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Legal Aspects of Using Deepfake in Political Campaigns: A Threat to Democracy? Lestari, Mugi; Ibrahim, Riza
International Journal of Humanities, Law, and Politics Vol. 3 No. 1 (2025): International Journal of Humanities, Law, and Politics
Publisher : Communication in Research and Publications (CRP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijhlp.v3i1.193

This study examines the legal aspects of the use of deepfake technology in political campaigns and its implications for the integrity of democracy. Through a qualitative approach with normative legal analysis methods, this study maps regulations related to deepfake in various countries, analyzes case studies of the use of deepfake in political campaigns, and evaluates the effectiveness of regulations in Indonesia. The results show that regulations on deepfake in political campaigns vary significantly between countries, with some countries such as the United States (at the state level) and the European Union having implemented specific regulations, while Indonesia still relies on the Electronic Information and Transactions Law (UU ITE) which does not specifically regulate deepfake. Case study analysis reveals the use of deepfake for positive purposes such as translating political speeches (India) and negative purposes such as spreading disinformation (United States, Russia, Philippines). The main challenges to regulation in Indonesia include the lack of public awareness, limited detection technology, the speed of information dissemination on social media, and the absence of specific sanctions for the misuse of deepfake in a political context. Based on these findings, this study recommends the establishment of specific regulations related to deepfake, strengthening multi-party collaboration in developing detection systems, increasing public education through digital literacy campaigns, and implementing strict law enforcement with specific sanctions. A synergy between comprehensive regulation, advanced detection technology, and high public awareness is needed to mitigate the negative impact of deepfakes on democratic integrity and ensure that this technology is used ethically in political communication.

The Role of Explainable Artificial Intelligence (XAI) in Drug Discovery: A Study of Opportunities and Barriers to Implementation Ibrahim, Riza; Azkiyah, Hilda
International Journal of Health, Medicine, and Sports Vol. 3 No. 2 (2025): International Journal of Health, Medicine, and Sports
Publisher : Corespub

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijhms.v3i2.216

Drug discovery is a complex, lengthy, and costly process with a high failure rate, especially during clinical trials. The integration of Artificial Intelligence (AI) has revolutionized various stages of drug discovery by enabling faster and more accurate analysis of biological and chemical data. However, most AI models in this field operate as “black boxes,” where their decision-making processes are opaque and difficult to interpret. This lack of transparency poses significant challenges in terms of trust, validation, and adoption of AI-generated predictions in both clinical and regulatory settings. To address this issue, Explainable Artificial Intelligence (XAI) has emerged as a promising approach to improve the interpretability of AI models without compromising their predictive power. This study aims to systematically review the opportunities, challenges, and future directions of XAI implementation in drug discovery. Using a qualitative method with a systematic literature review approach, data were collected from reputable databases including Scopus, PubMed, IEEE Xplore, SpringerLink, and ScienceDirect, focusing on publications from 2018 to 2024. The analysis identified five main themes: the role of XAI in molecular target identification, application of XAI in compound screening and molecular structure optimization, interpretation of drug toxicity predictions, challenges in XAI implementation, and future research directions. XAI techniques such as SHAP and LIME have proven useful in explaining AI model predictions, improving biological validation, and enabling more informed decision-making by scientists. However, significant challenges remain, including the trade-off between interpretability and accuracy, lack of universal standards, and the complexity of modeling biological systems. This study highlights the critical need for developing standardized interpretability frameworks, user-friendly interfaces, and collaborative environments between data scientists and healthcare professionals to foster XAI adoption in real-world drug discovery processes. Ultimately, XAI has the potential to increase transparency, trust, and efficiency, paving the way for safer and more effective therapeutic developments.

Revitalization of Traditional Knowledge in Natural Resource Management: An Ethnobotanical Study in the Dayak Kenyah Indigenous Community Ibrahim, Riza; Surya, Dhika
International Journal of Ethno-Sciences and Education Research Vol 5, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v5i2.937

This study aims to document the ethnobotanical knowledge of the Dayak Kenyah community in East Kalimantan as an effort to preserve culture and the environment. Using a qualitative-descriptive approach with data collection techniques through semi-structured interviews, walking transects, and visual documentation, this study identified 47 plant species utilized by the Dayak Kenyah community. The results showed that the plants were categorized based on their main functions, namely as medicine (38%), food (30%), building materials (19%), and ritual purposes (13%). Several types of key plants such as Tengkawang (Shorea spp.), Sungkai (Peronema canescens), Ulin (Eusideroxylon zwageri), and Katimpun (Uncaria gambir) have important values in the cultural and ecological structure of the community. Custom-based conservation practices such as Tana' Ulen are concrete evidence of the implementation of Traditional Ecological Knowledge (TEK) that supports the sustainability of forest ecosystems. However, there are indications of a decline in the interest of the younger generation in this local knowledge, with only 27.8% of young informants being able to identify more than 10 types of local plants. This study recommends the integration of TEK with modern conservation strategies and the revitalization of traditional knowledge through local culture-based education to ensure the sustainability of valuable traditional ecological knowledge.

Tanjung Jaya Village and the Potential for Developing Ethno-Sciences for Climate Change Mitigation in Panimbang District, Banten Sulistya, Astrid; Ibrahim, Riza
International Journal of Ethno-Sciences and Education Research Vol 5, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v5i1.838

Climate change is a major challenge for global society, especially in coastal areas that are vulnerable to the impacts of environmental change. Tanjung Jaya Village in Panimbang District, Banten, is an example of a coastal area that faces major threats due to climate change, such as coastal abrasion, tidal flooding, and changes in weather patterns. The people of Tanjung Jaya Village have rich local knowledge, which has been passed down from generation to generation and has proven effective in managing natural resources and mitigating disasters. This study aims to explore the potential of ethno-sciences or ethnology in addressing the impacts of climate change in this village, with a focus on mangrove management, sustainable agriculture, and coastal ecosystem protection. The results of the study indicate that the integration of traditional knowledge with modern science, such as satellite-based monitoring technology, can increase the effectiveness of climate change mitigation. Therefore, the development of policies that support the strengthening of the role of local wisdom in mitigating climate change is very much needed to increase community resilience to natural disasters

Dynamics of Intergenerational Transmission of Traditional Agricultural Knowledge in the Dayak Kenyah Community: Challenges and Opportunities for Strengthening Ethno-Sciences in the Modern Era Ibrahim, Riza; Yuningsih, Siti Hadiaty
International Journal of Ethno-Sciences and Education Research Vol. 5 No. 3 (2025): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v5i3.1046

In an era of increasingly rapid modernization, the sustainability of TAK faces serious challenges, ranging from rural-urban migration, formal curricula that ignore local content, and declining interest among the younger generation in agricultural practices based on local wisdom. Using a qualitative ethnographic approach, this study uncovers inheritance patterns, transmission channels, inhibiting factors, and potential for revitalization. The results indicate a generational gap, with elders still maintaining knowledge retention rates above 90%, while younger generations retain only around 25%. Nevertheless, opportunities for revitalization emerge through community initiatives, the integration of TAK into local curricula, and the development of technology-based agroecological entrepreneurship. This study confirms that cross-generational collaboration, policy support that recognizes the value of local knowledge, and the synergy between tradition and innovation are key to TAK's sustainability. Thus, TAK is not only a cultural heritage, but also a strategic resource for food security, biodiversity conservation, and strengthening the ecological identity of indigenous communities in an era of socio-ecological change.

Circular Economy Socialization for the General Public Around the Citarum River Basin Ibrahim, Riza; Jaizul, Alim
International Journal of Research in Community Services Vol. 6 No. 3 (2025): International Journal of Research in Community Service (IJRCS)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v6i3.1043

Environmental issues are becoming increasingly concerning and endangering the life of the ecosystem on this earth. The environmental problems faced will certainly endanger human life now and for future generations. The issue of sustainability has become a very interesting study to discuss, both by academics, practitioners, relevant authorities, and even the general public, especially in developing countries. The concept of the five Rs: Reduce, Reuse, Recycle, Repair, and Recover, which has long been offered as a solution to reduce industrial waste, community waste, and family (household) waste, continues to be developed and is now the concept of the circular economy. The trending concept of the circular economy must be disseminated so that everyone realizes the importance of playing a role in realizing a circular economy for the sustainability of life in the future. This is the background to the implementation of the Student Creativity Program (PKM) with the topic of the Circular Economy for the General Public. The PKM was carried out offline and targeted the participants' understanding and interest in the circular economy. After the PKM implementation, understanding of the circular economy increased; where initially the community was not familiar with and familiar with the circular economy, after participating in the socialization showed a change in paradigm and attitudes, as well as knowledge about the circular economy.

Tanjung Jaya Village and the Potential for Developing Ethno-Sciences for Climate Change Mitigation in Panimbang District, Banten Sulistya, Astrid; Ibrahim, Riza
International Journal of Ethno-Sciences and Education Research Vol. 5 No. 1 (2025): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v5i1.838

Climate change is a major challenge for global society, especially in coastal areas that are vulnerable to the impacts of environmental change. Tanjung Jaya Village in Panimbang District, Banten, is an example of a coastal area that faces major threats due to climate change, such as coastal abrasion, tidal flooding, and changes in weather patterns. The people of Tanjung Jaya Village have rich local knowledge, which has been passed down from generation to generation and has proven effective in managing natural resources and mitigating disasters. This study aims to explore the potential of ethno-sciences or ethnology in addressing the impacts of climate change in this village, with a focus on mangrove management, sustainable agriculture, and coastal ecosystem protection. The results of the study indicate that the integration of traditional knowledge with modern science, such as satellite-based monitoring technology, can increase the effectiveness of climate change mitigation. Therefore, the development of policies that support the strengthening of the role of local wisdom in mitigating climate change is very much needed to increase community resilience to natural disasters

Revitalization of Traditional Knowledge in Natural Resource Management: An Ethnobotanical Study in the Dayak Kenyah Indigenous Community Ibrahim, Riza; Surya, Dhika
International Journal of Ethno-Sciences and Education Research Vol. 5 No. 2 (2025): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v5i2.937

This study aims to document the ethnobotanical knowledge of the Dayak Kenyah community in East Kalimantan as an effort to preserve culture and the environment. Using a qualitative-descriptive approach with data collection techniques through semi-structured interviews, walking transects, and visual documentation, this study identified 47 plant species utilized by the Dayak Kenyah community. The results showed that the plants were categorized based on their main functions, namely as medicine (38%), food (30%), building materials (19%), and ritual purposes (13%). Several types of key plants such as Tengkawang (Shorea spp.), Sungkai (Peronema canescens), Ulin (Eusideroxylon zwageri), and Katimpun (Uncaria gambir) have important values in the cultural and ecological structure of the community. Custom-based conservation practices such as Tana' Ulen are concrete evidence of the implementation of Traditional Ecological Knowledge (TEK) that supports the sustainability of forest ecosystems. However, there are indications of a decline in the interest of the younger generation in this local knowledge, with only 27.8% of young informants being able to identify more than 10 types of local plants. This study recommends the integration of TEK with modern conservation strategies and the revitalization of traditional knowledge through local culture-based education to ensure the sustainability of valuable traditional ecological knowledge.

Comparison of Machine Learning Models for Breast Cancer Diagnosis Classification Ibrahim, Riza; Yuningsih, Siti Hadiaty; Ismail, Muhammad Iqbal Al-Banna
International Journal of Global Operations Research Vol. 6 No. 4 (2025): International Journal of Global Operations Research (IJGOR), November 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i4.431

Breast cancer remains one of the most pressing global public health challenges, with approximately 2.3 million women diagnosed worldwide in 2022 and around 670,000 deaths attributed to the disease. Despite the widespread application of machine learning algorithms for breast cancer classification, findings across studies remain highly varied, and there is still no consistent conclusion regarding which algorithm is most superior for breast cancer diagnosis. This study aims to analyze and compare the performance of four machine learning algorithms Logistic Regression, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN) in predicting breast cancer. The dataset used was the Breast Cancer Wisconsin (Diagnostic) Data Set obtained from Kaggle, containing morphological characteristics of tumor cells. Data preprocessing involved cleaning, label encoding, feature normalization using StandardScaler, and an 80:20 train-test split. Model performance was evaluated using confusion matrix, precision, recall, F1-score, accuracy, and ROC-AUC. The results showed that all four models achieved excellent performance with overall accuracy ranging from 95.61% to 97.37%. SVM emerged as the most accurate model (97.37%) with perfect recall (1.00) for the Benign class. Logistic Regression demonstrated the highest ROC-AUC value (0.9960), indicating excellent discriminative ability. Random Forest and KNN showed slightly lower performance, particularly in detecting Malignant cases with recall of 0.90. These findings confirm that machine learning can serve as an effective tool to support breast cancer diagnosis, with algorithm selection depending on data characteristics and clinical priorities.

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