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Pesatnya kemajuan teknologi digital telah mempercepat adopsi *Internet of Things* (IoT) dalam berbagai aspek kehidupan sehari-hari. IoT memungkinkan perangkat fisik untuk berkomunikasi dan bertukar data melalui konektivitas internet, sehingga proses menjadi lebih otomatis, efisien, dan responsif. Studi ini bertujuan untuk menganalisis pemanfaatan IoT dalam meningkatkan efisiensi kehidupan sehari-hari melalui pendekatan tinjauan pustaka. Metode penelitian yang digunakan adalah *Systematic Literature Review* (SLR), yang mengkaji dan menyintesis temuan dari berbagai studi terdahulu mengenai penerapan IoT di berbagai sektor, seperti rumah pintar (*smart home*), layanan kesehatan, transportasi, pertanian, dan pemantauan lingkungan. Hasil analisis menunjukkan bahwa IoT berkontribusi signifikan terhadap peningkatan efisiensi operasional, penghematan energi, perbaikan proses pengambilan keputusan, serta peningkatan kenyamanan pengguna. Terlepas dari berbagai manfaat tersebut, masih terdapat sejumlah tantangan, termasuk keamanan data, masalah privasi, interoperabilitas antarperangkat, biaya implementasi, dan ketergantungan pada infrastruktur internet yang stabil. Temuan studi ini menunjukkan bahwa IoT telah menjadi salah satu teknologi kunci yang mendukung transformasi digital dan peningkatan kualitas hidup. Pengembangan di masa mendatang diharapkan berfokus pada penguatan aspek keamanan, perluasan standar interoperabilitas, dan integrasi kecerdasan buatan (*artificial intelligence*) guna memaksimalkan efektivitas penerapan IoT dalam aktivitas sehari-hari.  

The Society 5.0 era brings major changes in various aspects of life, including the economic and financial systems. The integration of digital technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Big Data, and Financial Technology (Fintech) has created both opportunities and challenges for the development of the sharia economy. This article aims to: (1) analyze the challenges of the sharia economy in the Society 5.0 era, (2) identify opportunities that can be utilized to strengthen the sharia economy, and (3) formulate strategies for strengthening the sharia economy based on digital transformation and the maqashid sharia. This research uses a qualitative approach through literature study (library research) by analyzing various journals, books, reports of sharia financial institutions, and relevant official documents. The results show that the sharia economy faces challenges in the form of low sharia financial literacy, limited human resources, unequal access to technology, and regulations that are not yet fully adaptive to digital developments. However, Society 5.0 also opens up significant opportunities through the development of Islamic Fintech, the digitalization of the halal industry, the optimization of digital zakat and waqf, and the strengthening of Islamic financial inclusion. Therefore, strategies to strengthen the Islamic economy need to be implemented through increasing Islamic digital literacy, developing an Islamic Fintech ecosystem, strengthening Governance based on the principles of Islamic principles (maqasid) and synergy between the government, academia, industry, and the community.

Urban plant care is increasingly important for sustainable living, but many users face inconsistent watering, insufficient care knowledge, unsuitable plant selection and delayed disease recognition. This study presents Easy Grow Plants, an integrated web and Internet of Things (IoT) ecosystem that connects plant care guidance, soil-moisture monitoring, automated watering, plant recommendation, image-based plant health assistance, marketplace functions, community interaction and plant exchange. The prototype was implemented using a React frontend, Django REST backend, SQLite database and an Arduino UNO R4 WiFi smart pot with a soil moisture sensor, relay module and DC water pump. Functional, interface, API, IoT connectivity, sensor calibration, watering control and LAN deployment tests were conducted. The results show that the core modules operated together as an integrated academic prototype. The system demonstrates a practical foundation for smart urban gardening, although cloud deployment, multi-device testing and stronger AI validation remain future improvements

This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems

Penelitian ini bertujuan untuk merancang dan membangun perangkat yang mampu membaca, mengukur, serta mengirimkan data mengenai tegangan, arus, daya, frekuensi, dan energi secara real-time melalui aplikasi Blynk Internet of Things. Perangkat ini memungkinkan data ditampilkan pada smartphone dan layar komputer dari lokasi mana pun, dan direncanakan untuk digunakan di gardu distribusi milik PT PLN (Persero) ULP Darmo Permai. Alat ini berguna sebagai langkah tindakan korektif ketika terjadi ketidakseimbangan beban, mempermudah pengawasan dalam proses penyeimbangan, serta sebagai acuan untuk pemindahan fasa saat diperlukan. Selain itu, alat ini juga berfungsi sebagai langkah preventif untuk menjaga keseimbangan beban gardu dan sebagai referensi jika ada penambahan pelanggan baru. Metode penelitian ini melibatkan penggunaan mikrokontroler ESP32 sebagai Modbus master untuk mengatur sistem. Mikrokontroler ini menerima data digital melalui RS485 dari Power Meter Toky DS9L/7L yang mengukur tegangan dan arus dari PHB-TR. Data tersebut kemudian dikirimkan ke Cloud Blynk Internet of Things melalui nodemcu ESP32 dengan bantuan modem Wi-Fi router. Data yang diproses meliputi tegangan fasa netral RN, SN, TN (220-230 Volt), tegangan fasa-fasa RS, ST, RT (380-400 Volt), arus maksimal 400 Ampere sesuai spesifikasi sensor arus CT 400/5 A, energi (kWh), daya (Watt) untuk fasa R, S, T, daya total, serta frekuensi 50 Hz. Semua informasi ini ditampilkan pada antarmuka pengguna (UI) di smartphone dan PC.

Medical waste is a by-product of healthcare activities that may have negative impacts on human health and the environment if not properly managed. This study aims to examine strategic planning for medical waste management in healthcare facilities and evaluate the risk of environmental contamination in the digital era. The method used was a literature review by examining various relevant scientific sources. The findings indicate that medical waste management still faces several challenges, including non-compliance with established standards, limited human resources, and the risk of environmental pollution. The utilization of digital technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), RFID, and Blockchain has the potential to improve the effectiveness of monitoring and managing medical waste. Therefore, strategic planning supported by digital technology, human resource capacity building, and regulatory compliance is essential for achieving safe and sustainable medical waste management.

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.

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

One of the goals of a building is to create a comfortable environment that does not affect the health and operations of its occupants, therefore a system needs to be created to ensure comfort in classrooms. To fulfill a comfortable situation, there is a standard that regulates comfort, especially thermal and visual comfort. Thermal comfort is regulated in SNI 03-6572-2001 and visual comfort is regulated in SNI 03-6575-2001. The aim of this research is to design a tool to automatically monitor temperature and lighting, determine greater accuracy, determine temperature and lighting comfort distances, and test Smart Comfort measurement results in accordance with the SNI-03-6571-2001 and SNI-03-6575-2001 conformity standards. This design uses ESP32 with IoT-based LDR and DHT11 sensors which can be seen on the web and application, determines the accuracy and range of Smart Comfort values for monitoring temperature and lighting and determines the suitability of measurement quantities in the SDN PINANG 3 classroom.

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

This study presents an innovation design for an Internet of Things (IoT)-based watering and liquid fertilizer control system for chili plants using a NodeMCU ESP32. The main problem addressed is the manual watering and fertilizing process, which makes it difficult for farmers to monitor soil moisture, temperature, air humidity, and light intensity in real time. The recommended method used in this draft is Research and Development (R&D) with a prototyping approach because the study focuses on designing, building, integrating, and testing an IoT device through iterative stages. The system is designed using a soil moisture sensor, DHT11, LDR sensor, two-channel relay, two 12 V DC pumps, 16x2 I2C LCD, and the Blynk Mobile application for remote monitoring and control. Sensor data are transmitted to Blynk as percentage values and plant condition statuses, while the water and fertilizer pumps can be controlled using virtual buttons. The control logic defines the optimal condition for chili plants based on soil moisture of 60-80%, temperature of 25-30°C, air humidity of 60-80%, and light intensity of 50-90%. Prototype documentation and functional testing data will be completed in the next stage.

National milkfish aquaculture production in 2024 reached 792,864 tons, highlighting the strategic role of this commodity in supporting the economy of coastal communities. central java,as one of the main contributors, has significant potential for pond development, including in Ujungwatu Village, Jepara Regency. However, pond management still faces challenges regarding human resource (HR) quality and the optimal utilization of technology. This community service program aims to enhance students’ capacity through outreach and training on Internet of Things (IoT)-based milkfish pond development as an effort to strengthen HR at the Sidomaju 2 SME. Implementation methods include the stages of observation and problem identification, work program planning, WebGIS design, IoT outreach and implementation, and evaluation. The materials covered included an introduction to IoT concepts based on ESP8266, pond monitoring, milkfish feed management, and the implementation of WebGIS as a digital mapping system for the Kalingga milkfish ponds.The activity was attended by 20 students from MA NU Ujungwatu and was conducted in a participatory manner through experiential learning. The results of the activity demonstrated an increase in students’ understanding of the application of technology in milkfish farming, as well as heightened awareness of the importance of efficient and sustainable pond management. The main output of the program is the Kalingga Milkfish Pond WebGIS, which can be utilized as a digital medium for education and monitoring of pond areas. This program contributes to improving students’ technological literacy while supporting the strengthening of pond operational systems based on digital innovation.

Fase brooding merupakan periode paling krusial dalam siklus hidup unggas yang menentukan keberhasilan performa produksi pada tahap berikutnya. Namun, manajemen lingkungan pada peternakan rakyat, seperti Kelompok Ternak Asri Wijaya, masih menghadapi tantangan besar terkait fluktuasi mikroklimat dan akumulasi gas beracun. Penelitian ini bertujuan untuk mengintegrasikan teknologi Internet of Things (IoT) ke dalam sistem ventilasi guna memitigasi risiko mortalitas akibat tingginya konsentrasi amonia (NH₃) dan karbon dioksida (CO₂). Melalui pendekatan Research and Development (R&D), sistem dikembangkan menggunakan mikrokontroler ESP32 yang terintegrasi dengan sensor DHT22, MQ-135, dan MQ-2 untuk memantau parameter lingkungan secara real-time. Data diakuisisi secara berkelanjutan dan diolah untuk menggerakkan aktuator berupa exhaust fan serta sistem pemanas berupa lampu pijar melalui logika kontrol ambang batas. Hasil pengujian menunjukkan bahwa sistem mampu menjaga stabilitas suhu pada rentang optimal 33 °C dan secara efektif mereduksi konsentrasi gas berbahaya di bawah ambang batas kritis (NH₃ < 25 ppm; CO₂ < 3.000 ppm). Kontribusi ilmiah penelitian ini terletak pada rancang bangun arsitektur sistem kendali lingkungan yang adaptif dan ekonomis untuk skala peternakan rakyat. Implikasi penelitian ini tidak hanya meningkatkan efisiensi operasional dan kesejahteraan hewan (animal welfare), tetapi juga mendorong transformasi digital menuju Precision Livestock Farming yang berkelanjutan di tingkat lokal.

Perkembangan teknologi mendorong penerapan sistem otomatis dalam bidang pertanian, termasuk pada proses penyiraman tanaman. Kelembapan tanah merupakan faktor penting yang mempengaruhi pertumbuhan tanaman, namun pada Persemaian RBC Perhutani BKPH Bawang, penyiraman masih dilakukan secara manual sehingga berpotensi tidak terkontrol dan menyebabkan kelebihan air. Penelitian ini bertujuan merancang dan mengimplementasikan sistem monitoring serta penyiraman tanaman otomatis berbasis ESP8266 dengan sensor kelembapan tanah. Sistem ini menggunakan sensor kelembapan tanah sebagai input, ESP8266 sebagai pengendali utama, relay untuk mengontrol pompa air, modul RTC sebagai penjadwalan, serta aplikasi Blynk untuk monitoring jarak jauh. Metode penelitian meliputi perancangan, pembuatan, dan pengujian sistem. Hasil menunjukkan bahwa sistem mampu bekerja dengan baik, di mana pompa aktif saat kelembapan tanah di bawah 60% V/V dan berhenti saat di atas 60% V/V, serta dapat melakukan penyiraman terjadwal dua kali sehari. Sistem ini diharapkan dapat meningkatkan efisiensi dan akurasi dalam penyiraman tanaman.

The implementation of smoking prohibition policies in Islamic boarding schools continues to depend largely on manual monitoring methods, which often face challenges related to consistency and supervision range. This study aims to design an Internet of Things (IoT)-based cigarette smoke detection system as an alternative monitoring approach that is more effective, measurable, and sustainable. The system design combines an MQ-2 gas sensor with a NodeMCU ESP8266 microcontroller programmed through the Arduino IDE platform. When smoke levels detected by the sensor exceed the predetermined limit, the system automatically triggers a buzzer and LED as warning indicators while simultaneously sending monitoring data to cloud-based platforms such as Firebase or ThingSpeak for real-time observation through web interfaces. The research outputs include a comprehensive system design consisting of system architecture, electronic circuit schematics, flowcharts, and pseudocode that are systematically arranged to support future prototype development and implementation. Through this design, the proposed system is expected to provide an initial technological solution that can enhance the effectiveness of monitoring and enforcing smoke-free regulations within Islamic boarding school environments.

The increasing demand for electrical energy and the limited availability of fossil fuels have driven the development of renewable energy sources, including marine current energy, which remains underutilized in coastal and remote maritime regions. This study presents the design and realization of a small-scale marine current power generation prototype using a horizontal axis propeller turbine with a NACA S814 blade profile and analyzes the effect of turbine rotational speed on electrical power output. The system converts marine current kinetic energy into mechanical energy through turbine rotation and subsequently into DC electrical energy using a generator, which is stabilized by a Buck–Boost Converter and Maximum Power Point Tracking (MPPT) for charging a 12 VDC battery. Real-time monitoring of electrical and mechanical parameters is implemented using an Internet of Things (IoT)–based system comprising an ESP32 microcontroller, a PZEM-017 sensor, and an RPM sensor. Experimental results demonstrate a positive correlation between water flow rate, turbine rotational speed, and generator output voltage. The system begins operating at a minimum flow rate of 35.2 L/s at 56 RPM, producing 0.2 V, while optimal performance is achieved at 45.3 L/s and 516 RPM, generating up to 13.3 V. These results indicate that the proposed prototype is a viable alternative renewable energy source for marine applications.

Digital transformation has brought fundamental changes to the structure and dynamics of modern industrial economics. Technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and big data not only modify production and distribution processes but also revolutionize marketing strategies and patterns of industrial competition. This study is motivated by the need to understand how digital marketing transformation influences the development of competitive advantage through changes in digital market structure from an industrial economics perspective. In this context, digital marketing functions as a strategic instrument that integrates technology, data, and consumer behavior into market mechanisms. The analysis shows that digitalization creates a network-based market structure characterized by the concentration of economic power in major digital platforms and dominance in data control. This structure affects the intensity of competition, the direction of innovation, and patterns of industry differentiation. Digital marketing transformation enhances efficiency, expands market access, and lowers entry barriers for new players, yet it also creates competitive imbalances due to the dominance of large platforms.Through a digital Structure–Conduct–Performance (SCP) approach, the study finds that market structure acts as an intermediary variable that channels the impact of digitalization on competitive advantage. Digitalization significantly promotes industrial efficiency, innovation, and profitability. Proposed strategic solutions include strengthening digital literacy, developing adaptive regulations, and fostering cross-sector collaboration to create an inclusive, competitive, and sustainable digital industrial ecosystem

Waste management remains a critical environmental issue due to the lack of public awareness in separating organic and inorganic waste, resulting in accumulation and environmental pollution This study aims to analyze and evaluate the development of automatic waste sorting systems based on proximity sensors with full-capacity notification using a Systematic Literature Review (SLR) approach.. The proposed system utilizes a combination of sensors, including proximity sensors for material identification and ultrasonic sensors for detecting object presence and bin capacity, integrated with a microcontroller for real-time processing. Additionally, the system is equipped with IoT-based monitoring that allows users to receive notifications when the waste bin reaches its capacity. The research method involves system design, hardware and software integration, and functional testing to evaluate system performance. The results indicate that the system is capable of sorting waste automatically with a high level of accuracy and responsiveness, while also providing real-time monitoring to support waste management operations. The implementation of this system can reduce manual intervention, increase operational efficiency, and promote better waste segregation practices. Furthermore, this study highlights the potential of integrating smart technology into environmental management systems, contributing both theoretically and practically to the development of sustainable waste management solutions.

The rapid growth of the Internet of Things (IoT) has driven numerous innovations in wireless communications that not only demand technical efficiency but also raise philosophical questions about the nature of scientific knowledge. One such innovation is Physical Layer Network Coding (PLNC), a communication technique that utilizes signal interference as a source of information to enhance system performance. This paper examines the philosophical dimensions of science within PLNC, focusing on three fundamental aspects: ontology, epistemology, and axiology. Ontologically, PLNC represents a new paradigm in wireless communication that reinterprets interference not merely as noise but as an opportunity. Epistemologically, knowledge of PLNC is derived through scientific methods such as mathematical modeling, experimentation, and simulation—yielding intersubjective and verifiable truths. Axiologically, PLNC holds practical value in terms of energy efficiency, data reliability, and contributions to the sustainability of IoT ecosystems, while also raising ethical considerations regarding privacy and information security. Thus, this study demonstrates that the development of PLNC cannot be separated from philosophical reflection, emphasizing the profound interconnection between technological advancement, scientific methodology, and human values.

Peningkatan kasus gangguan kardiovaskular menuntut adanya sistem pemantauan detak jantung yang tidak hanya akurat, tetapi juga mampu memberikan respons cepat terhadap kondisi abnormal. Penelitian ini bertujuan mengembangkan sistem pemantauan detak jantung berbasis Internet of Things (IoT) yang terintegrasi dengan algoritma Extreme Gradient Boosting (XGBoost) untuk mendeteksi pola detak jantung abnormal secara real-time serta memicu respons otomatis melalui Miniature Medical Car. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan metode simulasi, di mana data denyut jantung dikumpulkan menggunakan Pulse Armband berbasis ESP32-C3 Mini dan sensor MAX30102, kemudian dikirim ke server untuk diproses melalui pipeline data mining. Dataset penelitian terdiri dari 800 sampel yang dibagi menjadi kelas normal dan abnormal, kemudian diolah menjadi 14 fitur statistik dan variabilitas detak jantung untuk proses pelatihan model. Hasil penelitian menunjukkan bahwa model XGBoost mencapai akurasi 94%, precision 93,3%, recall 93,3%, F1-score 0,933, serta ROC-AUC 0,96, yang mengindikasikan kinerja tinggi dalam membedakan pola detak jantung normal dan abnormal. Integrasi model dengan miniature car memungkinkan sistem memberikan respons fisik secara otomatis saat mendeteksi kondisi berisiko. Kontribusi penelitian ini mencakup pengembangan sistem IoT–ML real-time yang efisien, penggunaan fitur statistik ringkas untuk data fisiologis, serta implementasi respons berbasis robotik. Implikasi penelitian membuka peluang penerapan pada sistem monitoring medis jarak jauh, lingkungan publik, maupun perangkat asistif berbiaya rendah.