Binus University

We demonstrate a low-cost, stable, and tunable laser system by phase-locking a commercial telecom-grade tunable laser (ITLA) to an Optical Frequency Comb (OFC) within the C-band. Using minimal optical hardware and sub-mW OFC power, we achieved short-term integrated phase noise of 10 mrad and long-term frequency stability of ±0.01 Hz over 10 h. This system enables scalable, OFC-locked tunable lasers and holds promise for applications like tunable THz wave generation and comb-locked transmitters in DWDM systems, supporting scalable phase-locking of multiple lasers with OFC power in the nW regime, making it highly adaptable for various OFC generators.

The rapid growth of the Internet and the increasing reliance on digital systems have significantly expanded the global digital footprint, creating new challenges for cybersecurity. Artificial Intelligence (AI) technologies, particularly Machine Learning (ML) and Deep Learning (DL), have become central to addressing these challenges by enabling the automation of complex and data-intensive tasks across antivirus solutions, intrusion prevention systems, threat intelligence platforms, and email security tools. While these technologies provide high levels of accuracy in detecting anomalies, malware, and other forms of malicious activity, they are often criticized for operating as “black-box” systems. The lack of interpretability in their decision-making processes limits the ability of cybersecurity professionals to fully understand, validate, and trust the outcomes of AI-driven models, thereby restricting their practical adoption in high-stakes environments. To mitigate these limitations, Explainable Artificial Intelligence (XAI) has emerged as a promising paradigm that aims to make AI outputs transparent, interpretable, and actionable. By providing human-understandable explanations of automated decisions, XAI can bridge the gap between technical performance and practitioner usability, enabling analysts to make informed decisions, improve incident response, and strengthen organizational resilience against both known and emerging threats. This paper reviews recent state-of-the-art developments in XAI for cybersecurity, with a particular emphasis on anomaly detection a critical area for identifying insider threats, zero-day exploits, and atypical system behavior. The review follows a structured literature analysis of peer-reviewed studies published between 2018 and 2025, identified through systematic searches in major academic databases including IEEE Xplore, Scopus, Web of Science, and ACM Digital Library. After applying predefined inclusion and exclusion criteria focused on XAI applications in cybersecurity, 53 relevant studies were analysed to synthesize methodological trends, application domains, and evaluation practices. Drawing on these findings, the paper consolidates fragmented research contributions, identifies current gaps, and provides recommendations for advancing the design and adoption of explainable, trustworthy AI systems in cybersecurity. The analysis further highlights a critical deployment challenge: the integration of explainability mechanisms often introduces trade-offs between predictive accuracy, computational efficiency, and real-time scalability factors that are essential in operational cybersecurity environments.

Generational differences in interpreting digital communication symbols, including emoji, slang, and memes, create significant communication barriers between Generation Z/Alpha and older generations, with potentially serious social and legal consequences. This study examines how such gaps produce blind spots in which concerning online behaviors, including cyberbullying, can develop undetected by adult authority figures. Using Netflix’s limited series Adolescence (2025) as a case study, this research employs qualitative content analysis of the film’s portrayal of digital communication alongside quantitative analysis of social media discussions about the series to identify patterns of symbolic misinterpretation across generational lines.Three key areas of intergenerational misinterpretation were identified: context-dependent emoji usage, rapidly evolving digital slang, and generation-specific cultural references embedded in memes. These communicative gaps systematically prevent adult authority figures from recognizing warning signs of concerning online behavior, reduce opportunities for timely intervention, and risk the misinterpretation of digital evidence in legal contexts. This study contributes to digital literacy literature by characterizing the specific mechanisms through which intergenerational communication failures occur. By mapping these communicative spaces, it establishes an empirical and theoretical foundation for developing targeted educational interventions in family and institutional settings, as well as informing future prevention strategies for online risk behaviors.

This study analyzes the reasons for waste in the production processes of a paper-based food packaging company in Indonesia and proposes improvement measures to make the operations more efficient. Through the application of Value Stream Mapping (VSM) and Single-Minute Exchange of Dies (SMED), three significant waste categories were charted: product defects (24.74%), unnecessary movements (22.23%), and unnecessary inventory (13.29%). Implementation of SMED successfully reduced the time of tool change from 60.24 minutes to 27.12 minutes, a setup time improvement of 54.98%. Pursuit of labeling system improvement as well as putting in place a Just-In-Time (JIT) inventory practice also played an important role in the reduction of wastes and fast responsiveness. Consequently, Process Cycle Efficiency (PCE) increased from 70.37% to 73.41%, production capacity increased by 1,241 units per cycle, and complaint counts for defective products declined appreciably over a period of six months. These results underscore the actual benefits of Lean methods and underscore the standard for performance excellence for the same pursuit of sustainable operational excellence within the same industry.

Plastic waste has become one of the most urgent environmental challenges, particularly at the household level where waste management practices remain limited and underutilized. This community service project was carried out to empower PKK as Family Welfare Empowerment community groups from Kelurahan Gumuruh, Bandung, Indonesia, by integrating eco-branding strategies into sustainable plastic waste management. The main objective was to transform household plastic waste into value-added products that could strengthen community-based economic independence. The method applied was participatory action through design workshops in which 40 PKK participants were actively involved. They were guided by design lecturers and supported by students in generating ideas, developing concepts, and producing product prototypes. A total of 30 prototypes were successfully produced and curated, reflecting both functional and aesthetic qualities, as well as the principles of eco-branding. These products are planned to be exhibited and marketed through local bazaars, providing visibility and potential economic opportunities for the community. The results demonstrated that collaboration between PKK communities and academic facilitators significantly improved participants’ knowledge, creativity, and confidence in plastic waste management. In conclusion, this program highlighted the importance of community and academic partnerships in addressing environmental issues while simultaneously fostering local economic development.

Brawijaya Museum is a military history center in Malang City that has a wealth of valuable collections. Unfortunately, this museum is not yet fully known by the digital community. Limitations in digital communication strategies, especially through social media, have limited the museum's reach to visits by educational institutions such as student groups facilitated by teachers. This community service activity aims to provide training in the use of social media, especially Instagram, to the management of the Brawijaya Museum to increase the visibility and appeal of the museum's collections to the wider public. The methods used in this activity include technical training, assistance in creating visual content and interactive narratives, optimizing Instagram features, and preparing guidelines for implementing Instagram social media management for the sustainability of the museum's existence on social media. The active participation of partners in each stage of the activity strengthens the sustainability of the program, while increasing their capacity to manage digital media independently. The results of this activity show an increase in the ability of partners to produce and manage content on Instagram, as well as the preparation of a digital communication strategy for managers. This activity is expected to be a model for strengthening digital capacity for other institutions in reaching the digital community more effectively

The increase in waste production, along with population growth, is filling landfills. Organic waste leachate formation. Leachate management is crucial because it can pollute soil, surface water, and groundwater. Waste is the solid remains of human activities and natural processes. Waste will be processed at the final processing site. Waste produces leachate due to decomposition and rainwater infiltration. Leachate testing requires laboratory analysis. Analysis can be done by measuring the physical and chemical parameters contained in leachate. Response Surface Methodology (RSM) is a statistical method for analysing data. This study determines the correlation between the main pollutant parameters, Temperature, pH, total suspended solids (TSS), and COD at the final treatment site. Data were collected from several sampling points over a period, and statistical analyses, including Pearson correlation and regression, were used to assess the relationship. The results showed a significant positive correlation with COD. The COD coefficient of determination (R-squared) of 66.74% was found to be quite high for all functional relationships, and mathematical models have been obtained. Contribution to Sustainable Development Goals (SDGs):SDG 6: Clean Water and SanitationSDG 11: Sustainable Cities and Communities

The adoption of multi-accreditation schemes in Indonesian higher education has increased the complexity of self-evaluation processes at the study program level. Study programs must align internal quality assurance practices with multiple national and professional accreditation standards, often leading to overlapping indicators, fragmented evidence management, and increased administrative burden. This study aims to design an AI-assisted self-evaluation framework to support structured and coherent self-assessment in multi-accreditation contexts. A design-based qualitative approach was employed through document analysis of the BAN-PT IAPS 5.1 instrument and selected Lembaga Akreditasi Mandiri (LAM) accreditation frameworks, followed by indicator harmonization and framework development. The results reveal a strong convergence of core quality indicators across accreditation instruments, enabling the formulation of unified indicators linked to shared evidence sources. Based on these findings, an AI-assisted analytical layer supporting indicator-evidence mapping, evidence completeness checking, and performance trend identification, while maintaining human-in-the-loop validation. The framework enhances efficiency, consistency, and evidence traceability in self-evaluation under multi-accreditation condition and provides a practical foundation for future empirical implementation and integration with international accreditation and ISO-based education quality standards.