1 10 18 https://repository.horizon.ac.id/files/original/e2bf4d364786b20c98ff625bfe7827ee.pdf 2ecd7bd3edf690d2b760aef6a62e24b5 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 6 (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 Reducing Training Time in Skin Cancer Classification Using Convolutional Neural Network with Mixed Precision Implementation Subject The topic of the resource Skin Cancer,MobileNetV3Large,Transfer Learning,Mixed Precision,Metric Evaluation Description An account of the resource In the field of skin cancer classification, machine learning and deep learning have been extensively utilized, particularly with convolutional neural network (CNN) architectures. However, there remains room for exploration to achieve optimal performance. This study investigates the use of the MobileNetV3Large architecture for transfer learning, chosen for its efficiency in low-power and memory-constrained applications. To further enhance performance, black-hat morphological transformation and oversampling techniques were applied to the ISIC 2020 dataset. Additionally, mixed precision training was implemented to reduce training time. The research aimed to compare the accuracy, precision, recall, F1-score, and training time of models trained with and without mixed precision. The findings revealed that while the model without mixed precision achieved superior performance with accuracy, precision, recall, and F1-score metrics reaching 98%, both models yielded an AUC-ROC of 1. Notably, mixed precision training significantly reduced training time by 1,646 seconds (27 minutes and 26 seconds), representing an 8.39% speed increase. These results suggest that mixed precision can meaningfully accelerate model training while maintaining competitive performance. The practical implications of this research include its potential to improve the efficiency of skin cancer classification models, making them more suitable for real-time clinical applications, particularly in resource-constrained environments Creator An entity primarily responsible for making the resource Raka Ryandra Guntara1*, Hendriyana2, Indira Syawanodya Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5996/1002 Publisher An entity responsible for making the resource available Rekayasa Perangkat Lunak, Universitas Pendidikan Indonesia, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 31-12-2024 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/b79dcab2ca37c44f70908e4152c27c0d.pdf c276936fee63fc28af09eae3c32c99f0 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 6 (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 Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification Subject The topic of the resource Disease Identification;Leaf Classification;K-Nearest Neighbors (KNN);Particle Swarm Optimization (PSO); ComputationalEfficiency Description An account of the resource Palm oil plantations in Indonesia face challenges in enhancing productivity and profitability, notably due to pest attacks that reduce production. Early identification and classification of plant conditions, particularly palm oil leaves, are crucial for mitigating losses. This study explores the application of artificial intelligence, specifically computer vision and machine learning, for disease detection. Various machine learning techniques, including Local Binary Pattern (LBP), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), have been used in different studies with varying accuracy. This research focuses on modifying Particle Swarm Optimization (PSO) for feature selection in identifying diseases in palm oil leaves. The PSO modification combined with logistic regression and Bayesian Information Criterion (BIC) significantly enhances KNN performance. Accuracy improved from 95.75% to 97.85%, while precision, recall, and F1-score reached approximately 98.80%. Additionally, the modified KNN+PSO achieved the shortest computation time of 0.0872 seconds, indicating high computational efficiency. These results demonstrate that the PSO modification not only improves accuracy but also computational efficiency, making it an effective method for enhancing KNN performance in detecting palm oil leaf diseases Creator An entity primarily responsible for making the resource Veri Julianto1*, Ahmad Rusadi Arrahimi2,Oky Rahmanto3,Mohammad Sofwat Aldi Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6049/1001 Publisher An entity responsible for making the resource available Departmentof Information Technology, Politeknik Negeri Tanah Laut, Pelaihari, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 29-12-2024 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/a54579d3b98f7e084aa276cc6b9626d8.pdf faa85bb1de891f1601c9721c8540e17c 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 6 (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 Knowledge Management Foundation and SolutionsImplementationin IndonesianGovernment HigherEducational Institution Subject The topic of the resource business intelligence; foundation;government highereducational institution; knowledge management;partial least squares-structural equation model; solutions Description An account of the resource The performance of XYZ, a Government Higher Educational Institution (GHEI) in Indonesia is assessed through two unintegrated applications. The 2023 targetperformancewas missed due to miscalculations outside applicationswhiletransforminglarge data amounts. Thus,business intelligence (BI) serves as a knowledge management (KM) toolto integratethoseapplicationsto achieveXYZ'starget. BecauseBI is costly and 70% failure rate of developmentplans, a research model was evaluated to look at currentXYZ innovation capabilityfor successful BI adoptionfrom the KM foundation and KM solutions implementation. Thisstudy useda quantitative method, employinga questionnaire for 94 civil servantsand the partial least squares-structural equation model (PLS-SEM) for data analysis. Results indicate in the KM foundation,organizational (O) negatively influences KM process application (KMP)(β = -0.292,Pv = 0.010)while KM infrastructure (I)and process (P)positively influence KMP,butKM technology (T)does not.In KM solutions,KMP is provenlinked to innovation capability whenKM systemsarelacking.Hence,severalactivitiesare suggestedto activateTthrough T, O, P, and I. The model validated80% of the hypotheses, laying the groundworkfor future studiesintowhich aspectsofT strengtheninnovation capabilities in GHEI Creator An entity primarily responsible for making the resource Boy Sandi Kristian Sihombing1, Fatoumatta Binta Jallow2, Ghina Fitriya3, Dana Indra Sensuse4, Sofian Lusa5, Damayanti Elisabeth6, Nadya Safitri Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6005/999 Publisher An entity responsible for making the resource available Teknologi Informasi, Fakultas Ilmu Komputer, UniversitasIndonesia, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 29-12-2024 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/76cf35b046f282713d6086a455b162bb.pdf db90e7d18dc001b317e9afcd2e6d208d 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 6 (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 Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine Subject The topic of the resource sentiment analysis;hotel; clustering; naïve bayes; support vector machine Description An account of the resource Visitor reviews play a crucial role in determining the success of a business, particularly those offering hospitality and services, such as hotels. The growth of internet technology has made it easier for guests to share their experiences, which can influence potential customers. Google Maps is one of the platforms used for giving and searching reviewsThis research usesdata crawled from Google Maps Review using the playwright library. However, the large volume of reviews can make analysis and topic-based categorization—such as service quality, hotel location, and operational hours—challenging. To address this, DBSCAN is used to cluster reviews based on these topics. Clustering helps improve sentiment classification, making it more targeted and allowing a comparison of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Naïve Bayes achieved higher accuracy(0.87) in the operational hours cluster, while SVM scored 0.78. However, SVM showed improved accuracy in the location (0.89) and service (0.88) clusters, with Naïve Bayes maintaining a stable 0.86 accuracy in both. Both models demonstrated an average training time of less than one second, excluding preprocessing. Creator An entity primarily responsible for making the resource Bayu Yanuargi1*, Ema Utami2, Kusrini3,Arli Aditya Parikesit4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6139/998 Publisher An entity responsible for making the resource available Department of Magister of Informatics Engineering, UniversitasAMIKOM Yogyakarta, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 29-12-2024 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/4bc7fbe6bdacd423866aa157463657cb.pdf 90697805c6aa50c595faf65364fc0cba 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 6 (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 Efficient Pattern Recognition of Sundanese ScriptVariantsUsing CNN Subject The topic of the resource CNN, MobileNetV2, Pattern Recognition, Sundanese Script Description An account of the resource This research aims to apply pattern recognition technology, specifically through the Convolutional Neural Network (CNN) approach, in identifying and translating Sundanese script accurately. This research is focused on recognizing rarangken script patterns based on ngalagena script in Indonesian cultural heritage. This study uses the MobileNetV2based CNN model, utilizing transfer learning and trained for 50 epochs using the Adam optimizer with a learning rate of 0.0001, to achieve a training accuracy of 98.75% and test accuracy of 96.95% in 1 hour and 23 minutes, respectively. The results of the study show that the simpler CNN architecture without augmentation achieved the highest accuracy of 99.26%, and the augmented CNN model achieved 94.42% accuracy in 2 hours and 22 minutes. These results enable practical applications in both education and cultural preservation, demonstrating how modern technology can effectively contribute to maintaining traditional cultural elements in the digital era Creator An entity primarily responsible for making the resource Muhammad Husni Wahid1*, Erik Iman Heri Ujianto Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6122/997 Publisher An entity responsible for making the resource available Informatics Study Program, Faculty of Science and Technology, Universitas Teknologi Yogyakarta, Sleman, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 28-12-2024 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/dd427b3c6b4184e60a137e66dcb13dc9.pdf c99e97cae369d61f154da125a97f3c39 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 6 (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 Utilization of the Convolutional Neural Network Method for Detecting Banana Leaf Disease Subject The topic of the resource agricultural sector;artificial Intelligence;Banana leaf disease; CNN Model; technology development Description An account of the resource anana leaf diseases such as Sigatoka, Cordana, and Pestalotiopsis pose a significant threat to banana productivity, with implications for food security and the global economy. Early detection of this disease is an important step to reduce its spread and maintain crop yield stability. This research utilizes the Convolutional Neural Network (CNN) method to detect banana leaf diseases based on image analysis of infected and healthy leaves. The dataset used includes 937 images consisting of four maincategories, namely healthy leaves, Sigatoka, Cordana, and Pestalotiopsis. The dataset is processed through augmentation to increase data diversity and quality. The CNN model was applied for classification, with evaluation results reaching 92.85% accuracy, 95.73% recall, 93.52% precision, and 94.60% F1-score. This research contributes to the development of Artificial Intelligence-based technology for applications in the agricultural sector, especially in supporting farmers to detect banana leaf diseases quickly, accurately and efficiently. The research results also provide recommendations for exploring additional data augmentation and increasing dataset variety to improve model detection performance in the future. This shows CNN's potential to supportsustainable agriculture in the modern era Creator An entity primarily responsible for making the resource Nita Helmawati1*, Ema Utami Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6140/996 Publisher An entity responsible for making the resource available Magister of Informatics, Universitas Amikom Yogyakarta, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 28-12-2024 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/0050af8bdb7789cb665a4555060db33b.pdf f073112ff67cbf972e9273205ea8066b 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 6 (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 Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms Subject The topic of the resource cluster purity; comparative study;earthquake clustering; K-Means Description An account of the resource Indonesia’s frequent earthquakes, caused by its position at the convergence of multiple tectonic plates,Indonesia's frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, necessitate precise seismic zone identification to improve disaster preparedness. This research evaluates the effectiveness of five clustering algorithms—K-Medoids, K-Means, DBSCAN, Fuzzy C-Means, and K-Affinity Propagation (K-AP)—for analyzing earthquake data from January 2017 to January 2023. Using a dataset from BMKG encompassing 13,860 seismic events, each algorithm was assessed based on Silhouette Score and Cluster Purity metrics. Results indicated that K-Means provided the best balance, forming six clusters with a Silhouette Score of 0.3245 and Cluster Purity of 0.7366, making it the most suitable for seismic zone analysis. K-Medoids closely followed with a Silhouette Score of 0.3158 and Cluster Purity of 0.7190. Although DBSCAN effectively handled noise, its negative Silhouette values indicated poor clustering quality. Fuzzy C-Means and K-AP underperformed, with K-AP generating animpractically high number of clusters (196) and the lowest Silhouette Score (0.2550). This study offers a novel, comprehensive comparison of clustering algorithms for Indonesian earthquake data, emphasizing a dual-metric evaluation approach. By identifying K-Means as the most effective algorithm, provides valuable insights for disaster mitigation and seismic risk analysis Creator An entity primarily responsible for making the resource Nurfidah Dwitiyanti1, Siti Ayu Kumala2, Shinta Dwi Handayani Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5514/993 Publisher An entity responsible for making the resource available Informatics Engineering, Fakultas Teknik dan Ilmu Komputer, Universitas Indraprasta PGRI, Jakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 27-12-2024 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/58b664c27acf7dc5441fc4cd345be85d.pdf cbafccef8fd67b6221112431b94c5e69 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 6 (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 Ingredients Identification Through Label Scanning Using PaddleOCR and ChatGPT for Information Retrieval Subject The topic of the resource food identification, optical character recognition, PaddleOCR, information retrieval, ChatGPT Description An account of the resource Human health depends on choosing food ingredients that align with dietary needs and avoid allergens. However, consumers often encounter unfamiliar ingredients that require additional information. Traditionally, they search online by typing in the ingredient's name which can be time-consuming and may not yield relevant results. Therefore, a system to identify and display ingredient information is necessary. This studyproposesa new system that identifies ingredients by scanning the composition label on packaging using PaddleOCR and retrieving information through ChatGPT on a smartphone. The process begins with capturing an image of the composition label.ThenPaddleOCR is employed to extract text from the scanned label, enabling identification of the listed ingredients. Subsequently, ChatGPT retrieves detailed information about the desired ingredients and displays it, allowing users to easily understand the ingredients. The system's effectiveness in text recognition is assessed using the character error rate (CER). The results show robustperformance by achieving an average CER of 0.14, with flat packaging reaching an impressive CER of 0.05. Additionally, the system's usability was assessed through pilot testing which received significant positive user feedbackachieving a 4.37 satisfaction level on the Likert scale, particularly regarding the clarity and relevance of the ingredient information provided Creator An entity primarily responsible for making the resource Ahmad Wahyu Rosyadi1*, Siti Ma’shumah2, Muhammad Qomaruz Zaman3, Moh.Rizki Fajar Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6119/992 Publisher An entity responsible for making the resource available Department of Informatics Engineering, Faculty of Engineering, Universitas Qomaruddin, Gresik, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 27-12-2024 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/25010dc52afdba70e40cbb45badbc31d.pdf fb0b6e3b8dd1728cd9c1b5152b1393e1 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 6 (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 Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification Subject The topic of the resource BERT models;education data; finetuning; multilabel classification;sequence length;student feedback Description An account of the resource Student feedback plays a crucial role in enhancing the quality of educational programs, yet analyzing this feedback, especially in informal contexts, remains challenging. In Indonesia, where student comments often include colloquial language and vary widely in content, effective multilabel classification is essential to accurately identify the aspects of courses being critiqued. Despite the development of several BERT-based models, the effectiveness of these models for classifying informal Indonesian text remains underexplored. Here we evaluate the performance of three BERT variants—IndoBERT, IndoBERTweet, and mBERT—on the task of multilabel classification of student feedback. Our experiments investigatethe impact of different sequence lengths and truncation strategies on model performance. We find that IndoBERTweet, with a macro F1-score of 0.8462, outperforms IndoBERT (0.8243) and mBERT (0.8230) when using a sequence length of 64 tokens and truncation at the end. These findings suggest that IndoBERTweet is well-suited for handling the informal, abbreviated text common in Indonesian student feedback, providing a robust tool for educational institutions aiming for actionable insights from studentcomments. Creator An entity primarily responsible for making the resource Fatma Indriani1*, Radityo Adi Nugroho2,Mohammad Reza Faisal3, Dwi Kartini Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6100/991 Publisher An entity responsible for making the resource available 1Department of Computer Science, Lambung Mangkurat University, Banjarmasin, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 27-12-2024 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/4b6f0a142cdbaaf97a1da5652d63c0b2.pdf cb7632d0bb07a683fdbd6eb98b00fcc7 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 6 (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 Lightweight Models for Real-Time Steganalysis: A Comparisonof MobileNet, ShuffleNet, and EfficientNet Subject The topic of the resource deep learning; lightweight; steganography;steganalysis; security Description An account of the resource In the digital age, the security of communication technologies is paramount, with cybercrime projected to reach $10.5 trillion annually by 2025. While encryption is vital, decrypted data remains vulnerable, prompting the exploration of steganography as an additional security layer. Steganography conceals data within digital media, but its misuse for cyberattacks—such as embedding malware—has highlighted the need for steganalysis, the detection of hidden data. Despite extensive research, few studies have explored lightweight deep-learningmodels for real-time steganalysis in resource-constrained environments like mobile devices. This research evaluates MobileNet, ShuffleNet, and EfficientNet for such tasks, using the BOSSbase-1.01 dataset. Models were assessed based on accuracy, computational efficiency, and resource usage. MobileNet achieved the highest computational speed but with only 63.8% accuracy, falling short of practical application. ShuffleNet and EfficientNetperformed at random-guessing levels with50% accuracy, reflecting the challenges of steganalysis on mobile platforms. Future work aims to improve accuracy by integrating advanced preprocessing techniques, attention mechanisms, and hybrid architectures, as well as leveraging ensemble methods for improved detection. Data augmentation, transfer learning, and hyperparameter tuning will also be explored to optimize model performance. This study contributes by identifying these challenges and offering insights for future research, focusing on optimizing models and preprocessing techniques to enhance detection accuracy in resource-constrained environments Creator An entity primarily responsible for making the resource Achmad Bauravindah1*, Dhomas Hatta Fudholi Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6091/990 Publisher An entity responsible for making the resource available Master Program inInformatics, Faculty of Industrial Technology, Islamic University of Indonesia, Yogyakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 26-12-2024 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