Data Retrieval Method Research Articles

Philosophical analysis of Mortimer J. Adler's Christian education and global education management

The present research shows the absurdity of education, especially education management, in the posthumanist schema. Posthumanists seem to want to form their own group, but researchers see that posthumanism is not new, especially in the world of education. Philosopher Mortimer J. Adler, in the framework of perennialism, shows the structure of education, especially in America, which, according to researchers, is relevant in Indonesia. Research findings are based on qualitative research with data retrieval methods from literature studies. Results showed that Mortimer J. Adler's philosophical thinking emphasizes the expansion of education management in both depth and fragility by and through learners, parents, teachers and related parties in the mecca of an independent curriculum.

EBH-IoT: Energy-efficient secured data collection and distribution of electronics health record for cloud assisted blockchain enabled IoT based healthcare system

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation, distribution and storage for EHR data. Therefore, the integration of H-IoT and Blockchain technologies can become a reasonable choice for the design of decentralized H-IoT-based e-healthcare systems. But the H-IoT network has some intrinsic challenges like low computation, energy constraint, security, energy optimization, data storage, and real-time data analytic. Also, conventional EHR-based systems suffer from issues such as the potential loss of data, inadequate security and consensus on the unchangeable nature of health records, fragmented connections between different institutions, and ineffective clinical data retrieval methods, among other challenges. In this article, first, we study the performance of blockchain technology in the healthcare system. Second, we propose an improved Harris Hawk Optimization algorithm (HHO) based clustering mechanism for the collection and sharing of EHR. The proposed system was tested and validated using the Hyperledger-fabric based electronic healthcare record (EHR) sharing system along with Matlab. The proposed system achieves 12%, and 7% incremental improvement in terms of latency, throughput for the Blockchain networks. While the proposed clustering technique achieves 10%, 12%, 14%, and 16% improvements in alive node, energy consumption, throughput and average transmission delay compared to existing state of the art.

A Flexible and Accurate Additive Manufacturing Data Retrieval Method Based on Probabilistic Modeling and Transformation-Invariant Feature Learning

A Flexible and Accurate Additive Manufacturing Data Retrieval Method Based on Probabilistic Modeling and Transformation-Invariant Feature Learning

Data recovery methods for DNA storage based on fountain codes

Today's digital data storage systems typically offer advanced data recovery solutions to address the problem of catastrophic data loss, such as software-based disk sector analysis or physical-level data retrieval methods for conventional hard disk drives. However, DNA-based data storage currently relies solely on the inherent error correction properties of the methods used to encode digital data into strands of DNA. Any error that cannot be corrected utilizing the redundancy added by DNA encoding methods results in permanent data loss. To provide data recovery for DNA storage systems, we present a method to automatically reconstruct corrupted or missing data stored in DNA using fountain codes. Our method exploits the relationships between packets encoded with fountain codes to identify and rectify corrupted or lost data. Furthermore, we present file type-specific and content-based data recovery methods for three file types, illustrating how a fusion of fountain encoding-specific redundancy and knowledge about the data can effectively recover information in a corrupted DNA storage system, both in an automatic and in a guided manual manner. To demonstrate our approach, we introduce DR4DNA, a software toolkit that contains all methods presented. We evaluate DR4DNA using both in-silico and in-vitro experiments.

A novel framework for set-based steel connection design automation

Existing computational design tools for steel connection design predominantly employ a point-based design (PBD) approach, which requires iterative re-evaluation whenever there are changes in design specifications. This paper introduces a new framework that adopts a set-based design (SBD) approach, aiming to substantially reduce the iteration and time cost associated with steel connection design and rework. The framework integrates a component-set connection design model with a database storage and query-based data retrieval method. The first method enables the flexible and efficient generation of a large connection design space from all possible component combinations, and automated identification of valid connection configurations within it. The latter method allows for automated design space refinement from preference-based evaluation of connection design efficiency and high-speed comparison and selection of optimal connection designs. To evaluate the relative performance of SBD and PBD approaches, the framework is applied to a steel floor system design case study with 62 connections. Results showed that the SBD approach achieved near-instantaneous connection design automation, with a total execution time of fewer than 55 milliseconds, making it over 10 times faster than the corresponding PBD approach.

The Diversity of Mollusks (Bivalve and Gastropod) in the Intertidal Zone of Mutun Coastal, Padang Cermin, Lampung

Mutun Coastal has a diversity of biota species. Biota that are commonly found include the mollusk phylum (Bivalve and Gastropod classes). The purpose of this study was to determine the diversity of mollusks (Bivalves and Gastropods) on Mutun Coastal Lampung. This research was conducted on February 5-7, 2021. The method of data retrieval at both stations used the belt transect method by purposive sampling. Station I is substrated fine sand, while station II is substrated by coarse sand. Based on the research conducted, 26 individuals were found consisting of 7 families, namely 6 families of the Gastropod class (Trochidae, Strombidae, Cerithiidae, Muricidae, Potamididae and Pisaniidae) and 1 family of the Bivalve class, namely the family Mactridae. The value (H') at station I is 1.44. At station II it has an H' value of 0.86. The value of H' at station I belongs to the medium category. The H' value at station II belongs to the low category. The value of E at station I is 0.72. While at station II it has a value of 0.1. The evenness value at station I belongs to the high category. The value (E) at station II of the evenness index belongs to the low category. The value (D) at station I is 0.57. While at station II it has a value of 0.35. The value of D at station I belongs to the medium category. The value of D at station II belongs to the medium category. Based on data analysis, Mutun Coastal has suitable habitat for mollusk and it is also supported by abiotic parameter conditions.

Implementing and Evaluating a National Integrated Digital Registry and Clinical Decision Support System in Early Intervention in Psychosis Services (Early Psychosis Informatics Into Care): Co-Designed Protocol.

Early intervention in psychosis (EIP) services are nationally mandated in England to provide multidisciplinary care to people experiencing first-episode psychosis, which disproportionately affects deprived and ethnic minority youth. Quality of service provision varies by region, and people from historically underserved populations have unequal access. In other disease areas, including stroke and dementia, national digital registries coupled with clinical decision support systems (CDSSs) have revolutionized the delivery of equitable, evidence-based interventions to transform patient outcomes and reduce population-level disparities in care. Given psychosis is ranked the third most burdensome mental health condition by the World Health Organization, it is essential that we achieve the same parity of health improvements. This paper reports the protocol for the program development phase of this study, in which we aimed to co-design and produce an evidence-based, stakeholder-informed framework for the building, implementation, piloting, and evaluation of a national integrated digital registry and CDSS for psychosis, known as EPICare (Early Psychosis Informatics into Care). We conducted 3 concurrent work packages, with reciprocal knowledge exchange between each. In work package 1, using a participatory co-design framework, key stakeholders (clinicians, academics, policy makers, and patient and public contributors) engaged in 4 workshops to review, refine, and identify a core set of essential and desirable measures and features of the EPICare registry and CDSS. Using a modified Delphi approach, we then developed a consensus of data priorities. In work package 2, we collaborated with National Health Service (NHS) informatics teams to identify relevant data currently captured in electronic health records, understand data retrieval methods, and design the software architecture and data model to inform future implementation. In work package 3, observations of stakeholder workshops and individual interviews with representative stakeholders (n=10) were subject to interpretative qualitative analysis, guided by normalization process theory, to identify factors likely to influence the adoption and implementation of EPICare into routine practice. Stage 1 of the EPICare study took place between December 2021 and September 2022. The next steps include stage 2 building, piloting, implementation, and evaluation of EPICare in 5 demonstrator NHS Trusts serving underserved and diverse populations with substantial need for EIP care in England. If successful, this will be followed by stage 3, in which we will seek NHS adoption of EPICare for rollout to all EIP services in England. By establishing a multistakeholder network and engaging them in an iterative co-design process, we have identified essential and desirable elements of the EPICare registry and CDSS; proactively identified and minimized potential challenges and barriers to uptake and implementation; and addressed key questions related to informatics architecture, infrastructure, governance, and integration in diverse NHS Trusts, enabling us to proceed with the building, piloting, implementation, and evaluation of EPICare. DERR1-10.2196/50177.

Canine Disease Prediction using Multi-Directional Intensity Proportional Pattern with Correlated Textural Neural Network

Data optimization is crucial for enhancing prediction accuracy and similarity identification in texture learning systems, especially for predicting canine diseases. Traditional data retrieval methods often struggle with accuracy and efficiency, particularly when dealing with large datasets. This study presents a novel approach combining Multi-Directional Intensity Proportional Pattern (MDIPP) with a Similarity Measure (SM) system to improve data relevance and similarity estimation. The model organizes data into a paged database structure, which speeds up search operations. A neural network, Correlated Textural Neural Network (CTNN), forecasts the relevance of feature attributes and sorts matching indexes to predict canine diseases based on Test data. The CTNN model incorporates a correlation factor among features to enhance prediction accuracy. The relevance of data is determined using an upgraded neural network that accounts for these correlations. The study evaluates performance based on precision, recall, F1-score, and data retrieval accuracy, comparing the results with state-of-the-art techniques. By improving the organization and indexing of data and refining the prediction process, this approach aims to advance data validation and the prediction of canine diseases in large-scale texture learning systems

TAAC: Secure and Efficient Time‐Attribute‐Based Access Control Scheme in SDN‐IoT

The convergence of software‐defined networking (SDN) and the Internet of Things (IoT) provides a scalable method for handling the considerable volumes of data produced by IoT devices. However, the lack of appropriate security measures can lead to unauthorized access to sensitive data, potential breaches, and privacy violations, as well as time‐consuming and inefficient data retrieval methods in SDN‐IoT systems that require decrypting the entire dataset. To address these challenges, this article proposes the time‐attribute‐based access control scheme in SDN‐IoT (TAAC). The TAAC scheme combines ciphertext‐policy attribute‐based encryption with a novel time‐attribute‐based access tree to ensure fine‐grained access control on time and attributes, enabling secure ciphertext interaction and information sharing across domains. Furthermore, the TAAC scheme also incorporates searchable encryption, which enhances the efficiency of data retrieval. By implementing searchable encryption techniques, the data receiver can generate trapdoors to search and retrieve specific encrypted data without the need to decrypt the entire dataset. In summary, the TAAC scheme improves storage efficiency and computation, enhances scalability, and provides robust security, offering an efficient and secure solution for ciphertext sharing in SDN‐IoT environments. Experimental results have demonstrated that the TAAC scheme shows excellent performance and outperforms other attribute‐based searchable encryption algorithms.

SPARRING: Sistem Rekomendasi Peneliti Terintegrasi Google Scholar via SerpAPI dan Latent Dirichlet Allocation pada Konteks Perguruan Tinggi

The researcher partner recommendation system plays a crucial role in fostering academic collaboration in universities, where a challenge for new users is finding suitable research partners. In addressing the limitations of Naïve Bayes classifiers, this article introduces an innovative approach in the form of a non-linear sigmoid activation function. We highlight the urgency of this solution, detail its implementation steps, and describe its substantial contribution to research partner recommendations. This article not only identifies existing obstacles but also proposes revolutionary solutions to enhance the effectiveness of consultation systems in academic environments. A gap in this research is the manual input method for data retrieval, creating weaknesses, susceptibility to human errors, and reduced efficiency in collecting journal data. We propose SPARRING, a researcher recommendation system connected to Google Scholar, in the context of higher education. This approach uses a dataset of faculty members from the Faculty of Information Technology and Business at a private university in Indonesia. The results from Google Scholar extraction, with topic keywords determined by Latent Dirichlet Allocation, are then classified using the Naïve Bayes algorithm. Additionally, we integrate web scraping tools, particularly SerpAPI, to access data from Google Scholar. Through the integration of SerpAPI, the proposed web-based system is capable of providing more accurate recommendations, especially for new users with limited collaboration experience. By incorporating SerpAPI, the proposed web-based system can offer more accurate recommendations, particularly for new users without extensive collaboration experience.

QGIS AND OPEN DATA CUBE APPLICATIONS FOR LOCAL CLIMATE ZONES ANALYSIS LEVERAGING PRISMA HYPERSPECTRAL SATELLITE DATA

Abstract. Climate change poses a significant threat to humans and biodiversity, impacting various aspects of livelihoods, infrastructure, and ecosystems. Understanding climate change and its interaction with the environment is crucial for achieving Sustainable Development Goals. Local Climate Zones (LCZ) play a key role in comprehending climate change by categorizing urban areas also based on their thermal characteristics. This study presents prototype open-source software tools developed to integrate ground and satellite data for LCZ analysis in the Metropolitan City of Milan (Northern Italy). These tools consist of a QGIS plugin to access and preprocess ground-based meteorological sensor data and a client-server platform, based on the Open Data Cube and Docker technologies, for the exploitation of multispectral and hyperspectral satellite data in LCZ mapping and analysis. The tools’ architecture, data retrieval methods, and analysis capabilities are described in detail. The QGIS plugin facilitates the access and preprocessing of ground-based sensor data within the user-friendly QGIS environment. The platform enables seamless ground-sensor and satellite data management and analysis, using Jupyter Notebooks as an interface to support programmatic operations on the data. The proposed tools provide a framework for studying climate change and its local impacts on urban environments, with the potential of empowering users to effectively analyze and mitigate its effects.

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things.

Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related to e-healthcare. Different measurement conditions affect the daily routine monitoring, including the sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present in this domain that include a variety of techniques for pre-processing, descriptor extraction, and reduction, along with the classification of data captured from multiple sensors. However, such models consisting of multiple subject-based data using different techniques may degrade the accuracy rate of locomotion decoding. Therefore, this study proposes a deep neural network model that not only applies the state-of-the-art Quaternion-based filtration technique for motion and ambient data along with background subtraction and skeleton modeling for video-based data, but also learns important descriptors from novel graph-based representations and Gaussian Markov random-field mechanisms. Due to the non-linear nature of data, these descriptors are further utilized to extract the codebook via the Gaussian mixture regression model. Furthermore, the codebook is provided to the recurrent neural network to classify the activities for the locomotion-decoding system. We show the validity of the proposed model across two publicly available data sampling strategies, namely, the HWU-USP and LARa datasets. The proposed model is significantly improved over previous systems, as it achieved 82.22% and 82.50% for the HWU-USP and LARa datasets, respectively. The proposed IoHT-based locomotion-decoding model is useful for unobtrusive human activity recognition over extended periods in e-healthcare facilities.

Query Model Framework Design for Conservation History and Endowments Database: A Case Study on the Digitization of the Sumedang Larang Kingdom’s History and Endowments in Indonesia

The use of structured query formulation languages is a method for gaining access to data in information management systems. Writing structured queries is a powerful method of data retrieval, and end users can generate complex database queries by becoming proficient in a particular query language. Digitization of database information systems, history and endowment, and technology conservation are some examples of database conservation techniques in the process of displaying data that require optimal query techniques. Historical and endowment properties comply with a certain set of laws governing waqf recipients, which sets them apart from heritage and cultural assets. Trusted property typically comprises real estate, valuables, or both and has an infinite preservation term so long as it may still be put to use. A solid information technology infrastructure is essential for ensuring comprehensive data security. Given the availability of knowledge and the quick development of information technology, ensuring its sustainability is a challenge. This research is aimed at preserving historical and waqf databases based on the case study of the Kingdom of Sumedang Larang (KSL), Indonesia, through examination of the database, synchronization of digital data with physical data, as well as looking at its relationship with organizations and management. The proposed method, namely the Design and Analysis of Database Query Model for Preservation Information System (DAQMP), creates a recurring query model from the original tabular data placed in the next table as a special table check for data specifications containing important data. The results must be accurately aligned between digital and physical records regularly to serve as a record of any changes and as evidence to determine whether data has been corrupted, altered, or lost.

WordPPR: A Researcher-Driven Computational Keyword Selection Method for Text Data Retrieval from Digital Media

ABSTRACT Despite the increasing use of digital media data in communication research, a central challenge persists – retrieving data with maximal accuracy and coverage. Our investigation of keyword-based data collection practices in extant communication research reveals a one-step process, whereas our cross-disciplinary literature review suggests an iterative query expansion process guided by human knowledge and computer intelligence. Hence, we introduce the WordPPR method for keyword selection and text data retrieval, which entails four steps: 1) collecting an initial dataset using core/seed keyword(s); 2) constructing a word graph based on the dataset; 3) applying the Personalized PageRank (PPR) algorithm to rank words in proximity to the seed keyword(s) and selecting new keywords that optimize retrieval precision and recall; 4) repeating steps 1–3 to determine if additional data collection is needed. Without requiring corpus-wide sampling/analysis or extensive manual annotation, this method is well suited for data collection from large-scale digital media corpora. Our simulation studies demonstrate its robustness against parameter choice and its improvement upon other methods in suggesting additional keywords. Its application in Twitter data retrieval is also provided. By advancing a more systematic approach to text data retrieval, this study contributes to improving digital media data retrieval practices in communication research and beyond.

Survey of Information Encoding Techniques for DNA

The yearly global production of data is growing exponentially, outpacing the capacity of existing storage media, such as tape and disk, and surpassing our ability to store it. DNA storage—the representation of arbitrary information as sequences of nucleotides—offers a promising storage medium. DNA is nature’s information-storage molecule of choice and has a number of key properties: It is extremely dense, offering the theoretical possibility of storing 455 EB/g; it is durable, with a half-life of approximately 520 years that can be increased to thousands of years when DNA is chilled and stored dry; and it is amenable to automated synthesis and sequencing. Furthermore, biochemical processes that act on DNA potentially enable highly parallel data manipulation. While biological information is encoded in DNA via a specific mapping from triplet sequences of nucleotides to amino acids, DNA storage is not limited to a single encoding scheme, and there are many possible ways to map data to chemical sequences of nucleotides for synthesis, storage, retrieval, and data manipulation. However, there are several biological, error-tolerance, and information-retrieval considerations that an encoding scheme needs to address to be viable. This comprehensive review focuses on comparing existing work done in encoding arbitrary data within DNA in terms of their encoding schemes, methods to address biological constraints, and measures to provide error correction. We compare encoding approaches on the overall information density and coverage they achieve, as well as the data-retrieval method they use (i.e., sequential or random access). We also discuss the background and evolution of the encoding schemes.

Design of a Data Acquisition, Correction and Retrieval of Na Doppler Lidar for Diurnal Measurement of Temperature and Wind in the Mesosphere and Lower Thermosphere Region

Based on the resonance fluorescence scattering mechanism, a narrowband sodium (Na) lidar can measure temperature and wind in the mesosphere and lower thermosphere (MLT) region. By using a narrowband spectral filter, background light noise during the day can be suppressed, allowing for continuous observations. To obtain full-diurnal-cycle temperature and wind measurement results, a complex and precise retrieval process is required, along with necessary corrections to minimize measurement errors. This paper introduces the design of a data acquisition unit for three frequencies in three directions of the Na lidar system in the Chinese Meridian Project (Phase II) and investigates the calibration and retrieval methods for obtaining diurnal temperature and horizontal wind in the MLT region, using a Na Doppler lidar with Faraday anomalous dispersion optical filter (FADOF). Furthermore, these methods are applied to observations conducted by a Na lidar in Beijing, China. The wind and temperature results over full diurnal cycles obtained from the all-solid-state Na Doppler lidar are reported for the first time and compared with temperature measurements from satellite, as well as wind observations from a meteor radar. The comparison demonstrates a reasonable agreement between the results, indicating the rationality of the lidar-retrieved results and the feasibility and effectiveness of the data correction and retrieval method.

Devising a method for rapid data retrieval using explorers for blockchain analysis

The object of this study is blockchain explorers and their usefulness in efficiently gathering data for blockchain network analysis. The process of blockchain analysis typically involves deploying and synchronizing a blockchain node, which requires significant computational resources and time for synchronization. Analyzing multiple blockchain networks simultaneously demands substantial effort and requires even greater costs. The developed method involves utilizing publicly accessible blockchain explorers, which allows for rapid data retrieval with minimal computational resources for further analysis. Additionally, obtaining supplementary information from blockchain explorers provides valuable details that may be inaccessible using traditional data retrieval methods. The efficiency of the proposed method was verified through the development of a prototype system. Data was collected for 14 specified blockchain networks to analyze smart contracts within these networks. Information about accounts (including balance statistics) was gathered, smart contracts were identified among the accounts, data on existing tokens owned by smart contracts was obtained, and bytecode and source code (where available) of contracts were collected and decompiled. The process took nearly 24 hours on a cloud computing machine with minimal configuration. Based on the collected data, an example smart contract was analyzed to demonstrate the completeness of the process. The results of this research minimize computational resource expenses and allow for a simplified and rapid data gathering process without manual configuration, enabling researchers and analysts to concentrate on subsequent stages of analysis

Assimilating sense into disaster recovery databases and judgement framing proceedings for the fastest recovery

The replication between the primary and secondary (standby) databases can be configured in either synchronous or asynchronous mode. It is referred to as out-of-sync in either mode if there is any lag between the primary and standby databases. In the previous research, the advantages of the asynchronous method were demonstrated over the synchronous method on highly transactional databases. The asynchronous method requires human intervention and a great deal of manual effort to configure disaster recovery database setups. Moreover, in existing setups there was no accurate calculation process for estimating the lag between the primary and standby databases in terms of sequences and time factors with intelligence. To address these research gaps, the current work has implemented a self-image looping database link process and provided decision-making capabilities at standby databases. Those decisions from standby are always in favor of selecting the most efficient data retrieval method and being in sync with the primary database. The purpose of this paper is to add intelligence and automation to the standby database to begin taking decisions based on the rate of concurrency in transactions at primary and out-of-sync status at standby.

Enhancing Healthcare Management during COVID-19: A Patient-Centric Architectural Framework Enabled by Hyperledger Fabric Blockchain

The highly transmissible COVID-19 virus has wreaked havoc on the global economy, health, and lives. The abrupt burst and exponential spread of this pandemic has shown the inadequacies of existing healthcare institutions in handling a public health emergency. As governments around the world strive to re-establish their economies, open workplaces, ensure safe journeys, and return to regular life, they require solutions to reduce losses. The proposed framework provides virtual assistance from various medical practitioners and physicians. Furthermore, it promotes the accuracy of information gathered from COVID-19 patients, which can aid in the launch of a variety of government decisions and public guidelines aimed at combating health exigencies. The authors present a revolutionary blockchain-based solution that builds trust between the medical professionals and patients while preventing accidental coronavisrus transmission. This solution also keeps track of COVID-19 patients and improves EHR management, which can be a viable solution for common EHR challenges such as lowering the risk of patient data loss, maintaining privacy and security, and obtaining immutable consensus on the maintenance of health records, gaps in hospital communication, and inefficient clinical data retrieval methods. This research work describes a COVID-19 patient-centric blockchain-based EHR employing JavaScript-based smart contracts for a decentralized healthcare management system. The proposed Hyperledger fabric and a Composer-based working prototype ensure the model’s security and the authenticity of the health records. The authors used the Hyperledger Caliper benchmarking tool, which measures latency, throughput, resource utilization, etc., under different conditions and control parameters. The findings highlight the importance of the proposed blockchain-enabled architecture in revolutionizing healthcare administration during and after the COVID-19 pandemic, promoting enhanced clinical outcomes and supporting patient-centered care.

Herpetofauna trade via e-commerce in Indonesia

Sales of wildlife on a commercial scale using various online platform has been occurring for some time however, research related to online wildlife trade in Indonesia, especially herpetofauna, is still limited. This study aims to examine the herpetofauna trade on a popular e-commerce platforms in Indonesia, namely: Tokopedia, Shopee, Lazada, and Bukalapak, using a web scraping application data retrieval method. Web scraping is a technique to get information from the website automatically without having to copy it manually. The results recorded 117 species of the herpetofauna from 38 families, comprising 106 species of reptiles (90.6%) and 11 species of amphibians (9.4%). Eublepharis macularius (leopard gecko) was the most offered species in each e-commerce, whereas Trachemys scripta (red-eared slider) was the most sold species. Six species were included under the protected animals list according to the Ministry of Environment and Forestry No. 106 from 2018, which were Crocodylus porosus (saltwater crocodile), Morelia viridis (green tree python), Python bivittatus (Burmese python), Orlitia borneensis (Malaysian giant turtle), Varanus nebulosus (clouded monitor) and Varanus panoptes (argus monitor). Based on the province, the highest trade occurs in West Java, followed by Jakarta. The total economic value during the period of 2017 to 2022 is IDR 3,355,750,470.