3 10 31 https://repository.horizon.ac.id/files/original/2b10ee0d532b7d6e0fd37ab45df8a8af.pdf 1d5dcdd481064260118ce96151ae8c9d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Employee Education and Training Recommendations using the Apriori Algorithm Subject The topic of the resource training; competency; data mining; association rule; apriori algorithm Description An account of the resource The Ministry of Finance (MoF) aims to enhance employee performance through suitable education and training opportunities. Based on the data of education and training implementation in 2022 at the Central ICT Department in MoF, only 27.35% of employees participated in education and training according to proposed needs for both positions and individuals. This is partly due to mandatory training that must be attended by some or all employees, urgent needs in the current year, or the substitute participants that is not from the same team or function. To address this issue, the association method of data mining techniques can be utilized to analyze historical data of employees. The study used the apriori algorithm to analyze historical data of employee positions, organizations, and education and training from 2011 to 2021. This research involved comparing various minimum support values, assuming that employees attended at least 2, 3, and 4 training courses, to calculate the corresponding minimum support values. The evaluation results of the model show that the best rules are generated with a minimum support value of 0.013 and a minimum confidence value of 0.6, which is a total of 10 rules. One of the training recommendations is that if an employee has taken the Enterprise Service Bus (ESB)-API Management training, they will take the ESB API Integration Platform training. Furthermore, it can be used by the Human Resource Unit to provide education and training aligned with organizational needs and improve employee competency in line with their duties and functions, leading to better overall organizational performance. Creator An entity primarily responsible for making the resource Arief Wibowo, Vasthu Imaniar Ivanoti, Megananda Hervita Permata Sari Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Apriori algorithm association rule Competency data mining training https://repository.horizon.ac.id/files/original/0f00e3655c6b781b0bad9477568fcddb.pdf 81a0497e47bc62bd52f39af9553d7840 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Network Attacks Classification for Network Forensics Investigation: Literature Reviews Subject The topic of the resource network attacks; classification; machine learning; investigation Description An account of the resource Every branch of technology must constantly be on guard and anticipate the possibility of numerous cybercrimes due to the ongoing cyber-attacks. Crimes committed in this era of digitalization will undoubtedly have a negative impact on individuals or groups. In order to allow any cybercriminal to operate freely without worrying about getting caught, mitigation after a cyber-attack is often considered a trivial problem. Digital forensics not only plays an important role in the digitization cycle but is also critical to the digital industry's ability to respond to events as they occur. The standard methods used to support the pace of progress in digital forensics are very time-consuming and ineffective given the frequency of cybercrime. It is expected that collaboration between technology disciplines, such as the application of machine learning to digital forensics, will improve the efficiency of the forensic analysis and investigation process. These recommendations propose the application of machine learning techniques for automated attack classification using network logs. Specifically, machine learning algorithms would be trained to detect DDoS, SQL Injection, and XSS attacks based on the traffic logs on the router. The chosen method for this classification task is Support Vector Machine (SVM), which has been extensively employed in data-driven classification tasks according to previous research. By leveraging machine learning, the goal is to streamline the investigation of computer network attacks, making it faster and more efficient Creator An entity primarily responsible for making the resource Muhamad Maulana, Ahmad Luthfi, Dwi Kurnia Wibowo Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text classification investigation machine learning network attacks https://repository.horizon.ac.id/files/original/24ba77588a59657068ca347daa8ea132.pdf 5e06b320cd7b27fdf2f1dbf58d522eb7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Personality Detection on Reddit Using DistilBERT Subject The topic of the resource personality detection; reddit; distilBERT Description An account of the resource Personality is a unique set of motivations, feelings, and behaviors humans possess. Personality detection on social media is a research topic commonly conducted in computer science. Personality models often used for personality detection research are the Big Five Indicator (BFI) and Myers-Briggs Type Indicator (MBTI) models. Unlike the BFI, which classifies personalities based on an individual’s traits, the MBTI model classifies personalities based on the type of the individual. So, MBTI performs better in several scenarios than the Big Five model. Many studies use machine learning to detect personality on social media, such as Logistic Regression, Naïve Bayes, and Support Vector Machine. With the recent popularity of Deep Learning, we can use language models such as DistilBERT to classify personality on social media. Because of DistilBERT’s ability to process large sentences and the ability for parallelization thanks to the transformer architecture. Therefore, the proposed research will detect MBTI personality on Reddit using DistilBERT. The evaluation shows that removing stopwords on the data preprocessing stage can reduce the model’s performance, and with class imbalance handling, DistilBERT performs worse than without class imbalance handling. Also, as a comparison, DistilBERT outperforms other machine learning classifiers such as Naïve Bayes, SVM, and Logistic Regression in accuracy, precision, recall, and f1-score. Creator An entity primarily responsible for making the resource Alif Rahmat Julianda, Warih Maharani Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text DistilBERT personality detection reddit https://repository.horizon.ac.id/files/original/43f6116c538335f5059043f7f03de69a.pdf 8b8b62dd88f42fc6121bfc1a5932bbf2 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Comprehensive Bibliometric Analysis of Deep Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023) Subject The topic of the resource breast cancer; deep learning; image segmentation; bibliometric; WOS Description An account of the resource The objective of this study is to perform a comprehensive bibliometric analysis of the existing literature on breast cancer segmentation using deep learning techniques. Data for this analysis were obtained from the Web of Science Core Collection (WOS-CC) that spans from 2019 to 2023. The study is based on a comprehensive collection of 985 documents that cover a substantial body of research findings related to the application of deep learning techniques in segmenting breast cancer images. The analysis reveals an annual increase in the number of published works at a rate of 16.69%, indicating a consistent and robust increase in research efforts during the specified time frame. Examining the occurrence of keywords from 2019 to 2023, it is evident that the term "convolutional neural network" exhibited a notable frequency, reaching its peak in 2021. However, the term "machine learning" demonstrated the highest overall frequency, peaking around 2021 as well. This emphasizes the importance of machine learning in the advancement of image segmentation algorithms and convolutional neural networks, which have shown exceptional effectiveness in image analysis tasks. Furthermore, the utilization of latent Dirichlet Allocation (LDA) to identify topics resulted in a relatively uniform distribution, with each topic having an equivalent number of abstracts. This indicates that the data set encompasses a diverse range of topics within the field of deep learning as it relates to breast cancer image segmentation. However, it should be noted that topic 4 has the highest level of significance, suggesting that the application of deep learning for diagnosis was extensively explored in this study Creator An entity primarily responsible for making the resource Agus Perdana Windarto, Anjar Wanto, Solikhun, Ronal Watrianthos Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Bibliometric breast cancer deep learning Image segmentation WOS https://repository.horizon.ac.id/files/original/87d952811d405dfe487d7cd144b8ae5c.pdf 8d71f062b0b0b4f6d5c891a31573460a Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Service Automation Implementation for Delivering CaaS at the Ministry of Finance of Indonesia Subject The topic of the resource cloud service automation; cloud service implementation; container as a service; CaaS; finite state automata Description An account of the resource Evaluation is an essential aspect of service improvement. Within the Ministry of Finance, there is an organization responsible for providing IT services to various units and employees. One of the services offered by this organization is cloud computing, which supports the development of information systems. However, there are several challenges related to service fulfillment. For instance, the time required to fulfill a service is relatively long, taking two days, in contrast to public cloud providers who can deliver their services within minutes. Additionally, there is a potential for human errors in the manual process carried out by the Request Fulfillment Team (RFT) during service delivery. This study aims to explore the design and implementation of automated service fulfillment for containers, transforming them into self-service products. The author employs the Finite State Automata (FSA) model to test the input and output of the automation system using seven states and inputs. The results indicate that the service cycle for containers when compiled and tested with FSA using predetermined inputs, can generate containers according to user-selected specifications. As a result, the implementation of an automated and self-service model is proposed to reduce delivery time and mitigate potential errors in the Container-as-a-Service (CaaS) offering Creator An entity primarily responsible for making the resource Achmad Farid Rusdi, Teguh Raharjo, Bob Hardian, Tiarma Simanungkalit Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text CaaS cloud service automation cloud service implementation container as a service finite state automata https://repository.horizon.ac.id/files/original/c5bfdad063c8b942bd0a3c112bdb1e55.pdf 8bdc5c0a02010bd8ef0b9f71e0a041cf Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Hyperparameter Optimization of CNN Classifier for Music Genre Classification Subject The topic of the resource music genre classification; deep learning; GTZAN dataset Description An account of the resource Playing music through a digital platform that has a large database of songs requires automated classification of music genres, highlighting the need to develop a model for music genre classification that is more efficient and accurate. This study evaluated the hyperparameters in the music genre classification process using the CNN on the GTZAN dataset with 30-second duration data optimized using the MFCC feature extraction. The model that is formed with a time of 3 (three) seconds classifies music genres in the first 3 seconds of music. This model has a high potential for error because the first 3 seconds of initial music is varied and cannot be used as a benchmark in determining music genres. This study performed hyperparameters on batch size, epoch, and split dataset variables with various scenarios. The highest accuracy result was obtained at 72% with a data split of 85%:15%, 32 batch size,s and 500 epochs. Creator An entity primarily responsible for making the resource Rendra Soerkarta, Suhardi Aras, Ahmad Nur Aswad Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text deep learning GTZAN dataset music genre classification https://repository.horizon.ac.id/files/original/c8f4ebb5a12283b5282e450be357c594.pdf 574f38fdef1e264f2bbaa0cd74d31ea9 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Robust Digital Watermarking on Vital Archives using Hybrid SVD and DWT Methods Subject The topic of the resource vital archives; DWT; robustness; SVD; watermarking Description An account of the resource The development of internet technology affects the dissemination of data, especially in vital government archives. This research uses a hybrid Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) method, which aims to protect the copyright of vital archives. The stages of the insertion and extraction process are carried out to test the effect of the alpha value on the quality (imperceptibility) and robustness of the inserted image by measuring the Peak Signal to Noise Ratio (PSNR), verifying similarity by measuring the Normalized Cross-Correlation (NC) and Structural Similarity Index (SSIM). The results of research with ten vital archives and a protection watermark logo in JPEG format with a size of 512x512 pixels obtained a maximum PSNR with a value of α = 0.01 of 41.0567 dB, NC of 0.98904, and SSIM of 0.98023 on the Cibereum Land Certificate. So it can be proven that this method produces vital archive watermarks that can be extracted and are robust to JPEG compression attacks of 75%, median filtering 3x3, Gaussian noise 0.01, speckle noise 0.01, and salt and pepper noise 0.01 but not resistant to rotation 80˚ and cropping attacks 2 %. Creator An entity primarily responsible for making the resource Alita Wulan Dini, Shelvie Nidya Neyman, Toto Haryanto Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text DWT robustness SVD vital archives watermarking https://repository.horizon.ac.id/files/original/9c3708a332e34300a6d3025e8572f363.pdf 6ef0adecf0775e5aab1fcc32202330a0 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Designing A WSNs-based Smart Home Monitoring System through Deep Reinforcement Learning Subject The topic of the resource smart home monitoring system; wireless sensor networks; deep reinforcement learning; deep learning; internet of things (IoT) Description An account of the resource Smart home system technology has developed rapidly and provides convenience for human life. Several smart home technologies, especially monitoring systems, have been developed by integrating several aspects, including security systems, fuzzy methods, and energy-saving methods. However, the issue is how to build a smart home system that is accurate, convenient, and low-cost. In this research, the development of a smart home monitoring system that integrates wireless sensor networks (WSNs) and deep reinforcement learning (DRL) is carried out based on three parameters, i.e., temperature, humidity, and CO2 level. The experimental method is carried out by (1) validating the accuracy quality of WSNs; (2) determining the best model implemented in the system; and (3) measuring the quality of the DRL system on the smart home monitoring system. Based on the test results, several indicators were obtained: (1) Testing the WSNs resulted in an accuracy of 98.52%; (2) the accuracy of the modeling results implemented in the system is 97.70%; and (3) DRL system test on the smart home monitoring system through 21 test scenarios resulted in an accuracy of 95.52%. The indicators of testing this smart monitoring system prove that the developed system provides the advantages of accuracy, ease of use, and low cost Creator An entity primarily responsible for making the resource Ahmad Taqwa, Indra Griha Tofik Isa, Indri Ariyanti Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text deep learning deep reinforcement learning internet of things (IoT) smart home monitoring system wireless sensor networks https://repository.horizon.ac.id/files/original/e4c81385a15315d689ff65060f4cda58.pdf e3b3b4b547d5d341b5d7853a17b3a1de Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Process Mining on New Student Admission Process in Telkom University using Genetic Miner Subject The topic of the resource new student admission; telkom university; process mining; genetic process mining Description An account of the resource The selection process for new students at Telkom University, also known as SMB Telkom University has been running for years and already has its process flow. However, the existing process flow can be further improved to better reflect the actual field processes and become more accurate. Process mining can enhance this process flow by creating a new process flow based on event logs or previously executed processes. One of the algorithms in process mining is genetic process mining, where process mining is performed multiple times over several generations and genetic algorithms such as crossover and mutation are applied to generate a more accurate process model compared to other process mining algorithms such as heuristic and inductive mining. After conducting experiments, the best process model that was produced was at the 100th generation which has a fitness point of 0.755910819 and precision point of 0.742857143, after examining the parameters and the resulting Petri net or process flow that was produced it was concluded that the process model obtained from the application of Genetic Process Mining to SMB Telkom University is not very good because the resulting Petri net has several duplicate activities and appears to be non�linear. This could be due to several factors i.e., incompatible, or inaccurate data Creator An entity primarily responsible for making the resource Supra Yogi, Angelina Prima Kurniati, Ichwanul Muslim Karo Karo Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text genetic process mining new student admission Process mining telkom university https://repository.horizon.ac.id/files/original/01a16cf37477e36ac6ee4f47ecd94361.pdf 0d183fe992c06377482b4fac3b941c42 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL 7 NO 5 (2023) Contributor An entity responsible for making contributions to the resource Sri Wahyuni Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource MSER-Vertical Sobel for Vehicle Logo Detection Subject The topic of the resource car logo detection; maximally stable extremal region; vertical sobel Description An account of the resource Detecting a vehicle logo is the first step before the actual recognition of the logo. However, detecting logos can pose difficulties due to various factors, including logo variations, differing scales and orientations, background interference, varying lighting conditions, and partial obstruction. This paper presents a vehicle logo detection method using handcrafted features. We used a combination of Maximally Stable Extremal Region (MSER) and Vertical Sobel. We combine vertical Sobel with MSER to overcome MSER's limitation in recognizing objects of different sizes. These two features are merged using closing morphology operation to form blobs selected as logo candidate areas. Moreover, a Support Vector Machine (SVM) is implemented to choose a logo area by analyzing each candidate's Histogram of Oriented Gradient (HOG). The proposed method was compared to other methods by implementing them on the same dataset. The significant advantage of using MSER-Vertical Sobel is its fast computation time. It is faster than other approaches that use non-handcrafted features. The test results show that the MSER�Vertical Sobel can achieve high accuracy and the fastest computation time. Creator An entity primarily responsible for making the resource Gamma Kosala, Agus Harjoko, Sri Hartati Source A related resource from which the described resource is derived http://jurnal.iaii.or.id Publisher An entity responsible for making the resource available Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association Date A point or period of time associated with an event in the lifecycle of the resource October 2023 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource ISSN Media Electronic: 2580-0760 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text car logo detection maximally stable extremal region vertical sobel