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 695 Documents
 41   42   43   44   45 
Detection of Qur’anic Ikhfa Patterns in Digital Images Using Binary Similarity Distance Measures (BSDM) with 3W-Jaccard Formula Julianansa, Ririn; Fadlisyah; Yesy Afrillia
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9814

Abstract

Recitation rules in the Qur'anic script form various visual patterns. One of the selected rules for this study is the Ikhfa pattern. Ikhfa is a recitation rule pronounced subtly when the nun sukun (نْ) or tanwin (ـَــًـ, ـِــٍـ, ـُــٌـ) is followed by one of 15 specific letters, namely: ta’ (ت), tsa’ (ث), jim (ج), dal (د), dzal (ذ), za’ (ز), sin (س), syin (ش), shad (ص), dhad (ض), tha’ (ط), zha’ (ظ), fa’ (ف), qaf (ق), and kaf (ك). In this study, the primary challenge is the difficulty of automatically detecting the Ikhfa pattern in both digital and printed Qur'anic texts. This challenge arises from the subtlety of the recitation rule, which makes it difficult to distinguish from other recitation patterns. To address this, the Ikhfa pattern is detected using image processing techniques, and pattern classification is performed using the Binary Similarity and Distance Measures (BSDM) method. The results indicate that the pattern detection system, employing BSDM with the 3W-Jaccard formula, achieved a detection rate of 83.84%. This suggests that the 3W-Jaccard formula is an effective approach for detecting similar recitation patterns. One advantage of the 3W-Jaccard formula is its ability to recognize patterns with a relatively small amount of reference data, making it highly suitable for implementation in the detection system.
Clustering Korean Drama Viewers’ Preferences for Marketing Strategy Optimization Using the K-Means Algorithm on the MyDramaList Platform Nabilla, Sulistina; Nugroho, Agung; Mahaerni Putri, Isria Miharti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9820

Abstract

Korean dramas are highly popular in Indonesia, but it is a challenge for marketers to understand the diverse tastes of the audience. This study aims to identify audience preference segments by applying the K-Means algorithm to perform clustering. The analysis was conducted on 500 Korean dramas from the MyDramaList platform released in the period 2020 to 2023. Features used in the clustering process include rating, number of viewers (no_of_viewers), year of release (year), and genre. To overcome the multi-label genre, this research uses one-hot encoding technique to convert categorical data into numerical format. The optimal number of clusters was determined as four (4) based on analysis using the Elbow Method. The analysis successfully identified four distinct audience segments. Cluster 1 is the largest market segment which includes 323 dramas with a dominant genre of “Drama and Romance” and an average rating of 7.64. In contrast, Cluster 2 is a high-quality niche segment consisting of only 16 dramas but has the highest average rating (8.29) as well as the highest average viewership (25,739), with the dominant genre of “Drama and Mystery”. The other two segments are Cluster 0 (58 dramas) which focuses on the “Thriller” and ‘Mystery’ genres, and Cluster 3 (147 dramas) which features the “Comedy, fantasy and romance” genres. These data-driven findings enable the development of more specific and targeted marketing strategies for each audience profile, thereby improving promotional effectiveness as well as the viewing experience.
Analyzing and Controlling COVID-19 Using SageMath Toolbox: A case Study in the D.R. Congo MATONDO MANANGA, Herman; Patience, Pokuaa Gambrah; Marcial, Nguemfouo; Lea-Irène, Milolo Kanumuambidi; Peter, Kasende Mundeke; Benjamin, Consolant Majegeza
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9828

Abstract

Understanding the dynamics of an epidemic, to control, manage, or eradicate it, requires a wealth of knowledge in biology and mathematics. Computer tools also make significant contributions, thus, enabling us to carry out analyses and find approximate solutions, as well as run simulations to determine trends over time. In this study, we present a compartmental SVEIHAR model for the propagation and prevention of COVID-19. Using the computational and mathematical competencies of SageMath software (version 9.3) we simulate and evaluate the spread of the virus. Equilibria are calculated and adjusted according to the data. Again, the basic reproduction number, stabilities, and parameter sensitivities were studied. Our findings indicate that vaccination and cure rates are the most sensitive parameters, playing a crucial role in the fight against COVID-19. Again, the use of traditional plants, prayer, and meditation significantly decreases the value of the basic reproduction number. We also found that the disease will disappear after a time. Lastly, our study has shown the usefulness of SageMath software (version 9.3) which could be adapted to a variety of mathematical epidemic models.
Comparison of FMADM TOPSIS and FMADM WP in Determining Recipients of the Family Hope Program (PKH) Assistance Puspitasari, Novianti; Kurniati, Wendy; Hatta, Heliza Rahmania
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9852

Abstract

Fuzzy Multi-Attribute Decision Making (FMADM) TOPSIS and WP methods are frequently employed to identify potential recipients of government assistance. The Family Hope Program (PKH) is a government social assistance program designed to improve the welfare of underprivileged individuals. However, the process of distributing this assistance often faces obstacles in the form of inaccuracy in determining recipients. This study compares FMADM TOPSIS and WP to evaluate their effectiveness in objectively determining potential PKH recipients. The criteria for potential PKH recipients are eleven criteria obtained from the social service based on government regulations and PKH assistants. Meanwhile, the alternatives for this study are fifty samples of family data for potential PKH recipients. This study employs a sensitivity test method to assess the accuracy of the results obtained from each method. The results of the study show that FMADM TOPSIS produces a higher level of accuracy of 94% compared to FMADM WP. This study is expected to be able to contribute to choosing the right decision-making method to determine potential recipients of social assistance.
Spatial Distribution Patterns of Urban Growth, Dasymetric Mapping, and Air Pollution Standard Index (ISPU) in Bogor Regency (2018-2023) Ramadhan, Alfarisi; Hermawan, Erwin; Hudjimartsu, Sahid Agustian
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9862

Abstract

Bogor Regency has undergone rapid industrialization and urban growth, raising concerns about deteriorating air quality and its health impacts. This study aims to analyze the spatial distribution of urban expansion and its relationship with air pollution levels between 2018 and 2023. Using Sentinel-5P and Sentinel-2A satellite imagery processed via Google Earth Engine (GEE), the research maps changes in land use and monitors key pollutants such as nitrogen dioxide (NO₂) and sulfur dioxide (SO₂). The Air Pollution Standard Index (ISPU) is used to classify air quality conditions. The results indicate a notable increase (16%) in urban areas, with population density rising from 178 to 180 inhabitants per hectare, and a shift toward higher pollution categories, with significant portions of the region now classified as "Unhealthy" increased from 17% in 2018 to 31% in 2023. The urban growth model demonstrated high accuracy (92.5%) and strong alignment with local monitoring data. Model evaluation showed a Mean Absolute Error (MAE) of 1.295 µg/m³ and Root Mean Square Error (RMSE) of 1.478 µg/m³, demonstrating strong agreement between predicted and observed data from the Bogor Regency Environmental Agency. These findings highlight the need for integrated urban planning and effective air quality management to reduce future health risks in Bogor Regency.
Lung Segmentation in X-ray Images of Tuberculosis Patients Using U-Net with CLAHE Preprocessing Mabina, Ibnu Farid; Sari, Christy Atika; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9869

Abstract

Tuberculosis (TB) is an infectious disease that commonly affects the lungs and remains one of the leading causes of death from infectious diseases. Early detection is essential to prevent further spread and organ damage. Chest X-ray images are one of the main methods for diagnosing TB, but image quality is often affected by low contrast and noise. This study proposes the application of Contrast Limited Adaptive Histogram Equalization (CLAHE) method to improve X-ray image quality, combined with U-Net deep learning architecture for lung segmentation in X-ray images of tuberculosis patients. U-Net was chosen due to its excellent capability in medical image segmentation, thanks to its architectural structure that has encoder-decoder with skip connections, which allows the model to retain detailed information on high-resolution images, even on complex and noisy data. Experimental results using the Shenzhen and Montgomery datasets show that the U-Net model with CLAHE achieves Pixel Accuracy 97.96%, Recall 94.93%, Specificity 98.97%, Dice Coefficient 95.87%, and Jaccard Index (IoU) 92.07%.
A Banana Disease Detection Using MobileNetV2 Model Based on Adam Optimizer Aryanta, Muhammad Syifa; Sari, Christy Atika; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9870

Abstract

The main objective of this study is to develop a deep learning-based disease detection system for banana plants using the MobileNetV2 architecture through a comprehensive comparison with VGG16. This study utilizes a dataset of 3,653 images categorized into 12 classes, including Aphids, Bacterial Soft Rot, Bract Mosaic Virus, Cordana, Insect Pest, Moko, Panama, Fusarium Wilt, Black Sigatoka, Yellow Sigatoka, Pestalotiopsis, and healthy specimens. The methodological framework includes architecture comparison, data balancing, preprocessing techniques, and performance evaluation. The dataset was divided with a distribution ratio of 75% for training, 15% for validation, and 10% for testing. Comparative analysis shows excellent performance of MobileNetV2 with an accuracy of 96.21% compared to 90.15% for VGG16, while maintaining a significantly smaller model size of 10.0 MB compared to 57.8 MB for VGG16. Statistical validation through the McNemar test confirms significant superiority with a p-value of 0.008. The findings of this study contribute positively to the development of agricultural technology, particularly in the development of automated systems for disease detection in banana plants.
Implementation of the K-Nearest Neighbor Algorithm for Birth Rate Prediction Alhafiz, Akhyar; Kurniawan R., Rakhmat
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9886

Abstract

This study aims to predict the monthly birth rate using the K-Nearest Neighbor (KNN) regression algorithm. The dataset consists of historical data from 2010 to 2020, covering six districts and including variables such as total population, number of couples of reproductive age, family planning participation rate, and monthly birth rate as the prediction target. Data preprocessing involved handling missing values and applying Min-Max normalization. To maintain the time-series nature of the data, a chronological split was used, with 576 records from 2010 to 2018 for training and 216 records from 2019 to 2020 for testing. The model was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). The best performance was achieved at K = 7, with MAE = 19.94, RMSE = 30.91, and R² = 0.34. Additionally, the KNN model was compared with Linear Regression and Decision Tree, where KNN outperformed both alternatives. The final model was implemented in a web-based application to facilitate demographic data management and automatic birth rate prediction per district. This system is expected to support policy planning in the fields of population control and public health.
Classification of Nutritional Status Using the Fuzzy Mamdani Method : Case Study at Banjar City Hospital Nugraha, Muhammad Satria; Sanjaya, Fadil Indra
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9893

Abstract

The problem of nutritional status in adults requires accurate and adaptive classification methods. This study aims to develop a decision support system using the Fuzzy Mamdani method to classify nutritional status based on Body Mass Index (BMI). A dataset consisting of 237 anthropometric records from Banjar City Regional General Hospital was utilized. The system applies five fuzzy rules to map BMI values into nutritional categories: malnutrition, underweight, normal, overweight, and obesity. The classification process involves fuzzification, inference, and defuzzification using the centroid method. System performance evaluation shows an overall accuracy of 91.13%, with the highest classification precision achieved in the normal category (98.54%) and the lowest in the malnutrition category (30.77%). The results demonstrate that the Fuzzy Mamdani method is effective for nutritional classification, although refinement is needed for underrepresented categories. This system can serve as a useful tool for supporting clinical decision-making in public health services.
Application of Linear Discriminant Analysis Method With Gray Level Cooccurrence Matrix Method for Classification of Lung Disease Diagnosis Based on X-Ray Results Nuriana, Nuriana; Fitri, Zahratul; Razi, Ar
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9908

Abstract

This study aims to classify lung diseases from X-ray images using a combination of Gray Level Cooccurrence Matrix (GLCM) and Linear Discriminant Analysis (LDA) methods. GLCM was used to extract texture features such as contrast, correlation, energy, and homogeneity from 300 lung X-ray images representing four categories: Normal, Pneumonia, Tuberculosis, and Bronchitis. The LDA method was then applied for classification based on these features. The results showed that Tuberculosis had the highest classification accuracy at 80%, while the overall model accuracy was 61.67%. Evaluation using precision, recall, F1-score, and confusion matrix confirmed that the GLCM and LDA combination performed best in identifying tuberculosis cases. However, overlapping features between Normal, Bronchitis, and Pneumonia classes reduced the classification performance. These findings suggest that the proposed method provides promising results and could be improved further with advanced feature extraction or classification techniques.

Page 43 of 70 | Total Record : 695

 41   42   43   44   45 

Filter by Year

2017 2025


Reset
Filter By Issues
All Issue 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