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All Journal International Journal of Basic and Applied Science Journal Basic Science and Technology Asian Journal of Management, Entrepreneurship and Social Science
Alrasyid, Wildan
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences

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Modeling the Number of Acute Hepatitis Sufferers in DKI Jakarta using Negative Binomial Regression Wildan Alrasyid; Dian Lestari; Fevi Novkaniza; Arman Haqqi; Sindy Devila
Asian Journal of Management, Entrepreneurship and Social Science Vol. 3 No. 02 (2023): May, Asian Journal of Management, Entrepreneurship and Social Science
Publisher : Cita Konsultindo Research Center

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Abstract

Hepatitis is an inflammation of the liver due to viral infections. All viral hepatitis can cause acute hepatitis. Hepatitis is an infectious disease that is a major health problem in the community because of its relatively easy transmission. DKI Jakarta is the province in Indonesia with the highest cases of acute hepatitis. Therefore, efforts need to be made to reduce the number of acute hepatitis sufferers, especially in DKI Jakarta. Several factors are thought to be closely related to the high number of acute hepatitis cases. The purpose of this study is to find factors that can significantly explain the case of hepatitis disease in DKI Jakarta so that measures can be taken to prevent the emergence of acute hepatitis cases in the community. The data in this study was obtained from the DKI Jakarta health office in 2021. The appropriate modeling for the number of people with acute hepatitis is a poisson regression model because the number of people with acute hepatitis is a count of data. In overcoming cases of overdispersion in poisson regression models, a more suitable Negative Binomial regression model is used as an alternative. In this study, the estimation of model parameters was carried out using the Maximum Likelihood Estimation (MLE) method. The results of the analysis found 3 variables that significantly explain the number of acute hepatitis sufferers in DKI Jakarta, namely the number of places of management that meet health standards, the number of health workers, and the number of HIV sufferers.
Vulnerability Analysis and Mitigation Strategies of DDoS Attacks on Cloud Infrastructure Sihotang, Hengki Tamando; Alrasyid, Wildan; Delano, Aldrich; Jacob, Halburt; Rizky, Galih Prakoso
Journal Basic Science and Technology Vol 14 No 2 (2025): June: Basic Science and Technology
Publisher : Institute of Computer Science (IOCS)

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As cloud computing becomes increasingly central to modern digital operations, it has also become a primary target for Distributed Denial of Service (DDoS) attacks. This research investigates the major vulnerabilities within cloud infrastructure that are commonly exploited by DDoS attackers and evaluates the effectiveness of various mitigation strategies. The study employs a mixed-methods approach, combining vulnerability assessment, simulated attack scenarios, and comparative performance analysis of traditional and advanced defense mechanisms, including rate limiting, Intrusion Detection Systems (IDS), Software-Defined Networking (SDN), and machine learning-based anomaly detection. The findings reveal that key weaknesses in cloud systems such as shared resource models, unsecured APIs, and auto-scaling configurations can be leveraged to disrupt services or cause economic damage. The comparative evaluation highlights the limitations of conventional tools in handling sophisticated or large-scale attacks, while also showcasing the superior adaptability of SDN and AI-driven techniques under dynamic threat conditions. This research contributes to the field of cloud security by offering a comprehensive understanding of DDoS threat vectors, identifying effective defense combinations, and providing practical recommendations for strengthening the security posture of cloud systems. The study emphasizes the importance of proactive, layered, and intelligent defense frameworks to enhance the resilience of cloud-based infrastructures against evolving DDoS threats.
AI-Based Sentiment Analysis of Social Media to Detect Public Opinion on Government Policies Rizky, Galih Prakoso; Alrasyid, Wildan
Journal Basic Science and Technology Vol 14 No 2 (2025): June: Basic Science and Technology
Publisher : Institute of Computer Science (IOCS)

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In the digital age, social media has become a powerful platform for public expression and discourse, offering governments a real-time window into citizen sentiment. This research explores the application of Artificial Intelligence (AI), specifically Natural Language Processing (NLP) techniques, to analyze public sentiment on social media in response to government policies. Using data primarily sourced from Twitter, the study applies a BERT-based sentiment analysis model to classify public reactions into positive, negative, and neutral categories. The model achieved high performance with an accuracy of 89.2%, precision of 88.6%, and recall of 87.9%, outperforming traditional classifiers. Sentiment was analyzed across three key policy areas: fuel subsidy removal, education curriculum reform, and COVID-19 vaccination programs. Results indicate significant variations in public sentiment based on policy type, timing, and inferred demographic factors. A real-time sentiment analysis dashboard was developed to support policymakers in monitoring public opinion trends and improving communication strategies. This study demonstrates the potential of AI-driven sentiment analysis as a tool for enhancing data-informed governance, public engagement, and policy responsiveness.
A bayesian dynamic latent state model for predicting infant sleep-wake patterns under daily massage intervention A , Galih Prakoso Rizky; Rasenda, Rasenda; Dermawan, Budi Arif; Arifuddin, Nurul Afifah; Alrasyid , Wildan
International Journal of Basic and Applied Science Vol. 14 No. 1 (2025): Computer Science, Engineering, Basic and Applied mathematics Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i1.699

Abstract

Sleep disturbances in infants present a persistent challenge for caregivers and healthcare providers. This study proposes a Bayesian Dynamic Latent State Model to predict infant sleep-wake patterns in response to daily massage, a non-pharmacological intervention. The model captures latent sleep propensity as a dynamic hidden process influenced by current and previous massages, individual random effects, and autoregressive components. Observed outcomes include nocturnal sleep duration and nighttime awakenings, modeled using Gaussian and Poisson distributions respectively. Through numerical simulations and a real-world case study, the model demonstrates clear benefits: average nocturnal sleep duration increased by approximately 1.2–1.5 hours, while nighttime awakenings decreased by about 35–40% on intervention days, with residual improvements on subsequent days. Compared to traditional static and time-series models, the proposed Bayesian approach provides greater flexibility in handling uncertainty, explicitly models carry-over effects, and integrates individual heterogeneity in sleep responses contributions that have not been fully addressed in prior infant sleep studies. This research thus advances the scientific understanding of dynamic, intervention-driven sleep processes, while also offering practical implications for evidence-based pediatric nursing and personalized infant care strategies. While promising, validation is currently limited to a small dataset and simplified assumptions. Future work will involve larger-scale testing, incorporation of additional external factors, and benchmarking against alternative machine learning models.
Co-Authors Arman Haqqi Budi Arif Dermawan Delano, Aldrich Dian Lestari fevi novkaniza Hengki Tamando Sihotang Jacob, Halburt Nurul Afifah Arifuddin Rasenda, Rasenda Rizky A, Galih Prakoso Rizky, Galih Prakoso Sindy Devila