1 10 20 https://repository.horizon.ac.id/files/original/57b4479f535aac8dc536cc134950c1a0.pdf 0fefdcd3e57ce7daf9f8ef896c314540 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 8 NO 1 (2024) 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 Comparative Study of HTTP and MQTT for IoT Applications in Hydroponics Subject The topic of the resource hydroponics; IoT; web service; MQTT; HTTP Description An account of the resource Hydroponics is basedon nutrients in water. It mustbe regularlymonitoredto prevent plant defects. The Internet of Things has become a solution for remote hydroponic monitoring and is currently being tested on the Yuan HidroponikKelompok Wanita Tani (KWT).This system will send data every minute,and each data has a possibility of loss in transmission. There is a chance thatthis system will be implemented in other hydroponic organizations. As more devices are involved,it will affectserver resources. This research will compare Message Queue Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP) as popular protocols used in IoT. A test with increasing clients shows thatat 50 clients HTTP needs 87% CPU,while MQTT needs 22.63% CPU. A test with increasing payload shows thatat 10,000 payload HTTP needs 94% CPU while MQTT needs 28.35% CPU. A test with fixed clients and payloads shows thatHTTP has a CPU limit based on the clients involved. A transfer time test shows thatHTTP needs 177.344 seconds while MQTT needs 3.24 seconds. An acceptance rate is calculated by incrementing the count for every incoming payload. It shows thatHTTP can receive 30,000 payloads, unlike MQTT which can only receive 1680 payloads before losses Creator An entity primarily responsible for making the resource rvan Rizki Nugraha1, Widhy Hayuhardhika Nugraha Putra2,Eko Setiawan Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5561/899 Publisher An entity responsible for making the resource available Information System Department, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang, Indonesia3Informatics Engineering Department, Fakultas Ilmu Komputer, Universitas Brawijaya, Malang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 18-02-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/3a40d786af445f3245d2d0d0c53355ed.pdf 2fd0da996ee9acfeb461601ee1e00a21 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 8 NO 1 (2024) 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 Novel Framework for Information Security During the SDLC Implementation Stage: A Systematic Literature Review Subject The topic of the resource information security;implementation;system development life cycle (SDLC);secure software development life cycle (SSDLC Description An account of the resource This research delves into the critical aspects of information security during the implementationstage of the Software Development Life Cycle (SDLC). Usinga systematic review of the literature,the study synthesizes thefindings ofvarious digital repositories, including IEEE Xplore, ACM Digital Library, Scopus, and ScienceDirect, to outline a comprehensive framework that addressestheunique security challenges of the implementation stage.This research contributes to the field by proposing a novel assurance model for software development vendors, focusing on improvinginformation security measures during the implementation stage. The study's findings reveal 12 key steps organizationscan adopt to mitigate security risks and improveinformation security measures during this critical phase. These steps provide actionable insights and strategies designedto supportsecurity protocols effectively. The paper concludes that by incorporatingthese steps, organizationscan significantly improve their security posture, ensuring the integrity and reliability of the software development process, particularly during the implementation stage. This approach not only addressesimmediate security concerns but also sets a precedent for future research and practice in secure software development, particularly in the critical implementation stage of the SDLC Creator An entity primarily responsible for making the resource Mikael Octavinus Chan1, Setiadi Yazid Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5403/896 Publisher An entity responsible for making the resource available Information Technology, Computer Science, Universitas Indonesia, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 18-02-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/d19808990bc3ab4d17cb7b47400a3362.pdf ad4f495a4c72ec751e67a713c3410c98 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 8 NO 1 (2024) 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 Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms Subject The topic of the resource mushroom; deep learning; modified mobilenet; classification Description An account of the resource One food item that is easy to find in nature is the mushroom. In terms of form and features, mushrooms are similar. Arranging mushrooms into groups so that poisonousand non-poisonous ones can be told apart is important. Real-time analysis of mushrooms is still not used very often. Previous studies focused primarily on performance and accuracy, ignoring architectural computing and a significant amount of data preprocessing. The used dataset is more laboratory-conditioned. This will impede the process of widespread implementation. The study suggests changes to eight current architectures: Modified DenseNet201, DenseNet121, VGG16,VGG19, ResNet50, InceptionNetV3, MobileNet, and EfficientNet B1. The development of this architecture took place within the areas of classification and hyperparameter learning. In contrast to the other eight architectures, the MobileNet architecture exhibits the lowest computational performance and highest accuracy, according to the comparison results. By employing the confusion matrix for evaluation, an accuracy of 82.7% is achieved. Modified MobileNet has the best speed because it keeps a lower-computation architecture and cuts down on unnecessary pre-processing. This means that a lot of people can use smartphones with more realistic data conditions to make it work Creator An entity primarily responsible for making the resource Lia Farokhah1, Suastika Yulia Riska Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5498/902 Publisher An entity responsible for making the resource available Informatics, Technology and Design, Institut Teknologi dan Bisnis ASIA, Malang, Indonesi Date A point or period of time associated with an event in the lifecycle of the resource 19-02-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/94fba457f862583e7611c1aad6131b79.pdf e7ad120e81475bd84537d682fac116f6 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 8 NO 1 (2024) 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 Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method Subject The topic of the resource asinabu; convolutional neural network; identify bamboo; macroscopic images Description An account of the resource Bamboo is important to note that some species of bamboo are protected and considered endangered. However, distinguishing between traded and protected bamboo species or differentiating between bamboo species for various purposes is still a challenge. This requires specialized skills to identify the type of bamboo, and currently, the process can only be carried out in the forest for bamboo that is still in clump form by experienced researchers or officers. However, a study has been conducted to develop an easier and quicker method for identifying bamboo species. The study aims to create an automatic identification system for bamboo stems based on their anatomical structure (ASINABU). The bamboo identification algorithm was developed using macroscopic images of cross-sectioned bamboo stems, and the research method used was the Convolutional Neural Network (CNN). The CNN was designed to identify bamboo species with images taken using a cellphone camera equipped with a lens. The final product is an automatic identification application on Android, which can accurately detect bamboo species with an accuracy of 99.9%. Creator An entity primarily responsible for making the resource Dede Rustandi1, Sony Hartono Wijaya2, Mushthofa3, Ratih Damayanti Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5370/893 Publisher An entity responsible for making the resource available Computer Science Study Program, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource XX-02-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/0e599b01721e45df4d38b3cbb3caf094.pdf 61a9ad06b4b40dcecc7cc27fe4435f0e 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 8 NO 1 (2024) 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 Cattle Weight Estimation Using Linear Regression and Random Forest Regressor Subject The topic of the resource cattle; machine learning; linear regression; random forest regressor; prediction model Description An account of the resource The global cattle farming industry has benefits as a food source, livelihood, economic contribution, land environmental restoration, and energy source. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. The authors propose estimating cattle weighting linear regression and random forest regression. Linear regression can interpret the linear relationship between dependent and independent variables, and random forest regression can generalize the data well. The dataset used in this study consisted of ten variables: live body weight, withers height, sacrum height, chest depth, chest width, maclocks width, hip joint width, oblique body length, oblique back length, and chest circumference. To find out the model that produces the smallest MAE value. The results show that the linear regression algorithm can produce estimated weight values for cattle with the best performance. This model produces a mean absolute error (MAE) of 0.35 kg, a mean absolute percentage error (MAPE) of 0.07%, a root mean square error (RMSE) of 0.5 kg, and an R² of 0.99. Each variable has excellent correlation performance results and contributes to computer vision and machine learning Creator An entity primarily responsible for making the resource Anjar Setiawan1, Ema Utami2, Dhani Ariatmanto3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5494/894 Publisher An entity responsible for making the resource available Magister of Informatics Engineering, Universitas AMIKOM Yogyakarta, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 10-02-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/e132fa84b9f3fa52a88230a16d11d393.pdf 002a37f7e6aac46d1773315c5dc31e5e 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 8 NO 1 (2024) 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 Comparison of Machine Learning Algorithms in Detecting Tea LeafDiseases Subject The topic of the resource comparison;machine learning;disease detection;tea leaves Description An account of the resource Tea is one of the top ten most exported products sent from Indonesia toforeign countries. However, in recent years, the amount of tea leaf exports from Indonesia has decreased, even though the export value impacts the country’s economic structure. Besides market competition, Indonesia needs to maintain tea leaf production sothat the spike in export decline is not significant or even increases the export production of tea leaves. To improve the quality of production and reduce production costs, early detection of tea leaf diseases is necessary. This study aims to classify tealeaf images for early detection of tea leaf disease so that appropriate treatment can be carried out early on. This study compares Machine Learning algorithms to determine the best algorithm for detecting tea leaf diseases. The algorithms tested as performance comparisons in classifying the tea leaf diseases are Random Forest (RF), Support Vector Classifier (SVC), Extra Tree Classifier (ETC), Decision Tree (DT), XGBoost Classifier (XGB) and Convolutional Neural algorithms. Network (CNN). As a result, the average accuracy performance generated by ETC produces a higher value than other algorithms, i.e.,getting an average accuracy performance of 77.47%. Another algorithm, i.e.,SVC, has an average accuracy of 76.57%, RF of 76.12%, DT of 65.31%, XGB of 71.62%,and the lowest is CNN of 59.08%. ETC is proven to be the most superior Machine Learning algorithm for detecting tea leaf diseases in this study. Creator An entity primarily responsible for making the resource Candra Nur Ihsan1, Nova Agustina2,Muchammad Naseer3, Harya Gusdevi4,Jack Febrian Rusdi5, Ari Hadhiwibowo6, Fahmi Abdullah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5587/901 Publisher An entity responsible for making the resource available Department of Informatics, Sekolah Tinggi Teknologi Bandung,Bandung,Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 18-02-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/3be91f8a4590cb011028a7d270455310.pdf 4f030abb994ab144ceaa7ca0b62d2c53 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 8 NO 1 (2024) 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 Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions Subject The topic of the resource Image Processing, Indonesian Batik Motifs, Convolutional Neural Networks Description An account of the resource Although Indonesian batik patterns vary by location, they usually depict local customs and cultures. Each batik has a unique quality and,to correctlyidentify the batik designs, you need to understand the design patterns. However,many people struggle to identify and categorizethese kinds of motivationbecause they don't have the requisite knowledge, understanding, or access to sufficient information. This study used photo data to classify batikpatterns into 15different groups. Batik Kawung, Megamendung, Lasem, Pole, Machete, Gills, Nutmeg, Karaswasih, Cendrawasih, Geblek Renteng, Bali, Betawi, and Dayak are all included in this category. 1,350 images were used in the research. Google supports the collection of data. Toprovide the highest level ofprecisionand to evaluate how image dimensions affect the classification of batik designs, this study employs convolutional neural networks(CNNs). The results of this study show that Multi-Layer Perceptron (MLP) is a well-liked deep learning method for data classification, especially in domains where picture classification is involved. The size of the images utilizedaffects the accuracy ofcomputational neural network(CNN)algorithms. The resultsshowed that the testusing training datacomparisons of60%, 30%and 10%resulted in a 01.89% loss of 1.18%and a 100% improvement in accuracy Creator An entity primarily responsible for making the resource Siti Aisyah1, Rini Astuti2, Fadhil M Basysyar3 Odi Nurdiawan4, Irfan AlI Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5623/910 Publisher An entity responsible for making the resource available Teknik Informatika, STMIKIKMI Cirebon, Cirebon, Indonesia2Sistem Informasi STMIK LIKMI Bandung, Bandung, Indonesia3Sistem Informasi STMIK IKMI Cirebon, Cirebon, Indonesia4Manajemen Informatika STMIK IKMI Cirebon, Cirebon, Indonesia5Rekayasa Perangkat Lunak STMIK IKMI Cirebon, Cirebon, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 22-02-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/ca83b248757d68e66b290b1302ab0730.pdf 10df4c7a676ff96d4bab2cf734ed216a 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 8 NO 1 (2024) 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 Data Mining Techniques for Predictive Classification of Anemia DiseaseSubtypes Subject The topic of the resource anemia; data mining; J48 decision tree; naïve bayes; random forest Description An account of the resource Anemia, characterizedby insufficient red blood cells or reduced hemoglobin,hinders oxygen transport in the body. Understanding thevarioustypes of anemiais vital to tailor effective prevention and treatment. This research explores data mining's role in predicting and classifying anemia types, emphasizingComplete Blood Count (CBC) and demographic data. Data mining is key to building models that aidhealthcare professionals in thediagnosis and treatment of anemia.Employing the Cross-Industry Standard Process for Data Mining (CRISP-DM), with its six phases, facilitates this endeavour. Our study compared Naïve Bayes, J48 Decision Tree, and Random Forest algorithms using RapidMiner's tools, evaluating accuracy, mean recall, and mean precision. The J48Decision Tree outperformed the others, highlighting the importance ofalgorithm choicein anemia classification models. Furthermore,our analysis identified renal disease-related and chronic anemia as the most prevalent types, with ahigher incidenceamong women.Recognizinggender disparities in the prevalence ofanemiainforms personalizedhealthcare decisions. Understanding demographic factors in specific types ofanemiais crucial for effective care strategies. Creator An entity primarily responsible for making the resource Johan Setiawan1, Dita Amalia2, Iwan Prasetiawan3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5445/887 Publisher An entity responsible for making the resource available Department of Information Systems, Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Tangerang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 15-01-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/bfd20d7bf4fbb45618c5e3c077aa7ac7.pdf fc31eb56ea052ef47bfdcbee88a10df1 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 8 NO 1 (2024) 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 Development of an Early Warning System Using Social Media for Flood Disaster Subject The topic of the resource lood monitoring; water level measurement; thing speakAPI; user-friendly environment; field testing Description An account of the resource his research paper introduces an innovative prototype system that utilizes IoT technologies for monitoring floodwater levels. The integration of an ultrasonic sensor, ESP8266 microcontroller, Arduino IDE, and the ThingSpeak platform aims to establish a robust flood monitoring solution. The paper provides a thorough exploration of the system's background, the problem it addresses, the methodology employed, and the obtained results, along with insights into future research directions. The studymeticulously outlines the design, implementation, and programming code for data collection and transmission within the system. Through extensive field testing and meticulous data analysis, the paper evaluates the accuracy and effectiveness of the proposed flood monitoring solution. Notably, the research underscores the advantages of IoT, emphasizing real-time data collection, logging, and analysis as essential components for efficient flood management. In addition, the paper elucidates step-by-step instructions for configuring Telegram notifications through the ThingSpeak React app, enhancing the practical applicability of the developed system. The research effectively highlights the potential of IoT in flood monitoring, showcasing its superior accuracy and effectiveness compared to traditional methods. By demonstrating the feasibility and advantages of IoT in the context of flood monitoring, this study contributes valuable insights, enriching existing knowledge and paving theway for future advancements in the field. The research encourages continued exploration of advanced techniques to strengthen flood monitoring and management strategies. Ultimately, this work presents a comprehensive IoT-based prototype for floodwater monitoring, offering invaluable insights and fostering the promising role of IoT technologies in this critical domain Creator An entity primarily responsible for making the resource IKetut Kasta Arya Wijaya1,Ruben Cornelius Siagian2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5087/909 Publisher An entity responsible for making the resource available Universitas Warmadewa, Denpasar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 22-02-2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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 https://repository.horizon.ac.id/files/original/881f37e5b23381f2a64b3fdeddd0e137.pdf 5a5717e66569768bbe79db8104fa3663 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 8 NO 1 (2024) 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 Image Preprocessing Approaches Toward Better Learning Performance with CNN Subject The topic of the resource convolutional network; deep learning; face recognition; advanced preprocessing; classification Description An account of the resource Convolutional neural networks(CNNs) are at the forefront of computer vision, relying heavily on the quality of input data determined by the preprocessing method. An excessivepreprocessing approach will result in poor learning performance. This study critically examines the impact of advanced image preprocessing techniques on convolutional neural networks(CNNs) in facial recognition. Emphasizing the importance of data quality, we explore various preprocessing approaches, including noise reduction, histogram equalization, and image hashing. Our methodology involves feature visualization to improvefacial feature discernment, training convergence analysis, and real-time model testing. The results demonstrate significant improvements in model performance with the preprocessed data set:average precision,recall, precision, and F1 score enhancements of 4.17%, 3.45%, 3.45%, and 3.81%, respectively. Furthermore,real-time testing shows a 21% performance increase and a 1.41% reduction in computing time. This study not only underscores the effectiveness of preprocessing in boosting CNN capabilities,but also opens avenues for future research in applying these methods to diverse image types and exploring various CNN architectures for a completeunderstanding Creator An entity primarily responsible for making the resource Dhimas Tribuana1,Hazriani2*, Abdul Latief Arda3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5417/886 Publisher An entity responsible for making the resource available Departementof Computer System,Handayani University Makassar, Makassar, Indonesia1MNC Bank, Cabang Makassar, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 13-01-2024 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W 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