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Analyse Der Sprachhandlungen Von Lokution, Illokution Und Perlokution in Der Traditionellen Batak Toba-Hochzeit Situmorang, Rohani; Harahap, Ahmad Bengar
Jurnal Pendidikan Tambusai Vol. 9 No. 3 (2025): Desember
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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Penelitian ini mengkaji peran bahasa dalam komunikasi verbal pada upacara pernikahan adat Batak Toba melalui pendekatan teori tindak tutur Austin dan Searle, yaitu Lokution, Illokution, dan Perlokution. Metode penelitian yang digunakan adalah kualitatif deskriptif dengan pengumpulan data berupa ujaran, frasa, dan dialog dari prosesi Marhusip, Marhata Sinamot, dan Marunjuk. Analisis menunjukkan bahwa tindak tutur tidak hanya berfungsi sebagai penyampai informasi, tetapi juga sebagai wahana pembentukan dan pelestarian hubungan sosial serta nilai budaya Batak Toba. Penelitian ini mengkaji peran bahasa dalam komunikasi verbal pada upacara pernikahan adat Batak Toba melalui pendekatan teori tindak tutur Austin dan Searle, yaitu Lokution, Illokution, dan Perlokution. Metode penelitian yang digunakan adalah kualitatif deskriptif dengan pengumpulan data berupa ujaran, frasa, dan dialog dari prosesi Marhusip, Marhata Sinamot, dan Marunjuk.. Penelitian ini menegaskan pentingnya pemahaman konteks komunikasi guna mencegah kesalahpahaman dan memperkuat makna sosial dalam upacara adat. Hasil penelitian memberikan kontribusi pada kajian pragmatik dan dokumentasi budaya Batak Toba serta dapat menjadi bahan pembelajaran bahasa tradisional dalam konteks budaya.

A Comprehensive Review of Modern Machine Learning Architectures: From Statistical Models to Adaptive Intelligent Computing Systems Prakoso Rizky A, Galih; Situmorang, Rohani
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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This research presents a comprehensive review of the evolution, performance, and limitations of modern machine learning architectures, spanning from classical statistical models to advanced adaptive intelligent computing systems. By systematically comparing diverse architectural families including linear models, tree-based learners, convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformers, and emerging adaptive systems the study evaluates their computational complexity, training efficiency, scalability, data requirements, interpretability, robustness, and adaptability. The findings reveal that while traditional models remain valuable for their simplicity and transparency, deep learning and Transformer-based architectures significantly outperform earlier methods in handling large-scale, high-dimensional, and unstructured data. However, these performance gains come with notable challenges, including high computational and energy costs, adversarial vulnerability, data bias, lack of explainability, and difficulties in deployment on resource-limited devices. The study also compares current results with key findings from the past decade, highlighting both continuities and major advancements in model capabilities, scalability, and reliability. Overall, the research contributes an integrated framework that synthesizes technical, ethical, and practical considerations, offering deeper insights into the strengths, limitations, and future directions of modern machine learning architectures. The study underscores the need for more interpretable, energy-efficient, and ethically aligned AI systems to support responsible and sustainable technological development.

Analyzing the Limitations of Conventional Machine Learning Models in Handling Large-Scale and Heterogeneous Data Prakoso Rizky A, Galih; Situmorang, Rohani
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 2 (2025): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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The rapid growth of data volume, dimensionality, and heterogeneity has challenged the effectiveness of conventional machine learning models, which were originally designed for smaller and more homogeneous datasets. This study analyzes the structural and computational limitations of traditional models such as Logistic Regression, Naïve Bayes, Decision Trees, and Support Vector Machines in handling large-scale and diverse data. Using a combination of literature review, experimental evaluation, and comparative analysis, the research investigates how these models perform under increasing data size, varying feature complexity, and mixed data modalities. Key performance metrics, including accuracy degradation, training time escalation, memory consumption, and scalability constraints, are examined to identify critical thresholds where conventional techniques begin to fail. The results show that traditional models exhibit significant performance drops, resource saturation, and reduced robustness when faced with high-dimensional or heterogeneous datasets, particularly in comparison to modern deep learning and distributed learning approaches. These findings align with earlier theoretical studies but provide new empirical evidence that quantifies failure points and broadens the understanding of scalability limitations. The study concludes that while classical machine learning approaches remain effective for small and structured datasets, they are increasingly unsuitable for contemporary data-intensive environments. This research highlights the necessity of transitioning toward more scalable, adaptive, and representation-rich models to meet current and future data challenges.

Exploring Representation-Based Learning Techniques: Toward More Generalized and Self-Optimizing Models Prakoso Rizky A, Galih; Situmorang, Rohani
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Representation-based learning has become a foundational pillar of modern machine learning, enabling models to extract meaningful structure from complex, high-dimensional data. This study employs a mixed-method research design that integrates theoretical analysis, systematic literature review, and empirical evaluation to investigate the effectiveness of representation-based learning techniques in developing more generalized and self-optimizing machine learning models. Through an integrated review and empirical evaluation, the research investigates how different representation mechanisms influence model generalization, robustness, and adaptability across diverse data modalities. The findings show that deep, self-supervised, and contrastive representations consistently outperform traditional feature engineering, symbolic approaches, and classical statistical models, particularly in low-data and cross-domain scenarios. However, the study also identifies critical challenges including representation collapse, bias in embeddings, high computational overhead, interpretability limitations, and catastrophic forgetting that must be addressed to realize fully autonomous learning systems. In addition to synthesizing advances such as foundation models, multimodal fusion, neuro-symbolic frameworks, and efficient edge-compatible representations, this research proposes a structured framework for evaluating representation quality and outlines conceptual enhancements for self-optimizing learning systems. Overall, the study offers theoretical insights, practical evaluation tools, and forward-looking perspectives that contribute to the development of more generalized, flexible, and self-improving machine learning models capable of meeting the demands of evolving real-world applications.

Foundational Study on Integrating Machine Learning with Distributed Computing for Scalable Intelligent Systems Prakoso Rizky A, Galih; Situmorang, Rohani
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 4 (2025): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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The rapid growth of data-intensive applications and increasingly complex machine learning (ML) models has created an urgent need for computational architectures capable of supporting large-scale intelligent systems. This research presents a foundational study on integrating machine learning with distributed computing to achieve scalable, high-performance AI workflows. The study develops a conceptual integration model comprising four core layers data, compute, communication, and model designed to address scalability, fault tolerance, and resource optimization. Using experimental benchmarking and architectural analysis, the research evaluates multiple distributed frameworks, data partitioning strategies, and ML models to measure improvements in training speed, throughput, latency, and resource utilization across cluster-based and cloud environments. Results demonstrate significant performance gains compared to single-node execution, particularly for deep learning workloads, while also identifying critical bottlenecks such as communication overhead, synchronization delays, heterogeneous hardware constraints, and data imbalance. The findings highlight key trade-offs between accuracy and computational speed, as well as cost and system performance, underscoring the importance of strategic design decisions in large-scale ML deployments. This study contributes theoretical and practical insights into distributed ML integration and offers a framework that can guide the development of next-generation intelligent systems capable of operating across massively distributed environments.

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