Pub Date : 2024-06-04DOI: 10.21609/jiki.v17i2.1298
A. Nugroho, Budi Sunarko, Hari Wibawanto, Anggraini Mulwinda, Anas Fauzi, Dwi Oktaviyanti, Dina Wulung Savitri
Active Contour (AC) is an algorithm widely used in segmentation for developing Computer-Aided Diagnosis (CAD) systems in ultrasound imaging. Existing AC models still retain an interactive nature. This is due to the large number of parameters and coefficients that require manual tuning to achieve stability. Which can result in human error and various issues caused by the inhomogeneity of ultrasound images, such as leakage, false areas, and local minima. In this study, an automatic object segmentation method was developed to assist radiologists in an efficient diagnosis process. The proposed method is called Automatic Combinatorial Active Contour (ACAC), which combines the simplification of the global region-based CV (Chan-Vese) model and improved-GAC (Geodesic Active Contour) for local segmentation. The results of testing with 50 datasets showed an accuracy value of 98.83%, precision of 95.26%, sensitivity of 86.58%, specificity of 99.63%, similarity of 90.58%, and IoU (Intersection over Union) of 82.87%. These quantitative performance metrics demonstrate that the ACAC method is suitable for implementation in a more efficient and accurate CAD system.
主动轮廓（AC）是一种广泛用于分割的算法，可用于开发超声成像中的计算机辅助诊断（CAD）系统。现有的 AC 模型仍具有交互性。这是由于大量的参数和系数需要手动调整才能达到稳定。这可能会导致人为错误和超声图像的不均匀性引起的各种问题，如泄漏、虚假区域和局部极小值。本研究开发了一种自动物体分割方法，以协助放射科医生进行高效诊断。所提出的方法被称为自动组合主动轮廓（ACAC），它结合了基于全局区域的 CV（Chan-Vese）简化模型和用于局部分割的改进型 GAC（大地主动轮廓）。50 个数据集的测试结果显示，准确率为 98.83%，精确度为 95.26%，灵敏度为 86.58%，特异度为 99.63%，相似度为 90.58%，IoU（Intersection over Union）为 82.87%。这些定量性能指标表明，ACAC 方法适合在更高效、更准确的 CAD 系统中实施。
{"title":"Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method","authors":"A. Nugroho, Budi Sunarko, Hari Wibawanto, Anggraini Mulwinda, Anas Fauzi, Dwi Oktaviyanti, Dina Wulung Savitri","doi":"10.21609/jiki.v17i2.1298","DOIUrl":"https://doi.org/10.21609/jiki.v17i2.1298","url":null,"abstract":"Active Contour (AC) is an algorithm widely used in segmentation for developing Computer-Aided Diagnosis (CAD) systems in ultrasound imaging. Existing AC models still retain an interactive nature. This is due to the large number of parameters and coefficients that require manual tuning to achieve stability. Which can result in human error and various issues caused by the inhomogeneity of ultrasound images, such as leakage, false areas, and local minima. In this study, an automatic object segmentation method was developed to assist radiologists in an efficient diagnosis process. The proposed method is called Automatic Combinatorial Active Contour (ACAC), which combines the simplification of the global region-based CV (Chan-Vese) model and improved-GAC (Geodesic Active Contour) for local segmentation. The results of testing with 50 datasets showed an accuracy value of 98.83%, precision of 95.26%, sensitivity of 86.58%, specificity of 99.63%, similarity of 90.58%, and IoU (Intersection over Union) of 82.87%. These quantitative performance metrics demonstrate that the ACAC method is suitable for implementation in a more efficient and accurate CAD system.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"73 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141268471","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}
Pub Date : 2024-06-03DOI: 10.21609/jiki.v17i2.1280
Teja Endra Eng Tju, Muhammad Umar Shalih
The research lays the groundwork for further advancements in VR technology, aiming to develop devices capable of interpreting sign language into speech via intelligent systems. The uniqueness of this study lies in utilizing the Meta Quest 2 VR device to gather primary hand sign data, subsequently classified using Machine Learning techniques to evaluate the device's proficiency in interpreting hand signs. The initial stages emphasized collecting hand sign data from VR devices and processing the data to comprehend sign patterns and characteristics effectively. 1021 data points, comprising ten distinct hand sign gestures, were collected using a simple application developed with Unity Editor. Each data contained 14 parameters from both hands, ensuring alignment with the headset to prevent hand movements from affecting body rotation and accurately reflecting the user's facing direction. The data processing involved padding techniques to standardize varied data lengths resulting from diverse recording periods. The Interpretation Algorithm Development involved Recurrent Neural Networks tailored to data characteristics. Evaluation metrics encompassed Accuracy, Validation Accuracy, Loss, Validation Loss, and Confusion Matrix. Over 15 epochs, validation accuracy notably stabilized at 0.9951, showcasing consistent performance on unseen data. The implications of this research serve as a foundation for further studies in the development of VR devices or other wearable gadgets that can function as sign language interpreters.
{"title":"Hand Sign Interpretation through Virtual Reality Data Processing","authors":"Teja Endra Eng Tju, Muhammad Umar Shalih","doi":"10.21609/jiki.v17i2.1280","DOIUrl":"https://doi.org/10.21609/jiki.v17i2.1280","url":null,"abstract":"The research lays the groundwork for further advancements in VR technology, aiming to develop devices capable of interpreting sign language into speech via intelligent systems. The uniqueness of this study lies in utilizing the Meta Quest 2 VR device to gather primary hand sign data, subsequently classified using Machine Learning techniques to evaluate the device's proficiency in interpreting hand signs. The initial stages emphasized collecting hand sign data from VR devices and processing the data to comprehend sign patterns and characteristics effectively. 1021 data points, comprising ten distinct hand sign gestures, were collected using a simple application developed with Unity Editor. Each data contained 14 parameters from both hands, ensuring alignment with the headset to prevent hand movements from affecting body rotation and accurately reflecting the user's facing direction. The data processing involved padding techniques to standardize varied data lengths resulting from diverse recording periods. The Interpretation Algorithm Development involved Recurrent Neural Networks tailored to data characteristics. Evaluation metrics encompassed Accuracy, Validation Accuracy, Loss, Validation Loss, and Confusion Matrix. Over 15 epochs, validation accuracy notably stabilized at 0.9951, showcasing consistent performance on unseen data. The implications of this research serve as a foundation for further studies in the development of VR devices or other wearable gadgets that can function as sign language interpreters.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"26 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141270587","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}
Pub Date : 2024-06-03DOI: 10.21609/jiki.v17i2.1274
Made Prastha Nugraha, Laksmita Rahadianti
Image blur is one of the common degradations on an image. The blur that occurs on the captured images is sometimes non-uniform, with different levels of blur in different areas of the image. In recent years, most deblurring methods have been deep learning-based. These methods model deblurring as an imageto-image translation problem, treating images globally. This may result in poor performance when handling non-uniform blur in images. Therefore, in this paper, the author compared two state-of-the-art supervised deep learning methods for deblurring and restoration, e.g. BANet and Restormer, with a special focus on the non-uniform blur. The GOPRO training dataset, which is also used in various studies as a benchmark, was used to train the models. The trained models were then tested on the GOPRO testing test, the HIDE testing set for cross-dataset testing, and GOPRO-NU, which consists of specifically selected non-uniform blurred images from the GOPRO testing set, for the non-uniform deblur testing. On the GOPRO testing set, Restormer achieved an SSIM of 0.891 and PSNR of 27.66 while BANet obtained an SSIM of 0.926 and PSNR of 34.90. Meanwhile, for the HIDE dataset, Restormer achieved an SSIM of 0.907 and PSNR of 27.93 while BANet obtained an SSIM of 0.908 and PSNR of 34.52. Finally, on the non-uniform blur GOPRO dataset, Restormer achieved an SSIM of 0.911 and PSNR of 29.48 while BANet obtained an SSIM of 0.935 and PSNR of 35.47. Overall, BANet shows the best result in handling non-uniform blur with a significant improvement over Restormer.
{"title":"Deep Image Deblurring for Non-Uniform Blur: a Comparative Study of Restormer and BANet","authors":"Made Prastha Nugraha, Laksmita Rahadianti","doi":"10.21609/jiki.v17i2.1274","DOIUrl":"https://doi.org/10.21609/jiki.v17i2.1274","url":null,"abstract":"Image blur is one of the common degradations on an image. The blur that occurs on the captured images is sometimes non-uniform, with different levels of blur in different areas of the image. In recent years, most deblurring methods have been deep learning-based. These methods model deblurring as an imageto-image translation problem, treating images globally. This may result in poor performance when handling non-uniform blur in images. Therefore, in this paper, the author compared two state-of-the-art supervised deep learning methods for deblurring and restoration, e.g. BANet and Restormer, with a special focus on the non-uniform blur. The GOPRO training dataset, which is also used in various studies as a benchmark, was used to train the models. The trained models were then tested on the GOPRO testing test, the HIDE testing set for cross-dataset testing, and GOPRO-NU, which consists of specifically selected non-uniform blurred images from the GOPRO testing set, for the non-uniform deblur testing. On the GOPRO testing set, Restormer achieved an SSIM of 0.891 and PSNR of 27.66 while BANet obtained an SSIM of 0.926 and PSNR of 34.90. Meanwhile, for the HIDE dataset, Restormer achieved an SSIM of 0.907 and PSNR of 27.93 while BANet obtained an SSIM of 0.908 and PSNR of 34.52. Finally, on the non-uniform blur GOPRO dataset, Restormer achieved an SSIM of 0.911 and PSNR of 29.48 while BANet obtained an SSIM of 0.935 and PSNR of 35.47. Overall, BANet shows the best result in handling non-uniform blur with a significant improvement over Restormer.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"52 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141268900","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}
Pub Date : 2024-06-03DOI: 10.21609/jiki.v17i2.1284
I. Dewa, Made Bayu, Atmaja Darmawan, Linawati, Gede Sukadarmika, Ni Made Ary, E. D. Wirastuti, Reza Pulungan
Sign language (SL) is vital in fostering communication for the deaf and hard-of-hearing communities. Continuous Sign Language Translation (CSLT) is a work that translates sign language into spoken language. CSLT translation is done by changing continuous forms into isolated signs. Segmenting morpheme signs from phrase signs has several challenges, such as the availability of annotated datasets and the complexity of continuous gesture movements. The Indonesian Sign Language (SIBI) system follows Indonesian grammatical norms, including word formation, in contrast to other sign languages with rules derived from their spoken language. In SIBI, a word can consist of a root word and an affix word. Therefore, temporal action segmentation in SIBI is important to reconstruct the results of translating each sign into spoken Indonesian sentences. This research uses an optical flow approach to segment temporal actions in SIBI videos. Optical flow methods that calculate changes in intensity between adjacent frames can be used to determine the occurrence of sign movement or vice versa to determine the delay between sign movements. The absence of intensity differences between the two frames indicates the boundary between sign gestures. This study tested the use of dense optical flow on videos containing SIBI sentences taken from 3 signers. Evaluation is done on several parameters in the dense optical flow algorithm, such as threshold size, PyrScale, and WinSize, to obtain the best accuracy. This paper shows that the optical flow algorithm successfully performs segmentation, as measured by Perf and F1r. The experimental results showed that the highest Perf and F1r yields were 0.8298 and 0.8524, respectively.
手语（SL）对于促进聋人和重听者群体的交流至关重要。连续手语翻译（CSLT）是一种将手语翻译成口语的工作。CSLT 翻译是通过将连续的形式转换为孤立的符号来完成的。从短语手势中分离语素手势面临着一些挑战，如注释数据集的可用性和连续手势动作的复杂性。印尼手语（SIBI）系统遵循印尼语法规范，包括词的构成，这与其他手语的规则源自其口语形成鲜明对比。在 SIBI 中，一个词可以由一个词根词和一个词缀词组成。因此，SIBI 中的时间动作分割对于将每个手势翻译成印尼口语句子的结果重建非常重要。本研究采用光流方法来分割 SIBI 视频中的时间动作。计算相邻帧之间强度变化的光流方法可用于确定手势动作的发生，反之亦然，可用于确定手势动作之间的延迟。两帧之间没有强度差异则表示符号手势之间的边界。本研究测试了密集光流在包含 SIBI 句子的视频中的应用，这些句子来自 3 个手势。对密集光流算法中的几个参数（如阈值大小、PyrScale 和 WinSize）进行了评估，以获得最佳精度。本文通过 Perf 和 F1r 的测量结果表明，光流算法成功地进行了分割。实验结果表明，最高的 Perf 和 F1r 值分别为 0.8298 和 0.8524。
{"title":"Temporal Action Segmentation in Sign Language System for Bahasa Indonesia (SIBI) Videos Using Optical Flow-Based Approach","authors":"I. Dewa, Made Bayu, Atmaja Darmawan, Linawati, Gede Sukadarmika, Ni Made Ary, E. D. Wirastuti, Reza Pulungan","doi":"10.21609/jiki.v17i2.1284","DOIUrl":"https://doi.org/10.21609/jiki.v17i2.1284","url":null,"abstract":"Sign language (SL) is vital in fostering communication for the deaf and hard-of-hearing communities. Continuous Sign Language Translation (CSLT) is a work that translates sign language into spoken language. CSLT translation is done by changing continuous forms into isolated signs. Segmenting morpheme signs from phrase signs has several challenges, such as the availability of annotated datasets and the complexity of continuous gesture movements. The Indonesian Sign Language (SIBI) system follows Indonesian grammatical norms, including word formation, in contrast to other sign languages with rules derived from their spoken language. In SIBI, a word can consist of a root word and an affix word. Therefore, temporal action segmentation in SIBI is important to reconstruct the results of translating each sign into spoken Indonesian sentences. This research uses an optical flow approach to segment temporal actions in SIBI videos. Optical flow methods that calculate changes in intensity between adjacent frames can be used to determine the occurrence of sign movement or vice versa to determine the delay between sign movements. The absence of intensity differences between the two frames indicates the boundary between sign gestures. This study tested the use of dense optical flow on videos containing SIBI sentences taken from 3 signers. Evaluation is done on several parameters in the dense optical flow algorithm, such as threshold size, PyrScale, and WinSize, to obtain the best accuracy. This paper shows that the optical flow algorithm successfully performs segmentation, as measured by Perf and F1r. The experimental results showed that the highest Perf and F1r yields were 0.8298 and 0.8524, respectively.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"3 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141271981","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}
Pub Date : 2024-06-03DOI: 10.21609/jiki.v17i2.1237
P. A. Pramesti, Muhamad Supriyadi, Muhammad Reza Alfin, Rita Noveriza, D. Wahyuno, D. Manohara, Melati, Miftakhurohmah, Riki Warman, S. Hardiyanti, Asnawi
Clove is one of the precious plants produced in Indonesia. Clove has many benefits for humans, but clove cultivation often experiences problems due to disease attacks, including Leaf Blister Blight Disease(CDC). The handling of CDC disease is carried out based on the severity of the symptoms that can be seen on the affected leaves. This research was conducted to obtain a CDC disease classification model, so appropriate treatment can be carried out. This study used the pre-trained VGG16, InceptionV3, and ResNet models for classification. VGG16 got the highest average accuracy of 96.7%. Aside from that, k-fold cross validation improved the model's accuracy.
{"title":"Classification of Clove Leaf Blister Blight Disease Severity Using Pre-trained Model VGG16, InceptionV3, and ResNet","authors":"P. A. Pramesti, Muhamad Supriyadi, Muhammad Reza Alfin, Rita Noveriza, D. Wahyuno, D. Manohara, Melati, Miftakhurohmah, Riki Warman, S. Hardiyanti, Asnawi","doi":"10.21609/jiki.v17i2.1237","DOIUrl":"https://doi.org/10.21609/jiki.v17i2.1237","url":null,"abstract":"Clove is one of the precious plants produced in Indonesia. Clove has many benefits for humans, but clove cultivation often experiences problems due to disease attacks, including Leaf Blister Blight Disease(CDC). The handling of CDC disease is carried out based on the severity of the symptoms that can be seen on the affected leaves. This research was conducted to obtain a CDC disease classification model, so appropriate treatment can be carried out. This study used the pre-trained VGG16, InceptionV3, and ResNet models for classification. VGG16 got the highest average accuracy of 96.7%. Aside from that, k-fold cross validation improved the model's accuracy.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"14 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272017","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}
Pub Date : 2024-06-03DOI: 10.21609/jiki.v17i2.1236
Hanan Iqbal Alrahma, Anan Nugroho, A. F. Hastawan, U. Arief
The Digital Center (DC) building at UNNES is a new building on the campus that currently lacks evacuation routes. Therefore, it is necessary to create an evacuation route plan in accordance with the Minister of Health Regulation Number 48 of 2016. Creating a manual evacuation route plan can be inefficient and prone to errors, especially for large buildings with complex interiors. To address this issue, learning techniques such as reinforcement learning (RL) are being used. In this study, Q-learning will be utilized to find the shortest path to the exit doors from 11 rooms on the first floor of the DC building. The study makes use of the floor plan data of the DC building, information about the location of the exit doors, and the distance required to reach them. The results of the study demonstrate that Qlearning successfully identifies the shortest evacuation routes for the first-floor DC building. The routes generated by Q-learning are identical to the manually created shortest paths. Even when additional obstacles are introduced into the environment, Q-learning is still able to find the shortest routes. On average, the number of episodes required for convergence in both environments is less than 1000 episodes, and the average computation time needed for both environments is 0.54 seconds and 0.76 seconds, respectively.
{"title":"Application of Q-learning Method for Disaster Evacuation Route Design Case Study: Digital Center Building UNNES","authors":"Hanan Iqbal Alrahma, Anan Nugroho, A. F. Hastawan, U. Arief","doi":"10.21609/jiki.v17i2.1236","DOIUrl":"https://doi.org/10.21609/jiki.v17i2.1236","url":null,"abstract":"The Digital Center (DC) building at UNNES is a new building on the campus that currently lacks evacuation routes. Therefore, it is necessary to create an evacuation route plan in accordance with the Minister of Health Regulation Number 48 of 2016. Creating a manual evacuation route plan can be inefficient and prone to errors, especially for large buildings with complex interiors. To address this issue, learning techniques such as reinforcement learning (RL) are being used. In this study, Q-learning will be utilized to find the shortest path to the exit doors from 11 rooms on the first floor of the DC building. The study makes use of the floor plan data of the DC building, information about the location of the exit doors, and the distance required to reach them. The results of the study demonstrate that Qlearning successfully identifies the shortest evacuation routes for the first-floor DC building. The routes generated by Q-learning are identical to the manually created shortest paths. Even when additional obstacles are introduced into the environment, Q-learning is still able to find the shortest routes. On average, the number of episodes required for convergence in both environments is less than 1000 episodes, and the average computation time needed for both environments is 0.54 seconds and 0.76 seconds, respectively.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"17 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272917","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}
Pub Date : 2024-06-03DOI: 10.21609/jiki.v17i2.1181
I’zaz Dhiya ‘Ulhaq, Muhamad Arief Hidayat, Tio Dharmawan
The current research about the classification of coffee fruit ripeness based on multispectral images has been developed using the Convolutional Neural Network (CNN) method to extract patterns from highdimensional multispectral images. The high complexity of CNN allows the model to capture complex features but requires more time and computational resources for model training and testing. Therefore, in this study, classification is performed using a more straightforward method such as Naïve Bayes because its complexity only depends on the number of features and samples. The method only considers each feature independently, so it has high speed and does not require a lot of computational resources. Naïve Bayes is applied to color and texture features extracted from multispectral images of coffee fruit. There are 300 features consisting of 60 color features and 240 texture features. Experiments were conducted based on the comparison of training and testing data and the use of each feature. The combination of color and texture features showed better performance than color or texture features alone, with the highest accuracy reaching 91.01%. In conclusion, using Naïve Bayes is still reasonably good in classifying the ripeness of coffee fruit based on multispectral images.
{"title":"Classification of Coffee Fruit Maturity Level based on Multispectral Image Using Naïve Bayes Method","authors":"I’zaz Dhiya ‘Ulhaq, Muhamad Arief Hidayat, Tio Dharmawan","doi":"10.21609/jiki.v17i2.1181","DOIUrl":"https://doi.org/10.21609/jiki.v17i2.1181","url":null,"abstract":"The current research about the classification of coffee fruit ripeness based on multispectral images has been developed using the Convolutional Neural Network (CNN) method to extract patterns from highdimensional multispectral images. The high complexity of CNN allows the model to capture complex features but requires more time and computational resources for model training and testing. Therefore, in this study, classification is performed using a more straightforward method such as Naïve Bayes because its complexity only depends on the number of features and samples. The method only considers each feature independently, so it has high speed and does not require a lot of computational resources. Naïve Bayes is applied to color and texture features extracted from multispectral images of coffee fruit. There are 300 features consisting of 60 color features and 240 texture features. Experiments were conducted based on the comparison of training and testing data and the use of each feature. The combination of color and texture features showed better performance than color or texture features alone, with the highest accuracy reaching 91.01%. In conclusion, using Naïve Bayes is still reasonably good in classifying the ripeness of coffee fruit based on multispectral images.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"41 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141270112","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}
Pub Date : 2024-06-03DOI: 10.21609/jiki.v17i2.1273
U. N. Wisesty, T R Mengko, Ayu Purwarianti, Adi Pancoro
Detecting DNA sequence mutations in cancer patients contributes to early identification and treatment of the disease, which ultimately enhances the effectiveness of treatment. Bioinformatics utilizes sequence alignment as a powerful tool for identifying mutations in DNA sequences. We used the Needleman-Wunsch algorithm to identify mutations in DNA sequence data from cancer patients. The cancer sequence dataset used includes breast, cervix uteri, lung, colon, liver and prostate cancer. Various types of mutations were identified, such as Single Nucleotide Variant (SNV)/substitution, insertion, and deletion, locate by the nucleotide index. The Needleman Wunch algorithm can detect type and index mutation with the average F1-scores 0.9507 for all types of mutations, 0.9919 for SNV, 0.7554 for insertion, and 0.8658 for deletion with a tolerance of 5 bp. The F1-scores obtained are not correlated with gene length. The time required ranges from 1.03 seconds for a 290 base pair gene to 3211.45 seconds for a gene with 16613 base pairs.
检测癌症患者的 DNA 序列突变有助于疾病的早期识别和治疗，最终提高治疗效果。生物信息学将序列比对作为识别DNA序列突变的有力工具。我们使用 Needleman-Wunsch 算法来识别癌症患者 DNA 序列数据中的突变。使用的癌症序列数据集包括乳腺癌、子宫颈癌、肺癌、结肠癌、肝癌和前列腺癌。通过核苷酸指数定位，确定了各种类型的突变，如单核苷酸变异（SNV）/置换、插入和缺失。Needleman Wunch 算法可以检测出突变的类型和指数，所有类型突变的平均 F1 分数为 0.9507，SNV 为 0.9919，插入为 0.7554，缺失为 0.8658，容差为 5 bp。获得的 F1 分数与基因长度无关。所需时间从 290 碱基对基因的 1.03 秒到 16613 碱基对基因的 3211.45 秒不等。
{"title":"Detecting Type and Index Mutation in Cancer DNA Sequence Based on Needleman–Wunsch Algorithm","authors":"U. N. Wisesty, T R Mengko, Ayu Purwarianti, Adi Pancoro","doi":"10.21609/jiki.v17i2.1273","DOIUrl":"https://doi.org/10.21609/jiki.v17i2.1273","url":null,"abstract":"Detecting DNA sequence mutations in cancer patients contributes to early identification and treatment of the disease, which ultimately enhances the effectiveness of treatment. Bioinformatics utilizes sequence alignment as a powerful tool for identifying mutations in DNA sequences. We used the Needleman-Wunsch algorithm to identify mutations in DNA sequence data from cancer patients. The cancer sequence dataset used includes breast, cervix uteri, lung, colon, liver and prostate cancer. Various types of mutations were identified, such as Single Nucleotide Variant (SNV)/substitution, insertion, and deletion, locate by the nucleotide index. The Needleman Wunch algorithm can detect type and index mutation with the average F1-scores 0.9507 for all types of mutations, 0.9919 for SNV, 0.7554 for insertion, and 0.8658 for deletion with a tolerance of 5 bp. The F1-scores obtained are not correlated with gene length. The time required ranges from 1.03 seconds for a 290 base pair gene to 3211.45 seconds for a gene with 16613 base pairs.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"31 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273152","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}
Pub Date : 2024-06-02DOI: 10.21609/jiki.v17i2.1195
Andro Harjanto, R. F. Aji
IT Asset Management (ITAM) is crucial for organizations as it enables efficient utilization of IT resources, cost reduction, and risk mitigation. Horangi, a startup company, recognizes the importance of asset optimization and aims to enhance its ITAM service. To achieve this, researcher conducts research to identify a suitable framework as a solid foundation. ITIL 4, a widely adopted IT service management framework, is chosen, along with the Continual Service Improvement and Service Value Chain models. These models provide guidelines and recommendations to identify weaknesses and improve current processes while enabling continuous improvement in response to the dynamic IT landscape. The research employs a qualitative approach, utilizing in-depth interviews, document research, and the ITIL 4 guidebook. The study aims to provide recommendations and a foundation for developing guidelines and workflows in ITAM within the company. However, a limitation of this research is not much research related to ITIL 4 in ITAM area and cannot proceed until the implementation of recommendations due to funding constraints and approval processes. To overcome this limitation, it is suggested that future research includes the implementation process to obtain more optimal evaluation results.
IT 资产管理（ITAM）对于企业来说至关重要，因为它可以有效利用 IT 资源、降低成本和风险。Horangi 是一家初创公司，它认识到资产优化的重要性，并致力于加强其 ITAM 服务。为此，研究人员开展了研究，以确定一个合适的框架作为坚实的基础。研究人员选择了被广泛采用的 IT 服务管理框架 ITIL 4 以及持续服务改进和服务价值链模型。这些模型提供了指导方针和建议，可用于找出薄弱环节并改进当前流程，同时实现持续改进，以应对动态的 IT 环境。研究采用定性方法，利用深入访谈、文档研究和 ITIL 4 指南手册。本研究旨在为制定公司内部 ITAM 指南和工作流程提供建议和基础。然而，本研究的一个局限是，ITAM 领域与 ITIL 4 相关的研究不多，而且由于资金限制和审批程序，在实施建议之前无法进行。为克服这一局限性，建议今后的研究包括实施过程，以获得更优化的评估结果。
{"title":"Improving IT Assets Management with ITIL 4 Framework","authors":"Andro Harjanto, R. F. Aji","doi":"10.21609/jiki.v17i2.1195","DOIUrl":"https://doi.org/10.21609/jiki.v17i2.1195","url":null,"abstract":"IT Asset Management (ITAM) is crucial for organizations as it enables efficient utilization of IT resources, cost reduction, and risk mitigation. Horangi, a startup company, recognizes the importance of asset optimization and aims to enhance its ITAM service. To achieve this, researcher conducts research to identify a suitable framework as a solid foundation. ITIL 4, a widely adopted IT service management framework, is chosen, along with the Continual Service Improvement and Service Value Chain models. These models provide guidelines and recommendations to identify weaknesses and improve current processes while enabling continuous improvement in response to the dynamic IT landscape. The research employs a qualitative approach, utilizing in-depth interviews, document research, and the ITIL 4 guidebook. The study aims to provide recommendations and a foundation for developing guidelines and workflows in ITAM within the company. However, a limitation of this research is not much research related to ITIL 4 in ITAM area and cannot proceed until the implementation of recommendations due to funding constraints and approval processes. To overcome this limitation, it is suggested that future research includes the implementation process to obtain more optimal evaluation results.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"43 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273923","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}
Pub Date : 2024-06-02DOI: 10.21609/jiki.v17i2.1178
Ilma Ainur Rohma, Ade Azurat
Model-Driven Software Engineering (MDSE) is a software development approach that uses the Model to be the main actor of the development. MDSE can be applied to User Interface (UI) Development so that a model for the UI can be built, and then a transformation can be made to turn it into a running application. In this research, we develop UI Generator to support UI Development with the MDSE approach. This UI Generator can also support UI Development in Software Product Line Engineering (SPLE) paradigm. The UI is modeled with Interaction Flow Modeling Language (IFML) diagram. Then The IFML diagram is transformed into React-Based UI by the UI Generator. The UI Generator is developed with Acceleo on Eclipse IDE to transform IFML into React Code with the transformation rules defined in this research. The UI generator is also enriched with display settings and static page management to address user customization needs. The experimental results show that the UI Generator can generate a functional website. Besides evaluating the working product, UI Generator is evaluated qualitatively well based on six quality criteria as an SPLE supporting tool.
{"title":"Code Generator Development to Transform IFML (Interaction Flow Modelling Language) into a React-based User Interface","authors":"Ilma Ainur Rohma, Ade Azurat","doi":"10.21609/jiki.v17i2.1178","DOIUrl":"https://doi.org/10.21609/jiki.v17i2.1178","url":null,"abstract":"Model-Driven Software Engineering (MDSE) is a software development approach that uses the Model to be the main actor of the development. MDSE can be applied to User Interface (UI) Development so that a model for the UI can be built, and then a transformation can be made to turn it into a running application. In this research, we develop UI Generator to support UI Development with the MDSE approach. This UI Generator can also support UI Development in Software Product Line Engineering (SPLE) paradigm. The UI is modeled with Interaction Flow Modeling Language (IFML) diagram. Then The IFML diagram is transformed into React-Based UI by the UI Generator. The UI Generator is developed with Acceleo on Eclipse IDE to transform IFML into React Code with the transformation rules defined in this research. The UI generator is also enriched with display settings and static page management to address user customization needs. The experimental results show that the UI Generator can generate a functional website. Besides evaluating the working product, UI Generator is evaluated qualitatively well based on six quality criteria as an SPLE supporting tool.","PeriodicalId":31392,"journal":{"name":"Jurnal Ilmu Komputer dan Informasi","volume":"17 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273502","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}
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