through an IoT-based application with a Wi-Fi connection or applications such as Telegram. This trial aims to determine whether this system can provide feed and probiotics according to the specified time, and maintain air temperature in the optimal range (28°C–40°C) for catfish growth. In this way, the system is expected to increase efficiency in catfish farming automatically, reduce the need for human labor, and minimize errors in pond management.

The “Beginner Brewer” community activity organized by Koffie Rakjat Semarang stands out as an informal learning space for individuals new to coffee brewing techniques, but so far there has been little scientific study regarding its impact on participants’ sensory experiences. This study aims to analyze how beginners understand and evaluate the flavor profile of Aceh Gayo single-origin coffee “Drama” through practical brewing sessions. The research method used a qualitative descriptive approach with data sources consisting of activity documentation, observations of popular articles, and direct quotations from participants’ experiences. The results showed that mentoring with simple techniques such as the V60 (1:15) and French Press (1:12) contributed to participants’ increased understanding of the fruity character and flavor complexity of “Drama,” which has flavor notes of passion fruit, pineapple, strawberry jam, rum, and palm sugar. Participants also reported that the inclusive learning environment made the brewing process easy and enjoyable, and encouraged continued interest in specialty coffee. In conclusion, this community activity is effective as an initial educational tool that shapes flavor perception and increases the confidence of beginner brewers in understanding specialty coffee.

This research focuses on optimizing the control system to improve voltage stability in a 10 kW Solar Power Plant (PLTS) located in a tropical region. The main issue addressed is voltage fluctuation caused by the intermittent nature of solar radiation (200–1200 W/m²) and temperature variations (20–50°C), which result in up to 12% overshoot in the inverter. The proposed method implements a Proportional-Integral-Derivative (PID) controller optimized using the Particle Swarm Optimization (PSO) algorithm with real-time irradiation input data. The research integrates a 100 Hz digital low-pass filter to mitigate sensor noise under low irradiation conditions. Simulation results show that the PID-PSO system successfully reduces overshoot from 12.1% to 4.2% under high irradiation, and decreases settling time from 0.62 seconds to 0.31 seconds. The digital filter effectively reduces measurement deviation from 7.2% to 2.8% at 200 W/m² irradiation. The PSO optimization achieved optimal convergence within 37 iterations with an Integral of Time-weighted Absolute Error (ITAE) value of 0.18. This study concludes that the implementation of PID-PSO with a digital filter significantly enhances the voltage stability of the PLTS by 20.3% compared to conventional PID control and is ready to be applied in tropical-region smart grid systems.

Wheat has limited nutritional content, therefore fortification with other ingredients is required to complement these nutrients, one of which is fortification with pineapple peel flour and chicken bones in noodle products. Thus, this study was conducted to investigate the impact of adding chicken bone and pineapple peel flours on the characteristics of wheat flour noodles, with the goal of developing a product enriched in fiber and minerals, and exhibiting acceptable sensory qualities. The research design used a completely randomized design (CRD) with 2 experimental factors, namely pineapple peel flour concentration (0%,3%,5%) and chicken bone flour concentration (0%,5%,10%) with triplicate replication. Based on the optimal treatment by Zeleny method, sample N1T3 emerged as the most favorable formulation. This sample exhibited the following chemical profile: a moisture content of 8.77%, an ash content of 4.52%, a protein content of 14.45%, a fat content of 1.52%, and a fiber content of 0.4%. Furthermore, sensory evaluation of Sample C yielded scores of 3.11 for color, 3.11 for aroma, 3.00 for taste, 2.89 for texture, and 2.89 for overall acceptance.

The increasing demand for electrical energy, particularly in offices and commercial buildings, has made energy efficiency a critical aspect of sustainable development. Among various building components, lighting systems are recognized as one of the major consumers of energy. This study investigates the potential for energy savings through the adoption of a smart lighting system incorporating IoT-based sensors, motion detectors, and dimming controls. Employing a quantitative descriptive approach, the research was conducted at the workspace of Indie Light, comparing energy consumption before and after the implementation of the system. Data were collected using direct observation, light and power meters, and real-time monitoring devices to ensure accurate measurement. The results demonstrate that smart lighting systems can substantially reduce energy use without compromising lighting quality or comfort. By integrating intelligent sensors and adaptive control algorithms, the system not only optimizes energy efficiency but also aligns with national policies on energy conservation, supporting broader environmental sustainability efforts. These findings suggest that smart lighting solutions can play a significant role in promoting energy-efficient practices in commercial spaces while contributing to sustainable development goals.

This study aims to design and test a Dual Axis Solar Tracker to improve the energy absorption efficiency of solar panels on ships. The system is designed with a two-axis movement mechanism (horizontal and vertical) using a linear actuator motor controlled by Arduino Nano and ESP32. Testing was conducted on a 20 WP solar panel in Surabaya for 30 days, divided into three methods: 10 days using an LDR sensor, 10 days using an RTC, and 10 days in static conditions without a sensor. Voltage, current, and power data were measured every 30 minutes at 07.00–17.00 WIB. The test results show that the RTC method provides the highest and most stable output power, according to the sun's movement patterns in tropical areas, while the LDR method responds quickly to changes in light intensity but is less stable in changing weather. Static installation produces the lowest power. This system is able to maintain the panel orientation perpendicular to the sun's rays, thus increasing energy efficiency compared to static systems. These findings prove that dual-axis solar tracker technology, especially with an RTC sensor, is effective in dynamic maritime environments and can be a practical solution for optimizing renewable energy on ships. The most effective results using RTC sensors demonstrated the most stable and high power output, especially since the sun in tropical areas like Surabaya moves fairly consistently following a cyclical pattern. The success of this system not only increases the energy output of solar panels but also provides a practical solution for renewable energy applications in tropical climates.

Gingerbread merupakan kue kering yang terbuat dengan bahan dasar tepung terigu dengan campuran jahe dan bubuk kayu manis. Ketergantungan penggunaan tepung terigu pada pembuatan produk pangan sampai saat ini masih sangat tinggi, salah satu upaya untuk mengurangi penggunaan tepung terigu adalah dengan memanfaatkan tepung kentang sebagai pengganti tepung terigu. Jahe memiliki beragam jenis dan karakteristik yang berbeda, penggunaan jahe yang biasanya yang digunakan dalam pembuatan gingerbread yang sering dijumpai dipasaran berupa jenis jahe merah, jahe gajah dan jahe emprit. Penelitian ini bertujuan untuk mengkaji pengaruh jenis dan bentuk jahe terhadap kualitas gingerbread cookies. Penelitian ini menggunakan Rancangan Acak Lengkap (RAL) dengan 2 perlakuan jenis jahe yaitu jahe gajah dan jahe merah, dan dalam bentuk bubuk dan cair. Hasil penelitian menunjukkan perlakuan jenis jahe berupa bubuk dan cair menunjukkan pengaruh signifikan terhadap parameter fisik dan organoleptik produk. Penggunaan jahe bubuk cenderung meningkatkan kekerasan, serat kasar, dan daya kembang, serta menghasilkan warna yang lebih gelap dan tekstur yang lebih padat. Sebaliknya, jahe cair menghasilkan gingerbread yang lebih lembut, berwarna lebih cerah, dan tekstur lebih rapuh. Analisis sensoris menunjukkan preferensi konsumen terhadap gingerbread dengan perlakuan jahe cair dari segi warna dan aroma lebih disukai, sementara dari aspek tekstur dan kekerasan, jahe bubuk lebih disukai.  

This community service program was carried out to enhance the technological literacy of residents in Telang Sari Village through the introduction of an automated street lighting system based on sensor technology. The system presented to the community utilizes an Arduino microcontroller integrated with an LDR sensor to detect light intensity and an ultrasonic sensor to identify the presence of nearby objects. With this configuration, the street lights operate automatically: they turn on when the environment becomes dark and an object is detected, and turn off when the surroundings are bright or no activity is detected in the sensing area. The program activities included device installation, technical explanation, and a live demonstration to ensure that residents comprehended its functions and benefits. Additionally, the use of solar panels was introduced as an alternative power source to support sustainable operation without relying on grid electricity. The results of the program showed a positive response from the community, as the system was considered effective in improving nighttime safety, reducing energy consumption, and requiring minimal maintenance. Overall, this activity successfully increased public understanding of automation technology and renewable energy applications suitable for rural community development.

Energy efficiency in water heaters is a crucial factor in ship operational environments due to limited electricity resources that rely on generators. This study aims to design and build an IoT-based water heater monitoring system with an innovative heat storage medium in the form of a mixture of silica sand and paraffin wax to improve thermal efficiency. Although previous studies have developed temperature monitoring and control systems in IoT-based water heaters, this study specifically fills this gap by analyzing the performance of adding silica sand to overcome the low thermal conductivity of paraffin wax. Using the Research and Development (R&D) method, this system was built with an ESP32 microcontroller as the control center, a DS18B20 temperature sensor for accurate measurements, and the Blynk and Google Sheets platforms for real-time monitoring and data recording. Performance testing was conducted by comparing the water heating rate between pure paraffin wax media and the mixed media. The results showed that the monitoring system functioned reliably, and the main finding proved that the addition of silica sand to paraffin wax significantly increased heating efficiency. This was clearly seen from the reduction in time required to raise the water temperature to 40°C, from 2.5 hours to only 1 hour in the second heating cycle. The results of this study indicate that the integration of silica sand and paraffin wax media with IoT technology can increase the efficiency of water heaters and provide an innovative solution for energy-efficient and environmentally friendly temperature control.

This community service program was carried out to enhance the technological literacy of residents in Telang Sari Village through the introduction of an automated street lighting system based on sensor technology. The system presented to the community utilizes an Arduino microcontroller integrated with an LDR sensor to detect light intensity and an ultrasonic sensor to identify the presence of nearby objects. With this configuration, the street lights operate automatically: they turn on when the environment becomes dark and an object is detected, and turn off when the surroundings are bright or no activity is detected in the sensing area. The program activities included device installation, technical explanation, and a live demonstration to ensure that residents comprehended its functions and benefits. Additionally, the use of solar panels was introduced as an alternative power source to support sustainable operation without relying on grid electricity. The results of the program showed a positive response from the community, as the system was considered effective in improving nighttime safety, reducing energy consumption, and requiring minimal maintenance. Overall, this activity successfully increased public understanding of automation technology and renewable energy applications suitable for rural community development.

Penelitian ini mengevaluasi efisiensi tiga algoritma sorting hybrid, yaitu TimSort, IntroSort, dan Merge-Insertion Sort, pada dataset skala menengah yang memiliki jumlah elemen antara 10.000 hingga 1.000.000. Tujuan utama penelitian adalah untuk menganalisis performa algoritma berdasarkan waktu eksekusi, konsumsi memori, dan stabilitas, dengan pengujian dilakukan pada berbagai jenis dataset, termasuk data acak, terurut, hampir terurut, dan data dengan banyak elemen duplikat. Pengujian dilakukan melalui simulasi komputasi menggunakan bahasa pemrograman Python dalam lingkungan terkontrol untuk memastikan hasil yang konsisten. Dataset sintetis dibuat untuk mencerminkan kasus dunia nyata, seperti pengolahan log sistem, pengurutan data pelanggan dalam aplikasi e-commerce, atau pengolahan data sensor dalam sistem Internet of Things (IoT). Hasil pengujian menunjukkan bahwa TimSort memiliki performa unggul pada dataset hampir terurut dengan waktu eksekusi rata-rata 0,12 detik untuk 1.000.000 elemen, sedangkan IntroSort lebih cepat pada dataset acak dengan waktu 0,09 detik dan konsumsi memori rendah sekitar 120 MB. Merge-Insertion Sort menonjol dalam hal stabilitas, tetapi memerlukan memori lebih besar, yaitu sekitar 180 MB untuk dataset yang sama. Analisis mendalam menunjukkan bahwa pemilihan algoritma yang optimal sangat bergantung pada karakteristik dataset dan kebutuhan aplikasi, seperti kecepatan untuk data acak atau stabilitas untuk pengurutan data berurutan. Penelitian ini merekomendasikan TimSort untuk aplikasi yang memerlukan stabilitas tinggi, seperti pengolahan data transaksi keuangan, dan IntroSort untuk aplikasi yang mengutamakan kecepatan pada data acak, seperti analitik data real-time. Untuk pengembangan lebih lanjut, penelitian ini menyarankan eksplorasi optimasi paralel atau implementasi algoritma pada perangkat dengan sumber daya terbatas guna meningkatkan skalabilitas dan efisiensi.

Deteksi nyeri secara objektif merupakan tantangan penting dalam dunia medis, terutama bagi pasien yang tidak mampu menyampaikan rasa sakitnya secara verbal, seperti bayi, lansia, atau penderita gangguan komunikasi. Teknologi non- invasif berbasis sensor menjadi solusi potensial untuk mengatasi keterbatasan metode subjektif. Penelitian ini bertujuan meninjau secara sistematis literatur terkini mengenai penerapan Radar MIMO dan algoritma kecerdasan buatan dalam deteksi nyeri non-invasif. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan pedoman PRISMA 2020, melalui penelusuran basis data IEEE Xplore, ScienceDirect, PubMed, Google Scholar, dan SpringerLink untuk periode 2021– 2025. Dari hasil seleksi diperoleh 17 artikel inklusi yang mencakup penggunaan Radar MIMO, UNBC-McMaster, BioVid, Medical Imaging (CT/MRI), Radar SISO, serta studi review, survey, bibliometrik, dan teoretis. Dari sisi algoritma, CNN dan SVM menjadi pendekatan paling dominan, diikuti Neural Network dan metode lain, dengan tren yang mengarah pada penggunaan multimodal untuk meningkatkan akurasi. Hasil penilaian kualitas dengan GRADE menunjukkan mayoritas studi berkualitas sedang, dengan keterbatasan utama pada ukuran sampel kecil, pelabelan nyeri yang belum konsisten, bias populasi, serta kurangnya validasi klinis nyata. Kesimpulannya, Radar MIMO dan algoritma deep learning memiliki potensi besar untuk deteksi nyeri non-invasif. Namun, penelitian lanjutan perlu difokuskan pada pembangunan dataset yang lebih inklusif, standarisasi pelabelan nyeri, serta pengujian dalam konteks klinis, dengan memperhatikan aspek etika dan privasi agar teknologi ini dapat diimplementasikan secara luas dalam layanan kesehatan.