1 10 10 https://repository.horizon.ac.id/files/original/f39f54f52e690485109a0234d0a2c5e7.pdf 747701fc8c3b4b4d7dabf97abb8f9ad0 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. 18 NO. 1 (2025) 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 Conceptual Design e-Wallet for Rupiah Digital Subject The topic of the resource CBDC; rupiah digital; e-wallet; blockchain; private blockchain Description An account of the resource This research study the advancement of Central Bank Digital Currencies (CBDCs) spurred by fi-nancial technology progress. It focuses on Rupiah Digital, Indonesia's CBDC initiative led by the Bank of Indonesia (BI). The study explores the technical aspects of Wholesale and Retail Digital Rupiah, proposes an e-wallet system for seamless digital transactions in related to blockchain technology, specifically Permissioned Distributed Ledger Technology (DLT). The objective of this research to provide recommendations to BI regarding appropriate e-wallet conceptual design based on study literature review (LR) methods and qualitative research method by conducting interviews throughs forum group discussion (FGD) and e-mail with leading economic (banks), legal (BI and Government), and technical experts (banks, academic expert on this field, BI and Government) to get reviews and input regarding the e-wallet conceptual design that was proposed. As result, we recommended the architecture for Rupiah Digital using Hyperledger Fabric blockchain with two-tiered distribution and user layer backed by digital token using ID on mobile apps to enhance the security of the system. The FGD with experts and executor result in approval on those conceptual design to be part of the option on development of CBDC in Indonesia. Creator An entity primarily responsible for making the resource Mohamad Faisal Zulmy, Monica Vivi Kurniawati, and Setiadi Yazid Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v18i1.1309 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2025-02-08 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Blockchain CBDC E-wallet private blockchain rupiah digital https://repository.horizon.ac.id/files/original/4fb4346df5a5d21a8bcefa4ed1779387.pdf 57ad1b0f0408ac983f613989978c879a 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. 18 NO. 1 (2025) 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 E-Government Between Developed and Developing Countries: Key Perspectives from Denmark and Iraq Subject The topic of the resource Developing Countries;Developed Countries;E-Government;challenges;implementation; Iraq; Denmark Description An account of the resource E-government involves using technology to provide public information and services digitally. This study examines key factors addressing infrastructure, cultural, political, technical, and social challenges in e-government implementation. By exploring diverse contexts, from citizen engagement to data frameworks, it will elucidate best practices and lessons for overcoming hurdles on the bureaucratic and user sides. The research aims to uncover how states can successfully transition services online. Insights can inform policymakers seeking to digitize governance and leverage information and communication technologies to improve state-citizen relations. Additionally, it aims to compare and analyze e�government systems in a developing country (Iraq) and a developed country (Denmark) to highlight key differences that could inform e-government development efforts in developing nations. And in resreach the PRISMA approch was adopoted to help present the findings of a systematic review. Iraq and Denmark were chosen due to the disparity between their e-government systems, enabling the identification of weaknesses in Iraq's e-government initiatives and providing insights from Denmark's more advanced experience. This study has identified several common obstacles encountered across different contexts in the implementation of e-government systems, irrespective of whether the country is developed or developing. Examining this e-government gap between a developing and developed country will allow developing nations like Iraq to pinpoint areas for improvement and potentially benefit from Denmark's success in this area. Creator An entity primarily responsible for making the resource Ahmed Totonchi, Abd Rahman Ahlan, and Hazwani Bt Mohd Mohadis Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v18i1.1316 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2025-02-08 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Challenges Denmark Developed countries developing countries e-Government implementation Iraq https://repository.horizon.ac.id/files/original/9dd70adcddaf53ef8c310b6cd15f343c.pdf cecf9dc00eed86518cd11197cf86f7d4 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. 18 NO. 1 (2025) 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 Efficient Design and Compression of CNN Models for Rapid Character Recognition Subject The topic of the resource Lightweight CNN; model optimization; efficient deep learning; character recognition Description An account of the resource Convolutional Neural Networks (CNNs) are extensively utilized for image processing and recognition tasks; however, they often encounter challenges related to large model sizes and prolonged training times. These limitations present difficulties in resource-constrained environments that require rapid model deployment and efficient computation. This study introduces a systematic approach to designing lightweight CNN models specifically for character recognition, emphasizing the reduction of model complexity, training duration, and computational costs without sacrificing performance. Creator An entity primarily responsible for making the resource Onesinus Saut Parulian Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v18i1.1443 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2025-02-08 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Character recognition efficient deep learning Lightweight CNN model optimization https://repository.horizon.ac.id/files/original/e770e5c663a63b69a5f6d824edaec8de.pdf a808cf8f47e049625254d4083b908891 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. 18 NO. 1 (2025) 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 Estimating Passenger Density in Trains through Crowd Counting Modeling Subject The topic of the resource Commuter Line, Density Estimation, Crowd Counting, P2PNet, Individual Localization Description An account of the resource The Greater Jakarta Commuter Rail, also known as the KRL Commuter Line, is one of the primary transportation choices for many people due to its comfort and efficiency. However, the level of user dissatisfaction is still relatively high, particularly regarding the frequent and unpredictable overcrowding of trains. To address this issue, our research develops an Artificial Intelligence-based model to predict train passenger density through crowd counting. By utilizing the proposed k-F1 metric By utilizing the proposed k-F1 metric, which balances the impact of False Positives and False Negatives in crowd density predictions by measuring the proximity of predicted points to the nearest ground truth within a scaled threshold and a constructed dataset of train density, we compare three object detection approaches: bounding box prediction (YOLOv5), density map (CSRNet), and proposal point (P2PNet). In our experiments, YOLOv5 surpassed other models in performance, achieving a Mean Absolute Error (MAE) of 1.41 and a k-F1 score of 0.91, while maintaining a fast inference speed of 300 milliseconds per frame. This model’s strength lies in scenarios with fewer people and larger objects, such as passengers, within the frame. Conversely, P2PNet and CSRNet were less successful under these conditions, achieving MAEs of 3.49 and 4.98, and k-F1 scores of 0.77 and 0.35 respectively. However, it is important to note that P2PNet and CSRNet are better suited for denser and more congested environments, such as peak hours or at major transit hubs, where trains typically experience high crowd densities. The proposed density estimation method can be applied to real-time image-based CCTV systems to predict train congestion and facilitate transportation management decisions. Creator An entity primarily responsible for making the resource Bryan Tjandra, Oey Joshua Jodrian, Nyoo Steven Christopher Handoko, Alfan Farizki Wicaksono Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v18i1.1314 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2025-02-08 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Commuter Line Crowd Counting Density Estimation Individual Localization P2PNet https://repository.horizon.ac.id/files/original/277fe6b627597a8a36f7bfc744a49bb7.pdf d240665bcdde700411c3a47c39ba63f7 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. 18 NO. 1 (2025) 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 Forest and Land Fire Vulnerability Assessment and Mapping using Machine Learning Method in East Nusa Tenggara Province, Indonesia Subject The topic of the resource East Nusa Tenggara, forest and land fires, feature selection, machine learning, mapping Description An account of the resource Forest and land fires (FLF) severely damage forest ecosystems and reduce their functionality. Predicting areas prone to fires is crucial for effective management and prevention. Machine learning (ML) has shown potential in this field. By 2022, East Nusa Tenggara (NTT) experienced the highest incidence of fires in Indonesia, with 70,637 hectares burned. This study evaluates NTT's FLF vulnerability using seven ML methods: Gaussian Naive Bayes, Support Vector Machine, Logistic Regression, Artificial Neural Network, Random Forest, Gradient Boosting Machine, and Extreme Gradient Boost (XGB). A geospatial dataset combining NTT's 2022 fire data and fourteen fire-related factors was developed with ArcGIS. Using the Information Gain Ratio for feature selection, twelve key features were identified: Elevation, Slope angle, Slope Aspect, Plan Curvature, Land Cover, NDVI, Distance to Road, Distance to Buildings, Annual Rainfall, Average Temperature, Wind Speed, and Relative Humidity. The XGB model performed best, with AUC values of 0.959 for training and 0.743 for testing. The resulting vulnerability map revealed key fire factors: low elevation, gentle slopes, curved terrain, forest cover, poor vegetation health, human activity, distant firefighting resources, low rainfall, high temperatures, high wind speeds, and low humidity. Recommendations include land management, fire-resistant vegetation, policy enforcement, community education, and infrastructure enhancement. Creator An entity primarily responsible for making the resource Hans Timothy Wijaya and Aniati Murni Arymurthy Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v18i1.1304 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2025-02-08 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) East Nusa Tenggara feature selection forest and land fires machine learning Mapping https://repository.horizon.ac.id/files/original/3eb95428973e1846545d9ce055450d4d.pdf a68057923be508e71f79debe46e80906 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. 18 NO. 1 (2025) 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 Multilabel Hate Speech Classification in Indonesian Political Discourse on X using Combined Deep Learning Models with Considering Sentence Length Subject The topic of the resource deep learning; classification task; hate speech detection; sentence length; social media. Description An account of the resource Hate speech, as public expression of hatred or offensive discourse targeting race, religion, gender, or sexual orientation, is widespread on social media. Creator An entity primarily responsible for making the resource Revelin Angger Saputra and Yuliant Sibaroni Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v18i1.1440 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2025-02-08 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) classification task deep learning hate speech detection https://repository.horizon.ac.id/files/original/351698a2e99a8a938fa4e11af2482995.pdf e2efd35b69a0eb3489c40573602943cc 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. 18 NO. 1 (2025) 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 Optimizing Data Quality in Interagency Data Sharing: A Framework Subject The topic of the resource data quality, government, data sharing, data exchange, framework Description An account of the resource As governments strive for openness, inclusivity, and collaboration to deliver public value and inform policy with a data-driven approach, interagency data sharing (IDS) becomes increasingly critical. However, challenges remain, particularly regarding data quality. This study addresses this gap by proposing a novel framework for IDS within government agencies. This framework goes beyond traditional approaches by proactively managing data quality throughout its lifecycle. It aims to provide insights for fostering effective IDS practices that support collaborative and evidence-based decision-making, a cornerstone of modern government operations. This research employs a Systematic Literature Review (SLR) to examine current IDS practices and associated challenges. Subsequently, a case study conducted at Indonesia's Directorate General of Taxes utilizes semi�structured in-depth interviews with practitioners to explore practical implementation scenarios. The framework developed through this research aims to mitigate various challenges related to IDS, particularly focusing on enhancing data quality assurance. By ensuring high-quality exchanged data, this framework empowers governments to break down silos, fostering a more coordinated and efficient approach to public service delivery. Creator An entity primarily responsible for making the resource Monica Vivi Kurniawati, Mohamad Faisal Zulmy, and Yova Ruldeviyani Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v18i1.1310 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2025-02-08 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) data exchange data quality data sharing Framework Government https://repository.horizon.ac.id/files/original/88c23c85975b5e7902fd9c3379a8216f.pdf 4c4fcb0e2598c77e9d1c54ab0cf5791f 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. 18 NO. 1 (2025) 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 Preprocessing Impact on SAR Oil Spill Image Segmentation Using YOLOv8 Subject The topic of the resource SAR image with oil spill object, Flip Augmentation, Hyperparameter Configuration, Filter kernel sharpen, YOLOv8 hyperparameter default value Description An account of the resource Synthetic Aperature Radar (SAR) is a sensory equipment used in marine remote sensing that emits radio waves to capture a representation of the target scene. SAR images have poor quality, one of which is due to speckle noise. This research uses SAR images containing oil spills as objects that are detected using machine learning with the YOLOv8 model. The dataset was obtained from MKLab by preprocessing to improve the quality of SAR images before processing. Preprocessing involves annotating the dataset, augmenting it with flip augmentation, and filtering it using threshold and median filters in addition to a sharpen kernel that finds the optimal midway value. The default value of the YOLOv8 hyperparameter is used with addition of delta as well as subtraction of the same delta. The implementation of preprocessing and combination of hyperparameters is examined to optimize the YOLOv8 model in detecting oil spills in SAR images. Based on 10 experimental scenarios, initial results with the original MKLab image provide an mAP50 of 49.7%. Implementing Flip augmentation alone on the data set increases the mAP50 value by 18.8%. Followed by the sharpen 1.2 kernel filter increasing the mAP50 value to 68.89%, while the median and thresholding filters tend to reduce the mAP50 value. The combination of experiments with the best results was preprocessing with flip augmentation and sharpen 1.2 kernel filter with hyperparameters: epoch 200, warmup 4.0, momentum 0.9, warmup bias lr 0.01, weight decay 0.005, and learning rate 0.000714, resulting in an mAP50 value of 68.89%. In addition, it was found that the sharpening kernel with a real number midpoint of 1.2 and combination with flipping augmentation had the greatest impact on increasing the MAP50 value in SAR oil spill image segmentation by YOLOv8. Creator An entity primarily responsible for making the resource Nurjannah Syakrani, Dimas Kurniawan, Wili Akbar Nugraha, Priyanto Hidayatullah, Lukmannul Hakim Firdaus, Muhammad Rizqi Sholahuddin Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v18i1.1380 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2025-02-08 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Filter kernel sharpen Flip Augmentation Hyperparameter Configuration SAR image with oil spill object YOLOv8 hyperparameter default value https://repository.horizon.ac.id/files/original/2873c33a0a02d7951bc5c67c330b8c42.pdf c21248905cccb6227d1cb7dc60084571 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. 18 NO. 1 (2025) 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 Transformative Insights into Corrosion Inhibition: A Machine Learning Journey from Prediction to Web-Based Application Subject The topic of the resource corrosion, inhibitor, machine learning, web, streamlit Description An account of the resource This study focuses on the exploration and evaluation of machine learning (ML) models to analyze expired pharmaceutical data for their potential use as corrosion inhibitors. Additionally, the entire modeling process is integrated into a user-friendly platform through a Streamlit service-assisted corrosion inhibitor website, facilitating broader accessibility and practical application. The models are trained offline to ensure accurate performance, eliminating the need for users to retrain the models themselves. This approach simplifies the user experience by offering a ready-to-use prediction service directly on the website platform. Among the various ML models implemented, XGB demonstrated the highest performance with an R2-score of 0.99999999. Given that many chemists are not familiar with informatics coding, the researchers developed a Streamlit-based website that includes tools to customize the models. The end product of this work is a corrosion inhibitor experimentation tool that eliminates the need for users to code, making advanced ML techniques accessible to a broader audience within the chemistry community. Creator An entity primarily responsible for making the resource Dzaki Asari Surya Putra, Nicholaus Verdhy Putranto, Nibras Bahy Ardyansyah, Gustina Alfa Trisnapradika, Muhamad Akrom Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v18i1.1303 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2024-06-04 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) corrosion inhibitor machine learning streamlit Web https://repository.horizon.ac.id/files/original/77ba4753abcd274fa7928d9f6e2772a7.pdf a5dd6bda9e5ad17ed2f20a12b855be80 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. 18 NO. 1 (2025) 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 Utilizing X Sentiment Analysis to Improve Stock Price Prediction Using Bidirectional Long Short-Term Memory Subject The topic of the resource BERT, BiLSTM, sentiment analysis, stock price prediction, X Description An account of the resource The capital market is one of the important factors that influence the national economy. However, the stock price in capital market fluctuates over time. Therefore, the investors strongly need an accurate prediction of stock price for making profitable decision. However, with the pervasive influence of the internet, investors and investment institutions have started incorporating online opinions and news, including those found on social media platforms like X. This research aims to enhance stock price prediction by utilizing X sentiment analysis. The sentiment of tweets from X related to IHSG stock price is predicted by using BERT (Bidirectional Encoder Representations from Transformers), then its result isintegrated with the historical stock price data for predicting future stock price by using BiLSTM (Bidirectional Long Short-Term Memory). The experiment results show that the RMSE and MAPE of the proposed model with sentiment analysis is decreased by 0.042 and 0.595, resepectively, compared to the model without sentiment analysis. Therefore, it can be concluded that the inclusion of X sentiment analysis in conjunction with BiLSTM succeeded in improving the performance of stock price prediction. The study's outcome is expected to be valuable for investors to make profitable decisions, leveraging the information available on social media. Creator An entity primarily responsible for making the resource Marcella Komunita Pasaribu, Fahrel Gibran Alghany, Benhard Simanullang, Hilma Nur Khasanah, Hanan Nurul Hardyana Zain, Khadijah, and Rismiyati Source A related resource from which the described resource is derived http://dx.doi.org/10.21609/jiki.v18i1.1428 Publisher An entity responsible for making the resource available Faculty of Computer Science Universitas Indonesia Date A point or period of time associated with an event in the lifecycle of the resource 2025-02-08 Contributor An entity responsible for making contributions to the resource Sri Wahyuni Rights Information about rights held in and over the resource e-ISSN : 2502-9274 printed ISSN : 2088-7051 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 Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) BERT BiLSTM sentiment analysis stock price prediction X