cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 1,006 Documents
 86   87   88   89   90 
Multi-label Deep Learning for Thoracic Disease Co-occurrence in Chest Radiography Syahril Alamsyah Zainuddin; I Gusti Ngurah Lanang Wijayakusuma
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12840

Abstract

Chest radiography (Chest X-Ray) remains the primary imaging modality for thoracic disease diagnosis, yet remains susceptible to misdiagnosis due to anatomical complexity and overlapping pathologies. This study proposes a multi-label Deep Learning framework based on the MobileNetV2 architecture for simultaneous classification of 14 pulmonary pathologies. To address the extreme class imbalance inherent in medical datasets, a two-stage fine-tuning strategy, ColorJitter augmentation, and class weighting (pos_weight) in the Binary Cross-Entropy Loss function were implemented. Furthermore, probability threshold optimization was performed dynamically for each class using Youden’s J Statistic. Ablation study results indicate that the baseline model achieved Mean AUROC of 0.828, while the proposed method achieved Mean AUROC of 0.822. However, this marginal trade-off was strategically accepted to achieve the primary clinical objective: dramatically improving sensitivity (Recall) on critical minority pathologies, including Cardiomegaly (from 73.6% to 85.1%), Fibrosis (64.6% to 72.5%), and Hernia (71.9% to 75.0%). This framework enables simultaneous multi-label classification of 14 pulmonary pathologies using independent sigmoid activations, which inherently supports the detection of co-occurring conditions without enforcing mutual exclusivity. Consequently, the approach demonstrates enhanced clinical utility as a medical screening instrument by substantially suppressing false negative rates for high-risk pathologies. When deployed as a Computer-Aided Diagnosis (CAD) system with appropriate clinical validation, this framework demonstrates the potential to serve as a secondary screening tool in healthcare settings with limited access to specialist radiologists, particularly for detecting high-risk pathologies such as Cardiomegaly and Fibrosis.
Comparative Evaluation of Agentic Workflow Capabilities in AI IDE Agents for Web-Based Learning Media Development Erlangga Aditia; Ulva Elviani
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12841

Abstract

The rapid development of agentic IDEs calls for evaluation approaches that assess not only final outputs but also the agentic workflow enacted during software development. This study comparatively evaluates the workflow capabilities of four AI IDE agents, namely Cursor, Windsurf, Trae, and Antigravity, within the five-stage benchmark of developing a web-based learning media application, Next-Gen SPLDV. A descriptive comparative evaluation was conducted using five agentic maturity metrics: task decomposition (DC), tool-use effectiveness (TSR), autonomous recovery capability (ARC), human intervention cost (HIC), and time completion efficiency (TCT), complemented by interaction logs and internal artifacts. The findings indicate distinct performance trade-off profiles across systems. Antigravity appeared relatively more stable descriptively (TSR 96.0%; HIC 3; ARC 8), whereas the other systems exhibited context-dependent strengths: Cursor showed more selective tool use, Trae was efficient in several stages but more vulnerable during database integration, and Windsurf was more exploratory but required higher intervention and recovery effort. Qualitative evidence further suggests that these differences were associated with variations in plan-execute-verify strategies and error-response behavior. Overall, the evaluation of AI IDE agents is better interpreted as a contextual map of workflow trade-offs rather than the identification of a single winner across all settings.
Comparison of Sobel, Prewitt, and Canny Edge Detection Methods in Digital Images Diana Diana; Delima Agustina; Dian Yunita Situmorang; Dasril Kholid; Ahmad Rizki Pratama
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12860

Abstract

Edge detection is a fundamental operation in digital image processing, enabling identification of object boundaries in an image. This study presents a systematic comparative evaluation of three widely used edge detection algorithms Sobel, Prewitt, and Canny applied to a dataset of 20 grayscale images (512×512 pixels) from the USC-SIPI Image Database, spanning four object categories: simple geometric objects, complex textures, low-noise conditions, and medium-noise conditions. Each method was evaluated using four metrics: Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), precision/recall/F-measure (with Canny output as the reference ground truth for Sobel and Prewitt), and average computational time. All experiments were conducted in Python 3.10 with OpenCV 4.7 on a dedicated Intel Core i7 (11th Gen) workstation running Ubuntu 22.04 LTS with 16 GB RAM to ensure fair benchmarking. Results show that the Canny method consistently outperforms the others in detection quality, achieving the lowest average MSE (7.26), the highest average PSNR (40.06 dB), and the best F-measure (0.91), albeit at a 3–4× higher computational cost (8.76 ms vs. ~2.4 ms for Sobel/Prewitt). Sobel and Prewitt provide comparable speed with lower precision, making them suitable for real-time applications. Notably, under medium-noise conditions, Canny's MSE advantage widens markedly, highlighting its superior noise robustness. A brief comparison with modern deep learning-based approaches (HED, BDCN) contextualises classical methods within the current state of the art. The study concludes that Canny is the superior choice for high-accuracy tasks, while Sobel and Prewitt remain practical for latency-constrained environments..
OCR and NLP for Consumer Product Label Analysis: A Systematic Literature Review Musthofa Dzikry Pamungkas; Noor Falih; Muhammad Panji Muslim; Nanang Nasrulloh
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12861

Abstract

Composition labels on consumer products serve as an essential source of information for consumers in making purchasing decisions and for producers in ensuring regulatory compliance. However, manual reading of labels is prone to errors due to small font sizes, blurred images, low resolution, and perspective distortion, which reduce the accuracy of information recognition. This issue becomes increasingly critical as many label compositions contain ingredients associated with metabolic diseases, such as added sugar, artificial sweeteners, and carbohydrates, whose risks are well-documented in epidemiological literature. While numerous systematic reviews have addressed OCR and NLP in document intelligence, no prior review has systematically mapped the integration of OCR robustness, BERT-based semantic extraction, and food label-specific challenges in the context of multilingual Indonesian consumer products. To address this gap, this study conducts a Systematic Literature Review (SLR) following PRISMA guidelines, synthesizing recent advances in Transformer-based OCR and BERT-based NLP for consumer product label analysis. The review aims to map OCR performance under real-world conditions, identify best practices in preprocessing and post-correction, and evaluate end-to-end integration with BERT for semantic understanding of label compositions. The expected outcome is a theoretical contribution in the form of an integrative OCR–BERT framework proposal, along with practical design recommendations for future systems aimed at supporting nutritional literacy and informed consumer decision-making. Empirical validation of the proposed framework remains a direction for future research.
Multi-LoRa-Based Automatic Drinking Water Transmission System Sri Indah Rezkika; Adinda Juwita Nasution; Andri Ramadhan; Muhammad Fiza Lubis; Panangian Mahadi Sihombing; Aulia Agung Dermawan
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12862

Abstract

Drinking water transmission systems are needed to fill some reservoirs. However, the water level in each reservoir must be monitored constantly to ensure optimal water transmission. Therefore, a device is needed to monitor the water level and automatically control the transmission system. The purpose of this research is to develop a multi-LoRa-based automatic drinking-water transmission system that provides near-real-time water-level information with an average end-to-end latency of 1.2 seconds, meeting the typical requirements for water-level monitoring in remote reservoirs (acceptable delay < 10 seconds). This study uses a prototype experimental approach, validated with a Laser Distance Meter and sticker meters. The star topology connects several LoRa modules that transmit water-level data without internet access. The purpose system consists of several LoRa modules, water-level sensors, liquid crystal displays (LCDs), solenoid valves, and pressure-switch sensors. A pressure sensor switch is installed on the power-supply side of the water pump, controlling the pump based on the water-pressure difference. The result of this study is that the purpose system can automatically control the water pump to fill several reservoirs based on water-level information from each reservoir. This is evidenced by each solenoid valve opening when the water level is below 10 cm and closing when it reaches 70 cm. So that the water pump can be controlled through a pressure sensor switch. The purpose system can also accurately measure water levels, as evidenced by ME ≤ 0.5 cm, MAPE ≤ 4.4%, RMSE ≤ 0.7 cm, and R² ≥ 0.97. These findings demonstrate the potential of the proposed solution as a cost-effective and reliable solution for remote areas without internet infrastructure.
Development of an IoT-Based Broiler Chicken Coop Air Quality Monitoring Prototype Panangian Mahadi Sihombing; Maharani Putri; Tuti Adi Tama Nasution; Muhammad Fiza Lubis; Aulia Agung Dermawan; Muhammad Syahruddin
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12863

Abstract

Inadequate air quality in the cage causes broiler chickens to become stressed, resulting in suboptimal weight gain, even death, and crop failure. Adequate broiler chicken cage air quality parameters include: a temperature range of 22 °C to 32 °C (depending on the chicken's age), a humidity range of 45% to 65%, an O2 level of≥ 19.6%, an NH3 level of ≤ 10 ppm, a CO level of ≤ 10 ppm, and a CO2 level of ≤ 3,000 ppm. Therefore, a technology is needed to monitor the air quality of the chicken coop in near real time. Thus, farmers can provide appropriate and prompt handling to maintain good air quality in the chicken coop. The purpose of this research is to develop a prototype for monitoring the air quality of broiler chicken coops based on IoT. The method used is research and development (R&D) with a prototype manufacturing approach based on the Internet of Things (IoT). The prototype was developed using temperature and humidity sensors (DHT22), NH3, O2, CO, and CO2 sensors. Each sensor is connected to the ESP32, which processes the data and displays it on the liquid crystal display (LCD) and the Thingspeak dashboard. Prototype testing was conducted by comparing the measurement results with those from standard measuring instruments in a closed container. Based on the test results, the prototype measured air quality accurately. This is evidenced by the mean absolute error (MAE) values for each measurement result of the O2 sensor, CO sensor, CO2 sensor, DHT22 sensor, and A02YYUW sensor, which are 0.10 % Vol, 4.46 μmol/mol, 24.20 ppm, 0.71 °C, and 0.13 cm, respectively. Thus, this prototype offers an advantage in measuring air quality in near real time using IoT.
Analysis of Customer Churn Classification for Sinarmas Syariah Lhokseumawe Insurance Services Using Deep Learning Tabnet and Explainable AI Syarifah Muliana; Taufiq Taufiq; Munirul Ula
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12864

Abstract

Customer churn is a major challenge in the insurance industry because it directly affects customer retention, business sustainability, and company profitability. Early identification of customers at risk of churn is therefore essential for developing effective retention strategies. This study proposes an interpretable customer churn prediction framework for Sinarmas Syariah Lhokseumawe Insurance Services by integrating TabNet deep learning with Shapley Additive Explanations (SHAP). The dataset consists of 2,000 customer records containing demographic information, insurance transactions, premium payments, claims history, and customer interaction data. Due to the imbalanced class distribution, the Synthetic Minority Oversampling Technique (SMOTE) was applied exclusively to the training dataset to improve model learning while preventing data leakage. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The experimental results demonstrate that the proposed approach achieved an accuracy of 98.77%, precision of 86.67%, recall of 92.86%, F1-score of 89.66%, and ROC-AUC of 0.995, indicating excellent classification performance. Furthermore, SHAP analysis revealed that communication, premi_2025, and reason_to_purchase were the most influential features affecting churn predictions. These findings highlight the importance of customer engagement, premium management, and purchasing motivations in customer retention. The proposed TabNet-SHAP framework provides both high predictive performance and model interpretability, making it a valuable decision-support tool for customer retention strategies in the insurance sector.
Hybrid Cryptosystem for Image Encryption Using Lorenz Attractor and Neural Network Optimization i Wayan Rangga Pinastawa; Radinal Setyadinsa; Ihsan Tri Marseno; Gybran Kalmando
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12876

Abstract

Image encryption is required to protect visual data from unauthorized access. This study proposes a hybrid image cryptosystem based on the Lorenz chaotic attractor combined with artificial neural network optimization for adaptive chaotic parameter generation. The proposed method applies chaotic pixel permutation and bidirectional diffusion using chained XOR and bit rotation operations to improve ciphertext randomness and differential attack resistance. Experiments were conducted on three categories of generated RGB images with a resolution of 1024×1024 pixels. Performance evaluation was carried out using entropy, correlation coefficient, NPCR, UACI, PSNR, SSIM, key sensitivity, key space, histogram analysis, and encryption time analysis. The experimental results show that both encryption methods achieved entropy values close to the theoretical maximum approx ≈7.9999 and correlation values near zero, indicating strong randomness and successful removal of spatial pixel relationships. The proposed system also achieved NPCR values of approximately 99.6% and UACI values close to 333%, demonstrating strong resistance against differential attacks. In addition, the decryption process successfully reconstructed the original images without information loss, producing infinite PSNR values and SSIM values of 1.0. The obtained key sensitivity values exceeding 99.6% and the approximate key space of 〖10〗^45 further indicate strong dependence on secret key precision and resistance against brute-force attacks. Overall, the proposed hybrid cryptosystem demonstrated strong statistical security, stable computational performance, and effective encryption capability for high-resolution RGB images.
Identifying Fear of Missing Out (FOMO) in Adolescents Using K-Nearest Neighbors: An Experimental Study of k-Values and Distance Metrics Ricco Wahyu Pamungkas; Mochammad Anshori; Wahyu Teja Kusuma
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12879

Abstract

Fear of Missing Out (FOMO) is a psychological phenomenon commonly experienced by teenagers due to the high intensity of social media use, and has the potential to cause emotional and social impacts if not identified early. The main problem in identifying FOMO is its internal nature and the difficulty in measuring it objectively using conventional methods. This research proposes a data mining-based classification approach using K-Nearest Neighbor (KNN) to identify the level of FOMO in adolescents. The dataset was obtained from 136 respondents through a questionnaire that included demographic data and the ON-FoMO scale. The research stages include data preprocessing (encoding and Min-Max normalization), data splitting using stratified holdout (80:20), and experiments varying K (3–19) and distance metrics (Euclidean, Manhattan, Chebyshev). The experimental results show that the combination of Euclidean distance with K=11 yields the best performance with an accuracy of 85.71%, ROC AUC of 0.786, Precision–Recall AUC of 0.826, and sensitivity of 100%. The experimental results indicate that the selection of the K parameter and the distance method significantly affect classification performance. Overall, this study concludes that the KNN algorithm with the optimal configuration is effective as an initial screening method for the level of FOMO in adolescents in an systematic and data-based manner.
Clustering of Food Security Levels in North Aceh Using K-Medoids Tarisha Zhafira; Bustami Bustami; Nunsina Nunsina
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12881

Abstract

Food security is essential for ensuring food availability, accessibility, and utilization. This study applies a multidimensional indicator framework covering social, economic, and infrastructure aspects to cluster regions in North Aceh Regency, addressing limitations of previous studies that primarily focus on agricultural production indicators and lack policy-oriented analysis. The analysis uses 2023 data from 27 sub-districts at the village level, comprising 852 villages. The indicators include: (1) the ratio of agricultural land area to total population, (2) the ratio of food supply facilities and infrastructure to households, (3) the ratio of population with the lowest welfare status to total population, (4) the proportion of villages without adequate transportation access via land, water, or air, (5) the ratio of households without access to clean water, and (6) the ratio of population per health worker relative to population density. Data processing involves preprocessing, normalization, and K-Medoids clustering, evaluated using the Davies–Bouldin Index (DBI) and Silhouette Coefficient (SC). The results identify six clusters: highly food insecure (C1) with 63 villages, food insecure (C2) with 92 villages, moderately food insecure (C3) with 179 villages, moderately food secure (C4) with 42 villages, food secure (C5) with 49 villages, and highly food secure (C6) with 427 villages. Most villages fall within moderately food insecure to highly food secure categories, indicating disparities in food security distribution. The DBI value of 3.085 indicates moderate cluster compactness, while the SC value of 17.75% suggests weak separation between clusters. These findings provide policy recommendations for targeted and equitable food security interventions.

Page 88 of 101 | Total Record : 1006

 86   87   88   89   90 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol. 10 No. 4 (2026): August 2026 Vol. 10 No. 3 (2026): June 2026 Vol. 10 No. 2 (2026): April 2026 Vol. 10 No. 1 (2026): February 2026 Vol. 9 No. 6 (2025): December 2025 Vol. 9 No. 5 (2025): October 2025 Vol. 9 No. 4 (2025): August 2025 Vol. 9 No. 3 (2025): June 2025 Vol. 9 No. 2 (2025): April 2025 Vol. 9 No. 1 (2025): February 2025 Vol. 8 No. 2 (2024): December 2024 Vol. 8 No. 1 (2024): July 2024 Vol. 7 No. 2 (2023): December 2023 Vol. 7 No. 1 (2023): July 2023 Vol. 6 No. 2 (2022): December 2022 Vol 6 No 1 (2022): July 2022 Vol. 5 No. 2 (2021): December 2021 Vol 5 No 2 (2021): December 2021 Vol 5 No 1 (2021): July 2021 Vol. 5 No. 1 (2021): July 2021 Vol 4 No 2 (2020): Desember 2020 Vol. 4 No. 2 (2020): Desember 2020 Vol 4 No 1 (2020): Juli 2020 Vol 3 No 2 (2019): Desember 2019 Vol 3 No 1 (2019): Juli 2019 Vol 2 No 2 (2018): Desember 2018 Vol 2 No 1 (2018): Juli 2018 Vol. 2 No. 1 (2018): Juli 2018 Vol 1 No 2 (2017): Desember 2017 Vol 1 No 1 (2017): Juli 2017 More Issue