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All Journal TEMATICS : TECHNOLOGY MANAGEMENT AND INFORMATICS RESEARCH JOURNALS Journal of Dinda : Data Science, Information Technology, and Data Analytics Innovative: Journal Of Social Science Research Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Romdendine, Muhammad Fahrury
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Health Professions Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Public Health Other

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TWITTER SOCIAL NETWORK ANALYSIS AND SENTIMENT IDENTIFICATION OF “VAKSIN BOOSTER” KEYWORD Romdendine, Muhammad Fahrury; Martadireja, Okky Pratama; Danutirta, Alif Sofa; Sulasno, Mitsal Shafiq
TEMATICS: Technology Management and Informatics Research Journals Vol 6 No 2 (2024): TEMATICS: Technology ManagemenT and Informatics Research Journals
Publisher : Polteknik Imigrasi

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Abstract

Abstract. The low acceptance level and limited coverage of booster vaccines, despite their critical importance for public health, highlight the need for deeper insights into societal perceptions and behaviors. Social networks, as a significant medium for information dissemination, offer a valuable opportunity to understand public discourse and identify influential factors. This study leverages graph topology analysis to map and analyze the dynamics of vaccine-related discussions within social networks. By identifying key individuals who play pivotal roles in spreading booster vaccine information, the analysis reveals the structure and flow of information within the network. Furthermore, sentiment analysis indicates that neutral interactions dominate these discussions, followed by negative and positive sentiments. Notably, the neutral content largely pertains to travel procedures, which aligns with the "mudik" tradition during the data collection period. These findings provide a framework for understanding the sociotechnical landscape of vaccine acceptance and offer actionable insights for designing targeted, effective strategies to enhance booster vaccine uptake.
Systematic Literature Review : Population Density Mapping Using Data Mining Maftuh, Naufal; Nursanto, Gunawan Ari; Romdendine, Muhammad Fahrury
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1805

Abstract

Mapping population density plays a crucial role in designing and developing urban policies. Traditional methods are often unable to capture complex spatial patterns, making the application of data mining techniques crucial. In this study, we conducted a Systematic Literature Review (SLR) of various data mining techniques, including K-Means, KDE, DBSCAN, Random Forest, linear regression, Cellular Automata, and Fuzzy C-Means. The findings of this study show that although K-Means proved to be effective, it is quite sensitive to the presence of outliers. On the other hand, DBSCAN successfully detects irregular distributions, while KDE is able to track trends despite being computationally intensive. Random Forest and linear regression can predict growth, but both require large datasets to provide accurate results. Meanwhile, Cellular Automata and Fuzzy C-Means offer flexibility, but also require comprehensive data. For future optimization, we recommend using AI-GIS hybrid models.
The Effect of Auto Gate Systems on The Traveler Profiling System at Soekarno-Hatta International Airport Martadireja, Okky Pratama; Romdendine, Muhammad Fahrury
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.16282

Abstract

The growing numbers of international flights have resulted in the improvement of immigration processes at the airports. Therefore, self-service biometric gates have been developed to guarantee more speed and security at the border. In this paper, the authors evaluate the effects of implementing auto gate systems at Soekarno-Hatta International Airport, specifically looking into the effect of operational efficiencies, privacy, and data security. This study uses a qualitative approach to literature analysis. The author uses previous studies, Government papers, and industry documents to identify the mechanisms that facilitate effective implementation and acceptance of these systems and their relationship to privacy and data security issues. Implementation of the proper auto gate systems might ease immigration processes and encourage traveling around the world, but addressing internal aspects like perception, data safety, and even human beings will always be vital. The findings from this analysis recommend an increase in the data protection models in place, enhancing the accuracy of the systems and making the procedures more open.
Graph Analysis for the Discovery of Key Proteins in Type 2 Diabetes Mellitus Permana, Angga Aditya; Romdendine, Muhammad Fahrury; Perdana, Analekta Tiara
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 5 No. 4 (2023): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v5i4.189

Abstract

One of the metabolic diseases with a rising prevalence in Indonesia is Type 2 Diabetes Mellitus (T2DM). A collective effort from various sectors is required to seek solutions for T2DM. The proteomic approach, which focuses on proteins and their interactions related to T2DM, can be used to understand this condition. This research aims to model protein interactions associated with T2DM using a network graph, enabling the identification of key proteins that have the potential to serve as therapeutic targets or T2DM biomarkers. The graph analysis method used in this study involved four centrality measures: degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. The validation method used to confirm the identified proteins is gene set enrichment analysis. The results obtained from the graph analysis using four centrality measures highlighted that seven out of 27 T2DM-related proteins are key proteins; these are: ABCC8, HNF4A, INS, KCNJ11, NEUROD1, PDX1, and SLC30A8. This study concludes that graph analysis on the interaction graph of T2DM-related proteins successfully identified key proteins that could potentially serve as T2DM biomarkers. Further medical investigation is imperative because computational identification alone is not sufficient to confirm the validity of the findings in this study.
Co-Authors Angga Aditya Permana Danutirta, Alif Sofa Maftuh, Naufal Martadireja, Okky Pratama Nursanto, Gunawan Ari Perdana, Analekta Tiara Sulasno, Mitsal Shafiq