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 805 Documents
 76   77   78   79   80 
Detection of Diabetic Retinopathy Using Hybrid InceptionResNetV2-KELM Method Musfiroh, Musfiroh; Novitasari, Dian C Rini; Hakim, Lutfi; Damayanti, Adelia; Haq, Dina Zatusiva; Aisah, Siti Nur
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11967

Abstract

Diabetic Retinopathy (DR) is a complication of Diabetes Mellitus (DM), both type 1 and type 2 DM. Based on its severity, DR is divided into mild DR, moderate DR, severe DR, and proliferative DR stages. Manual detection is difficult because there is a fairly small difference between normal and DR. The Computer-Aided Diagnosis (CAD) system is a solution for detecting the severity of DR quickly and accurately so that DR sufferers do not get worse, which can cause blindness. This study uses fundus images from the Mesindor dataset consisting of four classes, namely normal, mild DR, moderate DR, and severe DR, with the InceptionResNetV2-KELM hybrid method. InceptionResNetV2 is used as a feature extraction and Kernel Extreme Learning Machine (KELM) as its classification. Several types of kernels are applied as model trials. The results show the highest sensitivity lies in the polynomial kernel experiment with a sensitivity value of 99.88%, an accuracy of 99.88%, and a specificity of 99.96%. The method used is able to detect very well and is quite time-effective compared to conventional CNN.
Analyzing Compost Fermentation Accuracy Through Fuzzy Logic and R-Square Techniques Putranto, Reza Firmansyah; Ningrum, Novita Kurnia
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11997

Abstract

The accumulation of unmanaged organic waste remains a critical environmental issue, highlighting the need for technological support to improve composting efficiency and monitoring. This study proposes an Internet of Things (IoT)-based system for monitoring compost fermentation conditions using temperature and humidity sensors, combined with Fuzzy Logic and R-square (R²) analysis to evaluate fermentation quality. The system employs a DHT11 sensor integrated with an ESP8266 microcontroller to collect temperature and humidity data in real time over a 20-day observation period, resulting in 1,008 data points. Fuzzy Logic is applied through fuzzification, rule-based inference, and defuzzification to classify compost conditions into four categories: poor, good, very good, and cooling needed. The model’s performance is further validated using multiple linear regression, with temperature and humidity as independent variables and average temperature as the dependent variable. The results show that compost temperature ranged between 28–32°C and humidity between 50–87%, indicating that the fermentation process was predominantly in the mesophilic or early composting phase. The fuzzy inference results demonstrate that most conditions fell within the “good” category, while the R² value of 0.87 indicates a strong relationship between the observed variables. These findings confirm that the integration of IoT, Fuzzy Logic, and statistical analysis is effective as a real-time monitoring and decision support system for compost management, while also highlighting the need for additional parameters to achieve a more comprehensive compost quality assessment.
Performance Evaluation of Face Mask Detection Using Feature Descriptor and Supervised Learning Method Suheryadi, Adi; Adhinata, Faisal Dharma; Wijanarko, Heru
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11999

Abstract

The use of masks as a measure to prevent the spread of dangerous diseases such as COVID-19 and others has become a social norm. Manual detection is less effective, especially in areas with high mobility. This study develops and evaluates an artificial intelligence (AI)-based face mask detection system using feature description and machine learning models. An optimal and lightweight model can help hospitals implement face mask detection systems in areas prone to disease transmission. Image preprocessing, feature description, supervised learning model studies, and performance evaluation were conducted using accuracy, precision, recall, and F1-score metrics, and a confusion matrix was used to assess the overall model performance. The performance evaluation results show that the combination of the LBP feature description with the random forest model is the best choice, with a relatively high and stable accuracy of around 96.3% with an average value, precision, recall, and F1-score of around 96% using K-Fold Cross-Validation. These findings suggest that this method is helpful in detecting mask use while minimizing error and computation rates. This study contributes to the development of lightweight mask detection systems that can be used in real time.
Experimental Comparison of Ground Plane Detection Speed Across Mobile Platforms Ladiesga, Laura; Pranata, Caraka Aji
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12010

Abstract

Markerless Augmented Reality (AR) technology has become increasingly important in various applications, yet its performance varies significantly across different platforms. This study conducts a comparative experimental analysis of ground plane detection performance between iOS and Android platforms using the Vuforia-based KreasiFurniture application. The research examines detection speed under varying lighting conditions (indoor and outdoor) and camera distances (50 cm, 100 cm, and 150 cm) through systematic testing with five repetitions per condition. Data were analyzed using Three-Way ANOVA with IBM SPSS Statistics 25. Results demonstrate that iOS achieves significantly faster and more consistent detection (mean = 1.402 seconds, SD = 0.143) compared to Android (mean = 1.541 seconds, SD = 0.235), with a statistically significant difference of 0.139 seconds (p = 0.003). The optimal detection distance was found at 100 cm for both platforms (p = 0.018). While lighting conditions showed no significant main effect (p = 0.129), a significant Platform × Light interaction (p = 0.038) was revealed, indicating that iOS maintains stable performance across lighting variations, whereas Android experiences substantial performance degradation in indoor conditions. These findings provide practical recommendations: iOS is preferable for applications requiring consistent indoor performance, 100 cm represents the optimal interaction distance for both platforms, and Android deployments should implement adaptive strategies for variable lighting conditions.
Hybrid Rainfall Analysis in Semarang by Integrating SARIMA Predictions with Meteorological Association Rules Agustin, Kristina; Novita Dewi, Ika
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12013

Abstract

Climate variability necessitates advanced analytical approaches to understand irregular rainfall patterns, particularly in coastal cities like Semarang, Central Java. This research employs a dual-analysis framework combining the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the Apriori algorithm to forecast rainfall and uncover hidden meteorological associations. Analyzing BMKG monthly climatological data from January 2020 to December 2024, the research addresses both temporal trends and variable dependencies. The SARIMA 〖(1,0,0)(2,1,0)〗_12 model projected rainfall dynamics for 2025, identifying critical wet periods (January-March, November-December) and dry intervals (July-September), achieving a MAPE of 44.97%. To complement temporal forecasting, the Apriori algorithm was applied with 50% minimum support and 50% confidence, generating association rules from daily discretized meteorological data. Results reveal that the combination of low temperature (Tx_Low, Tn_Low) and moderate wind speed (FFx_Medium) exhibits the strongest correlation with heavy rainfall events Lift Ratio 12.34, indicating a 12-fold increased risk compared to random conditions. By synergizing temporal forecasting with the identification of meteorological triggers, this research offers a robust basis for early warning systems, supporting flood mitigation and water resource management strategies in Semarang.
A Multi-Criteria Decision Approach to Livability Assessment Using Hybrid FUCOM–VIKOR Maharani, Felina Devi; Cholil, Saifur Rohman
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12031

Abstract

Persistent disparities in regional livability across Central Java pose challenges for effective and equitable poverty alleviation policies. Without objective prioritization, government interventions risk being inefficient and misdirected. This study aims to assess the livability level of 35 regencies and cities in Central Java and to identify regions that should be prioritized for policy intervention. Secondary data for 2024 were obtained from the Central Statistics Agency (BPS) of Central Java Province. A hybrid multi-criteria decision-making approach combining the Full Consistency Method (FUCOM) and VIKOR was employed. FUCOM was used to generate consistent and objective weights for six indicators (Human Development Index, Life Expectancy, Number of Poor Residents, Open Unemployment Rate, Access to Proper Sanitation, and GRDP per capita), while VIKOR was applied to produce compromise-based rankings of regional livability. The ranking results were visualized using a bar chart to enhance interpretability and facilitate regional comparison. The results indicate that Salatiga City, Magelang City, and Surakarta City exhibit the highest livability levels, whereas Brebes Regency, Banjarnegara Regency, and Pemalang Regency consistently rank lowest, indicating an urgent need for targeted government intervention. Model validation using Normalized Discounted Cumulative Gain (NDCG = 0.9835) and Spearman Rank Correlation (ρ = 0.883) demonstrates strong consistency with reference data. These findings suggest that the FUCOM–VIKOR hybrid approach provides a robust and practical decision-support tool for evidence-based regional development planning and poverty alleviation prioritization.
Comparison of Transfer learning Models MobileNetV3-Large and EfficientNet-B0 for Rice Leaf Disease Classification Abiyyu, Ahmad Naufal; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12033

Abstract

Rice productivity strongly depends on early detection of leaf diseases, while manual identification is often delayed and subjective. This study investigates the use of lightweight CNN architectures MobileNetV3-Large and EfficientNet-B0 based on transfer learning to classify six rice leaf disease classes, namely bacterial leaf blight, brown spot, healthy, leaf blast, leaf scald, and narrow brown spot. The dataset is obtained from Kaggle and consists of 2,628 images with a balanced class distribution, stratified into training, validation, and test sets with a ratio of 80%:10%:10%. The images are resized to 224×224 pixels and data augmentation was applied to the training set. Pretrained ImageNet weights are first used as frozen feature extractors, followed by partial fine-tuning of the last 30% backbone layers, with custom classification layers trained using the Adam optimizer with an early stopping mechanism. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrices, while computational efficiency is assessed based on parameter count and inference speed measured in frames per second. The results show that under partial fine-tuning MobileNetV3-Large achieves 95.83% test accuracy and 95.80% macro F1-score with 3.12 million parameters, while EfficientNet-B0 obtains 93.18% accuracy and 93.02% macro F1-score with 4.21 million parameters. Both models achieve inference speeds above 50 frames per second, suggesting their potential suitability for deployment on resource-constrained devices. Bootstrap analysis suggests the performance gap is clear in the frozen stage but becomes less conclusive after partial fine-tuning. Overall, MobileNetV3-Large provides the best trade-off between accuracy and efficiency for rice leaf disease classification.
Application of ADASYN and Optuna in the XGBoost Algorithm for Stunting Detection Putra Sadewa, Fastabyq; Kurniawan, Defri
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12035

Abstract

This study aims to develop an early detection model for childhood stunting risk using a machine learning approach based on Extreme Gradient Boosting (XGBoost), integrated with the Adaptive Synthetic Sampling (ADASYN) technique for data balancing and Optuna-based hyperparameter optimization. One of the main challenges in stunting prediction is class imbalance, where the number of stunting cases is significantly higher than non-stunting cases, thereby reducing the model’s ability to accurately identify the minority class. To address this issue, the study implements data deduplication, structured data splitting, and applies ADASYN exclusively to the training data to prevent data leakage and preserve the validity of the evaluation process. The proposed model (XGBoost with ADASYN and Optuna) is then compared with a baseline model that combines XGBoost and SMOTE. Experimental results show that the proposed model achieves an accuracy of 81.98%, a recall of 91.50%, and an F1-score of 89.14%, indicating improved sensitivity and a more balanced classification performance compared to the baseline. These findings demonstrate that the integration of ADASYN and Optuna-based hyperparameter optimization enhances model stability and generalization capability, making it a viable data-driven approach for stunting risk detection in environments with imbalanced class distributions.
Optimizing Feature Extraction for Naïve Bayes Sentiment Analysis Achmad, Achmad; Budiman, Fikri
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12041

Abstract

The rapid growth of e-commerce platforms such as Tokopedia has generated a large volume of user reviews containing diverse opinions about products and services. These reviews reflect consumer perceptions and provide valuable insights for business decision-making. This study aims to enhance sentiment analysis performance by optimizing the Naïve Bayes algorithm through a comparison of two feature extraction techniques, namely Bag of Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF). The dataset consists of 5,400 Tokopedia product reviews obtained from the Kaggle platform, which are categorized into positive and negative sentiments. The research process includes text preprocessing consisting of text cleaning, case folding, tokenization, stopword removal, and stemming, feature extraction using Bag of Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF), handling data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), and model training using the Naïve Bayes. The dataset is divided into 80% training data and 20% testing data, and model performance is evaluated using accuracy, precision, recall, and F1-score. The results show that BoW achieved the highest accuracy of 93%, while TF-IDF reached 83%, indicating that BoW provides more effective feature representation and more stable performance for Naïve Bayes-based sentiment analysis on this dataset.
Comparison of Random Forest and LSTM for Tokopedia Sentiment Analysis Saputra, Fahrizal Denta; Budiman, Fikri
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12042

Abstract

Tokopedia is one of the largest e-commerce platforms in Indonesia, where every transaction generates user reviews containing opinions about the products or services received. These reviews provide important information about product quality, but the very large quantity makes manual analysis inefficient. This study aims to automatically classify Tokopedia review sentiment and compare the performance of machine learning and deep learning methods. The dataset used was obtained from Kaggle and has undergone an initial cleaning stage, including removing irrelevant columns and manually labeling into two sentiment classes, positive and negative. The research methodology includes several stages, namely data preprocessing (cleaning, case-folding, stopword removal, tokenization, normalization, and stemming), feature extraction using TF-IDF for Random Forest and word embedding for LSTM, implementation of Random Forest and Long Short-Term Memory (LSTM) models, and model evaluation using confusion matrix. Experimental results show that LSTM provides the best performance with 94% accuracy, while Random Forest achieves 92% accuracy. These findings indicate that LSTM is more effective in understanding language context, resulting in more accurate sentiment classification and is useful for decision making in the e-commerce field.

Page 78 of 81 | Total Record : 805

 76   77   78   79   80 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue 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