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Teknika
Published by Institut Informatika Indonesia Surabaya
ISSN : 25498037     EISSN : 25498045     DOI : https://doi.org/10.34148/teknika
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
Teknika is a peer-reviewed journal dedicated to disseminate research articles in Information and Communication Technology (ICT) area. Researchers, lecturers, students, or practitioners are welcomed to submit paper which has topic below: Computer Networks Computer Security Artificial Intelligence Machine Learning Human Computer Interaction Computer Vision Virtual/Augmented Reality Digital Image Processing Data Mining Web Mining Computer Architecture Software Engineering Decision Support System Information System Audit Business Information System Datawarehouse & OLAP And any other topics relevant with Information and Communication Technology (ICT) area
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 356 Documents
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Enhancing Food Court Experience Through Design Thinking Deatri Arumsari Agung; Stefanus Amadeo Elbaharikusuma
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1452

Abstract

Despite widespread digital transformation in the food and beverage service industry, food courts in shopping malls largely remain dependent on conventional, queue-based ordering systems. This results in persistent inefficiencies and a diminished customer experience. This study advances existing research by developing and empirically validating a user-centered digital ordering and cashless payment prototype for food court environments. Food courts remain an underexplored service context compared to standalone restaurants and self-service kiosks. Employing a Design Thinking methodology, this qualitative study integrates empathize stage interviews with usability testing to achieve methodological triangulation. This ensures both experiential depth and design validation. Five Generation Z users in Surabaya participated as actual end users. This addresses a key limitation of prior studies that relied primarily on business owners or designers rather than customers. User insights were synthesized into a Point of View (POV) statement that directly informed ideation and iterative prototyping. The resulting prototype prioritizes visual clarity, intuitive navigation, and accessibility. These features are achieved through image-supported menus, readable typography, adequate color contrast, and icon-based interaction. Testing results indicate that the prototype eliminates physical queuing, while users reported enhanced efficiency and greater satisfaction. The study contributes theoretically by extending the application of Design Thinking to shared-service food court systems. Practically, it offers empirically grounded UI/UX design principles for scalable digital ordering solutions in high-traffic service environments.
Multi-Horizon LSTM Forecasting of Indonesian Food Commodity Prices to Support the Free Nutritious Meal (MBG) Program Deni Utama; Della Novita Sari
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1453

Abstract

This study develops a multi-horizon Long Short-Term Memory (LSTM) model to forecast food commodity prices in Indonesia, supporting the national Free Nutritious Meal (MBG) program. The MBG initiative increases daily and annual food demand for over 50 million beneficiaries, affecting rice, meat, eggs, vegetables, and cooking oil. Historical price data were used to train the model, which effectively captures nonlinear temporal patterns and seasonal variations. Forecast results indicate heterogeneous volatility patterns across commodities. Staple commodities such as rice and cooking oil exhibit relatively stable trends with minor monthly fluctuations, whereas perishable and climate-sensitive commodities such as chili, eggs, and sweet potatoes demonstrate higher short-term volatility. To ensure temporal robustness, the model was evaluated using a Rolling Forecast Origin (RFO) validation strategy with an expanding window approach. Evaluation on the test set yielded a MAE of 2,108.37, RMSE of 3,933.30, and MAPE of 6.61%, indicating approximately 93% accuracy. The model is implemented in an interactive interface allowing users to select commodities, set prediction horizons, and export results, providing a practical tool for procurement planning, distribution management, and national food security.
Beyond Accuracy: Cross-Validated and Threshold-Optimized Deep Learning for Primary and Metastatic Melanoma Classification from Histopathological Patches Raden Rara Kartika Kusuma Winahyu; Lathifah Alfat; Deyana Kusuma Wardani
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1454

Abstract

Accurate differentiation between primary and metastatic melanoma in histopathological assessment is critical for staging and therapeutic decision-making. Although deep learning models often report high classification accuracy, their robustness and threshold-dependent clinical behavior remain insufficiently examined. We propose a cross-validated and threshold-optimized deep learning framework for classifying 206 histopathological regions of interest (ROIs), partitioned in an 80:20 split into training (n = 164) and evaluation (n = 42) subsets, using a ResNet-18 backbone. On the hold-out evaluation set, the model achieved an AUC of 0.922. To evaluate generalization stability, stratified 5-fold cross-validation was conducted across all ROIs, yielding fold AUCs ranging from 0.904 to 0.973 and a mean AUC of 0.938 ± 0.024, with a pooled out-of-fold AUC of 0.916. At a decision threshold of 0.5, the model achieved 78.6% accuracy (macro F1 = 0.7846). Increasing the threshold to 0.8 improved accuracy to 85.7% (macro F1 = 0.856), accompanied by higher precision for metastatic melanoma (0.894) and improved recall for primary melanoma (0.904), underscoring clinically meaningful sensitivity–specificity trade-offs beyond AUC alone. Grad-CAM analysis demonstrated spatially coherent activation concentrated within tumor-dense regions in true positives, minimal activation in true negatives, and intermediate activation in a borderline false negative case (probability = 0.75), linking prediction confidence to morphologically relevant evidence. Collectively, these findings highlight the importance of cross-validation rigor, threshold calibration, and interpretability in advancing clinically reliable deep learning systems for melanoma classification.
Static Code Analysis for Identification and Prioritization of Code Quality Remediation in a Resource-Constrained University Academic Information System Muhammad Ridho Kurniawan Pratama; Deni Utama; Rauhil Fahmi; Ali Idrus
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1458

Abstract

Software quality directly impacts organizational effectiveness, yet university information systems in developing nations frequently face compounded challenges, including resource constraints, limited developer expertise, and inadequate quality assurance mechanisms. Although static code analysis systematically detects software defects, translating technical findings into actionable, context-appropriate strategies remains problematic for resource-limited academic institutions. This research investigated structural quality, security vulnerabilities, and maintainability characteristics of a university academic information system, formulating contextually informed recommendations that reconcile technical assessment outcomes with institutional constraints. The case study employed four sequential phases: organizational context evaluation, quantification of code-complexity metrics, comprehensive quality assessment via static analysis, and development of context-sensitive recommendations. Correlation analysis examined relationships between structural complexity indicators and maintainability degradation. Examination of 127 PHP files revealed 9,628 quality defects, with maintainability issues accounting for 89.9%, alongside 18 critical security vulnerabilities and severe complexity, with individual methods reaching cyclomatic complexity values of 263. Strong positive correlations emerged between complexity metrics and maintainability problems (r = 0.938, p < 0.001), indicating that complexity is a reliable predictor of quality deterioration. Issue distribution patterns across all examined files suggested systemic quality degradation rather than isolated problematic modules. Findings document critical security exposures, excessive structural complexity, and pervasive maintainability deficiencies, validating significant associations between complexity and maintenance burden. The study proposes a bifurcated improvement approach encompassing tactical measures targeting immediate technical debt, including security vulnerabilities and architectural complexity, complemented by strategic interventions, providing academic information system managers with a prioritized remediation action calibrated to institutional resource constraints.
A Hybrid Approach Using K-Means and Apriori Algorithms for Marketing Recommendation Systems in the Café Industry Kezia Elice Yulianto; Indra Maryati
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1461

Abstract

Many food and beverage (F&B) businesses struggle to survive because of a lack of understanding of their market and customer behavior. Therefore, this study aims to analyze transaction data using K-Means Clustering and Apriori algorithms to identify customer purchasing patterns and provide actionable insights for a café. This research applies the CRISP-DM method, including six primary phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. K-Means reveals that products can be grouped into three main clusters, namely the low-demand products, premium products, and best-selling products. Furthermore, the association rule mining successfully indicates six meaningful correlations among products, with the strongest lift value of 5.22 was found between Piccolo Latte and Extra Oatmilk. These findings are transformed into actionable insights, such as recommendations for implementing bundling and up-selling. An interview with the business owner validates that the recommendations are considered applicable to the current business operations. This proves that the integration of K-Means and Apriori algorithms can be used to design effective data-driven marketing strategies for an F&B business.
Robust Balinese Script Detection for Transliteration Using YOLOv8 Hans Juwiantho; Jonathan Eka Saputra
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1466

Abstract

YOLOv8-based detection system was developed to support automatic transliteration of Balinese script by addressing challenges related to small character size, visual similarity, and varying image quality. The study evaluates detection performance using a Balinese script dataset consisting of four dataset variations, Original, Grayscale, Augmented, and book-excluded dataset, designed to analyze the impact of preprocessing and data variation on model accuracy. The Original and Grayscale datasets demonstrating superior performance by achieving mAP@50 values above 0.93. Hyperparameter tuning experiments involving optimizer selection, epoch variation, and model scale indicated that the YOLOv8x variant with the auto optimizer and a fine-tuning strategy delivered the highest performance, reaching an mAP@50 of 0.9405 while maintaining balanced precision and recall. Confusion matrix analysis showed that most detection errors occurred between visually similar characters. Robustness evaluation further revealed that detection performance remains reliable when image clarity moderate, rotation does not exceed ±3°, shear remains within ±2°, and character bounding boxes occupy approximately at least 1% of the total image dimension. These findings contribute the importance of dataset preparation, model configuration, and robustness constraints to achieve accurate Balinese script detection for real-world transliteration applications.
Sequential Tourism Recommendation Using Dual-Input LSTM for Sustainable Destination Distribution Elyandri Prasiwiningrum; Ego Oktafanda; Junadhi
Teknika Vol. 15 No. 2 (2026): July 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i2.1468

Abstract

Tourism recommendation systems have become increasingly important in supporting intelligent travel planning and improving tourist experiences through personalized destination suggestions. However, most conventional recommendation approaches fail to effectively model sequential tourist behavior and contextual travel activities. This study proposes a predictive tourism destination recommendation system using a dual-input Long Short-Term Memory (LSTM) architecture capable of simultaneously learning destination sequences and tourist activity patterns. The proposed framework aims to generate contextual and adaptive destination recommendations based on tourist travel histories in Rokan Hulu Regency, Indonesia. Due to the limited availability of real-world sequential tourism datasets, a realistic synthetic dataset was constructed by considering destination categories, tourist typologies, geographical distance, popularity scores, visit duration, and activity diversity. The preprocessing stage involved tokenization, sequence padding, and tensor transformation to prepare the data for deep learning–based sequential modeling. The proposed dual-input LSTM model was trained using destination sequences and activity sequences as parallel inputs and evaluated using Top-K Accuracy metrics. Experimental results demonstrate that the proposed model achieved a Top-1 Accuracy of 39.72%, a Top-3 Accuracy of 76.21%, and a Top-5 Accuracy of 92.40%. Comparative evaluation also shows that the proposed architecture outperformed both a Random Forest classifier and a single-input LSTM model across all evaluation metrics. The findings indicate that integrating tourist activity information significantly improves contextual recommendation quality and predictive performance. Overall, this research contributes to the development of intelligent sequence-aware tourism recommendation systems and demonstrates the effectiveness of dual-input deep learning architectures for modeling contextual tourist travel behavior.
Dynamic Knowledge Map of Artificial Intelligence Literature: Longitudinal Trend Analysis Using Latent Dirichlet Allocation Aulia Khairunnisa; Syalwa Salsabillah Siregar; Ken Ditha Tania; Allsela Meiriza; Zaqqi Yamani; Shanti Dewi Siawanta
Teknika Vol. 15 No. 2 (2026): July 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i2.1470

Abstract

The rapid growth of Artificial Intelligence (AI) publications has increased the complexity of identifying thematic structures and understanding the evolution of research trends. Conventional citation-based bibliometric approaches are often limited in capturing semantic relationships within large-scale textual data. This study aims to analyze the knowledge structure and longitudinal dynamics of AI literature using a topic modeling approach within a knowledge mapping framework. The dataset consists of publication abstracts from the arXiv repository spanning 2015–2024, and the Latent Dirichlet Allocation (LDA) algorithm is employed with coherence-based evaluation to extract latent topics. The results indicate that the optimal model configuration consists of nine topic clusters, achieving a peak coherence score of 0.4605. Longitudinal analysis suggests a notable shift in research focus after 2021, marked by the rapid growth of Large Language Models (LLMs) and generative AI. This shift reflects a process of thematic integration, where foundational areas such as deep learning architectures are increasingly incorporated into more advanced domains. In addition, topics such as Graph Neural Networks (GNNs) exhibit relatively stable trends, which may indicate technological maturity rather than a decline in relevance. Overall, the findings provide data-driven insights into the evolving landscape of AI research and may support researchers and institutions in identifying emerging research directions within the scope of the analyzed dataset.
Implementation of Apache Airflow for Pipeline Automation: Case Study of Mental Health Issues on Social Media X Putri Indah Cahyani; Danur Wijayanto
Teknika Vol. 15 No. 2 (2026): July 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i2.1473

Abstract

The increasing use of social media as a platform for discussing mental health issues presents significant opportunities for psychological and social media research. However, the lack of automated, fail-resilient, and clean data provisioning remains a major infrastructure challenge. This study developed an automated ETL (Extract, Transform, Load) pipeline using Apache Airflow and Docker Compose to collect and process bilingual social media data from the X platform (Twitter). The system integrates a container-based architecture with persistent volumes to ensure infrastructure resilience and data continuity. A bilingual text preprocessing pipeline was implemented to support automated cleaning and normalization of English and Indonesian social media content. Experimental results show that the system successfully collected 1,768 unique tweets with a preprocessing throughput of 75 tweets per second and achieved a 39% reduction in average text length as part of preprocessing efficiency measurement. These measurements are intended to evaluate ETL preprocessing efficiency and throughput rather than analytical or linguistic quality. Resilience testing confirmed 100% data integrity with zero data loss across multiple failure scenarios, including container restarts and process suspensions. In addition, the implementation of ShortCircuitOperator successfully mitigated API credit exhaustion (HTTP 402) through a graceful degradation mechanism. This study contributes a reproducible and reliable data engineering framework for automated social media data collection and preprocessing, resulting in structured bilingual datasets ready for downstream analytical tasks. However, mental health in this study is positioned as a domain-specific case study, and the resulting dataset is not intended to serve as a clinical diagnostic tool.
Food Portion Weight Prediction and Nutritional Estimation from Images Using YOLOv8 Segmentation and XGBoost Regression Ery Setiyawan Jullev Atmadji; Freda Adi Ferdana; Husin; Aji Seto Arifianto
Teknika Vol. 15 No. 2 (2026): July 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i2.1474

Abstract

Understanding the nutritional content of food is essential for maintaining balanced dietary habits. However, most existing nutrition information sources rely on fixed portion sizes and do not reflect the actual amount of food consumed. This study proposes an image-based system for estimating food nutrition dynamically using computer vision and machine learning. The system integrates YOLOv8m-seg for food object detection and segmentation with XGBoost regression for food weight prediction. Images uploaded by users through a Telegram chatbot are processed to detect food containers and segment individual food objects. Features extracted from segmentation results, such as object area and dimensions, are then used to estimate the weight of each food item. Nutritional values including calories, fat, carbohydrates, and protein are calculated based on the predicted weights. Experimental results show that the segmentation model achieved an average accuracy of 87.68%, with several food categories reaching 97–98% accuracy. The weight prediction model obtained an MAE of 21.138 g, RMSE of 50.10 g, and R² of 0.7838, indicating reasonable predictive performance. The developed system demonstrates the potential of combining object detection, segmentation, and regression models to provide automated nutritional estimation through an accessible chatbot interface, supporting more practical and personalized dietary assessment.

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