JURNAL INFOTEL

Development of Smart Hydroponics System using AI-based Sensing

Thu, 08 Aug 2024 00:00:00 +0000

This paper proposes a smart hydroponic system that operates automatically using a fuzzy logic algorithm, integrating IoT functionalities to support smart agriculture. The system allows for remote monitoring and control via the internet, providing real-time data on water levels, pH levels, temperature, and nutrient solution temperature. Precise dosing and temperature control are critical for optimal plant growth, and the system schedules temperature measurements to ensure stability. Unstable temperature can affect pH levels, thereby impacting nutrient absorption. The proposed system employs sensors to continuously monitor the electrical conductivity (EC) and pH levels of the nutrient solution. Fuzzy control is utilized to regulate the nutrient solution pump, automatically adjusting EC and pH levels to promote optimal plant growth. This approach reduces the time burden on producers and provides more precise control over the nutrient solution, resulting in improved growth outcomes. The main contributions of this work are as follows: the development and implementation of an AI-based system integrating a controller, IoT environment, fuzzy logic algorithm, and NFT (nutrient film technology) hydroponics; the creation of a user-friendly interface for farmers through the Smart-Hydroponic application, enabling hybrid monitoring and control of hydroponic farms; the establishment of an IoT-based cloud environment for sensor data monitoring; the implementation of a smart hydroponic system for nutrient sensing, monitoring, and control; and a comparative analysis between smart and conventional hydroponics based on morphological results.

Recommender System for Group of Users using Matrix Factorization for Tourism Domain (Case Study: Bali)

Ruh Devita Widhiana Prabowo, Z. K. A. Baizal — Fri, 09 Aug 2024 00:00:00 +0000

Choosing a product that suits a customer's needs requires a recommendation system to provide suggestions on a collection of items of interest to the user. Recommendations can be applied in various fields such as entertainment, shopping sites, social networking, job portals, discovery of relevant web pages, and so on. There are many circumstances where recommendations are needed for a group such as in tourism and entertainment purposes. The development of a Group Recommendation System (GRS) was carried out in response to the need to provide several recommendations to a group of users. We conducted this research to build a GRS that can provide item recommendations using the Collaborative Filtering (CF) method with Matrix Factorization Technique, as well as three approaches, namely After Factorization (AF), Before Factorization (BF), and Weighted Before Factorization (WBF). Determine the best approach for the three categories of groups formed, namely small groups (three members), medium groups (five members), and large groups (ten members). The focus of this research is the tourism destination domain in Bali. In the evaluation results of the precision calculation, the medium group obtained the highest score for the AF, BF, and WBF approaches of 0.944. Meanwhile, in calculating recall, the small group achieved the highest scores for the AF, BF, and WBF approaches of 0.294, 0.259, and 0.259, respectively. From the results of this study, it appears that small groups are suitable for using the BF approach, while the AF method is more effective for large groups, and the best approach for medium groups is the WBF.

Rupiah Banknotes Detection Comparison of The Faster R-CNN Algorithm and YOLOv5

Muhammad Zuhdi Hanif, Wahyu Andi Saputra, Yit Hong Choo, Andi Prademon Yunus — Fri, 23 Aug 2024 01:16:47 +0000

Money is an essential part of human life. Humans are never separated from activities related to money. As time goes by, money is not only a means of transactions between humans but also between humans and machines. Machines can recognize money in various ways, including object detection. Object detection is one of the most popular branches of computer vision. There are many methods for carrying out object detection, such as Faster R-CNN and YOLO. Faster R-CNN has been widely used in various fields to perform object detection tasks. Faster R-CNN has advantages over its predecessor because it uses a Region Proposal Network (RPN) as a substitute for selective search, which requires less compilation time. YOLO (You Only Look Once) is the most frequently used object detection method. This method divides the image into grids; each part of the grid predicts objects and their probabilities. The main advantages of YOLO are its high speed and ability to recognize objects in various conditions and positions with reasonably high accuracy. This research compares the Faster R-CNN algorithm model using the ResNet-50 architecture with YOLOv5 to recognize rupiah banknotes. The dataset used is 1120 images consisting of 8 classes. The YOLOv5 model trained on RGB data had the best results, with calculation accuracy reaching 1. Test results on three images also showed suitable results. The hope is that this research can be applied in other research to build a system for recognizing rupiah banknotes.

Prediction Of Student Achievement Using Artificial Neural Network And Support Vector Regression At SMK TELKOM Lampung

Desi Susanti, Joko Triloka — Fri, 09 Aug 2024 00:00:00 +0000

The analysis of student performance is crucial in vocational schools because it helps identify the challenges students face in preparing themselves for the workforce. By integrating data mining techniques such as Artificial Neural Networks (ANN), educators can enhance their understanding of factors that improve student learning outcomes. An artificial neural network (ANN) is composed of interconnected artificial neurons that can learn from input data and make complex predictions, including academic achievements. The structure and function of the human brain inspire ANN. This study compares the effec- tiveness of the artificial neural network (ANN) method with other methodologies, such as support vector regression (SVR), to predict student achievement at SMK Telkom Lampung. Primary data collected from SMK Telkom Lampung includes 4939 examples with 550 cases, 26 features, and 4 meta-attributes. Performance evaluation involves metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). The coefficient of determination (R2) value of the Neural Network at 0.001 is higher than the R2 value of SVR, which reaches -0.036. Research find- ings indicate that the Artificial Neural Network model slightly outperforms the Support Vector Regression model, with lower prediction error rates and better ability to explain data variability.

Implementation of MOORA and MOORSA Methods in Supporting Computer Lecturer Selection Decisions

Zulham Sitorus, Abdul Karim, Asyahri Hadi Nasyuha, Moustafa H. Aly — Sun, 15 Sep 2024 00:00:00 +0000

The selection of computer science lecturers is an important process for educational institutions, requiring a balanced assessment of various criteria to find the most suitable candidates. This paper examines the implementation of Multi-Objective Optimization based on Ratio Analysis (MOORA) and its variant, namely Multi-Objective Optimization based on Ratio Analysis with a Subjective Attitude (MOORSA), as a tool to support decision making. in this case. This selection process is often complex, requiring consideration of various criteria, such as academic qualifications, teaching experience, research capabilities, and others. This research was conducted to support the decision-making process. by developing a Decision Support System (DSS) using the Multi-Objective Optimization on The Basic of Ratio Analysis (MOORA) and MOORSA methods. Many methods are used, such as SAW, AHP, Topsis and others. based on the calculation of the MOORA method, the highest result has been achieved by A1 worth 0.651819 and similarly, in the MOOSRA method the highest alternative result is A1 worth 0.592177.

Adoption Dynamics of Digital Payments: An Urban Case Study on E-Money Using the Technology Acceptance Model

Rio Guntur Utomo, Rahmat Yasirandi, Novian Anggis Suwastika — Tue, 17 Sep 2024 09:28:24 +0000

The utilization of e-money in Indonesia has surged, propelled by the expansion of digital payment platforms. Despite their growing prevalence, the dynamics of e-money acceptance within urban environments remain underexplored. This study innovatively extends the Technology Acceptance Model (TAM) by incorporating Perceived Security as a new variable, alongside traditional factors such as Perceived Usefulness, Perceived Ease of Use, Attitude Toward Using, Behavioral Intention to Use, and Facilitating Condition. The research focuses on Padang City, a representative urban landscape, where data was collected from 201 valid respondents through online platforms. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The integration of Perceived Security is a novel aspect of this study, reflecting its crucial role in the contemporary urban context of e-money utilization. Results reveal significant relationships among the studied variables, although the impacts of Facilitating Condition on Perceived Ease of Use and Perceived Usefulness on Behavioral Intention to Use were not supported. The findings underscore that positive attitude toward e-money significantly boost behavioral intentions to use it, primarily influenced by security perceptions and ease of use. These insights have substantial implications for policymakers and businesses focused on enhancing e-money adoption in urban settings. The study highlights the necessity of addressing user perceptions, particularly security, to foster broader acceptance. The limited influence of Facilitating Conditions suggests that improvements in infrastructure must be coupled with efforts to enhance user trust and ease of interaction with e-money platforms. This research contributes to the field by providing a deeper understanding of the factors driving e-money acceptance in urban areas, guiding targeted strategies for digital financial inclusion.

Development of a Prediction Model for Potential Forest and Land Fires using Machine Learning Algorithms Based on Patrol Data

Angga Bayu Santoso, Imas Sukaesih Sitanggang, Medria Kusuma Dewi Hardhienata — Tue, 24 Sep 2024 00:00:00 +0000

Indonesia allocates 120 million hectares or 64% of its land area as forest areas. Indonesia's forests continue to experience deforestation; one of the causes is forest and land fires (karhutla). The government conducts forest and land fire prevention through integrated patrols with the Forest and Land Fire Prevention Patrol Information System (SIPP Karhutla) facility for patrol data management. However, the patrol data are still primarily used for data observation and simple spatial analysis in the spatial module. Patrol data has not been used for further forest and land fire prevention studies. Based on these problems, this research aims to build a prediction model of potential forest and land fires using SVM, Random Forest, and XGBoost algorithms and compare model performance to get the best model. The preprocessing stage uses the SMOTE-ENN method to handle data class imbalance, and the k-fold cross-validation stage and hyperparameter tuning use the random search method. The confusion matrix evaluation method to see the model performance in terms of accuracy is XGBoost (94.81%), Random Forest (90.23%), SVM-linear (79.58%), SVM-polynomial model (73.99%), SVM-rbf (74.26%), and SVM-sigmoid (35.04%). Therefore, the best prediction model is XGBoost (94.81%) with boosting technique. The results of this study have implications for helping early prevention of forest and land fires on the islands of Sumatra and Kalimantan.

Design and Build Design and Build a Breeding House for IoT-based Goat Farming

Jumi Jumi — Tue, 24 Sep 2024 04:19:59 +0000

Abstract — Goat farming is an industry that supplies goat meat for food purposes. This makes goats have a high potential to boost the economy of the community as their meat products are needed by everyone. These conditions motivate farmers to improve the quality of livestock maintenance so that the livestocks produced are of the highest quality. The development of information technology has facilitated a wide range of human activities, including monitoring activities in goat cages. The use of Information Technology helps farmers to improve the quality of livestock maintenance. The large cage area and the distance between cages and communal settlements are the main reasons for the planned IoT-based cage.

This research has developed an IoT-based goat cage design that can be used to monitor livestock maintenance in real time via a website or Android. The methods used in this study were cattle identity information, cattle health through body temperature, cage.

MRI-Based Brain Tumor Classification using ResNet-50 and Optimized Softmax Regression

Muhammad Nazeer Musa — Tue, 24 Sep 2024 04:20:53 +0000

Accurate classification of brain tumors is crucial for effective treatment planning and patient management. This study presents a new hybrid deep learning classification method based on transfer learning by feature extraction to automate the categorization of MRI brain image datasets into four classes: meningioma, glioma, pituitary tumor, and no tumor. The proposed method combines a finely-tuned ResNet-50 model, a state-of-the-art convolutional neural network architecture, with optimized Softmax Regression (SR) for classification. The study explores the use of data augmentation techniques and evaluates the model's performance on both augmented and unaugmented images. The results demonstrate that the proposed method achieves an impressive accuracy of 98.4%, outperforming existing methods for automatic brain tumor detection. Furthermore, a detailed comparative analysis is presented to evaluate the proposed model's accuracy and efficiency against previous state-of-the-art hybrid models for brain tumor classification. The study suggests that the proposed methodology could be employed as a diagnostic tool to aid radiologists in identifying questionable brain regions, potentially improving the accuracy and efficiency of brain tumor diagnosis.

Coverage hetnet based picocell and femtocell for uplink condition around building environment with single knife edge method

Andrita Ceriana Eska — Fri, 09 Aug 2024 00:00:00 +0000

The development of HetNet (Heterogeneous Network) radio base stations has experienced many developments. This is indicated by the existence of microcells, macrocells, picocells, femtocells, and so on. In this research, the research is aimed at the propagation of user equipment communication systems in uplink conditions with HetNet picocells and femtocells. UE propagation is on a straight path between the building environment. The communication frequency used is 10 GHz. The communication between Tx and Rx is modeled as a diffraction mechanism, AWGN channel, and atmospheric attenuation. The Single Knife Edge (SKE) method is used to model the mechanism. The propagation channel is faced with AWGN (Additive White Gaussian Noise). The analysis of this research includes the SNR value, Adaptive Modulation and Coding (AMC) level, and percentage of communication coverage area. The AMC is based on the use of MCS (Modulation and Code Scheme). Some of the MCS modulations used QPSK, 16 QAM, and 64 QAM. As research results show that the percentage of communication coverage area obtained was gNB1 80.59 percent, gNB2 65.67%, and selection combining HetNet 95.52%.

Equal Incremental Cost Method with Adjustable Gamma Control to Solve Generator Scheduling

Fri, 09 Aug 2024 00:00:00 +0000

Generator scheduling remains an intriguing issue within the energy industry. It relates to the optimization of production costs, where system operators must select the optimal combination of available resources to minimize production costs. This paper proposes an enhancement to the Equal Incremental Cost (EIC) Method using Adjustable Gamma Control (AGC) in generator scheduling. Iterations begin with an initial lambda value, then gradually increase with the application of the factor until power demand is met. A variable of 10% is used as an adjustment step in the optimization method. The proposed method is capable of achieving convergence with 100% accuracy, where the power generated by all generators precisely matches the load demand (2,650 MW), at a cost of USD 32,289.03. EIC-AGC ranks second-best after VLIM, albeit with the consequence of consuming 195 seconds. This method is expected to have a significant impact on designing highly accurate economic dispatch techniques. Thus, generator scheduling will lead to a reduction in operational costs compared to current practices.

Utilization of PID Controller to Optimize Energy Consumption in Hybrid Forced Convection Dryer

Brahmantya Aji Pramudita — Tue, 24 Sep 2024 04:15:23 +0000

The drying process is essential in food processing, particularly for grains or beans. This process can influence the characteristics and quality of the dried material. However, the drying process needs massive energy. This condition can increase the cost of production. Therefore, optimizing energy is needed during the drying process. PID controller was proposed to be utilized in the dryer to maximize energy efficiency in this study. This method was implemented in forced convection and hybrid forced convection dryer. The temperature sensor was used as parameter control of the PID method to control the electric heater and exhaust fan. Moreover, PZEM-004T was used to measure the energy consumption. The objective of this study is to implement and fine-tune the PID controller to obtain the performance difference between two types of dryers, namely forced convection and hybrid forced convection. This will be achieved by collecting data on power and energy consumption. The results indicate that the hybrid dryer can significantly decrease power and energy consumption by 48.32% and 49.18%, respectively, compared to the forced convection dryer.

Magazine Identification on SS1-V1 Assault Rifle using Web-based HX711 Load Cell Sensor

Yasikha Farras Ismail — Tue, 24 Sep 2024 04:18:41 +0000

The SS1-V1 is an assault rifle model equipped with a magazine as one of its main components. The magazine plays a crucial role in storing and loading ammunition. However, magazines must be stored separately from the weapon as their integrated storage can pose a risk to a country’s security. Therefore, this research proposes a web-based system capable of identifying the presence of magazines in weapons in real-time. This system is supported by various hardware components, including a load cell sensor, HX711 sensor module, Arduino UNO R3, and an Ethernet shield for network connectivity. In addition, API is used for data management, which is then stored in the database. The results of this research indicate that the average response time for each rack within a cabinet is between 2.7s to 3.3s, while for racks serving as slaves, it ranges from 14.16s to 15.01s. Based on the results of the weight-based weapon identification testing, there is a weight difference of 0.1kg to 0.2kg. These results state that all tests were successfully identified by the web system according to the conditions of the weapons on the rack.