1 10 26 https://repository.horizon.ac.id/files/original/607f1f9d62c866d54e394dbdae8e73e7.pdf 6e4fea6b7b3973445817bcbf5741aaf2 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 6 (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 Analysis And Classification of Customer Churn Using Machine Learning Models Subject The topic of the resource data mining; machine learning models; imbalance data; SMOTE; confusion matrix Description An account of the resource Analysis studies of customer loss (customer churn) have been used for years to increase profitability and build customer relationships with companies. Customer analysis using exploratory data analysis (EDA) for visualizing data and the use of machine learning for the classification of customer churn are often used by past analysts. This study uses several machine learning models that can be used for customer churn classification, namely Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, AdaBoost, and Extreme Gradient Boosting (XGBoost). However, there is a class imbalance factor in the dataset, which is the biggest challenge that is usually faced by analysts to get good results in the classification of machine learning models. The Synthetic Minority Over-sampling Technique (SMOTE) method is a popular method applied to deal with class imbalances in datasets. The results of the analysis show that the classification of churn customers using the XGBoost algorithm has the best level of accuracy compared to other algorithms, with an accuracy value of 0.829424, and the oversampling method with SMOTE tends to reduce the accuracy value of each classification algorithm. The Permutation Feature Importance (PFI) technique from the XGBoost model gets the result that tenure, monthly contracts, and TV streaming are the features that affect customer churn the most. Creator An entity primarily responsible for making the resource Muhammad Maulana Sidiq, Dyah Anggraini 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 December 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 Confusion Matrix data mining imbalance data machine learning models SMOTE https://repository.horizon.ac.id/files/original/4ac6874068c807951883e7332c05f7ce.pdf aa4be149fc6c0b6dd38bffb73a6ee36b 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 6 (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 Forecasting Photovoltaic Output Power Based on Environmental Parameters Using Artificial Neural Network Methods Subject The topic of the resource artificial neural network; forecasting; output power; photovoltaic Description An account of the resource Photovoltaic is a system that can convert sunlight into electrical energy. However, photovoltaic efficiency tends to be low and its performance is affected by several environmental parameters such as dust, wind speed, humidity, temperature and other external factors. Because many factors can affect the power generated, we need a power output prediction system that can assist in planning and managing as well as increasing the efficiency of photovoltaic systems. In this research, a system is designed that can predict the photovoltaic output power in the short term using the Artificial Neural Network method or what is often called an artificial neural network. Predictions are made based on the effects of several environmental parameters such as wind speed, dust, humidity, and temperature on a 10 Wp photovoltaic system. Performance data for 7 days is used as a dataset and then processed using ANN with 1 input layer, 3 hidden layers 1 output layer and 3 sample epochs (10, 100, and 1000). The results of the study can predict the output of photovoltaic power for the next 4 days with an error value of Mean Square Error (MSE) of 0.0010, Mean Absolute Error (MAE) of 0.0155, Root Mean Square Error (RMSE) of 0.0229 with an increase in power reach 0.5 to 1 watt. Creator An entity primarily responsible for making the resource Desri Kristina Silalahi, Agnes Christy Margareth Rumapea, Wahmisari Priharti, Bandiyah Sri Aprillia 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 December 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 Artificial neural network Forecasting output power Photovoltaic https://repository.horizon.ac.id/files/original/3749e470dea486cfaa5545876dbde914.pdf a92487da3828e0f01ba68c50a23e2ef1 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 6 (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 The Examination of the User Engagement Scale (UES) in Small Medium Enterprise Social Media Usage: A Survey-Based Quantitative Study Subject The topic of the resource social-media; PCA; user engagement scale; user engagement; quantitative research; small medium enterprise Description An account of the resource Social media has proven to be an essential marketing tool for the success of any product, service, or business. User involvement affects the increase in revenue gain and creates long-term profit. The User Engagement Scale (UES) is one of the tools developed to measure user engagement and has been used in various digital domains. UES intends to compute six engagement dimensions: aesthetic appeal, perceived usability, focused attention, novelty, felt involvement, and endurability. This study investigates and verifies the three-factor structure of the UES. We used PCA to perform the analysis. The original data will be reanalyzed using UES, which consists of 220 valid responses. The result shows that the UES examination indicates good reliability in three factors: Factor 1 encompasses felt involvement (FI), aesthetic appeal (AE), novelty (NO), and endurability (EN). Factor 2 aggregates the perceived usability (PU) items. Factor 3 pertains to focused attention (FA) items. Our findings indicate that the User Engagement Scale is a valuable and suitable measurement tool for assessing user engagement in the context of social media within small and medium enterprises. Creator An entity primarily responsible for making the resource Rona Nisa Sofia Amriza, Khairun Nisa Meiah Ngafidin, Citra Wiguna 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 December 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 PCA quantitative research small medium enterprise social-media User Engagement user engagement scale https://repository.horizon.ac.id/files/original/74a531985e153444e8ca6793a6915e22.pdf 7c2fb5079b0427b132c25c347c44f030 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 6 (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 Predicting the Planting Time of Bird's Eye Chili Based on Environmental Conditions Using Internet of Things (IoT) and Neural Network Method Subject The topic of the resource Red Tabasco pepper, neural network, planting time, prediction Description An account of the resource In Indonesian cuisine, Red Tabasco pepper holds a significant place as a commonly used ingredient. However, the cultivation of this chili variety is not without its challenges, primarily due to the volatile nature of chili prices. Farmers often grapple with the critical decision of when to plant Tabasco pepper to optimize their yields and income. Understanding the complexities of this decision-making process in the context of varying environmental conditions is crucial. Thanks to recent advancements in Internet of Things (IoT) technology, innovative systems have emerged to address these challenges.This study delves into the development of an IoT-based solution aimed at assisting farmers in precisely determining the optimal planting time for Tabasco pepper. It leverages five key criteria—average temperature (°C), average humidity (%), rainfall (mm), length of sunlight (hours), and groundwater usage data (m3)—to make data-driven planting decisions. The pressing need for such a system becomes evident when considering the unpredictability of climate patterns and their direct impact on crop outcomes. Utilizing historical data from 2019, obtained from the DKI Jakarta Provincial Government Open Data, and climate data from the Meteorological Agency, Climatology, and Geophysics (BMKG), the authors have successfully developed an IoT-based prototype. This prototype employs a neural network algorithm to analyze the aforementioned criteria. The outcome is a reliable prediction system that boasts an impressive accuracy rate of 91.26%. By offering this level of precision in determining the ideal planting time for Tabasco pepper, the system extends invaluable support to farmers, helping them optimize their cultivation practices and navigate the uncertainties of the chili market. Creator An entity primarily responsible for making the resource Yan Mitha Djaksana, Agus Buono, Sri Wahjuni, Heru Sukoco 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 December 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 neural network planting time Prediction Red Tabasco pepper https://repository.horizon.ac.id/files/original/13ab22a332af0441ae125ab1b853f8b8.pdf 329ae6e7a9ccaa84badba0e58b4fa756 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 6 (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 Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm Subject The topic of the resource security system; convolutional neural network (CNN;, face recognition; biometric; VGG16 Description An account of the resource Currently, security systems use conventional security methods, which provide low levels of security. Therefore, some organizations now use biometric-based security systems, which include facial recognition-based systems. However, processing facial data requires computationally intensive feature extraction, making real-time implementation difficult. Additionally, most data used are from public datasets. In this study, we developed a facial recognition-based security system for door access using a convolutional neural network (CNN) for real-time face recognition. We used the primary data of 102 students. The datasets include two settings (i.e., outdoor and indoor) and three facial expressions (i.e., normal, smiley, and sleepy), amounting to 3060 samples. The training was performed using three deep-learning CNN architectures: Xception (model X), VGG16 (model Y), and modified VGG16 (model Z). The best accuracy results of the three training architectures of model X, model Y, and model Z for 100 epochs are 0.9469, 0.9971, and 1, respectively. In tests conducted on the 102 test data points, models X, Y, and Z achieved accuracies of 50%, 97.05%, and 97.05%, respectively. These results indicate that the modified VGG16 (model Z) is the best for real-time testing. In real-time tests conducted on the security system prototype with 15 respondents, the resulting accuracy of model Z is 86.6%. This demonstrates that the modified VGG16 model has excellent recognition capabilities and can be implemented as a room access security system. Creator An entity primarily responsible for making the resource Immanuel Morries Pohan, Suci Dwijayanti, Bhakti Yudho Suprapto, Hera Hikmarika, Hermawati 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 December 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 Biometric convolutional neural network (CNN) Face Recognition Security system VGG16 https://repository.horizon.ac.id/files/original/770407ccf013c42aeee0ead9e8d64d8e.pdf 831bcfe9013aca5ac41c78086169f26f 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 6 (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 MobileNetV3-based Handwritten Chinese Recognition Towards the Effectiveness of Learning Hanzi Subject The topic of the resource mandarin character writing; machine learning; digital learning tool; MobileNetV2; MobileNetV3; system usability scale Description An account of the resource Writing Mandarin characters is considered the most challenging component for beginners due to the rules and character formations. This paper explores the potential of a machine learning-based digital learning tool for writing Mandarin characters. It also conducts a comparative study between MobileNetV2 and MobileNetV3, exploring different configurations. The research follows the Multimedia Development Life Cycle (MDLC) method to create both the application and machine learning models. Participants from higher education institutions that offer Mandarin courses in Batam, Indonesia, were involved in a User Acceptance Test (UAT). Data was gathered through questionnaires and analyzed using the System Usability Scale (SUS) methods. The results show positive user acceptance, with an SUS score of 77.92%, indicating a high level of acceptability. MobileNetV3Small was also preferred for recognizing the user’s handwriting, due to comparable accuracy size, rapid inference time, and smallest model size. While the application was well-received, several participants provided constructive feedback, suggesting potential improvements Creator An entity primarily responsible for making the resource Suwarno, Tony Tan, Jonathan 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 December 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 digital learning tool machine learning mandarin character writing MobileNetV2 MobileNetV3 system usability scale https://repository.horizon.ac.id/files/original/10102ce6c3fd23676614b9b19f09ad02.pdf cedef94bff64ac1408854606162220c2 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 6 (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 Android Application for Tomato Leaf Disease Prediction Based on MobileNet Fine-tuning Subject The topic of the resource deep learning; computer vision; android application; tomato leaf disease Description An account of the resource Tomato is one of the most well-known and widely cultivated plants in the world. Tomato production result is affected by the conditions of the plants when they are cultivated. It may decrease due to leaf plant disease caused by climate change, pollinator decrease, microbial pets, or parasites. To prevent this, an image-based application is needed to identify tomato plant disease based on visually unique patterns or marks seen on leaves. In this paper, we proposed a CNN fine-tuned model that is based on MobileNet architectures to identify tomato leaf disease for mobile applications. Based on the results tested by K-fold cross�validation, the best accuracy achieved by the proposed model is 97.1%. In addition, the best average precision, recall, and F1 Score are 99.8%, 99.8%, and 99.5% respectively. The model with have best results is also implemented into Android-based mobile applications Creator An entity primarily responsible for making the resource Mutia Fadhilla, Des Suryani 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 December 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 android application Computer Vision deep learning tomato leaf disease https://repository.horizon.ac.id/files/original/d4f7153d3fe5c840adf4f43be256aaaf.pdf 7cafa90534518c39cfc8ccf10c117e63 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 6 (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 Balinese Script Handwriting Recognition Using Faster R-CNN Subject The topic of the resource balinese script; faster R-CNN; handwriting Description An account of the resource In Balinese culture, the ability to read Balinese script is one of the challenges young generations face. Advances in machine learning have proposed handwriting detection systems using both traditional and deep learning models. However, the traditional approach is usually impractical and is prone to inaccurate identification results. Convolutional Neural Network (CNN)-based models integrate feature extraction and classification into an end-to-end pipeline to increase performance. This research proposes that recognizing characters through an object detection approach makes an end-to-end process of localizing and classifying several characters simultaneously using the Faster R-CNN. Four CNN models, including ResNet-50, ResNet- 101, ResNet-152, and Inception ResNet V2 were tested to detect 28 Balinese characters in a single form covering 18 consonants and 10 digits using Intersection over Union (IoU) thresholds: 0.5 and 0.75. ResNet-50 and Inception ResNet V2 achieve 0.991 mAP at IoU of 0.5, while Inception ResNet V2 excels at IoU of 0.75. Further analysis showed that class “nol” had the lowest Recall due to many undetected ground truths. Meanwhile, class “ba” had the lowest Precision due to its similarity with classes “ga” and “nga”. This research contributes to experimenting with Faster R-CNN in detecting handwritten Balinese scripts. Creator An entity primarily responsible for making the resource Alif Adwitiya Pratama, Mahmud Dwi Sulistiyo, Aditya Firman Ihsan 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 December 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 balinese script Faster R-CNN handwriting https://repository.horizon.ac.id/files/original/757e856d6269fd156e466f664724c0b9.pdf e84a47f619070e463a80b60a17d11578 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 6 (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 Imputation Missing Value to Overcome Sparsity Problems in The Recommendation System Subject The topic of the resource missing value; stochastic hot-deck; imputation Description An account of the resource A recommendation system is a system that provides suggestions or recommendations for a product or service for its users. One of the problems encountered in the recommendation system is sparsity, namely the lack of available data for analysis, resulting in poor performance of the recommendation system because it cannot provide the proper recommendations. On this basis, this study proposes the mean method and the stochastic Hot-Deck Method to calculate missing values to improve the quality of the recommendations. The experimental results show that the hot-deck imputation method gives better results than the mean imputation method with smaller RMSE and MAE values, namely 2,706 and 2,691 Creator An entity primarily responsible for making the resource Sri Lestari, M. Elrico Afdila, Yan Aditiya Pratama 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 December 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 imputation missing value stochastic hot-deck https://repository.horizon.ac.id/files/original/1929dafa6071cb311b3395a2aa8d53f0.pdf 7cf7710f65dd5a72c61aaa4063d6fd6f 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 6 (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 Digital Image Encryption Using Logistic Map Subject The topic of the resource logistic map; digital image encryption; python Description An account of the resource This study focuses on the application of the Logistic Map algorithm in Python programming language for Digital Image Encryption and Decryption, the differentiating aspect of the present research in contrast to prior studies lies in its elucidation of the practical application of the Logistic Map within the Python programming language, as opposed to the antecedent investigations which primarily confined their discourse to the realm of scientific and mathematical abstraction. Not only that, but it also investigates the impact of image type, image size, and Logistic Map parameter values on computational speed, memory usage, Encryption and Decryption results. Three image sizes (300px x 300px, 500px x 500px, and 1024px x 1024px) in TIFF, JPG, and PNG formats are considered. The Digital Image Encryption and Decryption process utilizes the Logistic Map algorithm implemented in Python. Various parameter values are tested for each image type and size to analyze the Encryption and Decryption outcomes. This research has effectively implemented the logistic map algorithm, resulting in the discovery of several significant findings. The findings indicate that image type does not affect memory usage, which remains consistent regardless of image type. However, image type significantly influences Decryption results and computation time. Notably, the TIFF image type exhibits the fastest computation time, with durations of 0.17188 seconds, 0.28125 seconds, and 1.10938 seconds for 300px x 300px, 500px x 500px, and 1024px x 1024px images, respectively. Additionally, the Decryption results vary depending on the image type. The Logistic Map algorithm is unable to restore Encryption results accurately for JPG images. Furthermore, this research highlights those higher values of x, Mu, and Chaos result in narrower histogram values, resulting in better encryption results, as evidenced by experiments using x=0.102, Mu=3.9 and Chaos=6400. This study contributes to the field by exploring the application of the Logistic Map algorithm in Python and analyzing the effects of image type, image size, and Logistic Map parameter values on computation time, memory usage, and Digital Image Encryption and Decryption results. Creator An entity primarily responsible for making the resource Muhammad Rizki, Erik Iman Heri Ujianto, Rianto 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 December 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 digital image encryption logistic map Python