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All Journal Jurnal Biodjati 3BIO: Journal of Biological Science, Technology and Management Jurnal Penelitian Kehutanan Wallacea
Andriwibowo, Andriwibowo
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Immunology & microbiology Neuroscience

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Modeling suitable habitats of maleo (Macrocephalon maleo sal. müller 1846) in Gorontalo Andriwibowo, Andriwibowo; Maarif, Fadjri
Jurnal Penelitian Kehutanan Wallacea Vol. 12 No. 2 (2023)
Publisher : Foresty Faculty of Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24259/jpkwallacea.v12i2.31355

Abstract

Maleo (Macrocephalon maleo) is one of the endangered bird species in Indonesia. This avifauna species is an endemic bird to Sulawesi Island. It is distributed from the south to the north of Sulawesi, including Gorontalo. Currently, information on suitable habitat models for M. maleo is very limited, while this information is required to support the conservation of M. maleo. This study aimed to model the potential habitat for M. maleo using species distribution modeling (SDM) with vegetation cover variables as predictors. The model was built based on the M. maleo occurrence points. The suitable habitat was then evaluated using area under the curve analysis and the receiver operating characteristic curve (AUCROC). Based on the model, the AUC is valued at 0.729, which is considered reasonable and indicates that the model can be used to depict the potential habitats for the species. In this study, most of the west and east parts of Gorontalo were considered not suitable for Maleo. While the coastal areas of Gorontalo were considered very suitable. This was confirmed for both the north and south coastal areas of Gorontalo. Then it is strongly recommended to conserve and protect most of those coasts to ensure the Maleo conservation.
Modeling suitable habitats of stingless bee klanceng (Tetragonula laeviceps) in Merbabu Mountain areas related to elevation, temperature, and humidity variables Atmaja, Cornelius Devito; Sukirno, Sukirno; Purwanto, Hari; Andriwibowo, Andriwibowo; Prabowo, Hanindyo Adi
Jurnal Penelitian Kehutanan Wallacea Vol. 13 No. 2 (2024)
Publisher : Foresty Faculty of Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24259/jpkwallacea.v13i2.36342

Abstract

Klanceng is one of the stingless bee species in Indonesia with the scientific name Tetragonula laeviceps. This bee species has sustainable economic values since it has a role as a pollinator. This species is also common in mountainous areas, including Merbabu Mountain, Central Java. Despite this bee being very common, information about potential distributions of this bee is very limited, and this information is needed for its management. This study aimed to model the potential habitat for T. laeviceps using species distribution modeling (SDM) with elevation, temperature, and humidity as predictors. The model was built based on the T. laeviceps occurrence points gained through field surveys, with a total of 23 sampling points. According to the model, most of the west parts of Merbabu Mountain were considered not suitable for T. laeviceps. This suitability is also similar to the north and south parts. This condition is in contrast to the areas that bordered with the Merbabu Mountain directly. Most areas adjacent to the Merbabu Mountain were having high and very high suitability for T. laeviceps. Regarding altitudinal distribution, T. laeviceps was limited at elevations of 1000 m.
Using Machine Learning to Model Future Distributions of Babandotan Ageratum conyzoides L. Under Climate Change Scenarios (CMIP 5: RCP 2.6 and RCP 8.5) until 2070 in Bandung Areas Andriwibowo, Andriwibowo; Meylani, Vita
3BIO: Journal of Biological Science, Technology and Management Vol. 7 No. 2 (2025)
Publisher : School of Life Sciences and Technology, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/3bio.2025.7.2.3

Abstract

Ageratum conyzoides L., locally known as Babandotan, is an important plant in particular in West Java, including in Bandung, due to its medicinal uses. Currently, climate change is known to influence the distribution of organisms by altering climates and making habitats suitable or not suitable. Then, this present study is aiming to use machine learning to model future distributions of A. conyzoides under climate change scenarios CMIP 5 RCP 8.5 until 2070 in Bandung areas. The A. conyzoides occurrences were sampled from nine locations in Bandung and its surrounding areas. Machine learning using the R platform and MaxEnt algorithm was used to develop species distribution modeling (SDM). The model was then simulated using RCP 2.6 and 8.5 scenarios for the years 2050 and 2070. The quality of the model was assessed using AUC values. The current SDM model shows suitable habitats for A. conyzoides are sizing 1250 km2, mostly located in Bandung (56%), Kota Bandung (24%), and Sumedang (16%). The AUC value was 0.964, showing that the resulting model is good. Climate change will affect A. conyzoides in the future. Based on the RCP model, suitable habitats for A. conyzoides will be shifted northward, eliminating the suitable habitats in the south of Bandung, as can be seen in 2070.
Artificial Neural Networks (ANN) to Model Microplastic Contents in Commercial Fish Species at Jakarta Bay Andriwibowo, Andriwibowo; Basukriadi, Adi; Nurdin, Erwin; Meylani, Vita; Hasanah, Nenti Rofiah; Shiddiq, Zulfi Sam; Mulyanah, Sitiawati
3BIO: Journal of Biological Science, Technology and Management Vol. 6 No. 1 (2024)
Publisher : School of Life Sciences and Technology, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/3bio.2024.6.1.3

Abstract

Jakarta Bay is known as one of the marine ecosystems that have been contaminated by microplastics. Despite massive loads of microplasticcontamination, Jakarta Bay is also habitat to potential commercial fish species, including anchovy Stolephorus commersonnii and mackerel Rastrelliger kanagurta. While information on the microplastic contents and their determining factors is still limited, the goal of this study was touse artificial neural networks (ANN) as a novel and useful tool to model the determinants of microplastic content in fish in Jakarta Bay, using fish weight and length as proxies. Inside the stomachs of S. commersonnii and R. kanagurta, the order of microplastics from the highest to thelowest was fiber > film > fragment > pellet. Based on the RMSE values of 3.199 for S. commersonnii and 2.738 for R. kanagurta, the ANNmodel of fish’s weight + length ~ pellet was found to be the best fitted model to explain the correlation of fish weight and length with microplastic content in the stomach. The results indicate that ANN is suitable for solving large, complex problems in determining and projecting microplastic contents and provides better estimates that can be used to manage R. kanagurta and S. commersonnii along with microplastic contamination threats.
Habitat and Distribution Modeling of Prehistoric Hippos (Hippopotamus sivalensis spp.) During Pleistocene in Java Island Andriwibowo, Andriwibowo; Basukriadi, Adi; Nurdin, Erwin
Jurnal Biodjati Vol 6 No 1 (2021): May
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/biodjati.v6i1.10250

Abstract

Currently, there are only 2 extant species of hippos including common (Hippopotamus amphibius) and pygmy hippos (Choeropsis liberiensis) . But in prehistoric times, there were several species. During  Pleistocene these species were known to migrate to Java Island from Asian Continent and the species was Hippopotamus sivalensis spp. In this regard, this study aimed to model the habitat of H. sivalensis spp., ecology, and biodiversity of Hippopotamus sivalensis spp. based on the fossil record. The model was developed based on the Principal Component Analysis (PCA) method using the R statistical package. The results showed that there were 7 populations of H. sivalensis spp. that lived at various altitudes with an average of 177 m above sea level (95% CI : 123-232 m). According to PCA, there were at least 2 separate populations of H. sivalensis spp. One population occupies the forest while another occupies a habitat close to the coast. Currently the habitat for H. sivalensis spp. already changed. Based on habitat modeling, H. sivalensis spp. inhabit streams with submerged aquatic plants and shrubs and trees growing along river banks.
Habitat Preference Modeling of Prehistoric Giant Shark Megalodon During Miocene in Bentang Formation of West Java Coast Andriwibowo, Andriwibowo; Basukriadi, Adi; Nurdin, Erwin; Mubarok, Muh Aydava
Jurnal Biodjati Vol 6 No 2 (2021): November
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/biodjati.v6i2.14115

Abstract

In the Miocene era about 20 million years ago, the South Coast of West Java was a sea and habitat for marine organisms including giant sharks Megalodon measuring about 18 meters long. This study aimed to model the habitat preference of the prehistoric gigantic shark Otodus megalodon population based on the fossil record. From fossil teeth, it revealed that the rock layer where the teeth found was Bentang formation from Miocene era. Many fossils of Megalodon had been unearthed from Bentang formation which is part of the South Coast of West Java. The habitat model was developed using the Sea Level Rise Inundation Tool of ArcGIS to estimate the sea depth and Megalodon’s habitat during the Miocene. The length of the teeth of O. megalodon found was ranged from 13 to 19 cm, indicating the presence of juvenile and adult O. megalodon. Based on the model, in the Miocene era, half of West Java was a sea with a depth ranging from 0 to 200 meters. At that time, it was estimated that juvenile O. megalodon occupied waters with a depth of 0-40 meters with an area of 1365 km2. Meanwhile, adult O. megalodon prefers a depth of 80-160 m and the frequency of habitat use increases at a depth of 200 m. The declining population of O. megalodon is associated with climate change and declining prey populations.
Co-Authors ADI BASUKRIADI Atmaja, Cornelius Devito Erwin Nurdin Hari Purwanto Hasanah, Nenti Rofiah Maarif, Fadjri Mubarok, Muh Aydava Mulyanah, Sitiawati Prabowo, Hanindyo Adi Shiddiq, Zulfi Sam Sukirno Sukirno Vita Meylani