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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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Hybrid Framework Addressing Imbalanced Data in Indonesian E-commerce Multimodal Sentiment Analysis Shabrina Rasyid Munthe; Samsir; Rina Asriani Levianti
Teknika Vol. 14 No. 3 (2025): November 2025
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.v14i3.1332

Abstract

Severe class imbalance in Indonesian language e-commerce reviews hampers the detection of customer grievances, because about 88 percent of testimonials are positive whereas under one percent are negative. This study designs a simple yet effective framework to mitigate that bias. The data 2,611 authentic reviews covering electronics, fashion, and household goods and reflecting online shopping patterns were cleaned, structured, and represented using a combination of TF-IDF weighting and Word2Vec embeddings to condense meaning. Imbalance was addressed with the Synthetic Minority Oversampling Technique integrated with Edited Nearest Neighbour, equalising the proportions of positive, neutral, and negative classes. A Support Vector Machine was trained with five-fold cross-validation and benchmarked against Multinomial Naïve Bayes and a Decision Tree classifier. Experiments yielded 94.6 percent accuracy and a 94.8 percent F1-score, while precision for the negative class reached 100 percent, outperforming conventional approaches by up to twelve percent. The contribution of this research is the demonstration that minimal pre-processing, lightweight feature extraction, and targeted data balancing can markedly enhance grievance detection without resorting to complex model architectures. Practically, the framework enables online merchants to monitor service quality and respond to customer issues in real time, and it can be integrated into recommendation engines and business dashboards to accelerate data-driven decision making and strengthen transparency for stakeholders. Academically, the findings open avenues for integrating large language models, temporal analysis, and cross-domain adaptation to enrich the global e-commerce ecosystem.
Comparative Analysis of Machine Learning and Deep Learning Models for PM2.5 and PM10 Time Series Forecasting Using the SISANAPAS Web Platform Dwi Indra Prasetyo; Agustina Rachmawardani; Bayu Satrio; Derby Brylian Adinara
Teknika Vol. 14 No. 3 (2025): November 2025
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.v14i3.1335

Abstract

Particulate matter (PM2.5 and PM10) pollution remains a significant environmental concern in Indonesia. This study employed the SISANAPAS web platform to compare machine learning (ML) and deep learning (DL) algorithms for PM2.5 and PM10 forecasting. Using 3-hourly data from Cibeureum (January-October 2024), which underwent comprehensive pre-processing (K-Nearest Neighbors imputation, Z-score outlier removal, Min-Max scaling, feature engineering), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Random Forest, and XGBoost models were evaluated. XGBoost Regression provided the most accurate forecasts, with an R² of 0.85, RMSE of 5.05 µg/m³, and MAE of 3.19 µg/m³ for PM2.5, and an R² of 0.85, RMSE of 9.71 µg/m³, and MAE of 6.55 µg/m³ for PM10. These results, significantly outperforming LSTM and GRU, highlight XGBoost's potential for reliable air quality prediction and demonstrate SISANAPAS as a valuable tool for environmental data analysis, crucial for informing public health and environmental policies.
Design and Evaluation of a User-Centered Mobile Application for Elderly Activity Monitoring with Conceptual IoT-Based Emergency Response System Peter; Yana Erlyana
Teknika Vol. 14 No. 3 (2025): November 2025
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.v14i3.1336

Abstract

Elderly individuals living independently face increasing safety risks, including medication non-compliance and delayed emergency responses. This study presents the design and evaluation of a user-centered mobile application for elderly activity monitoring supported by a conceptual Internet of Things (IoT)-based emergency response system. The User-Centered Design (UCD) method was applied through five structured phases: empathize, define, ideate, prototype, and test. The design process incorporated survey data from 100 respondents and expert interviews to determine user needs. Key features include medication reminders, emergency buttons, automated alerts, and proposed integration with CCTV for remote monitoring. A system flow diagram was developed to illustrate the conceptual architecture for data communication between users and caregivers. The UI/UX prototype was created using Figma, prioritizing accessibility through high-contrast visuals, enlarged text, and simplified navigation tailored to elderly users. Usability testing using the system usability scale (sus) yielded an average score of 82, indicating excellent perceived usability. These results demonstrate the potential of combining UCD principles with a conceptual IoT framework to enhance home safety for elderly users through responsive mobile technology.
Analysis and Development of an Educational Game for Early Childhood Numeracy Learning through a Curriculum-Based and Functional Evaluation Approach Misna Asqia; Nurfajriah Halimatuz Zahra; Siti Hujaimah; Rizky Putra Mulyadi; Amalia Rahmah
Teknika Vol. 14 No. 3 (2025): November 2025
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.v14i3.1349

Abstract

Digital transformation in education has become a crucial necessity in the era of globalization, especially in Early Childhood Education. Generation Alpha tends to comprehend information more easily through digital media, which requires educators to be creative in delivering learning materials using technology. Video games as educational media have been proven to enhance children's learning motivation and create a joyful learning atmosphere. However, uncontrolled use of digital devices raises concerns regarding excessive screen time. To address this challenge, this study aims to design and develop a curriculum-based educational numeracy game for Early Childhood Education that can be collectively played in class using a projector and directly supervised by teachers. The method employed is Research and Development using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The research process includes curriculum analysis, numeracy content design, interactive game scenario development, and functional testing using black box testing. Test results show that all core features of the application function as designed, with 100% validation. The educational game consists of three levels featuring various numeracy activities, pattern and shape recognition, tailored for Kindergarten levels A and B. This study demonstrates that pedagogically designed educational games integrated with technology can serve as an engaging, interactive, and developmentally appropriate solution for early childhood numeracy learning.
Manual Clustering Approach for User Group Mapping in Facility Management System UI/UX Design Caylen Marli; Indah Lestari
Teknika Vol. 14 No. 3 (2025): November 2025
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.v14i3.1356

Abstract

The facility management process at Politeknik Caltex Riau is still conducted manually, using paper media and Excel records, which causes inefficiency, delays, and difficulties in monitoring real-time facility availability. To address these problems, a web-based information system was developed with a UI/UX approach using the Design Thinking method. The uniqueness of this study lies in the use of manual clustering as a user segmentation method during the initial stage, performed based on survey results. This technique resulted in three user groups: (1) active borrowers who need tracking and notifications, (2) infrequent borrowers who require clear information, and (3) non-borrowers who focus more on attractive and easy-to-use interface design. These clusters serve as the foundation for creating personas, defining problem statements, and designing key system features. This study was conducted in two cycles. The first cycle involved initial design and testing, while the second cycle involved iterative improvements based on previous evaluation results. Testing using the System Usability Scale (SUS) showed an increase in scores from 75 to 77. Meanwhile, the User Experience Questionnaire (UEQ) exhibited all dimensions in the positive range (>0.8), with the highest score in Stimulation (2.202) and the lowest in Novelty (1.721). These results demonstrate that the manual clustering approach is effective in identifying user needs contextually and supports the design of an efficient, relevant, and user-friendly system.
Hybrid Machine Learning Model for Risk Prediction and Action Recommendation Based on Artificial Mental Systems Hadi Asnal; Khusaeri Andesa; Fitry Erlin; Junadhi
Teknika Vol. 14 No. 3 (2025): November 2025
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.v14i3.1357

Abstract

Mental health problems are increasingly prevalent among the younger generation, particularly those active on social media, yet early detection efforts often remain limited. Previous studies have explored text-based approaches for identifying mental health issues, but many are constrained by low accuracy in differentiating multiple psychological states or lack integration into accessible tools for end-users. This study addresses these gaps by proposing a hybrid machine learning model for early detection of mental health conditions through social media text analysis. Five algorithms were evaluated, and a soft voting ensemble combining Logistic Regression and Support Vector Machine (SVM) was developed to improve classification across five mental states (Anxiety, Depression, Stress, Emotional Exhaustion, and Healthy) and three risk levels (Low, Medium, High). To ensure practical utility, the model was deployed in an Android-based application, SmartRisk, which allows users to input free text and receive automated assessments. The findings show that the proposed hybrid approach significantly improves detection performance, particularly in identifying depression and high-risk cases, while maintaining high usability in real-world application. The novelty of this study lies in combining hybrid ensemble learning with mobile deployment for practical, text-based early detection of mental health, offering both methodological advancement and societal impact.
Implementation of Blockchain Technology in E-Voting Using Smart Contract and ZK-SNARK Jimmy; Kenny Rimba; Vincent; Ronsen Purba; Darwin
Teknika Vol. 14 No. 3 (2025): November 2025
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.v14i3.1358

Abstract

E-voting systems are prone to challenges such as lack of transparency, risks of data manipulation, and dependence on centralized authorities, which can undermine trust in electoral processes. This research develops a blockchain-based e-voting system on the Polygon network, leveraging smart contracts and Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARK) to enhance security, transparency, and voter anonymity. The study employs an application development approach, implementing a structured methodology with initialization, registration, voting, and tallying phases. Smart contracts automate voter verification, vote casting, and result tabulation, while ZK-SNARK ensures voters can cast ballots anonymously without revealing their identities. The system’s transparency and immutability are tested using PolygonScan, demonstrating effective prevention of manipulations like double voting through cryptographic credentials (nullifier, commitment, and nullifier hash) and Merkle Tree structures. Results indicate that the system provides a secure, verifiable, and decentralized framework for elections. This implementation offers a robust foundation for future e-voting systems, promoting trust and integrity in digital voting processes.
An Integrated Framework for Automated Resume Screening Using RoBERTa, Random Forest and Explainable AI Kevin Frederick Yapiter; Alfin; Yoga Hasim; Ronsen Purba; Mustika Ulina
Teknika Vol. 14 No. 3 (2025): November 2025
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.v14i3.1359

Abstract

The resume screening process is a critical stage in recruitment, yet conventional methods and traditional applicant tracking systems (ATS) often rely on manual review or keyword matching, resulting in slow, biased, and less objective evaluations. This study proposes an integrated automated screening system that combines RoBERTa for contextual feature extraction, Random Forest for candidate classification, and SHAP-based Explainable AI for interpretable decisions, enhancing transparency, efficiency, and fairness beyond traditional ATS. The dataset consists of real resumes and synthetically generated ones designed to mimic the distribution of real data, with K-means clustering used to establish labeling thresholds. Experimental results show that RoBERTa achieved an F1 Score of 81.08% in feature extraction, while Random Forest reached 96% accuracy in suitability classification. SHAP-based explanations provide insights into feature contributions for each prediction, offering an actionable understanding for recruiters. This integrated framework not only improves the efficiency and fairness of resume screening but also demonstrates a practical application of explainable AI in recruitment.
IT Management Framework: a Practical Approach with IT Flowin Nexus (Applied in RSIA Stella Maris) Darwin; Binarwan Halim
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.v15i1.1360

Abstract

This research proposes and analyzes a novel hybrid IT management framework, IT Flowin Nexus, designed to address persistent challenges in the dynamic healthcare sector, specifically human resource inconsistency and lack of operational discipline. The framework addresses the conceptual gap in existing hybrid models by synthesizing structural foundations from Software Engineering, behavioral discipline from Rockefeller Habits, and quality assurance from Quality Management Systems (QMS). Using a qualitative case study at RSIA Stella Maris conducted over 23 weeks, the study employed rigorous data collection through semi-structured interviews with six key informants, direct observation of communication rhythms, and systematic document analysis. The framework's effectiveness was measured using the Project Consistency Index (PCI), a weighted metric (40% SOP, 30% WCM, 30% Task) developed to quantify team discipline. The findings show an absolute increase of 27 PCI points, shifting performance from a baseline of ~50 to ~77, with significant stabilization observed after the ninth week. Triangulated evidence confirms that the framework effectively translated strategic vision into tangible actions, resulting in improved project completion rates and a marked stabilization of system reliability. This research provides a practical roadmap for healthcare organizations to bridge the gap between flexible management and rigid operational consistency.
The Transformative Role of Artificial Intelligence in Modern Education Hasanain Mohammed Manji Al-Rzoky; Shuruq Khalid Abdulredha
Teknika Vol. 14 No. 3 (2025): November 2025
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.v14i3.1367

Abstract

The Fourth Industrial Revolution has led to significant advances in digital devices and social media, contributing to the emergence of artificial intelligence (AI) as a key tool for improving the educational process. Computing and information technologies have enabled the use of computers in education, particularly in computer-assisted instruction and improving classroom interaction. Artificial intelligence in education (AIEd) aims to support teaching strategies, enhance student learning, and improve educational outcomes through performance monitoring, adaptive learning, providing educational resource recommendations, and identifying educational gaps. The study focuses on exploring the role of AI applications in improving the quality of learning, assuming that these applications contribute to developing the educational process, addressing traditional challenges, and improving the performance of teachers and students. The study adopted a descriptive-analytical approach and collected data through a survey of academics and teachers in Iraq during the 2024-2025 academic year. The study recommends holding training workshops, providing necessary resources, promoting the effective use of AI applications, and developing future development plans, while proposing additional research on innovation and interactive lesson design.

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