The growing digitalization of industries like automotive, transportation, urban mobility, and telecommunications is highlighting the significance of point-to-multipoint communication services. These services are essential for tasks like maximizing the efficiency of hardware and software resources and ensuring that users consistently receive software updates and communications. In order to implement precise and dependable point-to-multipoint distribution services within the network, software-defined networking is used. SDN controllers like RYU, Floodlight, and others have become the de facto standard for operating these kinds of networks. An SDN multi-controller’s efficiency greatly affects the underlying SDN infrastructure network’s adaptability and capabilities. This research makes use of a Mininet emulator to develop a multi-controller SDN architecture with a Fat-Tree topology and two RYU controllers. Throughput, latency, and roundtrip time are some of the node-to-node performance measures that will be used to determine how successful a Domain Isolation Multi-controller (DiM) network architecture is. DiM has higher throughput and round-trip time than other multi-controller algorithms. DiM is 21% faster than HyperFlow, Kandoo, and Rama at 19.99 Gbps. DiM cuts RTT by 44%, 25%, and 86% compared to HyperFlow, Kandoo, and Rama. ONOS has a slightly lower RTT (10.0 ms) than DiM (11.25 ms), but the difference is small, and DiM matches ONOS in throughput. DiM latency matches other multi-controller methods. DiM high throughput and low RTT make it ideal for scalable and responsive SDN environments.

Real-world IoT network security generates traffic at big-data scale with extreme class imbalance, temporal non-stationarity, and continuously evolving attack strategies that overwhelm static supervised classifiers. This paper presents a cognitive computing framework for network intrusion detection: a CNN–LSTM–DQN architecture with Prioritized Experience Replay (PER) evaluated on a 5,000,000-flow naturalistic sample of the TON_IoT Processed_Network dataset (4,000,000 training/1,000,000 temporally held-out test flows; 94.5% attack ratio) under a strict temporal split. The cognitive agent optimizes detection decisions using an Alerts per Million Flows (ARMF)-aware reward function that encodes both alert-fatigue cost and missed-attack penalty. We conduct a cross-attack-family generalization study: the methodology—architecture template, reward design, and hyperparameter calibration—is inherited from a framework previously validated on CSE-CIC-IDS2018, re-instantiated and retrained on the structurally different TON_IoT environment, and compared against the previously published benchmark. Initialization sensitivity is characterized across five independent random seeds using paired Wilcoxon signed-rank and t-tests. Across the five seeds, the proposed X2 model attains recall 0.833 ± 0.306 and F1 0.874 ± 0.241 (mean ± sample SD), versus the supervised X1 baseline at 0.858 ± 0.178 and 0.912 ± 0.116; the best-performing seed (42) achieves 97.52% accuracy, 98.02% attack recall, 99.46% precision, and 98.73% F1-score on 1,000,000 held-out XSS flows—an attack family entirely absent from training—with temporal stability variances of 4.63 × 10−7 (recall) and 1.38 × 10−7 (F1). The X2 advantage observed among the four stable seeds is not statistically demonstrated at n = 5 (statistical power ≈ 5.1%); the initialization-sensitivity finding itself, including one degenerate alert-suppression seed, is reported as a primary contribution. A formal, exactly additive ARMF decomposition distinguishes the detected-attack (structural) component (99.46%) from the model-induced false-positive component (0.54%), and we report a multi-seed, ARMF-aware cognitive IDS evaluation on naturalistic TON_IoT traffic under an unseen-attack-family test condition that, to the best of our knowledge, has not been reported in the surveyed RL-based NIDS literature.

This study investigates the potential adoption of blockchain technology within the Indonesian electricity sector to address key challenges in digital infrastructure. Blockchain technology has the potential to address the challenges by facilitating immutable and distributed storage of data across multiple network points. A two-stage methodology comprising a comprehensive literature review and selection of case studies is employed to conduct the survey. Research from reputable databases is reviewed by focusing on blockchain applications in energy systems. Key criteria such as Regulation, Implementation Readiness, Urgency, Technology Readiness Level, and Business Maturity Level are analyzed to assess deployment readiness across the main use cases in the Indonesian landscape. The review finds that five main use cases in Indonesia can be enhanced by blockchain technology, including peer-to-peer energy trading, renewable energy certificate trading, electronic billing of electricity, microgrid transactions, and electric vehicle charging transactions. Furthermore, the deployment readiness analysis suggests that electronic billing and electric vehicle charging transactions emerge as the most viable options. It is supported by conducive regulations, high urgency, and existing technological infrastructure.

This study aims to determine the effect of transparency, accountability and the role of village officials on village financial management in Tinada sub-district, Pakpak Bharat district. The sample in this study amounted to 36 respondents. The data analysis method used multiple linear regression analysis, t-test, F-test, and coefficient of determination test. The regression equation in this study is Y = 0.951 - 0.495X1 + 0.211X2 + 1.003X3. Transparency has a significant effect on the financial management of the study village in Tinada sub-district, Pakpak Bharat district, where the t-value sig is 0.000 <0.05. Accountability has a significant effect on the financial management of the study village in Tinada sub-district, Pakpak Bharat district, where the t-value sig is 0.035 <0.05. The role of village officials has a significant effect on the financial management of the study village in Tinada sub-district, Pakpak Bharat district, where the t-value sig is 0.000 <0.05. Transparency, accountability, and the role of village officials simultaneously significantly influenced the financial management of the study village in Tinada sub-district, Pakpak Bharat Regency, with an F-value of 0.000 <0.05. The coefficient of determination test revealed that transparency, accountability, and the role of village officials influenced the financial management of the study village in Tinada sub-district, Pakpak Bharat Regency by 89.2%, while the remaining 10.8% was influenced by factors outside the research model. Keywords: Transparency, Accountability, Role of Village Officials, Village Financial Management

Data breach attacks are unique, particularly when attackers exfiltrate data from their target’s systems. As data breaches continue to increase in both frequency and severity, they pose escalating risks to organizations and society. Despite this, no prior research has focused on predicting exfiltration occurrences based on sequences of tactics identified from low-level logs. Additionally, integrating low-level logs with high-level conceptual frameworks remains a critical challenge. The urgency of automating the mapping process and developing advanced methods to assist defenders in analyzing exfiltration occurrences within their systems is evident. This paper addresses these gaps by developing a machine learning (ML) model to predict the occurrence of data exfiltration by analyzing the sequence of tactics employed by an attacker. We propose ARKAIV, which provides two main contributions: bridging the gap level between low-level logs and high-level data breach conceptual frameworks and integrating collected event logs and ML models to predict exfiltration tactics. To create our dataset, we extracted tactics from threat reports, refined the data to include ten features, and balanced using the Synthetic Minority Oversampling Technique with Edited Nearest Neighbor (SMOTE+ENN) technique. The ML model predicts exfiltration occurrences based on tactics identified from low-level logs as input. To optimize model performance, we benchmarked three resampling methods, five feature selection techniques, and five ML algorithms. Our key contributions include the creation of a novel dataset, the comprehensive techniques used to develop the ML model, and the proposed prediction method, which advances existing research. Additionally, we validate ARKAIV with case studies using event logs from real-world incidents. Our findings demonstrate that ARKAIV effectively predicts exfiltration occurrences with higher accuracy than existing approaches, providing a valuable tool for enhancing organizational cybersecurity.

Addressing class imbalance is critical in cybersecurity applications, particularly in scenarios like exfiltration detection, where skewed datasets lead to biased predictions and poor generalization for minority classes. This study investigates five Synthetic Minority Oversampling Technique (SMOTE) variants, including BorderlineSMOTE, KMeansSMOTE, SMOTEENC, SMOTEENN, and SMOTETomek, to mitigate severe imbalance in our customized tactic-labeled dataset with dominant majority class influence and weak class separability class imbalance. We use seven imbalance metrics to assess each SMOTE variant’s impact on class distribution stability and separability. Furthermore, we evaluate model performance across five classifiers: Logistic Regression, Naïve Bayes, Support Vector Machine, Random Forest, and XGBoost. Findings reveal that SMOTEENN consistently enhances performance metrics (accuracy, precision, recall, F1-score, and geometric mean) on an average of 99% across most classifiers, establishing itself as the most adaptable variant for handling imbalance. This study provides a comprehensive framework for selecting resampling strategies to enhance classification efficacy in cybersecurity tasks with imbalanced data.

Tujuan penelitian ini adalah untuk menganalisis dan mendeskripsikan tentang pengaruh gaya hidup dan literasi keuangan terhadap pengelolaan keuangan pada keluarga muda di Kota Padangsidimpuan. Jenis penelitian ini adalah asosiatif deskriptif dengan menggunakan data primer melalui penyebaran kuesioner. Teknik sampling yang digunakan yaitu purposive sampling yaitu keluarga muda yang sudah berumur satu s.d dua tahun yang berada di Kota Padangsidimpuan dengan jumlah 100 responden. Pengolahan data penelitian dilakukan dengan bantuan aplikasi SPSS yang selanjutnya dianalisis melalui uji validitas dan reliabilitas, uji asumsi klasik, uji hipotesis dan analisis regresi berganda. Temuan penelitian dimana gaya hidup dan literasi keuangan memberikan pengaruh yang signifikan dan positif terhadap pengelolaan keuangan keluarga muda yang ada di Kota Padangsidimpuan. Besarnya pengaruh yang diberikan variabel gaya hidup dan literasi keuangan sebesar 41,3 %.

The exponential growth of textual data in the digital era underlines the pivotal role of Knowledge Graphs (KGs) in effectively storing, managing, and utilizing this vast reservoir of information. Despite the copious amounts of text available on the web, a significant portion remains unstructured, presenting a substantial barrier to the automatic construction and enrichment of KGs. To address this issue, we introduce an enhanced Doc-KG model, a sophisticated approach designed to transform unstructured documents into structured knowledge by generating local KGs and mapping these to a target KG, such as Wikidata. Our model innovatively leverages syntactic information to extract entities and predicates efficiently, integrating them into triples with improved accuracy. Furthermore, the Doc-KG model's performance surpasses existing methodologies by utilizing advanced algorithms for both the extraction of triples and their subsequent identification within Wikidata, employing Wikidata's Unified Resource Identifiers for precise mapping. This dual capability not only facilitates the construction of KGs directly from unstructured texts but also enhances the process of identifying triple mentions within Wikidata, marking a significant advancement in the domain. Our comprehensive evaluation, conducted using the renowned WebNLG benchmark dataset, reveals the Doc-KG model's superior performance in triple extraction tasks, achieving an unprecedented accuracy rate of 86.64%. In the domain of triple identification, the model demonstrated exceptional efficacy by mapping 61.35% of the local KG to Wikidata, thereby contributing 38.65% of novel information for KG enrichment. A qualitative analysis based on a manually annotated dataset further confirms the model's excellence, outshining baseline methods in extracting high-fidelity triples. This research embodies a novel contribution to the field of knowledge extraction and management, offering a robust framework for the semantic structuring of unstructured data and paving the way for the next generation of KGs.

The objectives of this scientific research are: 1) To describe teachers' teaching strategies in building students' spiritual intelligence at Madrasah Ibtidaiyah Muhammadiyah, Fakfak Regency, West Papua. 2) To describe the supporting and inhibiting factors in implementing teachers’ teaching strategies in building students' spiritual intelligence at Madrasah Ibtidaiyah Muhammadiyah Fakfak Regency, West Papua. 3) To describe the impact of teachers' teaching strategies in forming students' spiritual intelligence at Madrasah Ibtidaiyah Muhammadiyah, Fakfak Regency, West Papua. This research applied descriptive a qualitative method. Primary data sources are class teachers, Islamic Education teachers, and Tahfiz Qur'an teachers for grades V and VI. Data collection methods are observation, interviews, and documentation. The data were processed and analyzed through reduction, presentation, and conclusion. The results show that: 1) Teacher teaching strategies in building students' spiritual intelligence are: a) Learning methods, b) Memorizing (Tahfiz) al-Qur'an, c) Islamic education methods, d) Extracurricular activities, e) Action social fundraising and joint prayer for Palestine.

Today's digital world is influential in every aspect, making people want to change things to be more practical. There is a high demand for using WiFi technology in every sector. The highest impact on network infrastructure planning was developed by the Software-Defined Network (SDN) to address all the complex problems and connectivity needs of the future. The connectivity needed in the future is something new that can be developed so that the network can better adapt to dynamic conditions and increase flexibility. Thus, the support of WiFi technology on SDN networks became a dream, and this paper aims to implement a domain isolation algorithm (DI) to be applied to WiFi networks on the SDN. The algorithm allows for the acquisition and transmission of packets between end devices using the network's shortest and least congested pathways. Furthermore, we assessed the effectiveness of the employed algorithm by evaluating their performance based on different quality of service (QoS) metrics, utilizing the iPerf tool. The experimental results demonstrate that the proposed system achieved favorable outcomes in quality of service (QoS) metrics.

Ransomware is a dangerous malware that blocks access to data through encryption, and it exploits device vulnerabilities to perform chain attacks from one system to another. This study results in modeling the threat of ransomware attacks using Bayesian Network. The structure of the model is created using device vulnerabilities that can be exploited. As the basis for calculating the probability of the model, the EPSS vulnerability score is used. The risk exposure rating is calculated through the joint probability distribution formulation based on attack scenarios. Our model shows that ransomware attacks are most likely to exploit the chain of vulnerabilities CVE-2021-26855, CVE-2021-26857, CVE-2021-27065, CVE-2021-36942, and CVE-2017-0144 which has a probability value of 0.046534. In addition, the use of the EPSS also makes the risk assessment more factual, accurate, and effective. The threat modeling method can help in identifying ransomware attacks through a chain of vulnerabilities, making risk assessment more precise.

Text simplification is one of the domains in Natural Language Processing (NLP) that offers an opportunity to understand the text in a simplified manner for exploration. However, it is always hard to understand and retrieve knowledge from unstructured text, which is usually in the form of compound and complex sentences. There are state-of-the-art neural network-based methods to simplify the sentences for improved readability while replacing words with plain English substitutes and summarising the sentences and paragraphs. In the Knowledge Graph (KG) creation process from unstructured text, summarising long sentences and substituting words is undesirable since this may lead to information loss. However, KG creation from text requires the extraction of all possible facts (triples) with the same mentions as in the text. In this work, we propose a controlled simplification based on the factual information in a sentence, i.e., triple. We present a classical syntactic dependency-based approach to split and rephrase a compound and complex sentence into a set of simplified sentences. This simplification process will retain the original wording with a simple structure of possible domain facts in each sentence, i.e., triples. The paper also introduces an algorithm to identify and measure a sentence's syntactic complexity (SC), followed by reduction through a controlled syntactic simplification process. Last, an experiment for a dataset re-annotation is also conducted through GPT3; we aim to publish this refined corpus as a resource. This work is accepted and presented in International workshop on Learning with Knowledge Graphs (IWLKG) at WSDM-2023 Conference. The code and data is available at www.github.com/sallmanm/SynSim.

Abstrak: Di sekolah alam process pembelajaran mengedepankan interaksi dengan alam. Terkadang sekolah alam memiliki beberapa fasilitas sebagai media pembelajaran yang berhubungan dengan alam seperti kolam. Tidak terkecuali di Sekolah Alam Gaharu sebagai mitra di pengambdian masyarakat ini. Sekolah Alam Gaharu yang memiliki beberapa kolam ikan yang digunakan untuk fasilitas observasi alam. Untuk mempermudah fasilitas tersebut digunakan sebagai media pembelajaran, Sekolah Alam Gaharu membutuhkan satu sistem pemantauan semua kolam dan media belajar. Oleh karena itu, untuk memenuhi kebutuhan tersebut, dikembangkan satu sistem pemantauan kolam ikan dibeberapa titik untuk parameter keasaman dan temperature air kolam yang terintegrasi oleh satu sistem IoT. Sistem yang diterapkan sebagai solusi di Sekolah Alam Gaharu adalah dua sensor node yang digunakan untuk mengakuisisi data PH dan temperatur dan satu sistem gateway sebagai sistem integrasi kedalam satu sistem IoT. Kemudia semua parameter PH dan temperatur kolam dapat dipantau di aplikasi android. Untuk mengetahui apakah sistem berjalan sesuai kebutuhan Sekolah Alam Gaharu, dilakukan survei umpan balik ke guru sekolah tersebut. Dari Umpan balik yang dilakukan ke lima belas guru, didapatkan respon bahwa seluruh guru sangat setuju bahwa semua komponen sistem yang telah diimplementasikan sangat membantu dalam process pembelajaran. Seluruh responden menyatakan sangat setuju 100% bahwa sistem mampu membantu process pembelajaran di Sekolah Alam Gaharu. Abstract: In natural schools, the learning process emphasizes interaction with nature. Sometimes natural schools have several facilities as learning media related to nature, such as ponds. Gaharu Natural School is no exception as a partner in this community service. Gaharu Nature School has several fish ponds used as natural observation facilities. Gaharu Natural School requires a monitoring system for all ponds and learning media to make it easier for these facilities to be used as learning media. Therefore, to meet these needs, a fish pond monitoring system has been developed at several points for the parameters of acidity and pond water temperature, which are integrated by an IoT system. The system implemented as a solution at the Gaharu Natural School is two sensor nodes used to acquire PH and temperature data and a gateway system as an integration system into an IoT system. Then all PH and pool temperature parameters can be monitored in the Android application. A feedback survey was conducted on the school's teachers to determine whether the system is running according to the needs of the Gaharu Natural School. From the feedback provided to the fifteen teachers, the responses were obtained that all teachers strongly agreed that all the components of the system that had been implemented were very helpful in the learning process. All respondents strongly agreed 100% that the system could assist the learning process at Gaharu Natural School.

The purpose of this study was to analyse and describe the effect of Non-Performing Financial, Capital Adeuacy Ratio and bank size on the operational efficiency of Islamic Commercial Banks for the period 2017-2022. This research approach is quantitative with the source of data used is secondary, namely quarterly reports from Islamic Commercial Banks. In determining the sample used with purposive sampling technique, so that the data that met the criteria were 8 banks with 20 quarters from 2017-2022. Data analysis was carried out through statistical methods, namely descriptive analysis, classical assumptions hypothesis testing with the help of the Eviews application. The results showed that Non-Performing Financial affects the level of operational efficiency of Islamic Commercial Banks while Capital Adequacy Ratio and bank size cannot affect the operational efficiency of banks.

System frequent Linux operations are used on critical systems. Part systems big pay attention to safety and reliability. Ubuntu Linux is one of all Lots common Linux distributions used for system server operation. To improve security on Ubuntu Linux is required to strengthen the security system process. Strengthening security system operation is one solution for the system operation more stand to attacks and vulnerabilities. Center for Internet Security (CIS) is one caring organization for cyber security and provides benchmarks for configuration system safe Ubuntu operation. Benchmarks cover recommended settings for various component systems like file permissions, application, configuration networking, logging, and management of users. The study aims to improve security system operation with the use of control strengthening security based on CIS Benchmark v1.1.0 servers’ level 2 with the automatic model use packers application. The developed methodology consists of four phases. The first phase is Packer server installation and configuration. The second phase is to build a configuration base Ubuntu installation with user data. The third phase is the application Ansible playbook in runtime Packer automation for automation reinforcement at the time of installation and produces image virtual machine. In phase, lastly, apply structure using image virtual machine-generated and verified percentage reinforcement and optimization achieved. After strengthening security, use research methods. This generated a score conformity audit of 218 controls or 99.54% of the total 219 CIS Benchmark controls.

Many studies have been related to the Intrusion Detection System (IDS) performance analysis. Still, most focus on inspection performance on high-capacity networks with packet drop percentage as a performance parameter. Few studies are related to performance analysis in the form of detection accuracy based on the number of rules activated. This research will analyze the performance of IDS Suricata based on the number of active rules in the form of Indicator of Compromise (IoC), including IPRep, HTTP, DNS, MD5, and JA3. The analysis method focuses on the detection accuracy of varying the number of active rules up to 1 million, expressed in 5 scenarios. In scenarios 1 to 4, where IoC rules are tested separately, the reduction in detection accuracy performance starts to occur when the number of active rules is at 100,000 and continues to decrease when the number reaches 1 million. However, in scenario 5, where the IoC rules are tested together, the percentage of rules detection accuracy decreases when the number of active rules from each IoC is less than 10,000. The percentage decrease in detection accuracy performance in scenario five can occur with an average reduction of 19.64%. Even further in scenario 5, when the total number of rules reaches 1,000,000 or 200,000 from each IoC, IDS Suricata fails to detect all rules (detection percentage is 0%). This research show that the higher number of rules activated, the decrease in the Suricata IDS performance in terms of detection accuracy.

This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation (PoIR) as an alternative to traditional Proof of Work (PoW). PoIR addresses the limitations of existing reputation-based consensus mechanisms by proposing a more decentralized and fair node selection process. The proposed PoIR consensus combines Bidirectional Long Short-Term Memory (BiLSTM) with the Network Entity Reputation Database (NERD) to generate reputation scores for network entities and select authoritative nodes. NERD records network entity profiles based on various sources, i.e., Warden, Blacklists, DShield, AlienVault Open Threat Exchange (OTX), and MISP (Malware Information Sharing Platform). It summarizes these profile records into a reputation score value. The PoIR consensus mechanism utilizes these reputation scores to select authoritative nodes. The evaluation demonstrates that PoIR exhibits higher centralization resistance than PoS and PoW. Authoritative nodes were selected fairly during the 1000-block proposal round, ensuring a more decentralized blockchain ecosystem. In contrast, malicious nodes successfully monopolized 58% and 32% of transaction processes in PoS and PoW, respectively, but failed to do so in PoIR. The findings also indicate that PoIR offers efficient transaction times of 12 s, outperforms reputation-based consensus such as PoW, and is comparable to reputation-based consensus such as PoS. Furthermore, the model evaluation shows that BiLSTM outperforms other Recurrent Neural Network models, i.e., BiGRU (Bidirectional Gated Recurrent Unit), UniLSTM (Unidirectional Long Short-Term Memory), and UniGRU (Unidirectional Gated Recurrent Unit) with 0.022 Root Mean Squared Error (RMSE). This study concludes that the PoIR consensus mechanism is more resistant to centralization than PoS and PoW. Integrating BiLSTM and NERD enhances the fairness and efficiency of blockchain applications.

One of the WHO’s strategies to reduce road traffic injuries and fatalities is to enhance vehicle safety. Driving fatigue detection can be used to increase vehicle safety. Our previous study developed an ECG-based driving fatigue detection framework with AdaBoost, producing a high cross-validated accuracy of 98.82% and a testing accuracy of 81.82%; however, the study did not consider the driver’s cognitive state related to fatigue and redundant features in the classification model. In this paper, we propose developments in the feature extraction and feature selection phases in the driving fatigue detection framework. For feature extraction, we employ heart rate fragmentation to extract non-linear features to analyze the driver’s cognitive status. These features are combined with features obtained from heart rate variability analysis in the time, frequency, and non-linear domains. In feature selection, we employ mutual information to filter redundant features. To find the number of selected features with the best model performance, we carried out 28 combination experiments consisting of 7 possible selected features out of 58 features and 4 ensemble learnings. The results of the experiments show that the random forest algorithm with 44 selected features produced the best model performance testing accuracy of 95.45%, with cross-validated accuracy of 98.65%.

Blockchain has emerged as an important technology, offering safe, decentralized, and transparent platforms for recording and validating transactions. Blockchain technology consist of several main components, i.e., consensus algorithm. The consensus algorithm guarantees that all participating nodes in a blockchain network agree over the data control. Traditional consensus methods, such as Proof of Work (PoW) and Proof of Stake (PoS), present issues in terms of energy consumption and attack vulnerability. To overcome these constraints, there has been a rising interest in incorporating Artificial Intelligence (AI) techniques, especially deep learning, into blockchain consensus algorithms. We highlight blockchain fundamentals in this article, while also stressing the importance of the consensus algorithm. Furthermore, we address the most recent advancements in blockchain consensus methods in both performance-based and reputation-based paradigm, emphasizing the use of AI inside these decentralized systems. The use of AI, especially deep learning, in consensus algorithms has the potential to overcome the limitations of previous techniques. Blockchain networks may improve its performance by employing AI capabilities. However, incorporating AI into blockchain consensus algorithms is having its own challenges. Therefore, we also highlight several issues related with AI-based techniques in blockchain consensus algorithms, such as dealing with the quality and variety of data utilized by consensus algorithms and maintaining the openness of the AI models. In addition to those challenges, we propose a future direction for the AI-based approach in blockchain, which includes merging the mechanisms of performance-based and reputation-based consensus algorithms to incorporate the merits of both methods.

—Teknologi informasi menjadi aset yang sangat berharga bagi organisasi dikarenakan menjadi pendukung utama proses bisnis pada organisasi. Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) sebagai Lembaga Pemerintah Non Departenen (LPND) yang memberikan pelayanan informasi cuaca, iklim, gempabumi dan tsunami perlu untuk menjaga keamanan informasi terutama aspek confidentiality, integrity, availability, maupun accountability dari informasi yang diberikan untuk masyarakat. Penilaian risiko organisasi menjadi salah satu syarat utama dalam proses pengamanan informasi. Dalam manajemen risiko perlu dilaksanakan proses penentuan kriteria dampak risiko .