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Electricity has emerged as an essential requirement in modern life. As demand escalates, electricity costs rise, making wastefulness a drain on financial resources. Consequently, forecasting electricity usage can enhance our management of consumption. This study presents an IoT-based monitoring and forecasting system for electricity consumption. The system comprises two NodeMCU micro-controllers, a PZEM-004T sensor for collecting real-time power data, and three relays that regulate the current flow to three distinct electrical appliances. The data gathered is transmitted to a web application utilizing the k-Nearest Neighbor (k-NN) algorithm to forecast future electricity usage based on historical patterns. We evaluated the system's performance using four weeks of electricity consumption data. The results indicated that predictions were most accurate when the user’s daily consumption pattern remained stable, achieving a Mean Absolute Error (MAE) of approximately 1 watt and a Mean Absolute Percentage Error (MAPE) ranging from 1% to 1.7%. Additionally, predictions were notably precise during the early morning hours (3:00 AM to 8:00 AM) when k=6 was employed. This study demonstrates the effectiveness of integrating IoT-based systems with machine learning for real-time energy monitoring and forecasting. Furthermore, it emphasizes the application of data mining techniques within embedded IoT environments, providing valuable insights into the implementation of lightweight machine learning for smart energy systems.

This study aims to improve the performance of roadside supervision services for Baggage Towing Tractor (BTT) movements at Kualanamu International Airport, Deli Serdang. The research background is based on the high level of operational risk on the airside, which requires strict supervision of Ground Support Equipment (GSE) vehicles. The method used is descriptive qualitative with data collection techniques in the form of direct observation, semi-structured interviews, and field documentation. The results of the study indicate that violations of Standard Operating Procedures (SOP) such as excessive speeding, inappropriate lane use, and compliance with procedures are still common. The main causes include lack of training, weak monitoring systems, and low awareness of safety culture. From the results of the observation, it was found that supervision is still manual and not optimally supported by monitoring technology such as CCTV with a real-time integration system. In addition, the number of supervisory personnel is also not able to cover the entire service area of the road which is quite extensive, especially during peak operating hours. The lack of a firm reward and punishment system also contributes to the low discipline of BTT drivers. Interviews with several BTT operators showed that they have not received regular safety training, and most do not understand the importance of complying with established signs or markings. Therefore, it is recommended that airport authorities implement a monitoring system through the use of sensor-based technology and GPS tracking, as well as increase the intensity of occupational safety training. Furthermore, a dedicated unit should be established to continuously monitor GSE movements and integrate a digital reporting system to ensure prompt action on violations. Improving safety culture can also be achieved through internal campaigns and ongoing outreach.

This research was conducted from June to July 2025 in Binjai City, with the primary focus being analyzing the readiness of the Binjai City Regional Disaster Management Agency (BPBD) to implement a flood early warning system utilizing artificial intelligence (AI). The data collection process was conducted through a literature review, which involved reviewing various theories and previous research results regarding the application of AI and Internet of Things (IoT) technology in the context of disaster mitigation. Based on the results of the study, it was found that the use of technologies such as ultrasonic sensors, microcontrollers, fuzzy logic, and automatic notification systems can provide real-time warnings with a high level of accuracy and a fast response. This system enables early detection of rising river levels through automatic measurements, intelligent data processing, and sending notifications to authorities and affected communities within seconds. By integrating historical data and machine learning-based predictions, this system is also able to depict potential flooding before it occurs, providing a longer response time for evacuation. However, the readiness of the Binjai City BPBD still faces various challenges, such as limited digital infrastructure, the need for human resource training in the technology field, and inadequate budget allocation. Therefore, cross-sector collaboration and ongoing policy support are needed for optimal implementation of this system. The use of AI and IoT in early warning systems is not only technically relevant but also urgent in the face of increasing climate change and flood risks. A strategy involving cross-sector collaboration between government, academia, and the private sector is needed to develop an adaptive and sustainable early warning system.

This quantitative study investigates the antecedents of Brand Loyalty in Genshin Impact players by examining the mediating role of Customer Value in the relationship between Experiential Marketing and Product Quality. Data were collected from 80 active players in Malang City using a purposive sampling technique and analyzed using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach to examine the relationship between variables. The results revealed two distinct pathways to loyalty formation. First, Experiential Marketing demonstrated a significant influence on Brand Loyalty, both directly and indirectly through strong mediation by Customer Value. This suggests that emotional, sensory, and relational experiences during gameplay play a crucial role in creating perceived value and long-term engagement. Second, although Product Quality demonstrated a significant direct influence on Brand Loyalty, its influence was not significantly mediated by Customer Value. This indicates that while the game's graphical quality, system performance, and technical stability are highly appreciated, they do not automatically translate into customer value without a meaningful experience. This study concludes that in the Games-as-a-Service model, loyalty is built through a dual strategy: a combination of superior product appeal and holistic value creation derived from a rich, curated player experience. Therefore, game developers need to synergistically integrate experience and quality strategies to build long-term brand loyalty. The practical implications of these findings are highly relevant for game developers and digital marketers. Marketing strategies are no longer sufficient to simply highlight technical features or product specifications; they must also address the emotional and social dimensions experienced by players.

The Paiton Steam Power Plant (PLTU) is one of the main sources of electrical energy in East Java, which plays a vital role in maintaining a sustainable electricity supply. The reliability of generator units is a key element in maintaining stable energy distribution. However, the high frequency of sudden generator failures poses serious challenges, such as increased downtime and increased maintenance costs. To address these challenges, this study aims to design a generator maintenance prediction model based on the Naive Bayes algorithm with a predictive maintenance approach. This study uses historical maintenance data and key sensor parameters such as temperature, oil pressure, and vibration as input. The data is analyzed through several stages, namely data preprocessing, selection of relevant features, and labeling generator conditions into three categories: Normal, Warning, and Critical. The Naive Bayes model is trained to classify the data probabilistically to generate predictions of future generator conditions. Model evaluation using accuracy metrics and a confusion matrix shows that the model successfully achieved an accuracy rate of 89% and was able to provide early warnings of potential failures up to 3 days before failure occurs. The implementation of this system is expected to support the shift in maintenance strategies from reactive and scheduled systems to data-driven predictive systems. Implementing failure predictions allows the technical team at the Paiton PLTU to conduct planned maintenance, avoid sudden disruptions, and extend equipment lifespan. Thus, this model has the potential to reduce operational downtime by up to 25%, while providing significant savings in operational and logistics costs. This research also shows that integrating machine learning technology into energy facility management can improve the efficiency and resilience of the overall electric power system.

The agricultural sector has a vital role in meeting national food needs, improving community welfare, and strengthening regional food security. However, the sector still faces various challenges, including land limitations, climate change, price fluctuations, and low production cost efficiency. Therefore, innovation is needed through the application of digital technology that is able to increase the productivity and sustainability of farming businesses. One of the solutions offered is the use of Artificial Intelligence of Things (AIoT), which is the integration of artificial intelligence (AI) with the Internet of Things (IoT) that allows agricultural systems to run more intelligently, efficiently, and scalably. This community service activity was carried out with the Satria Tani Hanggawana Farmers Group located in Kalisapu Village, Slawi District, Tegal Regency, with the main focus on premium melon cultivation. The method of the activity included material presentations on the concept of smart farming, the introduction of IoT-based sensor devices for monitoring temperature, humidity, and plant nutrition, field practices in greenhouses, simulations of the use of AI-based applications for crop prediction, and interactive discussions. In addition, material was also provided on simple agribusiness management, financial recording, and crop marketing strategies so that farmers are able to manage their businesses more professionally. The results of the activity showed a significant increase in participants' knowledge related to the application of AIoT in modern agriculture. Farmers not only understand the benefits of technology, but are also able to practice using tools and applications directly. The enthusiasm of the participants was reflected in their active involvement in discussions, willingness to try new technologies, and awareness of the importance of innovation to face agricultural challenges in the digital era.

The Caesarean Section (C-Section) procedure is one of the most common surgical methods for childbirth, performed through an incision in the abdominal wall (laparotomy) and the uterine wall (hysterotomy). Despite its effectiveness in ensuring maternal and fetal safety, one of the major postoperative complaints reported by patients is pain. Pain itself is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, which is subjective and can only be described by the person experiencing it. Unmanaged pain may negatively affect postoperative recovery, emotional well-being, mobility, and breastfeeding initiation. Therefore, appropriate nursing care interventions are essential to help patients achieve comfort and recovery. This scientific paper explores the implementation of sacral plexus therapy as part of nursing interventions to address pain problems in post-C-section patients. The study employed a case study approach that applied the nursing care process, which includes comprehensive assessment, formulation of nursing diagnoses, planning, implementation of interventions, and evaluation. Nursing care was carried out over a period of 72 hours (3x24 hours), with pain intensity and patient comfort levels monitored throughout the process. The evaluation results showed that all nursing diagnoses related to pain were successfully resolved within the given timeframe. Specifically, the application of sacral plexus therapy proved effective in reducing the intensity of postoperative pain and improving overall comfort, enabling patients to gradually regain mobility and adapt to their postpartum condition. In conclusion, sacral plexus therapy can serve as a complementary and non-pharmacological intervention within nursing care to effectively manage pain in post-C-section patients. These findings highlight the importance of innovative and holistic approaches in nursing practice to improve patient recovery and quality of care after surgical childbirth.

To support the smooth operation of maritime transportation, particularly in the transportation of liquids such as FAME oil, factors that can influence cargo loss must be properly managed. Deterioration in quality or loss of cargo volume can occur for several technical and non-technical reasons. One of the main factors causing FAME cargo loss is a leak in the ship's hull or poorly maintained tank structures, resulting in partial cargo loss during the voyage. Furthermore, inappropriate vessel characteristics, such as suboptimal carrying capacity, are also a major cause of cargo loss. Inaccurate unloading and unloading procedures, such as careless handling, also contribute to this problem. The impact of FAME cargo loss is significant, both financially and on the company's reputation. Claims received from charterers regarding losses can be detrimental to the company, and decreased customer confidence can impact shipping volumes and revenue. To minimize the risk of cargo loss, companies need to implement effective strategies, such as routinely inspecting vessels and storage tanks to ensure there are no leaks. Training crews in proper loading and unloading procedures is also crucial to avoid human error that can lead to losses. Implementing a regular visual and technical inspection system will improve operational quality and minimize cargo loss during FAME transportation. Furthermore, the use of more advanced monitoring technology, such as sensors to detect leaks or changes in tank pressure, can help detect problems early. Data-driven maintenance management systems can also be used to monitor vessel condition and optimize maintenance schedules, thereby reducing the risk of damage that could lead to cargo loss.

Measuring Liquefied Petroleum Gas (LPG) levels and inventory in storage tanks is a critical aspect of terminal operations, directly influencing safety, operational efficiency, and accurate stock management. The Integrated Terminal Palembang – Pulau Layang LPG Site has implemented a servo-type Automatic Tank Gauging (ATG) system, namely the Proservo NMS81, to provide precise and real-time volume measurements. Despite its operational importance, there remains a limited in-depth understanding of how this ATG system functions, particularly regarding the complete process from sensing LPG levels to converting them into accurate inventory data. This study aims to analyze the working mechanism of the Proservo NMS81 in measuring LPG height and generating digital stock data for monitoring purposes. The research method combines a literature review of the Proservo NMS81 technical datasheet and related scientific references with direct field observation (KSM-LP) at the LPG Pulau Layang site. The analysis covers the operating principle of the servo sensor, which relies on displacement measurement via a mechanical float and wire system; the analog-to-digital conversion process utilizing a 12-bit ADC to transform continuous signals into discrete digital values; the communication protocols employed, including HART and Modbus RS-485, for transmitting processed data to the control system; and the algorithmic data processing that converts tank level measurements into standardized inventory figures based on tank calibration tables and product density. The results of this study are expected to improve technical knowledge regarding servo-type ATG systems, enhance the accuracy and reliability of inventory monitoring, and contribute to more informed operational decision-making. Furthermore, the research findings are intended to serve as a valuable academic reference for the application of advanced instrumentation technology in the energy industry, supporting both professional practice and further scholarly exploration in the field.

Internet of Things (IoT)-based digital transformation has become a major catalyst in improving the efficiency of operational systems in various sectors, including the modern retail industry. One of the common logistics problems found in supermarket environments is the accumulation of unorganized shopping trolleys, which can hinder service flow and increase staff workload. This study presents a design of an IoT-based autonomous smart trolley system and automatic navigation to address these problems in a structured manner. The system design utilizes the integration of ESP32 and Arduino UNO microcontrollers, ultrasonic sensors for distance detection, line sensors for automatic path navigation, and Raspberry Pi modules for visual image processing in location tracking. The system is designed to be able to independently reposition the trolley to a predetermined parking station. Conceptual analysis shows that this system has significant potential in reducing operational costs, increasing labor efficiency, and strengthening customer service automation. Initial evaluation of technical and economic feasibility aspects strengthens the opportunity for widespread system implementation in the future. This design is the first step in developing a smart retail solution based on adaptive technology that is in line with the principles of Society 5.0. Furthermore, the development of this smart trolley system also considers user safety and comfort through additional features such as anti-collision sensors, an early warning system in the event of technical problems, and a manual control option as an alternative in emergency situations. The integration of Internet of Things-based technology also enables real-time monitoring and management systems through a web-based dashboard or mobile application, which can be accessed by supermarket management for operational analysis. Thus, this system not only addresses internal logistics needs but also contributes to improving the overall customer experience.

Maintaining stable cage temperature is a crucial factor in the success of broiler chicken farming, especially in close house systems that rely on optimal microclimate control. Temperature instability can lead to thermal stress, reduced growth rates, and increased mortality in broilers, particularly during the early stages of life (0 to 30 days old). This study aims to design and implement an automatic temperature control system based on the Arduino Uno microcontroller integrated with a DHT11 temperature and humidity sensor in the broiler chicken cages of PT. Barokah Restu Utama. The system is designed to read temperature and humidity in real-time and automatically activate or deactivate cooling devices such as fans or heating devices like incandescent lamps, depending on the temperature range required for each growth phase. The ideal temperature range used as a reference in this system includes 30–32°C for chickens aged 0–7 days, 29°C for ages 8–14 days, 28°C for ages 15–21 days, and 26–27°C for chickens aged 22–30 days. Testing results show that the system is capable of maintaining stable temperatures according to the specified standards for each growth phase. With this automatic control system in place, broiler chicken maintenance becomes more efficient and effective. The risk of mortality due to heat stress is significantly reduced, and chicken growth becomes more optimal. This technology offers a practical and economical solution, especially for small- to medium-scale broiler chicken farmers who use close house systems. The system is easy to operate and relatively affordable to install, making it an accessible innovation that supports better livestock management through automation and smart farming practices.

Pemantauan suhu dan kelembaban secara real-time merupakan kebutuhan penting dalam berbagai bidang, seperti kesehatan, pertanian, industri, dan pengelolaan lingkungan, guna menjaga stabilitas kondisi ruangan dan mencegah kerusakan pada peralatan elektronik. Penelitian ini bertujuan untuk merancang dan membangun sistem monitoring suhu dan kelembaban berbasis mikrokontroler ESP32 dengan sensor DHT22. Sistem dirancang untuk menampilkan data secara langsung melalui layar OLED dan aplikasi Blynk sehingga memungkinkan pemantauan jarak jauh secara real-time. Penelitian ini menggunakan metode eksperimen dengan pengujian pada dua skenario, yaitu ruang tertutup dan area terbuka, dengan titik kendali kelembaban relatif (RH) sebesar 65%. Hasil pengujian menunjukkan bahwa sistem mampu bekerja secara efektif dalam kondisi ruang tertutup, di mana kelembaban meningkat dari 54,5% menjadi 65% RH dalam waktu 10 menit dan kemudian dapat dipertahankan stabil melalui mekanisme kendali otomatis. Namun, pada area terbuka, sistem tidak menunjukkan peningkatan kelembaban yang signifikan karena adanya sirkulasi udara bebas yang mempercepat dispersi uap air. Dengan demikian, dapat disimpulkan bahwa sistem monitoring berbasis ESP32 ini efektif digunakan pada ruang dengan kondisi lingkungan yang terkontrol, serta memiliki potensi untuk dikembangkan lebih lanjut pada aplikasi rumah pintar (smart home) dan sistem industri berbasis Internet of Things (IoT).

Infant growth and development are crucial issues that require serious attention, particularly during the early childhood phase, which is crucial for a child's future development. The golden age of infants is the most sensitive period to various forms of stimulation that influence their motor, sensory, cognitive, and emotional development. However, not all parents, especially mothers, fully understand the importance of appropriate and safe stimulation in supporting infant growth and development. One form of stimulation that is relatively easy to perform, has no side effects, and can be applied independently at home is gentle touch therapy. Gentle Touch Therapy is a complementary therapy method that involves gentle, affectionate touch on the baby's body, aimed at stimulating the nervous system, strengthening the emotional bond between mother and child, and increasing comfort and relaxation in the baby. Several studies have shown that this gentle touch can improve sleep quality, accelerate weight gain, relieve stress, and encourage infant neuromotor and socio-emotional development. This community service activity was carried out as an effort to increase mothers' awareness and skills in providing stimulation through gentle touch. The activity was carried out at the Andina Independent Midwife Practice (PMB), Palembang City, in June 2025. The main target of this activity was mothers with babies aged 0–12 months. The implementation method included providing education through interactive lectures, demonstrations of touch therapy techniques by health professionals, and direct practice by mothers accompanied by facilitators. The results of the activity showed a significant increase in mothers' understanding of the importance of growth and development stimulation, especially through gentle touch therapy. The participants showed high enthusiasm in participating in the activity and were able to practice gentle touch techniques correctly and confidently.

Tempoyak is a traditional Indonesian fermented food made from the flesh of durian (Durio zibethinus Murr.), which undergoes spontaneous fermentation driven by indigenous microorganisms. The fermentation process generally occurs under anaerobic conditions and is primarily dominated by lactic acid bacteria (LAB), including species such as Lactobacillus, Leuconostoc, and Pediococcus. These bacteria play a crucial role in modifying the physicochemical properties of the product, including a significant reduction in pH, an increase in the concentration of organic acids, and the formation of volatile compounds responsible for tempoyak’s distinctive aroma and overall flavor complexity. In addition to enhancing its unique sensory profile, the fermentation process also extends the shelf life and introduces probiotic potential to the final product. This study aims to observe and analyze the microbiological and chemical aspects of tempoyak fermentation and to evaluate its potential as a local functional food with health-promoting properties. Laboratory tests and microbial analyses confirmed that the fermentation process not only retains essential nutrients but also promotes the growth of beneficial bacteria, provided the production conditions are hygienically maintained at all stages. The study emphasizes the importance of controlled fermentation techniques to ensure product safety and consistent quality. Furthermore, the findings reveal that tempoyak could serve as a promising probiotic-rich food that supports digestive health, contributing to the diversification of traditional Indonesian fermented foods. With its appealing flavor, cultural value, growing consumer interest, and potential health benefits, tempoyak holds significant promise for future development and commercialization, particularly in the field of functional food innovation rooted in indigenous culinary practices.

This study aims to develop tempeh (Rhizopus oryzae) into tempeh flour to be utilized as the main ingredient in the production of a traditional Indonesian cookie known as kastengel. Tempeh is a rich source of plant-based protein and dietary fiber, yet its application in the processed food industry, particularly in dry baked goods such as cookies, remains underexplored. This innovation is expected to serve as a healthy food alternative while supporting the diversification of local food products. The research employed a Research and Development (R&D) approach, encompassing several key stages: processing tempeh into flour through slicing, drying, grinding, and sieving; formulating kastengel dough using tempeh flour as the base ingredient; and evaluating the final product through both organoleptic (sensory) testing and nutritional analysis. Organoleptic testing involved 10 panelists, including lecturers and students, who assessed the product based on color, taste, aroma, and texture. Nutritional content was analyzed at the Manado Industrial Research and Standardization Agency. The results showed that kastengel made from tempeh flour had a light brown color, a savory-sweet flavor, a distinctive yet pleasant aroma, and a crunchy texture. Laboratory tests revealed the nutritional composition of the product to include 46.12% carbohydrates, 29.82% fat, and 3.92% protein, indicating its potential as a nutritious food option. Most panelists reported a favorable response toward the product. In conclusion, tempeh flour can serve as an effective substitute for wheat flour in the production of kastengel cookies. The final product, branded as “Kastengel Eka,” demonstrates that tempeh-based food innovations can be well-accepted in terms of sensory qualities and possess promising potential as a functional, nutritious, and marketable food product.

The Message Queue Telementary Transport (MQTT) protocol is able to adjust the sending and receiving of messages to monitor in accordance with the user's preferences because the sending and receiving of messages is topic based on a specified topic, making it necessary to routinely monitor the condition of patients who have been diagnosed with heart problems from a distance. With the aim to perform a Quality of Service (QoS) analysis with throughput, delay, and packet loss parameters using Unshielded Twisted Pair internet transmission media (UTP) and Wireless, the goal of this research is to design and implement (MQTT) a heart rate monitoring device with an EKG module as a sensor and ESP32 as a microcontroller. On the Ubidots website, EKG signals are transmitted over the internet and shown in real time. QoS analysis is performed using the Wireshark application. Data was collected on two scenarios at intervals of 30 minutes, 1 hour, 2 hours, 5 hours, 8 hours, 12 hours, 18 hours, and 23 hours. The throughput, latency, and packet loss metrics used in this study's results cause different value variations; these are influenced by the weather, internet bandwidth, computer, and router specifications. According to testing, the tool is portable and has a 3000mAh battery, but it has the restriction that it can only be used with reliable internet and bandwidth.

Abstrak  - Coal handling system berfungsi menangani pekerjaan mulai dari pembongkaran batu bara dari kapal/tongkang (unloading area) yang dipindahkan ke conveyor - conveyor, penimbunan/penyimpanan di stock area, ataupun pengisian ke bunker yang digunakan untuk pembakaran di boiler. Dalam proses pembongkaran tersebut tidak boleh terjadi penyumbatan / plugging terutama pada chute conveyor, maka dari itu diperlukan sensor proteksi untuk mencegah hal tersebut. Sensor lama yang digunakan yaitu berjenis Tilt Switch yang selama pemakaian tidak efektif karena banyak masalah sehingga dilakukan pemeliharaan, akibatnya dilakukan penggantian menggunakan sensor berjenis MBD (Microwave Beam Detector). Dari analisa setelah penggantian menggunakan sensor MBD, permasalahan penyumbatan chute berkurang dapat dilihat dari frekuensi pemeliharaan yang semakin berkurang.

Mental health is a crucial aspect of modern life, with stress and anxiety being among the most common and impactful psychological disorders. This research proposes a stress and anxiety monitoring system based on the Internet of Things (IoT), integrating biometric sensors and Deep Neural Networks (DNN) for early detection and in-depth analysis. The system is designed using MAX30102 (heart rate and SpO₂), GSR (Galvanic Skin Response), and DS18B20 (body temperature) sensors, managed by an ESP32 microcontroller and communicating through the MQTT protocol. Physiological data is collected in real-time, formatted in JSON, and transmitted to both Android and web-based applications for visualization. The DNN model is developed using the TensorFlow framework with a layered architecture and ReLU activation functions to classify four mental states: relaxed, calm, anxious, and highly stressed. The training dataset comprises both primary and secondary data, including the WESAD dataset. Model performance is evaluated through k-fold cross-validation, showing high accuracy and strong generalization capabilities. The results indicate that the integration of sensor technology and deep learning significantly improves the effectiveness of stress and anxiety detection compared to traditional methods. This system demonstrates great potential for the development of AI-based wearable devices for autonomous, real-time, and adaptive mental health monitoring. 

Photogrammetry is a technique for measuring and modeling three-dimensional objects by utilizing digital imagery from various perspectives. In the context of reverse engineering, this technique serves to duplicate, reconstruct, and analyze the dimensions of physical objects with a high degree of accuracy. The main advantage of photogrammetry lies in its ability to capture the details of the shape and texture of objects without the need for physical contact. However, the quality of photogrammetry scan results is greatly influenced by a number of technical factors, especially lighting and camera sensor sensitivity (ISO) settings. Variations in these two parameters can cause deviations or dimensional deviations in the resulting 3D model. This study aims to quantitatively evaluate the influence of lighting intensity and camera ISO setting on dimensional deviation in photogrammetry scan results. The research method used is experimental, where the dimensions of the scanned object are compared to the original dimensions using precision measuring instruments. The results showed that both the lighting level and the ISO setting had a significant influence on the accuracy level of the 3D model. The ideal lighting intensity range was found to be in the range of 125–150 lux, where shadows and light reflections could be minimized. Meanwhile, the use of low ISO (around 200) is able to produce cleaner image textures and minimize noise, resulting in smaller dimensional deviations. Additionally, the interaction between moderate lighting and low ISO is proven to provide the best scanning accuracy. This combination is able to maintain a balance between image quality and surface detail of the object. These findings not only provide practical recommendations regarding the regulation of scanning conditions, but can also serve as a guideline for industry practitioners and academics in improving the quality of reverse engineering results. With a proper understanding of lighting and ISO variables, the photogrammetry process can be optimized to produce more accurate and efficient 3D models.

This research aims to be able to meet the water supply of lettuce plants automatically by using three sensors such as soil moisture, water level, and water discharge. The goal is to provide water needs to plants automatically and regularly. The developed tool uses YL-96 sensor for soil moisture, HC-SR04 for water level and YF-S201 for water discharge. Sensor data is sent to the arduino to be processed using the fuzzy mamdani method so that these three data values affect the movement of the tap servo motor that flows to the lettuce plant. Fuzzy logic here as a decision maker from the value of 3 sensor data and then processed automatically by arduino using fuzzy mamdani to determine how many degrees the servo motor moves. The result is that the Lettuce Plant Water Needs Analysis System Automation Tool is able to maintain the water supply of lettuce plants and soil moisture ideally at 76% with a servo motor movement system success rate of 100%.