Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal 3BIO: Journal of Biological Science, Technology and Management Jurnal Penelitian Kehutanan Wallacea
Andriwibowo, Andriwibowo
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Immunology & microbiology Neuroscience

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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.
Co-Authors ADI BASUKRIADI Atmaja, Cornelius Devito Erwin Nurdin Hari Purwanto Hasanah, Nenti Rofiah Maarif, Fadjri Mulyanah, Sitiawati Prabowo, Hanindyo Adi Shiddiq, Zulfi Sam Sukirno Sukirno Vita Meylani