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Artificial Intelligence (AI) is transforming mechanical engineering and industrial processes by introducing unprecedented levels of efficiency, precision, and innovation. From predictive maintenance and autonomous robotics to material optimization and digital twins, AI-enabled systems are reshaping the industry landscape. This article examines key applications of AI in mechanical engineering, exploring how they contribute to sustainable industrial innovation, improve productivity, and pave the way for future advancements.

This paper presents a comparative analysis of various battery management systems (BMS) in electric vehicles, with a focus on incorporating machine learning techniques to improve battery safety and extend battery life. The study evaluates conventional BMS against machine learning-enhanced models in predicting thermal runaway, state of charge (SOC), and state of health (SOH) under diverse operating conditions. Results indicate that machine learning algorithms outperform conventional methods, providing more accurate SOC and SOH estimations, thus enhancing vehicle safety and longevity.

This paper investigates the mechanical properties of eco-friendly concrete incorporating recycled aggregates and industrial by-products. An experimental program was conducted to assess the compressive strength, tensile strength, and durability of various concrete mixes. The results reveal that the use of recycled materials can produce concrete with comparable mechanical properties to conventional concrete. This study highlights the potential of eco-friendly concrete in reducing environmental impacts while maintaining structural integrity.

This study presents an advanced structural health monitoring (SHM) system for steel bridges based on wireless sensor networks (WSN) integrated with machine learning algorithms. The proposed system monitors and predicts structural integrity under various load conditions. The research focuses on developing a machine learning model capable of real-time anomaly detection, allowing for early warnings of potential failures. Experimental results from both simulation and field tests demonstrate the system’s effectiveness in prolonging bridge lifespan while reducing maintenance costs.

This article investigates recent innovations in industrial engineering and mechanical systems, emphasizing how technological advancements impact manufacturing efficiency, sustainability, and cost-effectiveness. Through a review of current technologies, such as additive manufacturing, automation, and smart materials, the study assesses the key challenges industries face and forecasts emerging trends in mechanical engineering and industrial innovation. The article also discusses the potential for artificial intelligence and machine learning to revolutionize industry standards and drive forward engineering solutions.

This study presents the development of a smart grid system designed to efficiently integrate renewable energy sources into the existing electrical grid. The proposed system employs advanced communication technologies and real-time data analytics to optimize energy distribution and consumption. A simulation model was created to evaluate the system's performance under various scenarios, demonstrating significant improvements in energy efficiency and reliability. The findings indicate that the smart grid system can enhance the stability of the electrical network while promoting the use of sustainable energy sources.

In carrying out teaching and learning activities, of course learning media is needed that can support student learning. E-module is a learning media that contains images, videos and quizzes that can provide feedback in learning for students. This research focuses on developmente-mode basedandroid on the use of hand tools. Developmente-mode based android uses the ADDIE development model which has several stages, namelyanalysis, design, development, implementation, andevaluation. This research uses a research designone group pretest-posttest. The subjects of this research were class X TAV SMKN 3 Surabaya with 32 students. Validation is carried out to measure feasibilitye-mode. Student responses are needed to measure practicalitye-mode during learning. Student learning outcomes in the knowledge domain and skills domain are analyzed using the T test to measure effectivenesse-mode in learning. The research results show that (1) the validity of the developmente-mode obtained a result of 83.89% with very valid criteria, which means it is very suitable for use in learning. (2) practicalitye-mode which was developed obtained student response results of 89.47% with very practical criteria, which meanse-modebased android very practical for use in learning to use hand tools. (3) effectivenesse-mode obtained from learning results in the knowledge domain and the attitude domain. In the knowledge domain, the average value is obtainedpre-test of 48.9, while the average valuepost-test amounted to 87.91, and the T test significance value was 0.000<0.05. In the skills domain, an average skill value of 91.92 was obtained, greater than the KKM value of 75, and a T test significance value of 0.000<0.05 was obtained. It can be concluded that there is a significant increase in student learning outcomes in the realm of knowledge and skills.

This study aims to analyze purchasing patterns in online transactions using the Apriori algorithm to support sales strategy optimization. The research was conducted on transactional data from an online store selling household and daily-use products. The Apriori method was applied to identify associations between items based on minimum support and confidence thresholds. Four experimental scenarios were tested to compare the reliability of generated rules and determine the strongest item relationships. Data preprocessing included item grouping, transaction coding, and elimination of non-frequent items. The results show several strong association rules with lift ratio values above 1, indicating meaningful item relationships. The strongest rule identified was the association between forks and spoons, forming a highly relevant combination for product bundling strategies. The findings demonstrate that the Apriori algorithm can assist online stores in planning stock, designing product bundling, and improving marketing effectiveness. The research contributes practical insights for business decision-making and highlights the significance of data mining in e-commerce environments.

Darul Musthofa Wirosari Grobogan Islamic Boarding School is a boarding school where the tuition payment system still uses a manual system which later the administration section will make a report to the head of the boarding school. The formulation of this problem is an information system so that there is no double recording and books are easily torn so that transaction records are easily lost. The purpose of this study is to create a tuition payment information system to facilitate the creation of reports/data recording is still manual in books. As well as a summary of data one by one manually which results in recording taking a long time. The system developed in this study is web-based visual code software, storage is one in a MySQL database and makes it easy for users to record tuition fees with accurate results so as to reduce the risk of errors, loss and data manipulation                                                                                                        

A study was conducted to identify the relationship between height and weight. A researcher claimed that on average, when height increases, weight increases. This is called the Benn parameter. Weight can be adjusted for height using the Benn parameter (kg/mB), where B is the power that minimizes the correlation with height. To investigate how the Benn parameter changes across age and differs between genders. Using linear regression. Multilevel modeling was used to develop a history showing how Benn's parameters change with age from childhood through mid-adulthood and into older age. The data used information from a sample of 40 adults and 40 children under five. Height was measured in centimeters, weight was measured in kilograms. At alpha equal to 0.05 (α=0.05), the hypothesis is that there is a relationship between height and weight. This is the data, which is height and weight. And here is the hypothesis.  (Hnull) there is no relationship between height and weight, ρ equals 0. And ,, there is a relationship between height and weight, ρ does not equal 0. And we test to claim that .

Penelitian ini mengkaji implementasi sistem informasi dalam konteks pendidikan di sekolah dasar (SD) dan sekolah menengah pertama (SMP) di Penajam Paser Utara. Tujuan utama penelitian ini adalah untuk mengevaluasi efektivitas dan kepatuhan sistem informasi yang ada dalam meningkatkan proses administratif dan pendidikan. Dengan menggunakan pendekatan penelitian kualitatif, data dikumpulkan melalui wawancara, observasi, dan analisis dokumen. Temuan menunjukkan bahwa meskipun ada kemajuan signifikan dalam adopsi sistem informasi, tantangan seperti integrasi sistem, pelatihan pengguna, dan dukungan teknis masih ada. Tantangan ini mempengaruhi efisiensi dan efektivitas keseluruhan sistem. Implikasi dari temuan ini menunjukkan perlunya perbaikan terarah dalam pelatihan sistem, dukungan, dan strategi integrasi untuk meningkatkan efektivitas sistem informasi di institusi pendidikan.

Penelitian ini bertujuan untuk mengaudit sistem informasi pada UMKM di Penajam Paser Utara dengan fokus mengidentifikasi kelemahan dan meningkatkan kepatuhan terhadap standar yang berlaku. Dalam era digitalisasi, UMKM menghadapi tantangan besar dalam mengadopsi teknologi informasi untuk meningkatkan efisiensi dan daya saing. Namun, banyak UMKM yang masih kurang memahami pentingnya kepatuhan terhadap standar sistem informasi, yang berpotensi menimbulkan risiko keamanan data dan menghambat kelangsungan bisnis. Metode penelitian ini menggunakan pendekatan kualitatif dengan studi kasus melalui wawancara mendalam dan observasi langsung. Temuan penelitian menunjukkan beberapa kelemahan dalam kepatuhan sistem informasi, termasuk kurangnya pelatihan dan kesadaran. Implikasi dari penelitian ini adalah memberikan rekomendasi strategis bagi UMKM untuk meningkatkan kepatuhan dan keamanan sistem informasi mereka, yang pada akhirnya dapat meningkatkan efisiensi operasional dan daya saing UMKM di Penajam Paser Utara.

The development of natural skincare products, such as spinach powder face masks, is becoming increasingly popular as a safe and environmentally friendly alternative. However, many beginner entrepreneurs lack understanding of proper packaging and labeling techniques, which can reduce product appeal and consumer trust. This community service activity aimed to enhance students’ skills in applying effective packaging and labeling techniques as part of product branding strategies. The implementation methods included knowledge delivery through training sessions, literature review on cosmetic packaging standards, creative design of attractive packaging and labels, and hands-on practice in packaging and labeling the spinach powder face mask products. The results showed that students gained new insights into the importance of packaging and labeling in building brand image. Moreover, the professionally designed packaging and informative labels created by the students successfully improved the perceived quality of the product among consumers and increased its marketability.

Community service activities aim to provide training for local chicken farmers about herbal concoctions in the form of a mixture of lemongrass stalks, ginger, turmeric, galangal, garlic and brown sugar to improve the health of local chickens which will later support the growth and maintain the immunity of the local chickens. The method of implementing community service carried out to the local chicken farmer group in Werwaru Village, Southwest Maluku Regency is by using lecture methods, discussions and direct practice on making and using herbal concoctions that are given directly to local chickens. The results of the implementation of this community service are able to increase the knowledge of farmers, especially in making herbal concoctions to maintain the stamina and immunity of local chicken health. The conclusion of this community service is that training in making herbal concoctions in the form of a mixture of lemongrass stalks, galangal, ginger, brown sugar, garlic for local chickens is very interesting and of interest to farmers and increases farmers' understanding of the importance of using herbal ingredients that are around the farmer's environment to support the health of livestock, both local chickens and other livestock that are kept.

Artikel ini berfokus pada edukasi dan pendampingan sertifikasi halal dan digital marketing yang memberikan dampak peningkatan dalam penjualan UMKM. Pengabdian dilakukan dengan sosialisasi, pendampingan, dan pelatihan. Pengabdian ini menggunakan metode ABCD (Asset Based Community Development) yang didasarkan pada pemberdayaan aset masyarakat. Tahapan ABCD meliputi 5 tahap yakni, 1) Inkulturasi, 2) Discovery, 3) Design, 4) Define, dan 5) Reflection. Pengabdian menghasilkan bahwa UMKM di Desa Pesawahan telah berhasil memahami pentingnya sertifikasi halal dan pemasaran digital, meningkatkan daya saing mereka, dan memperluas jangkauan produk secara signifikan yang mencerminkan peran krusial kolaborasi antara pemerintah, masyarakat lokal, dan pemangku kepentingan lainnya dalam mendukung pertumbuhan UMKM di era digital.

This study explores the role of contact investigation (CI) in detecting suspected tuberculosis (TB) cases in Babussalam District, Southeast Aceh Regency, in 2023. The focus is on empowering health cadres through simulation, practice, and mentoring in TB case investigation. A total of 20 health cadres were trained to conduct the investigation, resulting in tracing of 5 index cases, 57 households, and 217 individuals. Activities included case assessment, home visits, TB screening, education, mentoring, monitoring, and reporting. Recommendations encompass the application of knowledge and skills acquired by health cadres in TB management, while maintaining close coordination with local health centers and government authorities.

Community Small and Medium Enterprises (MSMEs) have an important role in advancing the Indonesian economy. One of them is MSME UD. NATURAL FOOD which operates in the field of processing various types of onions. After observations, there are deficiencies in the onion chopping machines currently in use which cannot produce premium grade processed onions. The solution to this problem is to create a Transmission Design and Premium Horizontal Shallot Chopper Blade with an Output Capacity of 15 Kg Per Hour. It is hoped that this tool can help produce quality fried shallots. The method used in design is a design method that begins with observation and literature study, then continues with the planning stage including (planning the transmission system and chopper system). The results obtained in designing this machine show the size of the machine with dimensions of 70 cm wide, 145 cm long, 89 cm high, using 3 mm thick angle iron as part of the frame. And the results of the calculation of the load on the hopper is 231.4 N, the load on the transmission is 138.84 N, the load on the electric motor is 112.14 N. The heaviest load on the frame is on the hooper, amounting to 231.4 N. From the results of the data above it will then be Loading simulations were carried out, the results obtained were stress analysis (yield strength) of 25,000N, maximum displacement analysis on the frame of 5.287mm, and factor of safety analysis on the frame of 5.5. 4 then the frame is declared safe . And it has been designed and made safe to use, because no damage occurs to the frame when the machine is operated. Based on the design results of the transmission and premium horizontal premium  onion chopper blade with an output capacity of 15 kg per hour, the engine torque (T) is 545,04 Nm. The engine power requirement is 1,566 kW. The pulley ratio on the machine is (1:10). Pulley diameter 3.8. The shaft diameter is 19 mm and the resulting chopping capacity is 17,4 m^2/hour.

The rapid growth of data-intensive applications has posed significant challenges for classical machine learning (ML) algorithms, particularly in terms of computational efficiency and scalability. This study explores the role of quantum computing in optimizing machine learning performance through the implementation of Quantum Machine Learning (QML), specifically using the Quantum Support Vector Machine (QSVM) model. The research adopts a Design Science Research approach, involving problem identification, model development, system implementation, and performance evaluation. Both classical Support Vector Machine (SVM) and QSVM models are developed and tested using benchmark classification datasets. The results indicate that QSVM outperforms the classical SVM model across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Additionally, QSVM demonstrates improved computational efficiency by reducing training time, particularly when handling high-dimensional data. These improvements are attributed to the ability of quantum computing to utilize quantum kernel methods and map data into higher-dimensional feature spaces, enabling better pattern recognition and classification performance.  Despite these promising outcomes, the study also identifies several limitations related to current quantum hardware, such as noise, decoherence, and limited qubit availability, which may affect scalability and practical implementation. Therefore, further research is required to enhance quantum hardware reliability and develop hybrid quantum-classical models. In conclusion, quantum machine learning offers a promising solution to overcome the limitations of classical approaches, providing enhanced performance and efficiency for complex data processing tasks in future intelligent systems.

The rapid growth of digital technologies has significantly increased the complexity and frequency of cyber threats, making network security a critical concern in modern information systems. Traditional security approaches, such as rule-based and signature-based systems, are often limited in detecting sophisticated and unknown attacks. Therefore, this study proposes an Anomaly-Based Intrusion Detection System (AbIDS) utilizing machine learning and deep learning techniques to enhance detection capabilities. The research adopts a Design Science Research approach, involving stages of problem identification, data collection, preprocessing, model development, system implementation, and evaluation. Several models, including Decision Tree (DT), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are implemented and compared. The results indicate that deep learning models, particularly LSTM and CNN, outperform traditional machine learning methods in terms of accuracy, precision, recall, and F1-score, while maintaining a lower false positive rate. Additionally, the integration of incremental learning enables the system to adapt to new attack patterns without requiring complete retraining, improving scalability and real-time performance. Despite the promising results, challenges such as computational complexity and false positives remain. Overall, the proposed IDS model demonstrates strong potential as an effective and adaptive solution for enhancing network security in dynamic environments.

The rapid development of smart city initiatives has significantly increased the adoption of Internet of Things (IoT) technologies to enhance urban services, infrastructure efficiency, and quality of life. However, the large-scale deployment of interconnected IoT devices also introduces critical cybersecurity challenges, including unauthorized access, data breaches, and system vulnerabilities. This study aims to develop an integrated IoT security management model to improve cybersecurity resilience in smart city environments. The research adopts a Design Science Research (DSR) approach, which involves problem identification, literature analysis, model design, implementation, and evaluation. The proposed model incorporates key security components such as Identity and Access Management (IAM), device authentication, secure communication through encryption, firmware and patch management, and continuous monitoring with intrusion detection mechanisms. The model is evaluated through simulation in smart city scenarios, including transportation systems, environmental monitoring, and energy management. The results demonstrate significant improvements in security performance, with increases in threat detection rate, vulnerability reduction, access control effectiveness, and system stability under attack conditions. Quantitative analysis shows improvements of up to 37% compared to conventional approaches, indicating the effectiveness of the proposed model in mitigating IoT-related cybersecurity risks. This study contributes by providing a comprehensive and scalable framework for IoT device security management, which can be applied to enhance the reliability and sustainability of smart city systems. Future research is recommended to validate the model in real-world implementations and integrate advanced technologies such as artificial intelligence for predictive threat detection.