PERFECT: Journal of Smart Algorithms is an international, Computer Technology, peer-reviewed and open-access journal that provides a platform to produce high-quality original research, Reviews, Letters, and case reports in Information Technology, Computer Science, and all related fields. Our aim is to ameliorate the speedy distribution of new research ideas and results and allow the researchers to create new knowledge, studies, and innovations that will aid as a reference tool for the future. PERFECT is published twice in one year, namely in January and July. JOURNAL IDENTITY Journal Name PERFECT: Journal of Smart Algorithms e-ISSN 3064-0377 (Online - Elektronik) SK:30640377/II.7.4/SK.ISSN/10/2024 Prefix DOI 10.62671/perfect Language English Chief Editor Arie Budiansyah S.T., M.Eng | Universitas Syiah Kuala, Aceh, Indonesia Citation Analysis Scopus | Google Scholar | Sinta
LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)
Semantic Playlist Engineering: A Conceptual Approach to Digital Content Organization through Semantic Domains
The rapid growth of digital content enabled by Generative Artificial Intelligence (Generative AI) has shifted the primary challenge for creators from content production to the organization of continuously expanding digital collections. Existing content organization mechanisms, such as playlists, albums, and collections, are commonly based on technical characteristics including genre, publication chronology, or media type, making them less effective in representing the conceptual relationships among creative works. Meanwhile, semantic approaches reported in previous studies have primarily focused on recommendation systems, information retrieval, metadata enhancement, and automatic playlist generation rather than creator-centric content organization. This study introduces Semantic Playlist Engineering (SPE) as a conceptual approach to digital content organization through Semantic Domains. Within the proposed approach, a Semantic Domain is defined as a conceptual space that groups creative works sharing similar meanings regardless of their technical characteristics or platform implementation. These domains are subsequently represented through Semantic Playlist Organization (SPO), allowing platform-specific organizational mechanisms such as playlists, collections, albums, or series to serve as representations of semantic organization. The study adopts a conceptual descriptive research approach supported by a case study involving digital music produced by Studio Isi Pundi Sound™ and published through the FartressMusic YouTube channel. The case study demonstrates how semantically related creative works can be organized into illustrative Semantic Domains to support coherent creator-centric content organization. The primary contribution of this study is the introduction of Semantic Playlist Engineering, Semantic Domains, and Semantic Playlist Organization as a unified conceptual foundation for organizing creator-owned digital content. Rather than proposing a recommendation algorithm or an automated classification model, this study offers a new perspective that positions semantic meaning as the principal basis for long-term digital content organization across different digital platforms.
Copyright (c) 2026 Fardiansyah, Anhar, Sepri Kurniadi (Author) https://creativecommons.org/licenses/by-nc/4.0
Application Of The COPRAS Method In Selecting The Best Boarding House For Students
Selecting a boarding house is an important decision for students, especially those who come from outside the area and require a comfortable, safe, and suitable place to live. The large number of boarding house options with varying facilities and rental prices often makes it difficult for students to determine the best choice. Therefore, a decision support system is needed to assist the selection process objectively and effectively. This study aims to implement the Complex Proportional Assessment (COPRAS) method in selecting the best boarding house for students based on several predetermined criteria. The criteria used in this study include rental price, distance to campus, facilities, and security. The COPRAS method was chosen because it is capable of evaluating both benefit and cost criteria proportionally. The results of the study indicate that the COPRAS method can rank boarding house alternatives based on their priority values. Based on the calculation results, Alternative A1 (Kost M & U) was identified as the best boarding house with the highest priority value compared to the other alternatives. Therefore, the implementation of the COPRAS method can help students determine boarding house choices that best match their needs and preferences in a faster, more accurate, and objective manner
Copyright (c) 2026 Chairina Sakinah Sitorus, Nanda Afira (Author) https://creativecommons.org/licenses/by-nc/4.0
Prediction Of Repeating Object-Oriented Programming Course for Informatics Students at ITAF Kupang Using Extreme Gradient Boosting (XGBoost)
The Object-Oriented Programming course is one of the core courses in the Informatics Study Program which has a fairly high level of difficulty so that some students have the potential to fail and have to repeat the course. This study aims to build a prediction model for students of the Informatics Study Program at ITAF Kupang who have the potential to repeat the Object-Oriented Programming course using the Extreme Gradient Boosting (XGBoost) algorithm based on student learning behavior data. The data used amounted to 60 students with variables including attendance, assignment grades, accuracy of assignment submission, discussion participation, quiz scores, practicum activities, and mid-term/final exam scores. The research stages include data collection, data preprocessing, training and testing data distribution, XGBoost model training, and model evaluation using Confusion Matrix, Accuracy, Precision, Recall, and F1-Score. The results of the study showed that the XGBoost model was able to perform good classification with an Accuracy value of 83.33%, Precision of 80.00%, Recall of 80.00%, and F1-Score of 80.00%. Feature importance analysis showed that quiz scores were the most influential factor in students' potential to repeat courses, followed by mid-term/final exam scores and assignment scores. The results of the study proved that student learning behavior data can be used to build an early warning system that helps lecturers and study programs identify at-risk students early on so that more effective academic mentoring can be provided.
Copyright (c) 2026 Mohamad Iqbal Ulumando (Author) https://creativecommons.org/licenses/by-nc/4.0
ITAF Kupang New Student Admission Prediction Using The Random Forest Method
New student admission is a crucial aspect of higher education academic planning. The Alberth Foenay Institute of Technology (ITAF) Kupang requires a data-driven approach to predict the number of new students in each study program to support more accurate decision-making. This study aims to predict the number of new student admissions at ITAF Kupang in the 2026/2027 academic year using the Random Forest method. The data used comes from historical data on new student admissions over the past five years (2021–2025) in three study programs: Informatics, Environmental Engineering, and Mechanical Engineering. The year and study program variables are used as input variables, while the number of new students is used as the output variable. The research stages include data pre-processing, transformation and encoding of categorical variables, Random Forest modeling, and model evaluation using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The model evaluation results show an MAE value of 9.11 and an RMSE of 10.58, indicating that the model has quite good predictive performance. The prediction results show that the number of new students in the 2026/2027 academic year is estimated to be 41 students for the Informatics Study Program, 24 students for Environmental Engineering, and 16 students for Mechanical Engineering. This research is expected to be a supporting basis for planning new student admissions at ITAF Kupang.
Copyright (c) 2026 Mohamad Iqbal Ulumando, Orry Adrianus Mokola (Author) https://creativecommons.org/licenses/by-nc/4.0
Impact of Industrial Noise on Production Efficiency
Industrial noise is a common problem in manufacturing environments because it affects occupational comfort, machine reliability, and production efficiency. Objective: This study examines whether acoustic monitoring and sound pressure level measurement can be used to connect industrial machinery noise with output losses and maintenance needs. Methods: The method combined machine observation, acoustic signal recording, cutter cycle timing, production counting, and comparative analysis of a pasta packaging machine, lathe, multi-spindle drilling machine, and cigarette production machine. Results: The pasta packaging machine showed the clearest relationship between noise behavior and productivity. Its cutter was designed to operate every one and one-half seconds, but the measured acoustic rhythm indicated an average cycle of about one and two-thirds seconds. This timing difference reduced output from four hundred to three hundred sixty-one bags in ten minutes, equal to a loss of about ten percent. Other machines with higher sound pressure levels also showed signs of wear, vibration, and reduced operational stability. Conclusion: Industrial machinery noise is not only a workplace hazard but also a practical indicator of machine condition. The study supports using noise control, maintenance, acoustic monitoring, industrial machinery assessment, and production efficiency evaluation as an integrated approach for improving reliability and manufacturing performance.
Copyright (c) 2026 Osamah Ibrahim Ali Barka, Abdulqadir M. Alhadar, Musbag Ahedery, Omer I. A. Hmellah, Nuri Salem Ali Abosetha, Ahmed Alnagrat (Author) https://creativecommons.org/licenses/by-nc/4.0
GPON-Based FTTH Network Design in Koto Gunung Village
The rapid development of information and communication technology has increased the demand for fast and reliable internet access, especially in rural areas. However, Koto Gunung Village, Batang Kapas District, Pesisir Selatan Regency, still experiences limited and unstable internet connectivity. Therefore, this research aims to design a Fiber To The Home (FTTH) network based on Gigabit Passive Optical Network (GPON) technology to provide stable and high-quality internet services for the community. The research method used in this study is the Network Development Life Cycle (NDLC), which includes literature study, field survey, network route design using Google Earth Pro and AutoCAD, and network simulation using OptiSystem software. The network performance was analyzed based on attenuation, link power budget, rise time budget, and Bit Error Rate (BER) parameters. The results showed that the total attenuation values for distribution route 1 and route 2 were 23.8216 dB and 23.874 dB, respectively, which are still below the maximum allowable standard of 28 dB. The total rise time values obtained were 0.2636 ns and 0.264 ns, which also meet the maximum standard of 0.29 ns. In addition, the simulation results showed BER values of 1.42 × 10⁻¹⁹ and 2.322 × 10⁻¹⁹, indicating excellent transmission quality with very low error rates. Based on these results, the designed GPON-based FTTH network is considered feasible for implementation in Koto Gunung Village.
Copyright (c) 2026 Iqbal Rizantha, Aprinal Adila Asril, Sahid Ridho (Author) https://creativecommons.org/licenses/by-nc/4.0
LightEmoNet: Lightweight Deep Learning for Facial Emotion Recognition
Facial emotion recognition (FER) is a critical component of human-computer interaction, affective computing, and intelligent surveillance systems. Existing deep learning approaches, while achieving high accuracy, are often computationally expensive and unsuitable for deployment on resource-constrained or real-time systems. In this paper, we present LightEmoNet, a lightweight Convolutional Neural Network (CNN) architecture specifically designed for efficient and accurate facial emotion recognition. Our model is trained on the FER2013 benchmark dataset, which contains 35,887 grayscale images distributed across seven emotion classes: Happy, Neutral, Sad, Fear, Angry, Surprise, and Disgust. To address the inherent class imbalance within the dataset, we employ a dual strategy combining class-weighted loss penalization with targeted data augmentation applied selectively to underrepresented categories. The proposed architecture totals approximately 2.1 million trainable parameters and occupies only 8.3 MB on disk, making it deployable on edge and embedded platforms without GPU acceleration. Experimental results demonstrate that LightEmoNet achieves a training accuracy of 91.0% and a validation accuracy of 88.5% on the FER2013 test split, with an average inference latency of 4.2 ms per image on a standard CPU. The model exhibits robust performance across all seven emotion classes while maintaining a compact footprint suitable for real-time inference. These findings confirm that lightweight CNNs, when paired with principled augmentation strategies, can achieve competitive performance without the overhead of large-scale deep models.
Copyright (c) 2026 Ali Nadhim Kamber, Hussein Alaa Alkaabi (Author) https://creativecommons.org/licenses/by-nc/4.0
Optimization of Computer Network Performance through Traffic Management and Bandwidth Allocation
Computer network performance is highly dependent on effective traffic management and proper bandwidth allocation, especially in network environments with a large number of users and diverse service demands. Uneven bandwidth distribution often leads to degraded service quality, including low throughput, high delay, and increased packet loss. This study aims to analyze and optimize computer network performance through the implementation of traffic management and bandwidth allocation using the Simple Queue and Queue Tree methods on a MikroTik router. An experimental research approach was employed by comparing network performance before and after the application of bandwidth management mechanisms. The evaluation was conducted based on Quality of Service (QoS) parameters, namely throughput, delay, and packet loss, in accordance with the TIPHON standard. The experimental results indicate a significant improvement in network performance after the implementation of Simple Queue and Queue Tree. Throughput increased substantially, while delay and packet loss were considerably reduced, resulting in improved service quality categories. The Simple Queue method effectively ensured fair bandwidth distribution among users by limiting per-user bandwidth usage, whereas the Queue Tree method enhanced performance by prioritizing network traffic based on service types. The combination of these methods successfully minimized bandwidth monopolization, reduced network congestion, and improved overall network stability. Therefore, the implementation of Simple Queue and Queue Tree proves to be an effective solution for optimizing bandwidth utilization and enhancing Quality of Service in computer networks with high user density and heterogeneous traffic characteristics.
Copyright (c) 2026 Dirja Nur Ilham, Muhammad Khoiruddin Harahap, Muhammed Saat Talib, Arie Budiansyah, Rudi Arif Candra (Author) https://creativecommons.org/licenses/by-nc/4.0
Machine Learning Driven Decision Making in the Modern Data Era
The era of modern data has seen an unprecedented increase in the number of data generated, which generates an opportunity as well as a challenge to the decision making. Machine learning (ML) has become the significant solution to work with big and multifaceted data, recognize trends, and provide foresight and change the usual decision-making processes. This review examines the principles, methods and uses of ML-based decision systems in various industries, such as healthcare, finance, retail, transportation and education. It also analyses problems of data quality, bias, transparency, ethical considerations and developments related to explainable and trustworthy AI. Lastly, future trends, human-machine cooperation, and research perspectives are addressed, with a focus on the possibility of the ML to accelerate, more precise and answerable decisions in the world that runs on data.
Copyright (c) 2026 Hassan Raza, A Singh, Tsendayush Erdenetsogt, Muhammad Mohsin Kabeer, Muhammad Shahrukh Aslam, Mazhar Farooq (Author) https://creativecommons.org/licenses/by-nc/4.0
A Comprehensive Review on Data Science Frameworks for Big Data Analytics
The importance of big data analytics is now essential in deriving insights in large and complex information in various industries. This review discusses major data science frameworks, such as Apache Hadoop, Spark, Flink, and Storm, their architecture, capabilities, and a relative advantage of processing batches and in real-time. It also presents major challenges that can affect the framework efficiency, including scalability, latency, and heterogeneity of data, security, and the complexity of operational, among others. Lastly, the new trends such as the adoption of AI, cloud-native architecture, real-time streaming, and intelligent automation are discussed to demonstrate the changing environment. This review gives an in-depth insight into the concept of big data frameworks and how they facilitate the achievement of effective analytics.
Copyright (c) 2026 Hassan Raza, Tsendayush Erdenetsogt, A Singh, Mazhar Farooq, Muhammad Mohsin Kabeer, Muhammad Shahrukh Aslam (Author) https://creativecommons.org/licenses/by-nc/4.0
Harnessing Artificial Intelligence for Education Reform in Libya: Opportunities and Challenges
Many education systems are seeking ways to improve teaching quality, personalize learning, and increase administrative efficiency. Libya faces recovery challenges and regional disparities that make scalable solutions especially important. Objective: This study examines how artificial intelligence could contribute to education reform in Libya while safeguarding equity, privacy, and academic integrity. Methods: We conducted a scoping review of recent empirical studies and policy documents, compared implementation frameworks, and completed a Libya‑focused desk review on governance, infrastructure, human capacity, and curriculum‑aligned content. Results: The synthesis indicates that intelligent tutoring, adaptive practice, automated feedback for low‑stakes writing, and responsible data use can support gains in achievement and teacher efficiency when aligned with curriculum and accompanied by sustained professional development. Constraints include uneven connectivity, capacity gaps, and limited high‑quality Arabic content; these factors can widen disparities if not addressed. Conclusion: A staged roadmap is proposed that prioritizes national guidance and safeguards, teacher capacity building, targeted pilots in foundational literacy, mathematics, and writing support, and careful scale‑up based on evidence and inclusiveness across regions.
Copyright (c) 2025 Abdarahmah Kamees A Aboseta Aboseta , Asma Al Mokhtar Miftah Alhaj Salem, Ahmed Jamah Ahmed Alnagrat (Author) https://creativecommons.org/licenses/by-nc/4.0
Explainable AI for Medical Imaging: A Taxonomy Based on Clinical Task Requirements
Explainable Artificial Intelligence (XAI) has emerged as a critical enabler for deploying AI-driven medical imaging systems where transparency, trust, and accountability are paramount. However, most current taxonomies of XAI methods categorize techniques based on algorithmic families (e.g., saliency maps, attribution methods), which often fail to reflect the practical requirements of clinical tasks. This paper proposes a novel task-centric taxonomy of XAI in medical imaging that aligns explanation techniques with four key clinical tasks: classification, detection, segmentation, and prognostic assessment. For each task, we analyze how different XAI methods enhance model interpretability, their suitability for clinical decision-making, and their limitations in real-world applications. Our taxonomy aims to provide a practical framework for researchers and practitioners to select appropriate XAI strategies tailored to the specific demands of medical imaging workflows. Furthermore, we highlight the current gaps in task-specific explainability and propose future research directions towards clinically meaningful, task-driven XAI solutions. This work serves as a step towards bridging the gap between technical XAI developments and the functional needs of clinical practice.
Copyright (c) 2025 Ali Nadhim Kamber, Hussein Alkaabi, Mohammed Al-Rekabi (Author); Ali Kadhim Jasim (Translator) https://creativecommons.org/licenses/by-nc/4.0
An Analysis of User Satisfaction on the Official Website of Politeknik Aceh Selatan Using the EUCS Method
This study aims to assess user satisfaction with the evaluated information system by employing a quantitative research approach, specifically through the use of a five-point Likert scale questionnaire. Respondents were asked to rate their level of agreement with 16 structured statements designed to reflect five key dimensions of user satisfaction: content, accuracy, format, ease of use, and timeliness. The Likert scale ranged from 1 (strongly disagree) to 5 (strongly agree), allowing for the collection of measurable and standardized responses. The collected data were analyzed by calculating the Actual Satisfaction Score (SKN), which represents the total cumulative score based on all respondents’ answers. This score was then compared to the Ideal Satisfaction Score (SKI), which was determined by multiplying the total number of respondents by the total number of questions and the highest possible Likert score. In this case, with 112 valid respondents, the SKN was calculated at 7,841, while the SKI reached 8,960, resulting in a Satisfaction Percentage (PK) of 87.52%. This high level of satisfaction suggests that the system effectively meets user expectations, particularly in delivering high-quality information, ensuring timely services, and providing a user-friendly interface. These findings highlight the system’s strong performance in key usability areas and underscore its potential for further development. Furthermore, the results serve as an evidence-based foundation for future enhancements, especially in fostering user-centered, accessible, and sustainable digital services.
Copyright (c) 2025 Fardiansyah, M Arinal Ihsan, Sepri Kurniadi (Author) https://creativecommons.org/licenses/by-nc/4.0
From Static to Contextual: A Survey of Embedding Advances in NLP
Embedding techniques have been a cornerstone of Natural Language Processing (NLP), enabling machines to represent textual data in a form that captures semantic and syntactic relationships. Over the years, the field has witnessed a significant evolution—from static word embeddings, such as Word2Vec and GloVe, which represent words as fixed vectors, to dynamic, contextualized embeddings like BERT and GPT, which generate word representations based on their surrounding context. This survey provides a comprehensive overview of embedding techniques, tracing their development from early methods to state-of-the-art approaches. We discuss the strengths and limitations of each paradigm, their applications across various NLP tasks, and the challenges they address, such as polysemy and out-of-vocabulary words. Furthermore, we highlight emerging trends, including multimodal embeddings, domain-specific representations, and efforts to mitigate embedding bias. By synthesizing the advancements in this rapidly evolving field, this paper aims to serve as a valuable resource for researchers and practitioners while identifying open challenges and future directions for embedding research in NLP.
Copyright (c) 2025 Hussein Alkaabi, Ali Kadhim Jasim, Ali Darroudi (Author) https://creativecommons.org/licenses/by-nc/4.0
Serial Rectifier Antenna (Rectenna) Circular Microstrip Patch 2.4 GHz for RF Energy Harvesting
The rapid development of Radio Frequency (RF) usage at this time causes the abundance and waste of radio frequency (RF) electromagnetic wave energy sources in the air. This RF energy source can be used as an environmentally friendly alternative by harvesting energy using the rectenna system . The rectifier antenna (rectenna) system is designed using the serial rectenna system , this system is used to improve the performance of the rectenna in converting electromagnetic waves into a direct voltage (DC) source. To design a serial rectenna system, use 3 antennas and 3 series of rectifiers . The antenna used in the serial rectenna system is a 2.4 GHz circular microstrip patch antenna for WiFi signal reception. Meanwhile, the rectifier circuit uses a 6-stage voltage doubler using a Schottky 2860 diode and a 1nF smd capacitor. From testing and measuring the serial rectenna system , the circular patch microstrip antenna is able to capture and rectify the voltage to DC. Antenna system has return loss values of -26.29 dB, -21.75 dB, and -28.57dB, VSWR 1.11, 1.16, and 1.07, impedances 51.1, 55.56, and 50.22, bandwidth 60 MHz, 60 MHz and 50 MHz with resonant frequency 2.42 GHz, 2.36 GHz and 2.48 GHz. So that the voltage generated by a single rectenna system is 51.3 mV at a distance of 25 cm. Meanwhile, the rectenna serial system can convert direct voltage of 151.3 mV at a distance of 25 cm from the source of the Access Point transmitter.
Copyright (c) 2025 Salwa Asyifa, Pharmayeni Pharmayeni (Author) https://creativecommons.org/licenses/by-nc/4.0
MIMO Microstrip Antenna with Multiple Inputs at 2.4 GHz Frequency for Long Term Evolution (LTE)
LTE (Long Term Evolution) is a fourth-generation wireless communication technology that is currently under development, necessitating the use of efficient antennas. This research focuses on the design and realization of a Multiple Input Multiple Output (MIMO) microstrip antenna operating at a frequency of 2.4 GHz, which is essential for LTE applications. The MIMO technique employs multiple antennas on both the transmitter and receiver sides, aiming for a correlation coefficient below 0.2 to enhance communication quality and channel capacity without requiring additional bandwidth. The antenna design was simulated using CST Studio Suite 2019 software, followed by fabrication. The simulation results indicated a return loss of -25.411 dB, a Voltage Standing Wave Ratio (VSWR) of 1.113, a gain of 6.464 dBi, an omnidirectional radiation pattern, and a bandwidth of 20 MHz. Upon fabrication, the measured return loss was -18.071 dB, with a VSWR of 1.285 and an increased bandwidth of 50 MHz. The results demonstrate that the designed antenna meets the necessary specifications for LTE applications, confirming its suitability for practical use in modern wireless communication systems.
Copyright (c) 2025 Erwinsyah Sipahutar, Oktrison (Author) https://creativecommons.org/licenses/by-nc/4.0
Real-Time Classification of Local Orange Fruit Quality Using YOLO (You Only Look Once) and SVM (Support Vector Machine) Methods
Oranges are a fruit that we often encounter and are even consumed by people because of their various benefits. Oranges have commercial value in Indonesia and have a fairly wide reach. In order to increase competitiveness, oranges must also meet market standards, both domestic and foreign, so that they can be accepted by consumers. Of course, in this case, orange selection is very important. increasing sales and market competition by sellers, important indicators in selecting citrus fruit are in terms of size and color. In general, the selection of citrus fruit is done manually and based on human thinking, which causes several weaknesses that must be corrected, including requiring a long time, human visual limitations, and being influenced by human psychology itself. This is what causes inconsistencies in selection. oranges and does not comply with existing market requirements. So a research was carried out regarding the quality classification of local citrus fruit using the YOLO (You Only Look Once) and SVM (Support Vector Machine) methods in real time. In the comparison made between the two methods used, Yolo was found to be the best method for classifying citrus fruit.
Copyright (c) 2025 Muhammad Khoiruddin Harahap, Rudi Arif Candra, Arie Budiansyah, Romulo P. Aritonang, Zulfan, Devi Satria Saputra (Author) https://creativecommons.org/licenses/by-nc/4.0
Prototype Design and Development of an IoT-Enabled Monitoring and Control System for Public Street Lighting
This research discusses the Internet of Things (IoT)-based Public Street Lighting (PJU) system to improve energy efficiency and remote monitoring. The background problem is the need to improve operational efficiency and energy savings in the PJU system. This final project aims to design and test an IoT-based PJU system that transmits real-time data between nodes and gateways using LoRa technology and the MQTT protocol. The research process involves hardware and software design, as well as system testing under various conditions. The tests measured the data transmission time and analyzed the delay using LED indicators on the gateway and dashboard devices. The test results showed significant variations in data transmission time compared to the programmed time. The programmed transmission time was 10 seconds for node 1 and 20 seconds for node 2, but the test results showed an average time of about 15 seconds for node 1 and 21.89 to 36.02 seconds for node 2. This variation is due to factors such as network communication delay, processor load, and LoRa system efficiency.
Copyright (c) 2025 Dirja Nur Ilham, Rudi Arif Candra, Arie Budiansyah, Zulfan (Author) https://creativecommons.org/licenses/by-nc/4.0
Smart Algorithm Applications in Mechanical Engineering and Physical Sciences for Optimizing Systems and Materials
The increasing complexity of modern engineering systems and the demand for efficient, cost-effective, and high-performance designs have driven the adoption of intelligent computational strategies in mechanical engineering and physical sciences. Traditional simulation and optimization techniques often struggle with nonlinear, multi-objective problems that span both material and structural design spaces. This study aims to develop a unified smart algorithmic framework capable of optimizing both mechanical systems and material properties concurrently. The research focuses on integrating data-driven models with physics-informed techniques to improve predictive accuracy, computational efficiency, and practical applicability. The proposed framework combines artificial neural networks (ANNs), physics-informed neural networks (PINNs), genetic algorithms (GAs), and Bayesian optimization to form a hybrid multi-objective optimization system. A case study on an electric vehicle (EV) suspension system is used to validate the approach. Surrogate models were trained on finite element analysis (FEA) data and applied within a Pareto optimization loop to explore trade-offs among mass, fatigue life, and material cost. The framework achieved a 27% reduction in structural mass, a 35% increase in fatigue life, and a 13% decrease in material cost. Surrogate models attained R² values exceeding 0.90, with validation showing less than 5% deviation from FEA results. Sensitivity analysis confirmed design robustness under input variation. The findings demonstrate the effectiveness of smart algorithms in co-optimizing systems and materials. The proposed framework enhances the speed, accuracy, and physical validity of intelligent engineering design.
Copyright (c) 2025 Ali Soluman Ali Hassien, Nuri Salem Ali Abosetah (Author) https://creativecommons.org/licenses/by-nc/4.0
Flood Detection Tool Using Ultrasonic Sensor Based on Telegram and Sound in Krueng Kluet River Flow
Flooding is a problem that until now still requires special handling from various parties, both from the government and the community. Flooding is not a light problem because flooding can disrupt community activities and cause losses, such as the washing away of household equipment, valuables, and electronic goods. Flooding occurs due to rising water levels in rivers due to abnormal rainfall, damaged dams, obstruction of water flow at the site of the dam's destruction in the River Basin Area (DAK), and the construction of facilities and infrastructure. The series of "Flood Detection Devices using Ultrasonic Based on Telegram and Sound consists of three parts, namely the input section, the control section, and the display section. This design was made to simplify the process in the Ultrasonic Flood Detection Device Design using telegrams and sound. The Ultrasonic Flood Detection Device Design Circuit using telegrams and sound consists of three parts, namely the input section, the control section, and the display section. The first Flood Detection Device Test using Ultrasonic Sensors Based on Telegram and Sound has been tried as many as 10 The first experiment The water depth is 1 meter, the distance of the sensor to the water surface is 3 meters, it is said that the status is safe, there is no notification to the telegram and the siren does not sound. Experiment 2: The water depth is 2 meters, the distance of the sensor from the water surface is 2 meters, it is said to be on standby, then the flood detector provides notification to the telegram via the telegram bot, and the siren sounds. Experiment 3: The water depth is 3 meters, the distance of the sensor from the water surface is 1 meter, it is said to be on standby, then the flood detector provides notification to the telegram via the telegram bot, and the siren sounds.
Copyright (c) 2024 PERFECT: Journal of Smart Algorithms https://creativecommons.org/licenses/by-nc/4.0
Microcontroller-Based Automatic Bat Pest-Repellent Device
Bat habitats often coexist with human life, especially in homes, these mammals usually perch on the roofs of rooms or warehouses. In general, bats are parasites and pests for human life, therefore researchers have designed an automatic bat pest-repellent tool based on a microcontroller. This study aimed to create an automatic bat pest-repellent tool based on a microcontroller and electronic components as support for the circuit. This research method used three parts, namely input, output, and control. Where Arduino functioned as a controller for the entire circuit, while the ultrasonic sensor functioned as input while the Df player and speaker functioned as output. In this study, the researcher conducted 10 tests to determine the performance of the designed tool. After conducting 10 tests, the speaker produced a sound to repel bats, and the ultrasonic sensor was used as a detector of bat movements, in this test the distance of the ultrasonic sensor detected bats has been programmed, it was 5 cm to 50 cm.
Copyright (c) 2025 PERFECT: Journal of Smart Algorithms https://creativecommons.org/licenses/by-nc/4.0
Body Temperature Measuring Device Based On Nodemcu Esp8266
Health is a state of well-being of the body, soul, and society that allows everyone to live productively socially, and economically. Health maintenance is an effort to overcome and prevent health disorders that require examination, treatment, and care. The research method of the body temperature measuring device uses a thermal camera based on the Internet of Things (IoT). It uses two parts, namely input and output, where the input is a thermal camera sensor that functions to measure body temperature in humans while the production is in the form of a display on the LCD and in the Blynk application. The purpose of this tool is to produce a tool that can read human body temperature. The tool that is produced will later be able to read body temperature accurately like existing measuring instruments. The results of the thermal camera tool will detect human body temperature at a distance of 2 to 3 cm. Where each body temperature reading will be displayed on the LCD and the Blynk application, making it easier to find out the body temperature that has been detected by the sensor.
Fiber To The Home (Ftth) Network Infrastructure Design Using Gigabyte Passive Optical Network (Gpon) Technology South Solok District
Fiber To The Home (FTTH) Network with Gigabit Passive Optical Network (GPON) Technology in South Solok Regency, supervised by Ibu Amelia Yolanda, ST as supervisor 1 and Ibuk Popy Maria, ST STT as supervisor 2. Development of Telecommunications technology as a necessity Currently, communication has developed rapidly, transmission media facilities are needed that are capable of transmitting information with large capacities and adequate data transfer speeds. By implementing GPON technology, FTTH services can reach users at up to speed. This research aims to design an FTTH network using GPON technology in the South Solok Regency area, obtain results of identification and analysis of communication network needs for the Nagari Lubuk Gadang Selatan community in South Solok Regency, obtain results of FTTH network infrastructure design using GPON technology for Nagari Lubuk Gadang Selatan Regency. South Solok.
Design And Design Of A Temperature And Humidity Monitoring System For Broiler Chicken Cages Based Internet Of Things (IOT)
In this research, we designed a tool that is used to help chicken farmers to continue their daily activities in raising livestock. Problems often experienced by chicken farmers are frequent theft, and having to check the temperature manually in the coop. So a Security and Temperature Monitoring and Light Control System for Broiler Chicken Cages Based on Internet of Things (IoT) was created using NodeMcu Esp32 as the controller of the entire system. The tools used in this system are, Buzzer, DHT22, LCD 16x2, ESP32, Breadboard, Relay, and Incandescent Lamp. The tool making method uses engineering design methods in making the automation system. The test results show that the system that has been made is able to maintain the temperature in the chicken coop in accordance with the ideal conditions for Broiler chickens.
Improving Network Reliability with Traffic Steering Using Two ISP Links on Mikrotik Routers
Traffic Steering is an additional technique applied to network traffic especially when filtering, modification, or optimization is required. Network operators usually use traffic steering to expand the value of VAS or value-added service (value-added service that refers to additional services (content) based on existing telecommunication networks). This technique is applied so that the use of traffic from a link can be directed according to the client's needs and further maximize the functional use of the link itself. In general, the implementation of traffic steering is built based on three concepts, namely: NAT (Network Address Translation), Firewall RAW, and IP Routes. In this Traffic Steering implementation, the DSTNAT chain feature on the NAT firewall is used to direct access with the destination that has been set to a specific web server.
Implementation of The Internet of Things on Monitoring and Control Tools Hydroponic System
This Hydroponics is a way of growing crops without using soil. Soil that is actually a place for plants to grow can be supported with sand, husk charcoal and rockwool. In a hydroponic system, nutrients and the pH value of water are absolutely necessary for plant development, every plant that requires different levels of nutrients and pH values ​​of the air, if the nutrient levels are lacking then the plant will not grow as well as if the nutrient content is more then the plant will be poisoned. nutrients, water nutrients will continue to decrease along with the development of the plant itself. Therefore, a measuring tool for nutrient levels in hydroponic plant irrigation systems with android monitoring is made so that humans can monitor hydroponic plants. The tool used by Arduino nano as a microprocessor that is integrated into the TDS sensor works to measure nutrient levels and the pH sensor works to measure the pH value of the air, ESP8266 wifi module on NodeMCU as a gateway that aims to be a liaison from Arduino nano to the web server, Using websites and android applications that act as remote controls for nutrient pumps, pH up, pH down and hydroponic water pumps.
Design of Safety Worker Helmets Based on the Internet of Things
Safety helmets are used to protect the head from impacts, blows, or falls of sharp and heavy objects that float or slide in the air. This helmet can also protect the head from heat radiation, fire, chemical splashes, or extreme temperatures. The main benefits and uses of a safety helmet itself are to protect the worker's head, so that it can be protected from falling objects and others, and to minimize injuries that will befall the worker himself. The use of safety helmets is very much needed by workers who work in work areas such as oil mines, factories, building construction projects, and various other things. In this study, a helmet will be designed that can be used to detect the presence of its wearer with the case title Design of Safety Worker Helmets Based on the Internet Of Things, the working system of this tool is the entire series of tools from the smart helmet system to detect the location of work accidents installed on a helmet, precisely on the inside of the helmet with the position of Arduino UNO, shock sensor, GPS module, and battery in the rear position of the helmet. For the GSM module, limit switch, and tilt sensor at the top of the helmet. The voltage source from the tool is taken directly from the battery with a voltage of 12 volts.
Design of an automatic temperature and humidity measuring device in the oyster mushroom cultivation room
Advances in science and technology have become the idea of a tool for the development of oyster mushroom cultivation which has increased following the large market demand. Cultivating oyster mushrooms in areas with high temperatures requires special care techniques. Current technological developments make it easier to maintain oyster mushroom cultivation, namely by automatically regulating the temperature and humidity in the cultivation room. With automatic temperature control, it can make maintenance easier and reduce failure in oyster mushroom production. To realize the simulation of automatic temperature and humidity control, several pieces of hardware are needed, namely temperature and humidity sensors, DC fan, LCD, power supply, and Arduino Uno as the processor. which are then combined into a system. The growth of oyster mushrooms is greatly influenced by the surrounding environmental conditions. Therefore, we must know the conditions that are suitable for growth before we cultivate oyster mushrooms. Usually, the growth of oyster mushrooms will be optimal throughout the year if the cultivation location is by its natural habitat, namely in a mountainous area with an altitude of between 400 - 800 meters above sea level and an air temperature of around 21 - 29 oC with a humidity level of around 60 - 80%. In general, farmers cultivate natural mushrooms in a room to obtain environmental conditions suitable for growth. Because mushrooms are very sensitive to temperature and humidity, it is necessary to monitor the temperature and humidity in the growing room.
Arduino Based Automatic Banana Chip Slicer
Banana is a herbaceous fruit plant, in Latin banana Musa spp which belongs to the Musaceae family from Southeast Asia. What is meant by superior varieties is that banana plants are strong enough to withstand pests and diseases so they are capable of high production. Nowadays, many people who slice bananas still use simple or manual tools to slice raw bananas. An efficient machine mechanism must be needed in the manufacturing process to get these thin banana slices. This tool aims to avoid hand injuries and banana sap getting on your hands if you use a manual banana slicer. The working method of this tool is controlled by Arduino Uno R3 using an Ir sensor and with an output in the form of a relay which functions as a switch to rotate the blade. This test was carried out fifty times using three different types of bananas, namely, wak bananas, kapok bananas, and flower bananas. The results of testing three types of bananas, namely weak bananas, kapok bananas, and flower bananas, have the same thickness level, namely 1mm. This tool concludes that it makes it easier for people to slice bananas from manual to automatic so that the thickness is the same. The tool works based on input from two IR sensors using an analog signal pin connected to Arduino. The two IR sensors will detect if there is movement near the container.
Expert System for Diagnosing Dental and Oral Diseases Using the Forward Chaining Method
Dental and oral health is important to pay attention to and is an integral part of overall health that requires immediate treatment before it is too late and can affect a person's health condition. One of the obstacles experienced is the difficulty of diagnosing dental disease in remote areas due to the uneven distribution of experts or dentists. Therefore, in this research, an expert system was built that can diagnose dental disease and tooth-supporting tissue based on knowledge obtained from direct experts. An expert system built on a web basis using the PHP programming language and MySQL database. The problem-solving engine used is the Forward Chaining method. This method can diagnose disease by adjusting the facts experienced with the rules that have been created which are entered into the system.