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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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Evaluation of the Silent Center System Using Cobit 2019 at Disnakertrans Sukabumi Regency With the DSS03 Domain Raudya Kamila Bilqis; Habi Baturohmah; Hendri Ekasatria
Teknika Vol. 14 No. 2 (2025): July 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.v14i2.1222

Abstract

In the digital era, Information Technology (IT) plays a crucial role in improving the efficiency of public services, including at the Dinas Ketenagakerjaan dan Transmigrasi of Sukabumi Regency. One of the service innovations developed is the Silent Center website, which functions as an integrated system for issuing Job Seeker Cards (AK-1). However, this system faces various challenges, such as suboptimal performance, dependence on the main domain, and limitations in IT problem management. This study aims to evaluate the Silent Center system using the COBIT 2019 framework, focusing on the DSS03 – Managed Problems domain. The capability level analysis results indicate that the system's capability level is at Level 3 with a maturity level score of 61.30%, signifying that the system has achieved its purpose in a much more organized manner using organizational assets, with processes typically well-defined. The main issues identified include a lack of IT problem documentation, limited expert personnel, and reliance on vendors. Based on the evaluation results, this study provides improvement recommendations to enhance the efficiency and effectiveness of Silent Center system management, making it more reliable in supporting job seeker services.
Enhancing the Usability of ITG Virtual Tour Website Through UI/UX Design Using the Five Planes Method Ridwan Setiawan; Muhamad Faturrahman; Yosep Septiana; Dewi Tresnawati; Ade Sutedi
Teknika Vol. 14 No. 2 (2025): July 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.v14i2.1227

Abstract

The virtual tour website in higher education significantly enhances campus information accessibility; yet, the poor usability of the ITG Virtual Tour website leads to a subpar user experience. This research offers a redesign of the UI/UX utilizing the Five Planes technique, which prioritizes clarity in information and navigation. A System Usability Scale (SUS) was employed to evaluate the site's usability prior to and after the redesign, adhering to the five stages of The Five Planes: Strategy, Scope, Structure, Skeleton, and Surface. The test findings indicated that the initial SUS score of 51.17 (classified as "poor") rose to 74.7 (classified as "good") following the installation of the revised design, demonstrating a substantial enhancement in usability and user satisfaction. This study contributes by demonstrating how the Five Planes method can be effectively applied in the academic context to improve the usability of virtual tour websites, offering a structured framework that can be adopted by other institutions seeking similar improvements.
Fine-Hybrid: Integration of BM25 And Finetuned SBERT to Enhance Search Relevance Wan Ahmad Gazali Kodri; Muhammad Haris; Rifqi Fitriadi
Teknika Vol. 14 No. 2 (2025): July 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.v14i2.1229

Abstract

Legal information retrieval, particularly for tax law documents, faces significant challenges due to specialized terminology, complex hierarchical structures, and formal language patterns that existing search approaches inadequately address. Current methods either rely on lexical matching or use general semantic models, creating a critical gap in effectively retrieving relevant tax law information. This research develops a novel hybrid search system to enhance search result relevance for the General Provisions and Tax Procedures (KUP) dataset by integrating a lexical-based search method (BM25) with semantic search using Sentence-BERT (SBERT) that has been fine-tuned using a taxation corpus. Our methodology encompasses several innovative components: development of synthetic data using a two-stage LLM prompting approach for SBERT fine-tuning, implementation of a comprehensive query normalization system with taxation-specific terminology mapping, and integration of lexical and semantic results through Reciprocal Rank Fusion (RRF). We evaluate system performance with inputs from tax domain experts, demonstrating that the Fine-hybrid model consistently outperforms individual search methods, achieving a Precision@N of 66.021% and Average Recall of 76.51%. Our approach addresses the specific challenges of tax document retrieval while providing a generalizable framework applicable to other specialized domains with similar characteristics. This research contributes both theoretical advancements in hybrid search methodologies for legal documents and practical solutions for improving tax information accessibility, with implications for enhancing administrative efficiency and taxpayer compliance.
Experimental Modeling of Face Emotion Recognition Using Machine Learning Classification (SVM, KNN, Random Forest) and Deep Learning CNN Shane Ardyanto Baskara; Nina Setiyawati
Teknika Vol. 14 No. 2 (2025): July 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.v14i2.1232

Abstract

Facial Emotion Recognition (FER) is a technology that analyzes facial expressions to detect emotions, playing a growing role in psychology and Human-Computer Interaction. In Indonesia, mental health issues are rising, with emotional disorders increasing from 6.0% in 2013 to 9.8% in 2018. Over 19 million people aged 15+ were affected in 2018, a number likely worsened by the COVID-19 pandemic. Given the urgency of early detection, FER offers a non-invasive method to help identify mental health issues. It can support timely intervention and promote psychological well-being, especially in under-resourced settings. This study compares several Machine Learning (ML) and Deep Learning (DL) models—SVM, K-Nearest Neighbor, Random Forest, and Convolutional Neural Networks (CNN)—to classify facial emotions. The dataset used is the Facial Expression Recognition dataset by Jonathan Oheix from Kaggle. Images were preprocessed and used to train and evaluate each model. Traditional ML models relied on extracted features, while CNN learned features directly from images. Results show that CNN achieved the highest accuracy among the tested models. This suggests that FER, especially with CNN, can be a useful tool for early detection of emotional disorders in mental health contexts.
Development of a Modified CycleGAN Model with Residual Blocks and Perceptual Loss for Image Dehazing Sani Moch Sopian; Arief Suryadi Satyawan; Mokhammad Mirza Etnisa Haqiqi; Helfy Susilawati; Beni Wijaya; Khaulyca Arva Artemysia; Firman; Muhammad Ikbal Samie
Teknika Vol. 14 No. 2 (2025): July 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.v14i2.1235

Abstract

Fog reduces image contrast and clarity, creating challenges for applications such as autonomous driving and remote sensing. This study proposes a series of CycleGAN modifications for single image dehazing using unpaired data, integrating residual blocks, attention mechanisms, VGG19-based perceptual loss, and haze-aware loss. Among ten architectural variants, Modification 10 combining perceptual and haze-aware loss achieved the best overall performance. Quantitatively, it showed stable generator losses (0.91 for Gen G, 0.57 for Gen F), with improved discriminator performance (Disc X: 0.59, Disc Y: 0.47), indicating better training stability and image realism. Additionally, it offered competitive PSNR (7.99), strong SSIM (0.4202), and low LPIPS (0.6577), confirming its effectiveness in both pixel-level accuracy and perceptual quality. Qualitatively, this model generated clearer, more natural images with improved edge sharpness and detail preservation. These findings demonstrate that the modified CycleGAN significantly enhances dehazing performance and presents a valuable contribution to deep learning-based image restoration.
Development of a Mobile Application Using Convolutional Neural Networks for Recognizing Indonesian Traditional Snacks Njoto Benarkah; Joko Siswantoro; Muhammad Ikhsan
Teknika Vol. 14 No. 2 (2025): July 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.v14i2.1236

Abstract

Indonesian traditional snacks constitute a vital element of the country’s cultural heritage. However, growing modernization has contributed to a decline in public familiarity, particularly among younger generations. This study presents a mobile-based image classification desigend to automatically recognize Indonesian traditional snacks using convolutional neural networks (CNNs). A dataset of 3,240 images across 16 snack categories was collected using a smartphone camera. Five CNN architectures, which are, AlexNet, EfficientNetV2M, MobileNetV2, ResNet50V2, and VGG19, were evaluated for classification performance. MobileNetV2 achieved the highest accuracy and F1-score, both reaching 100%. The final model was deployed in a mobile application environment, with the backend developed using Flask and integrated into the Android platform. This research work demonstrates the potential of lightweight CNN models in preserving cultural knowledge through accessible mobile technology.
Adopt E-Learning for High School or Vocational School Students by Using Extended Unified Theory of Acceptance and Use of Technology Trian Wahyu Prasetyo; Edwin Pramana; Hartarto Junaedi
Teknika Vol. 14 No. 2 (2025): July 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.v14i2.1237

Abstract

This study uses the fundamental Unified Technology Acceptance and Use of Technology (UTAUT) model, which is based on earlier research, to identify potential characteristics that influence high school or vocational school students' success in embracing e-learning. The theoretical model is predicated on earlier studies that integrate elements from the UTAUT acceptance model with elements deemed pertinent to e-learning (computer anxiety and perceived enjoyment). 593 people from the Indonesian city of Gresik made up the sample. Theoretical models are developed and analyzed using structural equation modeling. Based on the examination of the six hypotheses put out, six of them can be accepted. Behavioral intention is positively and significantly impacted by performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived enjoyment, while behavioral intention is negatively and significantly impacted by computer anxiety. Although there are numerous research models on the uptake of e-learning, internet, and computer use are already widely prevalent in developing nations like Indonesia. However, e-learning's acceptability in many educational sectors is still in its infancy, particularly among students in high school or vocational schools. Thus, this study offers a thorough analysis of the variables influencing Indonesian high school or vocational school students' acceptance of e-learning
Classification of Anxiety Levels of IGD Patients at RSU Royal Prima Medan Using Support Vector Machine (SVM) Algorithm Kharisma Gunanta Ginting; Nugroho Prasetyo; Al Vino Gunawan; Magdalena Sihombing; Adli Abdillah Nababan
Teknika Vol. 14 No. 2 (2025): July 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.v14i2.1243

Abstract

The level of patient anxiety in the Emergency Department (ED) is an important indicator that affects the diagnosis and medical management process. However, the classification of anxiety levels is often hampered by data imbalance, which can reduce the accuracy of predictive models. This study aims to develop a patient anxiety level classification model in the ED using the Support Vector Machine (SVM) algorithm with the application of the Synthetic Minority Oversampling Technique (SMOTE) to address the class imbalance issue. The data used consists of 734 patient samples divided into 80% training data and 20% testing data, including physiological parameters such as systolic and diastolic blood pressure, respiratory rate, heart rate, and demographic data such as age and gender. The preprocessing process includes imputing missing values and normalizing numerical features so that the model can learn optimally. Performance evaluation shows that the use of SMOTE increases classification accuracy from 95% to 97%, as well as improving precision, recall, and F1-score metrics at almost all anxiety levels. Visualization of the relationships between numerical features also reveals significant correlation patterns between physiological variables and patient anxiety levels. The results of this study confirm the effectiveness of SMOTE in addressing data imbalance, thus producing a more accurate anxiety level classification model that can serve as an aid in clinical decision-making. Thus, the developed model has significant clinical utility potential as a diagnostic aid that can accelerate and improve the accuracy of patient management in the emergency department, thereby supporting the overall improvement of healthcare service quality.
Optimization of Village Grouping Using Comparison of K-Means and K-Medoids Methods Eza Rahmanita; Yeni Kustiyahningsih; Putri Nihayatul Husna; Adhelia Firdaus Al-Najib
Teknika Vol. 14 No. 2 (2025): July 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.v14i2.1248

Abstract

Sumenep Regency is the largest agricultural area in Madura and a major producer of food crops such as rice, corn, and vegetables. However, agricultural productivity in some areas has declined due to uneven fertilizer distribution and limited knowledge about plant diseases and their treatment. To address this, a village clustering system is needed to help the Agriculture Office identify areas with high and low productivity, enabling more targeted assistance and resource allocation. This study aims to classify villages based on agricultural productivity to support better decision-making in agricultural development. Two clustering methods, K-Means and K-Medoids, were applied and compared. K-Means determines cluster centers based on the average of data points, while K-Medoids selects the most representative data point within each cluster. The data used in this research is 690 datasets in 2023. Before clustering is carried out, the data is preprocessed first. Data Pre-processing steps include data transformation, label encoding, imputation, and Min-Max normalization. Cluster optimization was performed using the Sum of Squared Errors (SSE) method. The results show that K-Medoids offers more stable clustering, especially in the presence of outliers, while K-Means is more efficient in computation. The best clustering result was achieved using K-Means with five clusters (K = 5), producing the lowest SSE value of 29.8. These findings can assist local governments in prioritizing agricultural support based on village productivity profiles.
Skin Lesion Diagnosis Through Deep Learning and Hybrid Texture Feature Augmentation Irpan Adiputra Pardosi; Roni Yunis; Arwin Halim
Teknika Vol. 14 No. 2 (2025): July 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.v14i2.1253

Abstract

Skin cancer is a leading cause of cancer-related deaths globally, with melanoma being the most lethal subtype. Early detection remains critical for improving patient outcomes. However, dermoscopic image analysis faces challenges due to inter-class similarity between malignant melanoma and benign nevi. This study proposes a robust framework for optimizing Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) parameters using the ISIC 2023 dataset. The framework integrates handcrafted features with Convolutional Neural Networks (CNNs) to enhance classification accuracy. Key contributions include: Automated parameter tuning for GLCM and LBP using grid search and cross-validation; A hybrid model combining EfficientNet-B3 with full handcrafted features; Comprehensive evaluation on the ISIC 2023 dataset (10,015 images). Results demonstrate that the hybrid model (Scenario 2) achieves 93.7% accuracy and 92.8% F1-score, outperforming the standalone CNN model (Scenario 1) by 3.5%. The proposed framework reduces false positives by 15% compared to dermatologist assessments, highlighting its potential for clinical decision support. Future work will explore advanced architectures like EfficientNet-B4 and integration of external factors such as lesion location.

Page 29 of 36 | Total Record : 356

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