Herman Herman

The rapid expansion of large-scale computer networks and the exponential growth of big data have significantly increased the complexity and frequency of cyber threats, rendering traditional signature-based security mechanisms inadequate for adaptive detection. This study aims to develop a self-learning AI model capable of autonomously identifying evolving attack patterns and anomalous behaviors in large-scale networks without relying exclusively on pre-labeled datasets. The proposed framework integrates deep neural architectures, incremental learning, and behavior-based traffic analysis to enable continuous adaptation to dynamic threat environments while ensuring computational efficiency and scalability. The model was trained and evaluated using realistic network traffic datasets simulating distributed attacks, zero-day exploits, and advanced persistent threats across heterogeneous environments. Experimental findings demonstrate that the self-learning approach enhances detection accuracy, reduces false positives, and accelerates response times compared to conventional intrusion detection systems. In addition, the combination of deep neural architectures with incremental learning and scalable data processing further strengthens model robustness and adaptability in complex and evolving networks. The results indicate that integrating adaptive AI into cybersecurity frameworks enhances proactive defense capabilities, improves resilience in large-scale computer networks, and provides a scalable, intelligent solution for next-generation threat detection systems. This study highlights the practical relevance of combining AI, big data analytics, and cybersecurity strategies to support intelligent, adaptive security solutions capable of addressing emerging threats, minimizing operational risks, and fostering robust network protection in increasingly complex digital infrastructures.

The rapid adoption of Artificial Intelligence (AI) within digital business ecosystems has reshaped decision-making, data processing, customer services, and business automation, while simultaneously introducing significant challenges concerning accountability, transparency, data protection, and legal responsibility. This study investigates AI legal governance in sustainable digital business ecosystems by examining how regulatory principles can balance technological innovation with legal certainty, human rights, accountability, and sustainability. A normative legal research approach is employed through statutory, conceptual, and comparative legal analysis. The legal materials include regulations addressing AI-related activities, personal data protection, electronic transactions, digital governance, and relevant international frameworks, supported by scholarly literature and policy documents. The findings indicate that fragmented and technology reactive regulatory approaches are inadequate for addressing the autonomous and continuously evolving characteristics of AI-driven business activities. Effective governance therefore requires an integrated framework incorporating transparency, explainability, human oversight, risk-based regulation, data protection, accountability, and stakeholder responsibility. The proposed framework places human-centered governance at the core of sustainable digital business development by clarifying the responsibilities of AI developers, providers, and business users while reinforcing institutional oversight. This study advances digital legal governance scholarship by connecting AI regulation with sustainable business transformation and responsible technological innovation. Adaptive and accountable legal governance is crucial to ensuring that AI-driven digital ecosystems generate economic value while protecting fundamental rights, public interests, and longterm social sustainability.

The rapid advancement of autonomous technologies such as autonomous vehicles, drones, and service robots is transforming urban mobility systems and redefining how transportation and public services operate within smart cities. However, many countries still face regulatory uncertainty related to safety standards, liability allocation, data governance, and public accountability, as existing regulatory governance models tend to rely on static legal structures that are not sufficiently responsive to rapidly evolving technologies. This study aims to examine how adaptive legal frameworks can support the responsible deployment of autonomous mobility systems in smart city environments while ensuring safety, transparency, and regulatory effectiveness. The research employs a qualitative comparative policy analysis combined with thematic analysis by reviewing regulatory documents and policy frameworks from several technologically advanced jurisdictions selected based on their relevance to autonomous mobility regulation, availability of official policy documentation, and the maturity of smart city initiatives. Through iterative coding, key governance themes including regulatory flexibility, liability allocation, safety governance, and institutional oversight were identified to enable a systematic comparison of governance strategies. The findings indicate that adaptive regulatory mechanisms, including regulatory sandboxes, dynamic compliance models, and risk based governance structures, can significantly enhance the ability of urban legal systems to manage rapidly evolving autonomous technologies while balancing innovation and public safety. In conclusion, the development of adaptive legal frameworks is essential for enabling the safe, accountable, and sustainable integration of autonomous mobility systems into smart cities while supporting long term regulatory responsiveness and technological innovation.

This community service program was carried out to address students’ low confidence and difficulty in understanding spoken English texts. The purpose was to improve the listening comprehension of Grade VII-B students at SMP Swasta Teladan Pematangsiantar through the use of digital media provided by the British Council website (LearnEnglish Teens). The method used was a qualitative descriptive approach, involving socialization, material preparation, classroom implementation, and student reflection. The partners in this program were 27 Grade VII-B students, the English teacher, and the school principal. Evaluation was conducted through classroom observation and written student reflections. The results showed that although most students struggled to understand authentic English audio and lacked confidence, a small number of students demonstrated high interest and willingness to continue learning through the British Council platform. The activity successfully raised awareness of the importance of digital media in listening instruction and laid the groundwork for further development of similar programs in the future.

This research investigates the level of reading comprehension and TOEFL readiness among 12th-grade students at SMA Negeri 1 Pematangsiantar. A TOEFL style multiple choice test and a structured questionnaire were used to assess students’ reading abilities and their general awareness of TOEFL preparation. Findings revealed that the majority of students demonstrated low proficiency in reading, particularly in interpreting implied information and unfamiliar vocabulary. Despite acknowledging the importance of TOEFL, most students had limited exposure to test strategies and formal preparation. The study underscores the need for targeted instruction and institutional support to help students become more strategic, confident, and prepared for academic English assessments.

This study examines English idioms that have counterparts in Indonesian, with a focus on internal idioms as found in the animated film Samson. Due to limited research in contrastive analysis between English and Indonesian idioms, this study aims to address that gap. Semantics plays a vital role in understanding language, and idioms are an essential part of semantic expression. A qualitative descriptive method is employed to explore idiomatic expressions in both languages. Data were collected from idiom references and online sources, focusing on how idioms are used in English and their Indonesian equivalents. The analysis reveals that while many English idioms have direct counterparts in Indonesian, others exhibit nuanced differences. The idioms examined are categorized into three types. First, idioms with equivalent meanings and identical ways of expression in both languages. Second, idioms with similar meanings but slightly different interpretations, although the structure of expression is alike. Third, idioms that convey the same meaning but are expressed through different linguistic forms. These similarities and differences reflect cultural values embedded in both English and Indonesian languages. In the context of the Samson animation, the use of idioms helps convey deeper cultural and linguistic meaning. By examining idioms through contrastive analysis, the researcher hopes that viewers will gain a better understanding of idiomatic expressions, as well as translation strategies. This approach can assist in translating idioms accurately by focusing on both meaning and context within the target language.

The purpose of this study was to determine whether there is a difference in scientific literacy between students who follow the inquiry learning model and students who follow the STAD cooperative learning model to determine the difference between scientific literacy due to students' achievement motivation to describe the interaction between the learning model and achievement motivation on students' scientific literacy. This study used a Posttest-Only Control Group Design with a two-factor analysis. The population in this study were fifth-semester PGSD students at HKBP Nommensen University of Pematangsiantar, in 2024/2025 Academic Year, with totaling 186 students, which sample of 124 students determined by using Simple Random Sampling technique for the class. Data were obtained using the achievement motivation instrument and the science literacy test. Data analysis was carried out using two-way analysis of variance. The study found a significant difference in scientific literacy between students who followed the Inquiry Learning Model and those who followed the STAD Cooperative Learning Model, with a p-value of 0.000, which is less than 0.05. There is a difference in student science literacy based on achievement motivation with a value of Sig. 0.000 < 0.05 and there is no interaction between the learning model and achievement motivation on student science literacy, with a value of Sig. 0.230 > 0.05.

The transition to junior high school is a critical period for students, often accompanied by challenges in adaptation and learning. This study investigates the influence of the Masa Pengenalan Lingkungan Sekolah (MPLS), a school orientation program, on the adaptation process and learning abilities of seventh-grade students at SMP Methodist Pematangsiantar, North Sumatera, Indonesia. Employing a descriptive qualitative design, data were collected from 144 students across six classes during the MPLS held from July 14 to 16, 2025. The study analyzed three phases of the program: preparation, implementation, and evaluation. Findings indicate that the preparation phase effectively reduced student anxiety by clarifying expectations. The implementation phase facilitated social integration, familiarization with school culture, and student engagement through interactive activities. Finally, the evaluation phase showed improvements in students’ confidence, motivation, and overall adaptation to the new environment. These results suggest that MPLS significantly supports students in navigating the challenges of school transition, positively impacting their readiness to learn and participate. The study highlights the importance of structured orientation programs in promoting emotional, social, and academic adjustment in early adolescence. Recommendations include enhancing future MPLS programs by incorporating more academic exposure and peer collaboration opportunities to further strengthen student outcomes during school transitions.

This study examines the effectiveness of WordWall, a digital game-based learning platform, in improving students’ English vocabulary achievement. Conducted at SMP Negeri 4 Kota Pematangsiantar during the 2025/2026 academic year, the research involved 62 seventh-grade students divided into two groups: an experimental group taught using WordWall and a control group taught through traditional textbook-based instruction. Employing a quasi-experimental design with pre-test and post-test procedures for both groups, the study found a notable increase in the vocabulary scores of the experimental group compared to the control group. The experimental group’s mean post-test score (85.06) surpassed the school’s minimum mastery criterion (KKM) of 72, while the control group’s mean score was 71.6. Results from paired and independent sample t-tests indicated a significant difference (p < 0.05) between the two groups, validating the positive influence of WordWall on vocabulary acquisition. Moreover, students exposed to WordWall showed improved understanding and retention of word formation, such as recognizing different word classes (nouns, verbs, etc.). These findings highlight that incorporating digital game-based tools like WordWall can enhance both student engagement and learning outcomes. The study concludes that WordWall effectively supports vocabulary development while promoting a more interactive and learner-centered classroom environment, and recommends its adoption by teachers to overcome vocabulary learning challenges in English education.

This community service program aimed to improve students’ recount text writing skills by enhancing their mastery of past tense verbs through the application of clustering techniques within the Genre-Based Approach (GBA). The main problem addressed was the students’ limited ability to construct recount texts with appropriate structure, vocabulary, and chronological flow. The program involved socialization and training for 10 eighth-grade students at SMP YP HKBP 1 Pematangsiantar on May 13, 2025. The implementation methods included lectures, discussions, and writing practice, with evaluations conducted through observation, worksheet analysis, and student writing assessment. Results indicated that 80% of the participants showed significant improvement in text structure, use of past tense verbs, and idea organization. These findings suggest that the integration of clustering techniques and GBA is effective in enhancing students’ writing hardskills in the recount text genre.