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Computer Architecture and Signal Processing
Published by Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia
ISSN : -     EISSN : 31240003     DOI : 10.66472
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Aims This journal aims to disseminate research on computer architecture and digital signal processing as the foundation for high-performance, embedded, and intelligent computing systems. Scope Computer architecture and organization Embedded systems and IoT hardware Digital signal processing techniques Microprocessor and microcontroller systems Hardware–software co-design Communication and multimedia signal processing Energy-efficient and high-performance computing architectures
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Articles 4 Documents
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Search results for , issue "vol. 1 no. 2 (2026): june: computer architecture and signal processing" : 4 Documents clear
Data-Driven Learning Analytics Conceptual Framework for Automated Competency Mapping in Outcome-Based Education: A Design Science Research Approach Hasbu Naim Syaddad; Zainal Arifin Hasibuan; Bobi Kurniawan S; Sri Supatmi; Agus Nursikuwagus; Citra Noviyasari
Computer Architecture and Signal Processing Vol. 1 No. 2 (2026): June: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i2.396

Abstract

The implementation of Outcome-Based Education (OBE) in higher education demands precise measurement of Graduate Learning Outcomes (CPL) and Course Learning Outcomes (CPMK). However, current conventional Learning Management Systems (LMS) remain static and centered on final performance metrics (grades), thus failing to map student academic profiles into sub-competencies in a real-time and granular manner. This study proposes a conceptual artifact in the form of an Intelligent Tutoring System (ITS) architecture based on Learning Analytics (LA) and Knowledge Graphs to automate competency mapping. Through the Design Science Research Methodology (DSRM) approach, this framework designs a data fusion pipeline that integrates high-resolution academic log data with curriculum ontologies. The proposed architecture consists of three main layers: data acquisition, predictive modeling using Machine Learning, and a recommendation engine based on Explainable AI (XAI). This conceptual framework provides a blueprint for higher education institutions to transform from reactive curriculum evaluation into precise and auditable adaptive learning governance.
Adaptive-Cognitive Smart Farming Architectures for Food Security Resilience: A Systematic Literature Review of IoT and AI-Based Approaches Ridho Taufiq Subagio; Zainal Arifin Hasibuan; Bobby Kurniawan; Sri Supatmi; Hidayat Hidayat; Citra Noviyasari
Computer Architecture and Signal Processing Vol. 1 No. 2 (2026): June: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i2.445

Abstract

Food security resilience has become an increasingly critical global concern due to the combined effects of climate change, population growth, and resource scarcity. Conventional agricultural practices are no longer sufficient to meet rising food demands, thereby necessitating the adoption of intelligent and adaptive technological solutions. Smart farming, enabled by the integration of the Internet of Things (IoT) and Artificial Intelligence (AI), has emerged as a promising approach to enhance agricultural productivity, efficiency, and sustainability. However, existing smart farming systems remain fragmented and lack adaptive and cognitive capabilities required to dynamically respond to environmental variability. This study proposes an adaptive-cognitive smart farming architecture that integrates IoT, AI, edge-fog-cloud computing, federated learning, and digital twin technologies into a unified framework. A Systematic Literature Review (SLR) is conducted to synthesize insights from 60 high-quality publications indexed in IEEE, Elsevier, and Scopus databases. The proposed architecture adopts a multi-layered design consisting of sensing, edge-fog, cloud, cognitive, and application layers, enabling real-time data processing, distributed intelligence, and adaptive decision-making. To validate the proposed model, experimental simulations are performed using key performance indicators, including accuracy, mean squared error (MSE), latency, and resource efficiency. The results indicate that the proposed approach achieves superior performance, with an accuracy of 89%, a substantial reduction in latency, and improved resource utilization. These findings demonstrate that incorporating adaptive and cognitive intelligence significantly enhances system responsiveness and decision-making capabilities. This study contributes to both theory and practice by introducing a comprehensive framework for next-generation smart farming systems, ultimately supporting food security resilience in an increasingly uncertain environment.
Comparative Analysis of Hyperparameter Optimization Methods for ChineseBERT-Based Weibo Sentiment Classification Kartika Fanya; Almira Agwinanda
Computer Architecture and Signal Processing Vol. 1 No. 2 (2026): June: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i2.531

Abstract

Sentiment analysis has become an important research area in natural language processing (NLP), particularly for analyzing user opinions on social media platforms such as Sina Weibo. Transformer-based models have significantly improved sentiment classification performance, including ChineseBERT, which integrates glyph and pinyin information for Chinese language understanding. However, the performance of transformer models is highly influenced by hyperparameter configurations. Therefore, this study aims to compare the effectiveness of two hyperparameter optimization methods, namely Genetic Algorithm and Optuna, on ChineseBERT for Weibo sentiment classification using the WeiboSenti100k dataset. The experiments were conducted by fine-tuning the ChineseBERT model using baseline hyperparameters and optimized hyperparameters generated by both optimization methods. The optimized parameters include learning rate, dropout rate, batch size, and number of epochs. Model performance was evaluated using accuracy, precision, recall, and F1-score, with a primary focus on classification accuracy. The experimental results show that the baseline ChineseBERT model achieved an accuracy of 97.6%. Meanwhile, ChineseBERT optimized using Genetic Algorithm achieved an accuracy of 97.2%, while ChineseBERT optimized using Optuna achieved the highest accuracy of 98.2%. These results indicate that Optuna provides more effective hyperparameter optimization performance compared to Genetic Algorithm for ChineseBERT-based Weibo sentiment classification. Overall, this study demonstrates that appropriate hyperparameter optimization can improve the performance of transformer-based models for Chinese sentiment analysis tasks.
Phonics Recognition for Indonesian Dialects Using PNCC and RNN-GRU Nayya Kamila Putri Yulianto; Ratih Nur Esti Anggraini; Dwi Sunaryono
Computer Architecture and Signal Processing Vol. 1 No. 2 (2026): June: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i2.533

Abstract

Phonics plays an important role in helping learners develop reading and spelling skills by linking sounds to letters. However, applying phonics in multilingual environments such as Indonesia can be challenging due to the influence of regional dialects, especially for non-native English speakers. In this study, a phonics recognition system is developed using several deep learning approaches. The dataset consists of 986 audio recordings collected from 38 speakers, including both native and non-native English speakers from different regions in Indonesia. To improve data diversity, augmentation techniques such as pitch shifting and speed perturbation are applied. Feature extraction is performed using MFCC and PNCC, followed by classification using CNN, RNN-GRU, and Transformer models. The results show that the RNN-GRU model with PNCC features achieves the best performance, with an accuracy of 94.59% and an F1 Score of 0.946. Compared to previous work using SVM and MFCC, this approach provides better results. It is also observed that PNCC is more robust in handling pronunciation variations, and that dialect differences have a noticeable impact on model performance. Overall, this study highlights the importance of considering dialect variation in phonics recognition and shows how deep learning can be used to build more adaptive speech-based learning systems.

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