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
 58   59   60   61   62 
Comparative Study of SVM and Decision Tree Algorithms on the Effect of SMOTE Technique on LinkAja Application Faruq, Muhammad Kholfan; Umam, Khothibul; Mustofa, Mokhamad Iklil; Mahfudh, Adzhal Arwani
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.9806

Abstract

The widespread adoption of digital wallets like LinkAja in Indonesia has led to a surge in user-generated reviews, which are valuable for assessing service quality. This study compares the classification performance of Support Vector Machine (SVM) and Decision Tree algorithms on user reviews from the LinkAja application. 7.000 reviews were gathered through web scraping and processed with standard text cleaning, tokenization, stopword removal, and stemming, resulting in 6,261 usable entries. These were divided into training and testing sets in a 70:30 ratio. The performance of each algorithm was evaluated both before and after the application of Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Prior to SMOTE, SVM recorded an accuracy of 77.97%, precision of 0.74, recall of 0.33, and F1 score of 0.45, while Decision Tree reached 72.01% accuracy, 0.50 precision, 0.62 recall, and 0.55 F1 score. After SMOTE, SVM accuracy slightly improved to 78.29%, with notable increases in recall (0.74) and F1 score (0.60); Decision Tree also saw an accuracy rise to 74.56% but experienced a slight decline in F1 score to 0.52. These findings demonstrate that SVM, particularly when used with SMOTE, offers better overall performance and class balance in classifying reviews with imbalanced sentiment distribution, making it more suitable than Decision Tree for this application.
Opinion Classification on IMDb Reviews Using Naïve Bayes Algorithm Putri, Amiliya; Umam, Khothibul; Mustofa, Hery
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.9831

Abstract

This study aims to classify user opinions on IMDb movie reviews using the Multinomial Naïve Bayes algorithm. The dataset consists of 50,000 reviews, evenly distributed between 25,000 positive and 25,000 negative reviews. The preprocessing stage includes cleaning, case folding, stopword removal, tokenization, and lemmatization using the NLTK library. Text features are represented through the TF-IDF method to capture the significance of each word in the documents. The Multinomial Naïve Bayes model was trained using the hold-out validation technique with an 80:20 split for training and testing data. Hyperparameter tuning of α (Laplace smoothing) was conducted to enhance model stability and accuracy. The model’s performance was evaluated using accuracy, precision, recall, and F1-score metrics, supported by a confusion matrix visualization. The results show that the model achieved an accuracy of 87%, with precision of 87.9%, recall of 85.4%, and an F1-score of 86.6%. In comparison, Logistic Regression as a baseline algorithm achieved an accuracy of 91%. Nevertheless, the Naïve Bayes algorithm remains competitive and computationally efficient for large-scale text data, making it highly relevant for sentiment analysis of movie reviews.
Comparative Analysis of Penetration Testing Frameworks: OWASP, PTES, and NIST SP 800-115 for Detecting Web Application Vulnerabilities Imtias, Muhamad Bunan; Umam, Khothibul; Mustofa, Hery; Subowo, Moh Hadi
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.9846

Abstract

Web application security faces increasingly complex challenges as digital architectures evolve, necessitating the selection of appropriate and effective penetration testing methods. This study presents a comparative analysis of the OWASP Testing Guide, PTES, and NIST SP 800-115 frameworks in detecting web application vulnerabilities. Through experiments on DVWA and OWASP Juice Shop, the frameworks were evaluated based on detection speed, vulnerability count, and severity. The results highlight a clear trade-off: OWASP proved the most efficient (85 minutes average, 59 total vulnerabilities), making it ideal for rapid assessments. PTES demonstrated the most comprehensive technical depth (63 vulnerabilities, highest severity) but required the most time, while NIST SP 800-115 (49 vulnerabilities) excelled in compliance and risk management integration. The study recommends selecting OWASP for efficiency, PTES for deep technical audits, and NIST for regulatory alignment.
Evaluation of SMOTE Technique in the Comparison of XGBoost and Random Forest Algorithms for Liver Disease Prediction Rohman, Wahyutri Nur; Agastya, I Made Artha
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10239

Abstract

In many countries, including Indonesia, liver disease remains a major cause of morbidity and mortality. Early detection plays a crucial role in improving treatment outcomes. This study evaluates the performance of two widely used machine learning models Random Forest and XGBoost for predicting liver disease, employing the SMOTE balancing technique to address class imbalance. The primary objectives are to enhance model fairness, reduce overfitting, and improve sensitivity toward the minority class. Model performance is assessed using accuracy, precision, recall, and F1-score metrics. The XGBoost model achieved an average accuracy of 99.74%, precision of 99.77%, recall of 99.75%, and F1-score of 99.72%, while the Random Forest model attained an average accuracy of 99.82%, precision of 99.89%, recall of 99.75%, and F1-score of 99.75%. Both models demonstrated excellent predictive capability, with Random Forest slightly outperforming XGBoost. These results highlight the importance of data balancing and robust model validation in developing reliable machine learning models for healthcare decision-making.
A Smart Recommendation System for Crop Seed Selection Using Gradient Boosting Based on Environmental and Geospatial Data Aryanti, Aryanti; Iryani, Nanda; Khairunnisa, Khairunnisa
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10249

Abstract

The selection of appropriate crop seeds is a critical factor in enhancing agricultural productivity. Nevertheless, farmers frequently face challenges when trying to determine which crop seeds match the unique features of their surrounding environment and geographic location. To address this, the study introduces a smart recommendation model that leverages real-time environmental measurements alongside vital geographical characteristics to support informed seed selection. The environmental features include temperature, humidity, and rainfall, while the geographical attributes encompass nitrogen, phosphorus, and potassium content. A Gradient Boosting classification algorithm is employed to model the relationships between these features and the optimal crop seed types, based on a labeled dataset. Experimental results demonstrate that the model achieves strong classification performance, indicating its effectiveness in delivering accurate and context-specific seed recommendations. The proposed system highlights the potential of data-driven approaches in supporting agricultural decision-making and can be further integrated into smart farming platforms to optimize crop planning and seed selection, ultimately contributing to improved agricultural outcomes.
Mapping Influence Clusters: A Network Analysis of TikTok Influencer Co-Followership Among University Students Mutanga, Murimo; Fadillah, Muhammad Aizri; Chani , Tarirai; Anuardi, Sindiy Fortuna
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10340

Abstract

This study examines TikTok influencer co-followership patterns among university students through social network analysis to understand how shared influence functions within digital ecosystems. Using survey data from Indonesian university students who identified their top three most-followed TikTok influencers, we built a co-followership network comprising 266 unique influencers connected by 333 relationships. The research employed quantitative network analysis methods, such as centrality measures, community detection algorithms, and content categorisation, to map influence clusters and explore the network’s structural properties. Results reveal a fragmented network with a low density (0.0094) consisting of 49 connected components, indicating that student followership patterns form distinct thematic communities rather than a single, unified influence network. Centrality analysis identified key bridging influencers, with Tasya Farasya emerging as the most central figure, demonstrating broad appeal across multiple interest categories. Community detection uncovered clear clusters organised around lifestyle and entertainment content, comedy, food, educational material, and motivational themes. Content analysis revealed that travel and lifestyle influencers dominated the network (23.7%), followed by comedy and entertainment creators (16.9%), reflecting TikTok's dual role as both an entertainment platform and a lifestyle guide for university students. The findings show how algorithmic personalisation creates confined influence communities while some central figures act as bridges across different content domains. This research advances methodological approaches by pioneering network analysis methods for influencer co-followership, thereby enhancing the understanding of digital influence as a networked rather than individual phenomenon. The results provide valuable insights for marketing professionals aiming to understand network influence, educational institutions developing media literacy programmes, and platform designers creating algorithmic recommendation systems.
Comparative Analysis of Random Forest and XGBoost Models for Cervical Cancer Risk Prediction using SHAP-based Explainable AI Yudha, Muhammad Agung Reza; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10357

Abstract

Cervical cancer remains one of the leading causes of cancer-related deaths among women, particularly in developing countries such as Indonesia. This study aims to develop an accurate and interpretable predictive model for cervical cancer risk using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms. The dataset used is the Cervical Cancer Risk Factors from the UCI Repository, consisting of 858 patient records and 36 clinical and demographic features. The preprocessing stages include missing value imputation, class balancing using Synthetic Minority Oversampling Technique (SMOTE), and hyperparameter optimization through Randomized Search CV. Experimental results show that both models achieved high performance, with accuracy exceeding 96% and AUC above 0.95, while the XGBoost (Tuned + SMOTE) model slightly outperformed RF in detecting positive cases. The interpretability analysis using SHapley Additive exPlanations (SHAP) identified clinical features such as Schiller Test, Hinselmann Test, and Cytology Result as the most influential factors in the classification process, consistent with established clinical evidence. Therefore, the integration of XGBoost, SMOTE, and SHAP provides a predictive framework that is not only highly accurate but also clinically explainable, supporting the development of decision-support systems for early cervical cancer detection.
MRI Classification of Brain Tumors Using EfficientNetB0 Feature Extraction and Machine Learning Methods Jiven, Firza Findia; Rumini, Rumini
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10363

Abstract

Brain tumor classification using MRI images plays a crucial role in modern medical diagnostics, offering fast and accurate support for disease detection. This study proposes a classification approach that combines feature extraction using EfficientNet B0 with conventional machine learning algorithms. MRI brain images are preprocessed and resized to match EfficientNet B0 input dimensions. Feature vectors are extracted and subsequently processed using PCA for dimensionality reduction and SMOTE for class balancing. The resulting data are classified using various machine learning algorithms including Support Vector Machine, XGBoost, LightGBM, and others. Experimental results show that Support Vector Machine achieved the highest accuracy of 96%, followed by XGBoost and LightGBM at 94%. The combination of EfficientNet B0 feature extraction and lightweight classifiers proved to be effective, matching the performance of more complex deep learning models. This study does not focus on measuring computational cost directly, but rather demonstrates that combining EfficientNetB0 feature extraction with machine learning algorithms can achieve performance comparable to deep learning approaches. This highlights that lightweight models remain competitive in terms of accuracy without requiring highly complex architectures. Future work can explore this method on other medical imaging datasets and enhance model interpretability for clinical adoption.
Hypertension Risk Prediction Using Stacking Ensemble of CatBoost, XGBoost, and LightGBM: A Machine Learning Approach Alfath, Abisakha Saif; Wardhana, Ajie Kusuma; Rumini, Rumini
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10370

Abstract

Hypertension is a leading cause of cardiovascular diseases, chronic kidney failure, and strokes, affecting millions worldwide. Early detection and accurate risk prediction are crucial for effective management and prevention. This study aims to evaluate and compare the performance of different algorithms for predicting hypertension risk using a stacking ensemble approach. The model combines three gradient boosting algorithms XGBoost, LightGBM, and CatBoost as base learners, with Logistic Regression as the meta learner. The dataset, sourced from Kaggle, contains 4,240 instances with demographic and clinical attributes relevant to hypertension. The preprocessing steps included imputing missing values using the median, removing residual null entries, and addressing class imbalance through the SMOTE algorithm. Data were divided into 80% for training and 20% for testing. The evaluation showed that the stacking ensemble model achieved an overall accuracy of 92,65%, with precision, recall, and F1-scores consistently reaching 0.92 for both classes. The confusion matrix revealed minimal misclassification, indicating the model’s strong ability to differentiate between low and high risk individuals. These results emphasize that the primary goal of this research is to identify which algorithm provides the best performance for hypertension risk prediction. By evaluating and comparing different models, this study offers insights into choosing the most effective algorithm for clinical decision-making and early detection strategies.
Analysis of Factors Affecting the Delay in Completion of Student Final Projects Using the C5.0 Decision Tree Algorithm Islamy, Chaidir Chalaf; Anwar, Mochamad Choirul
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10374

Abstract

Delays in completing final projects are a common problem faced by students and can lead to delayed graduation, increased study load, and reduced readiness to enter the workforce. This study uses a quantitative predictive approach to analyze the factors influencing delays in completing student final projects by applying the C5.0 Decision Tree classification algorithm. Data were collected through a Likert-scale questionnaire from 204 students of the Faculty of Engineering, University of 17 August 1945 Surabaya, who graduated between 2019 and 2021. The analyzed factors include time management, student motivation, campus policies, faculty support, family support, surrounding environment, and academic skills. The C5.0 algorithm was selected for its higher accuracy and efficiency compared to earlier methods such as C4.5 and CART. The results show that the Surrounding Environment factor is the most dominant, followed by Student Motivation, Time Management, and Family Support. Evaluation of the model yielded excellent classification performance, achieving an accuracy of 95.31%, precision of 96.77%, recall of 93.75%, and an F1-score of 95.24%. These results indicate that the model effectively classifies students at risk of delay with strong predictive reliability. The findings provide insights for universities to develop targeted strategies to enhance student motivation, improve time management, and create a more supportive academic environment. In conclusion, the C5.0 algorithm demonstrates a strong capability to identify dominant delay factors and supports data-driven decision-making in academic management.

Page 60 of 70 | Total Record : 695

 58   59   60   61   62 

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