Building of Informatics, Technology and Science (BITS)最新文献

Penerapan Data Mining Untuk Penjurusan Kelas dengan Menggunakan Algoritma K-Medoids 利用 K-Medoids 算法将数据挖掘应用于类别对齐

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-30 DOI: 10.47065/bits.v5i2.4313

Jhiro Faran, Rima Tamara Aldisa

{"title":"Penerapan Data Mining Untuk Penjurusan Kelas dengan Menggunakan Algoritma K-Medoids","authors":"Jhiro Faran, Rima Tamara Aldisa","doi":"10.47065/bits.v5i2.4313","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4313","url":null,"abstract":"Class assignments are carried out to focus students on the subjects that will be studied during Senior High School (SMA). Class majors are generally carried out in class of all the main values used in the class majoring process. This is a problem with the class majoring process, where mistakes often occur in the class majoring process. Mistakes regarding class majors made by students will have quite a fatal impact on the student, apart from not being able to change classes, it will also have a laziness effect on the student because it does not match the student's abilities. Solving this problem can be done using a technique called data mining. The solution to this problem is done using clustering. The K-Medoids algorithm is the algorithm used to solve the problems in this research. The process of grouping or forming clusters in the K-Medoids algorithm is based on calculating the closest distance to each object, calculating the closest distance is based on determining the centeroid value first. The K-Medoids algorithm can form 2 (two) clusters according to existing class majors. The results obtained show that there are 3 (three) alternatives included in cluster 1 and also 12 (twelve) alternatives included in cluster 2.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analisa Perbandingan Complate Linkage AHC dan K-Medoids Dalam Pengelompokkan Data Kemiskinan di Indonesia AHC Complate Linkage 与 K-Medoids 在印度尼西亚贫困数据分组中的比较分析

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-30 DOI: 10.47065/bits.v5i2.4310

Rifqi Habibi Sachrrial, Agus Iskandar

{"title":"Analisa Perbandingan Complate Linkage AHC dan K-Medoids Dalam Pengelompokkan Data Kemiskinan di Indonesia","authors":"Rifqi Habibi Sachrrial, Agus Iskandar","doi":"10.47065/bits.v5i2.4310","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4310","url":null,"abstract":"The poverty rate in Indonesia has increased from 9.54 percent in March 2022 to 9.57 percent in September 2022 due to inflation and low wages and people's incomes. To overcome this problem, steps such as providing social assistance, creating decent jobs, and increasing wage standards are needed to increase people's purchasing power and reduce poverty in the future. The government needs to pay special attention to provinces with high poverty rates through special programs and efforts to increase income and the economy in these areas. Data Mining is a solution in solving this problem by utilizing the clustering method which is known as the clustering method. The clustering method used in this study is the AHC method and the K-Medoids method. In order to determine the provinces with the highest number of poor people, the AHC and K-Medoids clustering methods will be applied separately so that the final results of each will be analyzed. The results of the analysis show the formation of three clusters with different cluster locations. The application of the AHC method resulted in cluster 2 with the largest number of provinces, namely 22 provinces, followed by cluster 0 with 9 provinces, and cluster 1 with only 3 provinces. While the application of the K-Medoids method resulted in cluster 1 with the largest number of provinces, namely 22 provinces, followed by cluster 0 with 9 provinces, and cluster 2 with only 3 provinces. Although the location of the clusters is different between the two methods, the number of provinces in the cluster is the same so that a cluster with a total of 3 provinces is declared the province with the largest number of poor people.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Fruits High in Vitamin C Using Self-Organizing Map and the K-Means Clustering Algorithm 基于自组织图和K-Means聚类算法的高维生素C水果分类

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-30 DOI: 10.47065/bits.v5i2.4104

Nuke L Chusna, Nurhasan Nugroho, Umbar Riyanto, Ahmad Ari Aldino

{"title":"Classification of Fruits High in Vitamin C Using Self-Organizing Map and the K-Means Clustering Algorithm","authors":"Nuke L Chusna, Nurhasan Nugroho, Umbar Riyanto, Ahmad Ari Aldino","doi":"10.47065/bits.v5i2.4104","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4104","url":null,"abstract":"Vitamin C-rich fruits not only taste fresh and delicious but also have the potential to increase the body's resistance to various diseases and maintain a proper nutritional balance. Information about fruits high in vitamin C is very important in order to increase public knowledge about which fruits contain high levels of vitamin C. However, to classify fruits high in vitamin C based on their image, a model is needed that is able to analyze the characteristics present in the image of the fruit. The purpose of this study is to build a classification model for high-vitamin C fruits with a combination of the Self-Organizing Map (SOM) artificial neural network algorithm and K-Means Clustering. Prior to classification, an image segmentation process is carried out using the K-Means Clustering algorithm, which will separate the image into parts that have similar visual characteristics. After the segmented image, the features of the object are extracted based on shape and texture. After the features of the image have been obtained, proceed with classifying images using the SOM algorithm by mapping multidimensional data into a lower-dimensional spatial representation to obtain the appropriate group or class. The accuracy test results for the built model produce an accuracy value of 93.33% and are included in the good category","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Penerapan Algoritma K-Means Clustering untuk Daerah Penyebaran Sampah Kelurahan K-Means 聚类算法在城市垃圾分布区中的应用

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-30 DOI: 10.47065/bits.v5i2.4158

Yantria Gusta Nugraha, Maimunah Maimunah, Pristi Sukmasetya

{"title":"Penerapan Algoritma K-Means Clustering untuk Daerah Penyebaran Sampah Kelurahan","authors":"Yantria Gusta Nugraha, Maimunah Maimunah, Pristi Sukmasetya","doi":"10.47065/bits.v5i2.4158","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4158","url":null,"abstract":"Waste in Indonesia, especially in Magelang City, has become a serious problem due to rapid population growth. Waste management issues, including landfills and collection, need effective handling. Data mining methods, such as K-Means clustering, can help identify areas with the highest levels of waste generation. This approach provides insights for the development of a more focused and efficient waste management strategy, a significant contribution to the improvement of Magelang City. By identifying the areas with the highest waste generation, waste management measures can be directed more efficiently and effectively. This includes increasing the transparency, capacity, and role of waste banks, as well as other efforts to reduce the negative impact of waste on the environment and human health. After clustering, the waste in Magelang City was grouped into 3 clusters according to the supplier area and the volume of waste. Then after the evaluation stage with the silhouette score displays a value of 0.79 which is a good value because it is close to the value of 1.0. With this method, it is expected that the city government in handling waste in Magelang city can be done optimally, efficiently, and on target","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Penerapan Algoritma Adaptive Response Rate Exponential Smoothing Terhadap Business Intelligence System Penerapan算法自适应响应率指数平滑Terhadap商业智能系统

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-30 DOI: 10.47065/bits.v5i2.3955

Romindo Romindo, Jefri Junifer Pangaribuan, Okky Putra Barus

{"title":"Penerapan Algoritma Adaptive Response Rate Exponential Smoothing Terhadap Business Intelligence System","authors":"Romindo Romindo, Jefri Junifer Pangaribuan, Okky Putra Barus","doi":"10.47065/bits.v5i2.3955","DOIUrl":"https://doi.org/10.47065/bits.v5i2.3955","url":null,"abstract":"PT. XYZ is one of the companies in the field of furniture sales by offering its flagship product, namely spring bed. The company's business continues to grow every year, of course, the company must be able to complete its work quickly and precisely. One of the main problems of the company is that the increase in company sales is still not able to cover the company's expenses and sometimes the company still suffers losses. This happens because companies often make mistakes in purchasing product inventory stock. Not all types of spring beds sell well, so sometimes purchases are made of the type of spring bed that is not selling well, which results in stock accumulation and instability of the company's cash inflow and outflow. In this study, a Business Intelligence System was built, which is a form of information technology implementation to store, collect and analyze data into knowledge so that it can be used as prediction results. The prediction algorithm used in this research is the Adaptive Response Rate Exponential algorithm. The expected goal of this research is to build a Business Intelligence System that can calculate product sales predictions in the following month using the Adaptive Response Rate Exponential Smoothing (ARRES) algorithm. Based on the results of the MAPE test, it can be concluded that the percentage of prediction accuracy from the ARRES algorithm on the sales transaction data of PT. XYZ is 53.33% which is categorized as quite accurate and the percentage of prediction error from the ARRES method is 46.67% which is categorized as reasonable","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135132269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tomato Ripeness Detection Using Linear Discriminant Analysis Algorithm with CIELAB and HSV Color Spaces 基于CIELAB和HSV色彩空间的番茄成熟度线性判别分析算法

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-30 DOI: 10.47065/bits.v5i2.4192

Rini Nuraini, Teotino Gomes Soares, Popi Dayurni, Mulyadi Mulyadi

{"title":"Tomato Ripeness Detection Using Linear Discriminant Analysis Algorithm with CIELAB and HSV Color Spaces","authors":"Rini Nuraini, Teotino Gomes Soares, Popi Dayurni, Mulyadi Mulyadi","doi":"10.47065/bits.v5i2.4192","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4192","url":null,"abstract":"Tomatoes have a relatively short ripening period, making it essential to identify their ripeness level before distribution. The ripeness level of tomatoes can be detected based on their color. Therefore, the color of tomatoes serves as a crucial indicator in determining whether they are ripe and of good quality. However, classifying tomato ripeness levels manually has several drawbacks, namely requiring a long process, a low level of accuracy, and being inconsistent. The research aimed at developing a detection model for the ripeness level of tomatoes using the LDA algorithm based on color feature extraction, namely CIELAB (L*a*b) and HSV. The L*a*b and HSV color spaces are applied to obtain information about the color of the object being detected. Furthermore, the information obtained from feature extraction is then grouped by class using the LDA algorithm, which separates information for each class and limits the spread between classes through linear projection searches to maximize the covariance matrix between classes so that members within the class can be identified. This research produces a model that can detect the level of ripeness of tomatoes with an accuracy of 88.194%.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Penerapan Data Mining Untuk Klasifikasi Penerima Dana Bantuan Sosial Dengan Menggunakan Algoritma K-Nearest Neighbor 使用 K 近邻算法对社会援助基金受助人进行分类的数据挖掘应用

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-30 DOI: 10.47065/bits.v5i2.3972

Agung Triayudi

{"title":"Penerapan Data Mining Untuk Klasifikasi Penerima Dana Bantuan Sosial Dengan Menggunakan Algoritma K-Nearest Neighbor","authors":"Agung Triayudi","doi":"10.47065/bits.v5i2.3972","DOIUrl":"https://doi.org/10.47065/bits.v5i2.3972","url":null,"abstract":"The Social Assistance Fund (Bansos) is a government program carried out to assist in eradicating community poverty in Indonesia and improving the welfare of families in Indonesia. Social Assistance Funds (Bansos) are distributed from the central ministry, then forwarded to local social services and then distributed to the community through each sub-district office. After data collection is carried out, the process of determining and selecting the families who receive Social Assistance Funds (Bansos) is carried out. However, in the implementation process there were several obstacles, one of which was that the provision of Social Assistance Funds (Bansos) was still not on target for families who deserved to receive Social Assistance Funds (Bansos). This problem is an important matter that must be resolved, this is because the main aim of the Social Assistance Fund (Bansos) program is to help eradicate poverty in Indonesia. Reviewing and processing data again based on previous data can be completed using one of the computer techniques. Data mining is a technique used to reprocess data. Data processing returns to data mining based on data previously stored in a data collection or data warehouse. Classification is part of data mining which aims to find out certain models of data so that they can be divided into several classes or groups. The K-Nearest Neighbor (K-NN) algorithm is part of a data mining technique which aims to divide data into certain groups. The results obtained in the research are the K value used in the research, namely K=7, the result of the family data grouping process which has just determined that the family received Social Assistance Funds (Bansos).","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Komparasi Metode Maut dan Moora dalam Pemilihan Sunscreen untuk Kulit Menggunakan Pembobotan ROC 死亡方法和莫拉的比较皮肤选择用中华民国偷牛

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-30 DOI: 10.47065/bits.v5i2.4153

Gusti Tarisa Mareti, Afifah Trista Ayunda

{"title":"Komparasi Metode Maut dan Moora dalam Pemilihan Sunscreen untuk Kulit Menggunakan Pembobotan ROC","authors":"Gusti Tarisa Mareti, Afifah Trista Ayunda","doi":"10.47065/bits.v5i2.4153","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4153","url":null,"abstract":"The skin is the outer layer of the human body that has various important functions. However, high sun exposure in Indonesia can cause damage to the skin due to ultraviolet rays. The use of sunscreen becomes important in preventing sunburn and skin cancer. Women with combination skin types often have difficulty choosing the right sunscreen. This study applies the Multi Attribute Utility Theory (MAUT) and Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA) methods with the weighting of the Centroid Rank Order (ROC) method and the level of accuracy of MSE aims to produce decisions in choosing the right sunscreen for combination skin. At the methodology stage, data on criteria, sub-criteria, and alternatives are collected through observation and interviews. The criteria consist of 6 namely Benefit, Composition, Price, Vitamins, Side Effects, and Size. The ROC is used for weighting, while the MAUT and MOORA methods are used in the assessment and comparison of alternatives and MSE is used for the level of accuracy. The discoveries from this study hold the possibility to offer recommendations for choosing the best sunscreen for combination skin, namely A7 with the brand sunscreen Somethinc Comfort Correct for the MAUT method is 0.7134 and the MOORA method is 0.102. The results of the MSE calculation obtained a deviation value, namely the MOORA method with a value of 215.0091 better than the MAUT method of 207.4922. So that the MOORA method is the best method in choosing sunscreen for combination skin and aims to help people who are still having difficulty choosing the right sunscreen, so as to avoid mistakes in choosing inappropriate sunscreens that can have a negative impact on the skin","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Hyperparameters to the Ensemble Learning Method for Lung Cancer Classification 肺癌分类集成学习方法的超参数实现

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-30 DOI: 10.47065/bits.v5i2.4096

Ridlo Yanuar, Siti Sa’adah, Prasti Eko Yunanto

{"title":"Implementation of Hyperparameters to the Ensemble Learning Method for Lung Cancer Classification","authors":"Ridlo Yanuar, Siti Sa’adah, Prasti Eko Yunanto","doi":"10.47065/bits.v5i2.4096","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4096","url":null,"abstract":"Lung cancer is the most common cause of death in someone who has cancer. This happens because of remembering the importance of lung function as a breathing apparatus and oxygen distribution throughout the body. Early identification of lung cancer is crucial to reduce its mortality rate. Accuracy is crucial since it indicates how accurately the model or system makes the right predictions. High levels of accuracy show that the model can produce trustworthy and accurate findings, essential for making effective decisions based on available data. In this research, ensemble learning approaches, namely bagging and boosting methods, were employed for classifying lung cancer. Hyperparameters, a class of parameters, are crucial to this model's effectiveness. In order to increase the lung cancer classification model's accuracy, a thorough investigation was conducted to identify the best hyperparameter combination. In this study, the dataset used is a medical dataset that contains a history of patients who have been diagnosed with lung cancer or not. The dataset is taken from Kaggle mysarahmadbhat and cancerdatahp from data world. To evaluate the model's accuracy, this study used the confusion matrix method which compares the model's prediction results with the ground truth. the study findings revealed that employing a dataset split ratio of 70:30 produced the best results, with the Random Forest, CatBoost, and XGBoost models achieving an impressive 98% accuracy, 0.98 precision, 0.98 recall, and 0.98 f1-score. but for AdaBoost, the best results were obtained on a dataset with a ratio of 80:20 with an accuracy of 96%, 0.97 precision, 0.96 recall, and 0.96 f1-score","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polyp Identification from a Colonoscopy Image Using Semantic Segmentation Approach 基于语义分割方法的结肠镜图像息肉识别

Building of Informatics, Technology and Science (BITS) Pub Date : 2023-09-27 DOI: 10.47065/bits.v5i2.4083

Wahyu Hauzan Rafi, Mahmud Dwi Sulistiyo, Sugondo Hadiyoso, Untari Novia Wisesty

{"title":"Polyp Identification from a Colonoscopy Image Using Semantic Segmentation Approach","authors":"Wahyu Hauzan Rafi, Mahmud Dwi Sulistiyo, Sugondo Hadiyoso, Untari Novia Wisesty","doi":"10.47065/bits.v5i2.4083","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4083","url":null,"abstract":"Colorectal Cancer (CRC) is a major contributor to cancer-related mortality worldwide, necessitating early detection and treatment of polyps to prevent cancer progression. A colonoscopy is a critical diagnostic procedure for identifying colon abnormalities and removing premalignant polyps. However, accurately segmenting polyps in colonoscopy images poses challenges due to their diverse appearance and indistinct boundaries. In this study, we investigate augmentation techniques to enhance polyp semantic segmentation using the U-Net model. Our analysis reveals that the most effective technique is found in sub-scenario 2.6.c with an input size of 320×320, striking a favorable balance between accuracy and efficiency. Additionally, we explore the benefits of larger input sizes, taking into account resource considerations. Moreover, we conduct further testing of the best augmentation technique identified in previous experiments with the SegNet model. The results show a 3.5% improvement in the dice coefficient and slightly better qualitative outcomes. However, it is important to note that this enhancement comes with a nearly fivefold increase in training time. Moving forward, our objective is to develop a unified model for segmenting diverse medical images, pushing the boundaries of polyp detection and medical imaging. This research provides valuable insights and lays the foundation for more advanced applications in polyp detection and medical image analysis.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0