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Journal of Data Analytics, Information, and Computer Science (JDAICS)
Published by Yayasan Nuraini Ibrahim Mandiri
ISSN : -     EISSN : 30324696     DOI : https://doi.org/10.59407/jdaics.v1i2
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other
Journal of Data Analytics, Information, and Computer Science (JDAICS) is a national journal for scientific research Analytics, Artificial Intelligence, Bioinformatics, Big Data, Computational Linguistics, Cryptography & Information Security, Data Mining, Data Warehouse, E-Commerce / E-Health / E-Government, Internet of Things, Information Theory, Machine Learning, Multimedia & Image Processing, Software Engineering, Socio Informatics , Wireless & Mobile Computing, Data collection and integration, Data cleaning and preprocessing, Data analysis and exploration, Machine learning and predictive modelling, Data visualization and communication, Data-driven decision making, Ethical and privacy considerations, Designing data infrastructure and systems, Data pipeline development and management, Database design and management, Data integration and ETL (Extract, Transform, Load) processes
Arjuna Subject : Ilmu Komputer (semua kategori) - Teori Komputasi dan Matematika
Articles 7 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2024): April" : 7 Documents clear
MODELING FLOOD HAZARDS IN AMBON CITY WATERSHEDS: CASE STUDIES OF WAI BATU GANTUNG, WAI BATU GAJAH, WAI TOMU, WAI BATU MERAH AND WAI RUHU Rakuasa, Heinrich; Christi Latue, Philia
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 2 (2024): April
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jdaics.v1i2.635

Abstract

Flood hazard modeling in watersheds is an important step in natural disaster risk mitigation, especially in vulnerable areas such as Ambon City. This research focused on the Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah, and Wai Ruhu watersheds, using JRC Global Surface Water Mapping Layers data, NASA SRTM Digital Elevation 30 m data, and USGS Landsat 8 Level 2, Collection 2, Tier 1 data analyzed on the Google Earth Engine (GEE) platform. Prediction of built-up land in flood-prone areas was conducted by utilizing flood history analysis, hydrological modeling, and flood zone mapping. The results show that flood hazard modeling provides a better understanding of flood risk, assists in the development of safer land use planning, and increases public awareness of flood risk in Ambon City. It is hoped that the results of this research can contribute to flood risk management and sustainable regional development in the future.
SPATIAL ANALYSIS OF LAND USE CHANGE IN SLAWI SUBDISTRICT, TEGAL REGENCY, 2014 - 2024 USING HIGH RESOLUTION SATELLITE IMAGERY DATA Rakuasa, Heinrich; Nurul Achmadi , Panji
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 2 (2024): April
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jdaics.v1i2.645

Abstract

This research aims to analyze land use change in Slawi Sub-district, Tegal Regency, from 2014 to 2024 using high-resolution satellite image data. This study used Worldview-2 high resolution satellite image data in 2014 and Geoeye-1 in 2024. This study was classified into 14 land use classes consisting of rivers, roads, railways, agriculture, green open space, other vegetation, industry, housing, government, public service facilities, trade services, transportation, defense and security and open land. The spatial analysis method was used to map changes in land area based on the main use classes such as agriculture, housing, and infrastructure. The results showed a significant decrease in agricultural land area as well as an increase in residential land area and infrastructure, reflecting the changing pattern of regional growth and development. In conclusion, an in-depth understanding of land use dynamics in Slawi Sub-district is important to support environmental management policies, natural resource conservation, and sustainable regional development in the future.
URBAN LANDSCAPE TRANSFORMATION: LAND COVER CHANGE ANALYSIS IN SIRIMAU SUB-DISTRICT, AMBON CITY Rakuasa, Heinrich; Ria Karuna, Joan; Christi Latue, Philia
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 2 (2024): April
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jdaics.v1i2.649

Abstract

This study aims to analyze the Pattern of Land Cover Change in Sirimau District, Ambon City, Indonesia. The introduction of the research discusses the importance of understanding the dynamic interaction between human activities and land resources in the context of spatially distributed land cover. The research method used was descriptive quantitative with a spatial approach, conducted in Sirimau Sub-district, Ambon City, which experienced high population growth and development of built-up areas. Satellite image data analysis was used to identify patterns of land cover change from 2014 to 2024. The results showed the dominance of agricultural land in 2014, with certain areas converted to residential and built-up land in 2019 and 2024. The findings provide valuable insights in understanding the dynamics of land cover change in Sirimau sub-district and its relevance in sustainable land resource management.
REVIEW OF THE USE OF DRONES AND NON-METRIC CAMERAS FOR THE PROVISION OF LARGE-SCALE GEOSPATIAL DATA ACCORDING TO BIG REGULATION NO. 1 OF 2020 Rakuasa, Heinrich
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 2 (2024): April
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jdaics.v1i2.695

Abstract

Currently, the need for large-scale mapping for the entire territory of Indonesia is urgent. Therefore, accelerating the provision of large-scale Geospatial Data (DG) is essential for better spatial planning and regional development. The use of drone technology with non-metric cameras is starting to be used for the provision of large-scale DG. To regulate the use of drones and non-metric cameras, the Geospatial Information Agency issued the Head of Geospatial Information Agency Regulation No. 1 of 2020. The purpose of this paper will review the use of drones with non-metric cameras that have been regulated in the agency's regulations. The method used in this paper uses qualitative research with a data collection strategy or source using the literature method. The results show that the use of direct georeferencing in BIG Regulation No. 1 of 2020 has fulfilled the horizontal and vertical geometry accuracy requirements stipulated in BIG Head Regulation No. 6 of 2018 on Base Map Accuracy. The GSD value requirement in BIG Regulation No 1/2020 is too high compared to the GSD value requirement specified in the ASPRS Accuracy Standards for Digital Geospatial Data. This agency regulation is a standard / reference that must be met for all mapping industry players. Therefore, the implementation of this agency regulation requires further study to truly support the issue of accelerating large-scale mapping.
FUTURE POPULATION PREDICTION 2050 OF BANTEN PROVINCE, JAKARTA, JAWA BARAT, JAWA TENGAH, DAERAH ISTIMEWA YOGJAKARTA, JAWA TIMUR, USING WORLDPOP DATA WITH GOOGLE EARTH ENGINE Rakuasa, Heinrich; Lasaiba, Mohammad Amin
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 2 (2024): April
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jdaics.v1i2.712

Abstract

This study uses WorldPop data and the Earth Engine platform to predict population in Banten, Jakarta, West Java, Central Java, Special Region of Yogyakarta and East Java Provinces. Through high-resolution grid-based analysis, the study identifies patterns of population growth that are not visible with traditional, more aggregated data. Prediction results show significant population increases from 2020 to 2023, with Jakarta and West Java experiencing the most notable growth. Predictions for 2050 show significant population increases in all provinces, with Jakarta and West Java being the provinces with the highest populations. The implications of these population dynamics for infrastructure planning and public policy are critical to anticipate rapid population growth. This information can be used for more effective resource allocation, targeted infrastructure planning, and improved public welfare. Thus, this research is expected to make a significant contribution to regional planning and sustainable development, as well as in facing future demographic challenges.
IMPLEMENTATION OF DYNAMIC METHOD FOR MALWARE DETECTION IN EMAIL PHISHING ATTACKS ON LET'S DEFEND Sanjay, Sanjay; Ningsih, Rahayu; Wahidin, Ahmad Jurnaidi
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 2 (2024): April
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jdaics.v1i2.717

Abstract

In the rapidly evolving landscape of cybersecurity threats, the need for robust defenses against phishing attacks has become paramount. This study explores the efficacy of malware detection in phishing email attacks using dynamic analysis on the Letsdefend.io platform. Leveraging the insights provided by the Deloitte Center for Controllership regarding the escalating frequency of cyber adversaries targeting organizational data, this research investigates the effectiveness of the Letsdefend.io platform, particularly utilizing the SOC 146 rule, in identifying and mitigating phishing threats. Through a comprehensive analysis process encompassing dynamic malware analysis techniques, such as those employed by VirusTotal and URLHaus, alongside detailed examination of suspicious email attachments using the Mailbox feature, this study aims to provide insights into the evolving tactics of phishing attackers, specifically those utilizing Excel 4.0 Macros. The research methodology involves collecting malware samples for analysis, configuring sandbox environments with tools like Process Monitor and Regshot, and utilizing sophisticated analysis tools like ProcDot to visualize malware behavior. Additionally, the study examines the effectiveness of the Letsdefend.io platform in detecting phishing URLs and malicious domains reported by AnyRun and URLHaus databases. The findings reveal promising results in the detection and identification of phishing threats, shedding light on the potential of dynamic analysis methods in bolstering cybersecurity defenses against evolving phishing techniques. This research contributes to the ongoing efforts to enhance cybersecurity measures and protect organizational assets from the pervasive threat of phishing attacks.
APPLICATION OF REMOTE SENSING DATA AND GEOGRAPHIC INFORMATION SYSTEM FOR FLOOD MODELING IN WAI RUHU WATERSHED AMBON CITY BASED ON GEOGLE EARTH ENGINE Rakuasa, Heinrich
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 2 (2024): April
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jdaics.v1i2.739

Abstract

Flood hazard modeling in watersheds is an important step in natural disaster risk mitigation, especially in vulnerable areas such as Ambon City. This research focused on the Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah, and Wai Ruhu watersheds, using JRC Global Surface Water Mapping Layers data, NASA SRTM Digital Elevation 30 m data, and USGS Landsat 8 Level 2, Collection 2, Tier 1 data analyzed on the Google Earth Engine (GEE) platform. Prediction of built-up land in flood-prone areas was conducted by utilizing flood history analysis, hydrological modeling, and flood zone mapping. The results show that flood hazard modeling provides a better understanding of flood risk, assists in the development of safer land use planning, and increases public awareness of flood risk in Ambon City. It is hoped that the results of this research can contribute to flood risk management and sustainable regional development in the future.

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