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The Foraminifera Fossil Record of the Sedimentary Rock at Kotadjawa, Lampung, Indonesia: The Significance of Marine Paleontological Insight Harbowo, Danni Gathot; Sitinjak, Eri Sarmantua
Jurnal Riset Biologi dan Aplikasinya Vol. 6 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jrba.v6n2.p116-223

Abstract

Kotadjawa, located on the west coast of Lampung, directly faces the Indian Ocean. Lithified calcareous sedimentary beds are prevalently outcropped along this coastline. These beds likely belong to the Simpangaur Formation, which may be associated with the paleocoastal depositional environment and tectonic uplift of the Indian Ocean. Therefore, we investigated the paleontological record, focusing on foraminifera as potential indicators of the paleoenvironment. This study aimed to identify, record, and calculate the relative abundance of benthic foraminifera in the sedimentary beds of Kotadjawa, Lampung. The samples were prepared via chemical treatment of 10% H2O2 for 48 hours. Our results revealed a diversity of benthic foraminiferal fossils within the sedimentary rock. Notably, 11 benthic foraminifera genera were fossilized in the observed outcrop: Textularia (18.4%), Sigmoilopsis (16.5%), Rectobolivina (15.4%), Uvigerina (15.5%), Nodosaria (14.5%), Elphidium (11.5%), Lenticulina (7.7%), Hormosina (6.8%), Bolivina (6.8%), and Globobulimina (5.8%). These results suggested that the sedimentary beds exposed in our study area ranging from the foreshore to the deep ocean floor ecosystem. This suggests that a sediment mixing event, possibly triggered by a paleocatastrophic event, influenced the deposition of these beds. This study provides new insights into marine paleoenvironmental conditions and paleocatastrophic events along the west coast of Lampung, Sumatra.
Recognition of Voronoi Cell Distribution in Earthquake Epicenter Data in the Sunda Strait Region, Indonesia Muliawati, Triyana; Lestari, Fuji; Harbowo, Danni Gathot; S, Mika Alvionita
JOSTECH Journal of Science and Technology Vol 4, No 2: September 2024
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v4i2.9830

Abstract

The Sunda Strait is currently one of the busiest transportation hubs. However, this area has a significant history of geological disasters caused by the dynamic tectonic activity of the Eurasian and Indo-Australian tectonic plates. These disasters include the supervolcanic eruption of Krakatoa in 1883, the Sunda Strait tsunami in 2018, and decades of frequent earthquakes. To address these challenges, this study analyzes the frequency and distribution of seismic activity in the Sunda Strait region based on epicenter data recorded in the United States Geological Survey (USGS) Earthquake Catalog. We collected 440 multivariate earthquake data points between 1990 and 2023 (over three decades). The results of this study show that a machine learning approach accurately identified four relevant parameters for k-means clustering, followed by a silhouette value analysis to recognize the distribution of Voronoi cells. Based on earthquake data from the Sunda Strait from 1990 to 2023, the two highest silhouette analysis values, 0.40 and 0.39, are located at k=3 and k=5 in k-means clustering. This approach has recognized and identified the cell area of earthquake activity in the Sunda Strait, particularly around Anak Krakatoa. This study provides new insights into the spatiotemporal characteristics and identifies clusters of earthquake-prone areas. The information generated in this study facilitates the evaluation of future earthquake disaster risks, especially those with epicenters in the Sunda Strait region.
An assessment of the scientific value of Krakatoa, Indonesia from a geoheritage perspective Harbowo, Danni Gathot
Journal of Applied Geoscience and Engineering Vol 2, No 1 : Juni 2023
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jage.v2i1.19360

Abstract

Krakatoa is the most active volcanic complex located in Lampung Province, Indonesia. Throughout human history, several devastating Krakatoa eruptions have shocked the world and turned it into a global attraction. Recognizing its scientific value, Krakatoa has been designated as a geoheritage site. This study refers to the Standard Scientific Value Assessment published by Center for Geological survey of Indonesia, which applies seven main parameters, including well-published scientific reviews, to assess the feasibility of geoheritage sites. In conclusion, the Krakatoa volcanic complex is a highly regarded geoheritage site, scoring 92.5/100. Its significance extends globally, offering insights into the evolution of volcanic islands and their unique geological features. Additionally, the historical records of global catastrophes and the potential for future eruptions warrant further investigation. As a geoheritage site, Krakatoa serves as a reminder of the possibility of subsequent devastating eruptions and its natural history, making it crucial for sustainably maintaining, preserving, and managing its potential for educational, conservation, and scientific purposes. Considering the natural history, the study recommend further consideration of several sustain steps, particularly for sites around the Krakatoa area. Regular and systematic scientific observations and records of natural conditions are significant for maintaining and enhancing Krakatoa as geoheritage.
Recognizing the Spatial Distribution and Voronoi Patterns of the Recorded Earthquake Epicenters in Sunda Strait, Indonesia Muliawati, Triyana; Lestari, Fuji; Alvionita, Mika; Satria, Ardika; Harbowo, DG
Journal of Fundamental Mathematics and Applications (JFMA) Vol 7, No 2 (2024)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v7i2.21931

Abstract

Currently, Sunda Strait is one of the most active transportation hubs. However, this region also bears a notable history of geohazards associated with the dynamics of tectonic activity of the Eurasian and Indo-Australian tectonic plates, such as the super-eruption of Krakatoa volcano in 1883, the Sunda Strait tsunami in 2018, and decades of frequent earthquakes. To address these challenges, this study conducted a statistical analysis of the frequency and distribution of seismic activities in the Sunda Strait region based on recorded epicenter data in the United States Geological Survey's (USGS) Earthquake catalog. We assembled 440 multivariate earthquake data points between 1990 and 2023 (over three decades). The results of this study indicate that the machine learning approach precisely identifies four relevant parameters for -means clustering, followed by an analysis of silhouette values to recognize Voronoi patterns. These statistical patterns also have significant implications for the number of epicenter clusters and recognizing their spatial distribution. It provides a new understanding of the spatial-temporal characteristics and locates the list of frequent earthquake regions. Having all the necessary information would help to comprehensively evaluate geohazard risks in Sunda Strait region.
Reinforcing Local Food Security through Agrogeological Perspectives: South Lampung, Indonesia Danni Gathot Harbowo
Communication in Food Science and Technology Vol 3 No 2 (2024): Communication in Food Science and Technology, November Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat, Institut Teknologi Sumatera

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/cfst.v3i2.1973

Abstract

Local food security is a critical priority for the coming decades, especially in Indonesia. As the population grows, it is essential to meet local food needs before participating in the national supply. South Lampung, a key region in Sumatra, plays a vital role in the nation’s food supply. However, rapid population growth in this area has raised concerns about the conversion of agricultural land to residential use. To address these challenges, this study raises agrogeology as an alternative approach to reinforcing local food security. By re-evaluating the regional geological characteristics, the study aims to understand the distribution of productive, traditional paddy fields. Field surveys and spatial analyses were conducted for this study. The findings reveal that the silicate-rich lithology in the region’s pyroclastic deposits significantly contributes to maintaining fertile soils, particularly in areas such as Candipuro, Jatiagung, and Palas. Natural drainage systems further support rice cultivation. In alignment with the Sustainable Development Goals, the agrogeological perspective emphasizes the need to mitigate land conversion and improve land-use planning. This approach offers a sustainable pathway to enhance paddy field productivity and ensure water and nutrient availability, thereby supporting long-term local food security.
Towards a Geobotanical Insight into Vegetation Adaptation in Quaternary Mud Volcanoes: Java Island, Indonesia Sitinjak, Eri Sarmantua; Harbowo, Danni Gathot
Journal of Multidisciplinary Applied Natural Science Vol. 5 No. 2 (2025): Journal of Multidisciplinary Applied Natural Science
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.272

Abstract

Since the Lusi eruption in 2006, interest in studying mud volcanoes in Indonesia has significantly increased, with most research focusing on the onshore Northeast Java Basin (Kendeng and Rembang Zone), yet comprehensive studies on the vegetation surrounding these mud volcanoes, particularly from a geobotanical perspective, are lacking. This study aims to document and analyze the vegetation around various 18 mud volcanoes in Java Island, Indonesia and explore its relationship with the geological characteristics of the volcanoes. Through field mapping and site visits to all identified 18 mud volcanoes on Java and Madura Island, samples of mud and vegetation were collected for detailed laboratory analysis. By examining the vegetation that grows at the center and the periphery of the mud volcanoes, a relationship between vegetation composition and the mud volcanoes in Java Island, Indonesia, can be identified. It becomes more interesting as saline substrates play a key role in their adaptation. The vegetation surrounding quaternary mud volcanoes in Java Island is significantly influenced by the characteristics of the erupted mud material. Plants from the families Poaceae, Fabaceae, Asteraceae, Cyperaceae, and Euphorbiaceae are well-adapted to the vicinity of mud volcanoes in both the Kendeng zone, rich in marl from the Kalibeng formation, and the Rembang zone, rich in clay. These plants can thrive and adapt to substrates with distinctive chemical characteristics, such as high salinity levels (>10%) and dominant calcium and magnesium content (30–60%). This research indicates a complex interplay between geological factors and plant species distribution in the unique environment of mud volcanoes. This research is expected to inspire further studies on this unique geological feature and advance the field of geobotanical study.
Statistical Pattern Recognition of Lithosphere Anomalous Activity Along the Indonesian Ring of Fire S, Mika Alvionita; Satria, Ardika; Muliawati, Triyana; Lestari, Fuji; Harbowo, Danni Gathot
Journal of Science and Applicative Technology Vol. 9 No. 1 (2025): Journal of Science and Applicative Technology June Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v9i1.1850

Abstract

The introduction of statistical pattern recognition becomes highly important for assessing disaster threats such as earthquakes. This approach is significantly more comprehensive and suitable for long-term event forecasting. Therefore, in the future, efforts can be promptly made to reduce the risk of disasters resulting from anomalies in lithospheric activity, especially frequent earthquakes in the Sumatra Island region, Indonesia. Statistical pattern analysis of lithospheric activity anomalies can be categorized through classification. Earthquake classification is performed based on magnitude scale and mathematical calculations of earthquake parameter unit conversion. The classification method employed in this research includes machine learning methods like k-nearest neighbor and support vector machine. The evaluation metrics used for machine learning models are model accuracy and confusion matrix tables.