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This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.

Traditional association rule analysis is effective at uncovering co-purchase patterns but fails to provide a global structural view of the market, which often results in fragmented and isolated insights. This study proposes a hybrid framework that integrates the Apriori algorithm with a Minimum Spanning Tree (MST) in order to validate and contextualize association rules within a single structural backbone. Transaction data from a retail store are transformed into a weighted, undirected product graph using an inverse-support function, and an MST is then extracted to represent the market backbone, while frequent itemsets and strong rules are obtained using Apriori. Experimental results on 236 multi-item transactions show that the MST backbone comprises 10 products and 9 fundamental links, with 66.67% of these links being confirmed by strong association rules, indicating a substantial coherence between statistical and structural evidence. The proposed model identifies 41 Apriori patterns that can be embedded in the MST and ranks them using a new metric, Structural Distance, which enables the categorization of Core Patterns, Bridge Patterns, and Complex Patterns according to their structural tightness. This hybrid perspective distinguishes dense, strategically meaningful bundles from anomalous but frequent combinations that are structurally peripheral, thereby offering a more holistic and actionable alternative to conventional Market Basket Analysis. The validated framework can support various applications, including store layout optimization, cross-selling strategies, and the design of path-based recommender systems, and it opens avenues for future extensions based on dynamic graphs and Graph Neural Networks.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.

Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.

Information technology is a technology used to manage data, including processing, acquiring, organizing, storing, and manipulating data in various ways to produce high-quality information—namely, information that is relevant, accurate, and timely. This information is used for personal, business, and governmental purposes, serving as strategic information in decision-making. To anticipate changes in weather conditions, particularly rainfall, a valid and accurate report is needed that can be useful for the public. So far, the correlation or relationship between the factors influencing weather conditions—especially rainfall—has not been precisely determined, making it mathematically difficult to create a model that can describe the correlation among all these factors. This is where Artificial Neural Networks (ANN) come into play: to create such models and map out the existing problems purely based on the input data provided. One of the capabilities of neural networks is to make predictions based on previously learned data using the backpropagation method.

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.

Terrestrial digital television transmitter stations are the main facilities in the transmission of digital television broadcasts to the public. The quality of the transmitted signal is expected to reach the Central Java-1 service area well so as to provide optimal and reliable quality of digital television broadcast performance according to the needs of the community, but currently, complaints about signal problems such as service coverage and reception quality still occur a lot, coverage and signal quality received by community-owned television transmitters cannot be separated from the influence of the quality performance of digital television transmission stations. The purpose of this research is to analyse the coverage performance of digital television services of digital television transmitter stations using the K-Means Clustering Method, identify areas with the best signal coverage and group areas based on the level of signal performance. The data used includes field strength parameters collected through field measurements at 25 service area location points, topography factors and transmitter distance were found to be the main causes of signal quality differences. Data analysis shows that the K-Means Clustering method effectively clusters areas with signal reception quality categories of very good cluster 3 areas, good cluster 8 areas, fair cluster 5 areas and poor cluster 9 areas. The results of this study can be recommended in the evaluation and optimisation of tele-transmitting station networks.

Technological advancements have brought fundamental changes in the way we interact with digital images and photography. One significant milestone in this development is the Photoshop Express Photo Editor, which has become a primary platform for image processing and editing. Datasets are used to analyze sentiment and are utilized during the accuracy testing phase. Based on the testing results, the Convolutional Neural Network (CNN) algorithm achieved an average accuracy value of 86.50%, compared to the Naïve Bayes (NB) algorithm, which achieved an average accuracy value of 75%. The results of the research conclude that the choice of sentiment analysis method should be tailored to the needs and limitations of the system. If a fast, light, and easy-to-understand process is required, the Naive Bayes method is the right choice. However, if accuracy and context understanding are the top priorities, then CNN is a superior approach, although it requires more resources. Additionally, based on the Wordcloud data, it is known that the majority of comments are positive, indicating that the reviews or texts analyzed contain many positive expressions related to quality, usability, and ease of use.

The digital transformation in education has encouraged the adoption of computer-based tests (CBT) using video content, which demands stable and efficient network performance. This study aims to evaluate the performance of two queue management algorithms, namely Random Early Detection (RED) and Per Connection Queue (PCQ), in maintaining the quality of service (QoS) of school networks during online video-based examinations. A case study approach was applied using a real network topology in a school environment, and QoS parameters such as throughput, delay, packet loss, and jitter were measured. The implementation was conducted using a MikroTik RB450Gx4 router configured with simple queue settings for each algorithm. The results show that PCQ provides more consistent performance under high user loads, achieving an average throughput of 56,482 bps and lower delay compared to RED. Conversely, RED performs better in scenarios with a small number of users. The study recommends using PCQ for networks with dynamic and dense user environments, while RED is more suitable for low-traffic conditions where latency stability is crucial. These findings offer practical guidance for managing bandwidth and improving the quality of CBT delivery in educational settings.

Accurate rainfall prediction is needed to improve the performance of land that always uses rainfall data. Data mining or often called knowledge discovery in databases (KDD) is an activity that includes collecting, using historical data to find regularities, patterns or relationships in large data. In predicting rainfall, there are several conditions that can be observed as reference data to predict rainfall, namely wind speed, temperature, and air humidity. In this research, a backpropagation artificial neural network prediction method is developed that can be used in predicting future rainfall. The backpropogation artificial neural network method that was built produced an accuracy value of 95.36%, a precision value of 90.50%, a recall value of 97.50% and an f-measure value of 92.00%

At this moment, IoT technology was implemented in a broader area. The development of IoT technology can help human life become more manageable. IoT systems can help human activity through the natural time control system provided before. But there are some drawbacks when implementing the IoT system. One of them is the transmission process in the networking area. It’s happened because of weather, and other natural problems. Sometimes, IoT transmission can’t reach on time. It can make errors reading data on the user side. We proposed a transmission system, namely a Hybrid MP-QUIC transmission protocol, for helping in sending the data. Using this transmission protocol can reduce the response time between client and server.

Along with the development of technology, the use of computers as a tool really needs to be used, especially in the health sector. The Prabumulih City General Hospital is the only hospital owned by the Prabumulih City Government and is a first-level referral. The patient registration process at the Prabumulih City Hospital is still direct, by means of the patient/patient's guardian coming directly to the health service facility, then the patient is given a registration queue number, then the patient waits for a call according to the queue number, after the queue number is called, the officer will ask the type of patient new or old, poly patient purposes, outpatient or inpatient care, private payments or BPJS, after that the officer enters patient data to create a queue number for patient care in order to get health services. In this regard, it is necessary to have an application system for patient administration so that new patients can register online through the internet network, making it easy for old patients to check personal data, view doctor schedules, take queues and others.

Fresh or rotten meat is a different matter. Damage to the meat will produce a distorted odor, mucus, discoloration in certain areas and an undesirable taste due to the formation of metabolism. The odor is described as fishy, ​​rotten, containing sulfur and like ammonia. In this research, the author discusses a system for identifying the condition of meat based on the odor that arises from meat in three states, namely odorless odor, fresh odor and rotten odor. Fresh odor is taken from meat odor that is within 1 (one) day after being cut and rotten odor is taken from meat odor that is on the 2nd day. In this study, the test sample meat was placed in a closed container at room temperature for 2 days. Data was taken for 2 days from meat odors of known type. The sensor array consists of eight sensors made of conducting polymer material. The polymer materials used are silicon DC-200, PEG-20M, 0V-101, 0V-17, DEGA, PEG-200, PEG-1540, and PEG-6000 mixed with Carbon black. A two-layer artificial neural net consisting of eight input nodes and three output neurons, was trained using the Kohonen algorithm with a training process that was completed in 4 iterations. From 20 tests, 10 times exposure to steam from fresh odor and 10 times exposure to steam from rotten odor, carried out alternately, it was found that the system failed twice. Thus, the system success percentage reached 95 percent.

Dalam mengatasi masalah rendahnya kesadaran masyarakat dalam pemeriksaan kesehatan gigi dan mulut, machine learning dapat menjadi solusi praktis untuk mengatasi masalah tersebut. Solusi yang dapat diberikan berupa aplikasi yang dapat mengklasifikasikan dan mendeteksi secara dini gambaran penyakit gigi dan mulut. Ada banyak jenis pendekatan yang dapat digunakan untuk melakukan klasifikasi dan deteksi, namun yang paling banyak diteliti dan diterapkan adalah metode convolutional neural network yang merupakan salah satu dari beberapa jenis metode dari algoritma deep learning. Komputasi tanpa server dapat dilakukan dalam lingkungan yang terisolasi, ini dapat digunakan untuk menguji beberapa hipotesis secara paralel yang menguntungkan bagi pengembang, tetapi tantangan tetap ada pada komputasi tanpa server seperti jangka waktu dan kapasitas memori yang perlu ditangani, tetapi untuk saat ini komputasi tanpa server tampaknya telah menjadi alternatif yang layak untuk model proses pelatihan.

Mushrooms are very diverse with characteristics of each type, there are 1,433,800 types of mushrooms that have not been recognized. In this study, researchers used the Neural Network and Deep Learning Inception V3 methods as a feature extraction process in images to classify mushroom images based on genus with the Orange Data Mining application. There are 9 genera of mushrooms used in this study, namely Agaricus, Amanita, Boletus, Cortinarius, Entoloma, Hygrocybe, Lactarius, Russula, and Suillus. The total dataset used is 2,700, with 300 images for each genus. The test uses the cross-validation method which is applied to the confusion matrix to get precision, recall, F1-score, and accuracy values. In this study, the final classification results were obtained with an accuracy of 82.5% and the genus Boletus mushroom obtained the best results with an accuracy of 98.9%.

Internet Service Provider (ISP) is a company that provides internet services. The ISP network is a national and international scale network so that customers can be connected globally. There are many factors that must be considered in selecting an ISP, making choosing an ISP a difficult task. Factors that influence ISP selection include cost, bandwidth, coverage area and type of connection. ISP providers offer a variety of advantages that make it difficult for customers to choose the right provider. The method applied in determining ISP providers is the AHP method used for weighting criteria while the WASPAS method is used for evaluating ISP providers with the criteria of cost, bandwidth, coverage area and type of connection. The rating process using the WASPAS method uses four assessment criteria, namely cost with a weight of 0.54, bandwidth with a weight of 0.38, coverage area with a weight of 0.05 and type of connection with a weight of 0.03. The final results of the ranking show that ISPs with low cost, large bandwidth and wide coverage areas will make these ISPs the best choice, this is because the criteria for cost, bandwidth and coverage area have high weight. Conversely, ISPs with high costs and small coverage areas will be the worst choices in the ranking list.

Someone who is referred to as a technician is a person who has expertise in a particular field of technology. Telkom Group is the only state-owned telecommunications company and the largest telecommunications and network service provider in Indonesia. Evaluation of technician performance is important to support the smooth running of the company. Selection of the best technician, will increase the motivation of the technician's performance. Some of the problems encountered were the absence of a technician performance appraisal process, the absence of an appropriate selection method, and the absence of a Decision Support System (DSS) that could make it easier to assess the selection of the best technician. Designing and building a decision support system using the AHP and COPRAS methods on technician assessments at PT. Telkom Access Regional 4 (Semarang) to increase the morale of the technicians and appreciate them. In this study, the AHP-COPRAS method was used to create a web-based DSS, in providing a more objective assessment every month, and creating several reports that convey effective issues, such as rating reports and technician performance appraisal reports according to these criteria.

Honey is a natural liquid that contains a lot of sugar produced by bees (genus Apis) from flower nectar and has a sweet taste. Honey contains a multitude of benefits that are good for the body, including being a source of nutrients, improving body metabolism, anti-bacterial, and others. The purpose of designing the Honey Harvest Monitoring System with ARDUINO-based IOT is to help breeders maximize honey harvesting results. This study aims to design an intelligent system for controlling the temperature in bee hives, humidity in bee hives, and monitoring honey yields, measuring the temperature and humidity of the storage room using DHT 11 sensors, and monitoring honey harvest time using Load Cell sensors. The temperature and humidity controller in the cage uses a blower/fan. Sensor data will be processed using the Wemos D1 R1 microcontroller and then sent to an Android application via the internet network using a real-time Firebase database so that it can be accessed anywhere and anytime. The way this system works is that if the room temperature is >38 degrees Celsius, the blower/fan will turn on, and will turn off if the temperature is 40%, the artificial window will open and will close if the humidity is

Water is very important for all aspects of life on earth, agriculture is one of the fields that really need large amounts of water because of the use of water in the process of plant photosynthesis. Water that has good quality is if the water is not excessively polluted by harmful chemicals or minerals. One indicator that water is polluted is a change in temperature and pH (acidity) of the water. Temperatures that are too hot in the water will interfere with the growth of plants and other microorganisms. While the normal pH of water has a pH that ranges from 6.5 to 7.5. The quality of water and soil is very important in agriculture. The level of acidity (pH) and soil temperature are one of the things that affect plant fertility. Therefore, the quality of water and soil on agricultural land is one of the important things that needs special attention in its management. One solution so that water and soil quality can be monitored and managed efficiently is to utilize a Wireless Sensor Network based on the Internet of Things (IoT). The use of the ESP8266 Module as a WIFI module, is widely used by Internet of Things-based applications because the price is cheap so it reduces a lot of costs and has a fairly good speed of 80 MHz. This study aims to develop the concept of a Wireless Sensor Network by utilizing the ESP8266 module to monitor pH values using a pH Meter Analog Kit sensor and temperature from agricultural land using a DS18B20 Waterproof sensor and can be monitored at any time using a smartphone.