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INDONESIA
MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Published by Universitas Bumigora
ISSN : 18584144     EISSN : 24769843     DOI : 10.30812/matrik
Core Subject : Science,
Computer Science & IT
MATRIK adalah salah satu Jurnal Ilmiah yang terdapat di Universitas Bumigora Mataram (eks STMIK Bumigora Mataram) yang dikelola dibawah Lembaga Penelitian dan Pengabadian kepada Masyarakat (LPPM). Jurnal ini bertujuan untuk memberikan wadah atau sarana publikasi bagi para dosen, peneliti dan praktisi baik di lingkungan internal maupun eksternal Universitas Bumigora Mataram. Jurnal MATRIK terbit 2 (dua) kali dalam 1 tahun pada periode Genap (Mei) dan Ganjil (Nopember).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 437 Documents
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Topic Modeling Analysis of Indonesia Food-Security News: Methods,Interpretations, and Trend Insights Afiyati, Afiyati; Rochmad, Imbuh; Budiyanto, Setiyo; Jokonowo, Bambang; Santoso, Hadi; Budiana, Kelik
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5784

Abstract

The critical problem for food-security stakeholders in Indonesia is the lack of scalable, quantitative methods to systematically distill dominant themes and evolving trends from vast volumes of news media, which severely hinders timely policy monitoring and responsive intervention. This study aimed to develop and validate a reproducible topic modeling pipeline specifically designed to uncover the latent thematic structure and quantify the temporal dynamics within Indonesian food-security news discourse. The research method is a comprehensive natural language processing pipeline applied to a curated corpus of 770 news documents spanning 2012 to 2025. The process involved languageadaptive preprocessing of Indonesian text, n-gram (1-2) vectorization to capture nuanced phrases, and training multiple Latent Dirichlet Allocation (LDA) models. The optimal model, with K=10 topics,was rigorously selected through a perplexity-based grid search across a range of potential topic numbers. The resulting topics were then qualitatively interpreted and manually labeled into policy-relevant themes by domain experts. Subsequently, we computed monthly topic intensity series to conduct a longitudinal analysis. The results of this research are that the pipeline successfully generated semantically coherent topics that aligned perfectly with core policy pillars, including availability, access, and utilization. Furthermore, the analysis revealed significant temporal shifts, sustained intensification of price and inflation-related discussions throughout the 2022-2024 period. This study conclusively demonstrates that unsupervised topic modeling can effectively transform unstructured news streams into actionable, quantifiable intelligence, thereby significantly enhancing situational awareness and supporting evidence-based decision-making for food security stakeholders.
Identification of the Sub-motifs of Batik Kawung Using Deep Learning Sunarko, Budi; Subiyanto, Subiyanto; Wibawanto, Hari Wibayanto; Zakaria, Alfanza Rizky Zakaria; Alifian, Alifian; Muhammad, Naufal Muhammad; Rismawan, Yudha Andriano Rismawan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5818

Abstract

Batik is one of Indonesia’s cultural heritages, with motifs that are both diverse and intricate. The Kawung motif, characterized by repetitive circular patterns, is divided into sub-motifs such as Kawung Bribil, Kawung Sen, and Kawung Picis. Automatic classification of these sub-motifs is important for digital preservation but remains difficult due to subtle inter-class similarities. The aim of this research is to analyze the performance of VGG, ResNet, and DenseNet and determine the most effective CNN architecture in classifying the sub-motifs of Batik Kawung. The research method is a convolutional neural network-based image classification approach using a dataset of 300 Kawung Batik images evenly distributed across three classes. Preprocessing steps included grayscale conversion, resizing to 256 × 256 pixels, Canny edge detection, and normalization to the range [0,1]. The dataset was randomly split into 210 training, 60 validation, and 30 testing images. The results of this research are that VGG achieved the highest training accuracy of 97%, but only 67% on the testing set, indicating a tendency to overfit. In contrast, DenseNet achieved the best generalization performance with a testing accuracy of 80%, surpassing both VGG and ResNet. At the class level, DenseNet161 demonstrated consistent performance across all Kawung sub-motifs, with precision ranging from 67% to 91% and F1-scores between 71% and 95%. These results suggest that DenseNet161 not only performed effectively during training but also generalized well to unseen data, establishing it as the most robust architecture for sub-motif Batik Kawung classification. The results underscore the effectiveness of CNNs, particularly DenseNet, in classifying subtle batik sub-motifs. This research contributes to develope a reliable automated system for identifying Kawung batik, leveraging modern technology to support the preservation of Indonesia’s cultural heritage.
Comparative Analysis of Indonesian Pre-trained BERT Models for the Extractive Question Answering Task on an Indonesian-Translated SQuAD Dataset Suhendra, Fattah Al Ilmi; Darmayantie, Astie; Suhendra, Adang Suhendra; Pa Pa Min
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5847

Abstract

Transformer-based architectures have significantly advanced Natural Language Processing (NLP), with Bidirectional Encoder Representations from Transformers (BERT) serving as a strong baseline for extractive Question Answering (QA). This study aims to evaluate the performance of Indonesian BERT models on extractive QA tasks and to identify the most effective model for low-resource language settings. This research employed a comparative experimental method using two Indonesian BERT variants: indobert-base- ncased (IndoLEM) and indobert-base-p1 (IndoNLU/IndoBenchmark). Both models were fine-tuned on an Indonesian version of SQuAD 2.0, automatically translated via the Google Translate API. Answer-span alignment errors caused by translation were corrected using fuzzy string matching. Evaluation was conducted under identical hyperparameter settings and training schemes, using Exact Match (EM) and F1-score as performance metrics. The results indicate that IndoLEM achieved superior performance, with better loss convergence and a higher F1-score (71.58) than IndoNLU (63.59), and the difference was statistically significant (p < 0.001). In conclusion, IndoLEM is a more effective baseline model for Indonesian extractive QA systems. The findings also demonstrate that the composition and scale of pre-trained corpora substantially influence model performance in low-resource language contexts and highlight the importance of transfer learning for advancing NLP in underrepresented languages.
Performance Comparison of LSTM, XGBoost, and Residual-Correction Hybrid LSTM–XGBoost Models for Bitcoin Price Forecasting Anwas, Ihsan Maulana; Fahrianto, Feri; Shofi, Imam Marzuki; Ajif Yunizar Pratama
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5983

Abstract

The objective of this study is to systematically compare the predictive performance of Long Short- Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and a Hybrid LSTM–XGBoost model for next-day Bitcoin (BTC–USD) closing-price forecasting. The research method employs a quantitative time-series modeling approach using a decade-long daily Bitcoin price dataset. A strictly chronological train–test split and a one-step-ahead forecasting scheme are applied to prevent lookahead bias and ensure experimental validity. Model performance is evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), symmetric Mean Absolute Percentage Error (sMAPE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination R2 on the original price scale. The results demonstrate that the Hybrid LSTM–XGBoost model consistently outperforms the standalone LSTM and XGBoost models across all evaluation metrics, indicating superior predictive accuracy and robustness under high market volatility. The contribution of this study lies in providing a controlled, uniform, and methodologically rigorous head-to-head comparison of deep learning, machine learning, and hybrid architectures for Bitcoin price forecasting, thereby enriching the empirical literature and offering a reliable foundation for the development of adaptive decision-support systemsin volatile cryptocurrency investment environments.
Optimizing Content Recommendations Using a Hybrid Filtering Algorithm to Enhance User Relevance and Engagement Efrizoni, Lusiana; Junadhi; Agustin
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5988

Abstract

Recommender systems play an important role in helping users discover relevant content in environments characterized by information overload. However, existing approaches often struggle to balance recommendation relevance and user engagement. Collaborative filtering is constrained by data sparsity and the cold-start problem, whereas content-based methods that rely on textual features may not fully capture dynamic user preferences. This study aims to develop a hybrid deep learning-based recommendation model that improves both recommendation relevance and user engagement. The proposed method integrates collaborative filtering via Neural Matrix Factorization (NeuMF) with content-based filtering via a Long Short-Term Memory (LSTM) text encoder, employing an early-fusion strategy. An experimental research method was applied using synthetic user–item interaction data. Model performance was evaluated using ranking metrics (Precision@10, Recall@10, and NDCG@10) and engagement metrics (Click-Through Rate and Average Completion Ratio). The results show that the hybrid model outperforms the baseline models. It achieves Precision@10 of 0.143, Recall@10 of 0.112, and NDCG@10 of 0.139, which exceed those of both the NeuMF-only and LSTM-only models. In terms of engagement, the hybrid model also records the best performance with a CTR of 0.0017 and an ACR of 0.0090. These findings indicate that integrating user–item interaction patterns with semantic content representations can significantly enhance recommendation quality and user engagement, providing a more effective solution for content-rich digital platforms.
Multi-Criteria Hypervisor Selection Using Analytic Hierarchy Process with Ex-Post Evaluation Nugroho, Ronaldo Agung; Sensuse, Dana Indra; Sofian Lusa
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v15i2.6062

Abstract

reassessment of virtualization platform selection in banking Information Technology environments. The objective of this study is to develop a structured, scalable decision-making model to determine the most appropriate hypervisor platform based on technical and non-technical criteria. The research method used is the Analytic Hierarchy Process, developed from qualitative coding of expert interview results and validated through pairwise comparisons by internal infrastructure specialists. The analysis includes consistency measurements, sensitivity analyses, and an ex-post evaluation by comparing analytical ranking results with actual organizational decisions. The results show that technical criteria dominate the decision process, accounting for 64.10% of the total decision weight. At the alternative level, the final priority weights are 45.40–45.44% for Alternative 1, 38.00% for Alternative 2, and 16.55–16.60% for Alternative 3, with Alternative 1 identified as the most optimal choice. Notably, the proposed model achieves a 100% alignment between the analytical ranking and the actual organizational decision, representing a substantial improvement over prior studies, which were largely confined to ex-ante evaluations and lacked empirical validation of decision outcomes. The conclusion of this study confirms that integrating ex-post evaluation into a multi-criteria decision analysis approach enhances the validity of the results and demonstrates a strong fit between the analytical model and real-world decision-making in the context of banking information technology infrastructure.
Lightweight and Interpretable Coin Recognition and Counting UsingGeometric Detection and Fuzzy Score-Based Classification Dasriani, Ni Gusti Ayu; Triwijoyo, Bambang Krismono; Yasa, I Gede Yoga Sudarma; Priyanto, Dadang; Nguyen, Cong Dai
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.6067

Abstract

Deep learning-based coin recognition approaches typically require large, annotated datasets and substantial computational resources, yet offer limited interpretability. Such characteristics limit their applicability in lightweight, resource-constrained vision systems. Therefore, this study aims to develop and systematically evaluate a lightweight, interpretable coin recognition and counting method based on geometric detection and fuzzy-score-based classification. The main contribution of this work lies in integrating the Hough Circle Transform, contour-based circularity validation, and a weighted fuzzy score mechanism that aggregates diameter, circularity, and HSV color features without relying on data-driven model training. The proposed approach prioritizes computational efficiency and decision transparency, while maintaining robustness under varying lighting and object configurations. An experimental evaluation was performed on 40 test images containing 362 coins under both bright and dim lighting conditions, with aligned, scattered, and overlapping arrangements. The system achieved a detection rate of 87% and an object-level classification accuracy of 79%. Although image-level accuracy reached 50% under strict evaluation criteria, detailed error analysis indicates that performance degradation is primarily associated with segmentation limitations in overlapping configurations rather than instability in the fuzzy scoring mechanism. These findings demonstrate that a calibrated geometric and fuzzy-based approach can provide a transparent and computationally efficient alternative for small-scale vision applications without requiring large training datasets.

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