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
 59   60   61   62   63 
Balancing Student Specialization Class Placement Based on Interests and Talents Using K-Means Clustering and Genetic Algorithm Islamy, Chaidir Chalaf; Oktaviansyah, Muhammad Andika
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.v9i6.10425

Abstract

Student specialization placement in Indonesian secondary schools often produces imbalanced class distributions and misalignment between student interests and assigned tracks. This study develops a hybrid optimization system combining K-Means clustering and Genetic Algorithm (GA) to allocate 133 tenth-grade students from SMAN 1 Ngimbang into four specialization classes (Science, Mixed-Science, Mixed-Social, Social) while balancing operational constraints. Initial K-Means clustering (k=4, n_init=100) achieved a Silhouette Score of 0.287 but yielded severely imbalanced distribution (10, 51, 48, 24 students). GA optimization (population=300, generations=150, crossover=70%, mutation=10%, elitism=10%) with multi-component fitness function incorporating cosine similarity, distribution penalty, movement penalty, and entropy produced balanced classes (31, 35, 35, 32 students) within the 30-35 target range. Post-optimization metrics showed 73.7% retention rate, average match score of 0.792, entropy of 0.482, and execution time of 47.8 seconds. The Silhouette Score decreased to 0.080, reflecting an acceptable trade-off between cluster purity and operational feasibility. Sensitivity analysis confirmed weight configuration robustness. This system demonstrates practical applicability for real-time school implementation, reducing distribution gap by 90.2% while maintaining individual-class compatibility.
Machine Learning Based Prediction of Osteoporosis Risk Using the Gradient Boosting Algorithm and Lifestyle Data Salim, Edwin Ibrahim; 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.v9i6.10483

Abstract

Osteoporosis is a degenerative disease characterized by decreased bone mass and an increased risk of fractures, particularly among the elderly population. Early detection is essential; however, standard diagnostic methods such as Dual-Energy X-ray Absorptiometry (DEXA) remain limited in terms of availability and cost. This study aims to develop a machine learning-based risk prediction model for osteoporosis by utilizing lifestyle data with the Gradient Boosting algorithm. The secondary dataset was obtained from the Kaggle platform, consisting of 1,958 samples covering lifestyle and clinical attributes such as age, gender, physical activity, smoking habits, calcium intake, vitamin D consumption, and family history. Preprocessing involved normalization and categorical feature encoding, along with a balance check of class distribution, which indicated that the dataset was relatively balanced. The data were then divided using stratified sampling with an 80% training set and 20% testing set. Model performance was evaluated using accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). The results showed that the Gradient Boosting algorithm achieved an accuracy of 91%, precision of 90.8%, recall of 90.2%, F1-score of 90.5%, and an AUC of 0.92, outperforming baseline methods such as Logistic Regression and Random Forest. These findings demonstrate that Gradient Boosting is effective as a decision-support tool for early osteoporosis screening based on lifestyle data and has the potential to be integrated into clinical decision-making systems to enhance early detection in healthcare services. Nevertheless, since this study relied on a secondary dataset from Kaggle, the results require further validation using real clinical data from Indonesia to ensure representativeness for the local population.
A Hybrid Approach to Music Recommendations Based on Audio Similarity Using Autoencoder and LightGBM Aristawidya, Winda Ardelia; 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.v9i6.10516

Abstract

Music recommendation systems help users navigate large music collections by suggesting songs aligned with their preferences. However, conventional methods often overlook the depth of audio content, limiting personalization and accuracy. This study proposes a hybrid approach that uses PCA and Autoencoder to extract audio embeddings. These embeddings are processed using K-Nearest Neighbors to find similar tracks, followed by a reranking step with LightGBM based on predicted relevance. The system achieved strong results: 98% accuracy, 0.96 precision, 0.96 recall, and 0.96 F1-score for the Similar class, with 0.99 precision and recall for Not Similar. Cross-validation confirmed model robustness, with an average accuracy of 97.99%, precision of 0.9577, recall of 0.9624, and F1-score of 0.9600, all with low standard deviations. These outcomes show that combining deep audio features with machine learning ranking enhances recommendation quality. Future improvements may involve incorporating metadata and genre-based visualizations for more diverse and interpretable results.
Comparative Sentiment Analysis on Mobile JKN Application Using Logistic Regression with SMOTE Based Statistical Feature Selection Awaliyah, Rafika Farkhul; Hendrawan, Aria
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.v9i6.10520

Abstract

This study investigates public sentiment on the Mobile JKN application using Logistic Regression enhanced with SMOTE-based statistical feature selection. Unlike prior works that relied solely on conventional feature combinations such as TF-IDF or Word2Vec, this research performs a comparative evaluation of three statistical feature selection techniques: Recursive Feature Elimination (RFE), Chi-Square, and Mutual Information, under both TF-IDF and Word2Vec representations in a low-resource Indonesian language setting. The dataset consists of 2,382 user reviews from the Google Play Store, balanced using SMOTE to mitigate class imbalance. The best configuration, TF-IDF combined with Mutual Information, achieved an accuracy of 73.38% and an F1-score of 50%, indicating a moderate yet consistent performance. A confusion matrix-based error analysis revealed that most misclassifications occurred between neutral and negative classes due to semantic overlap. The relatively low F1-score highlights challenges in sentiment separability, while the superior performance of Mutual Information demonstrates its ability to capture discriminative linguistic features. The superior performance of Mutual Information is attributed to its ability to capture non-linear dependencies between features and sentiment labels, yielding richer discriminative information compared to Chi-Square or RFE. This research establishes a comparative methodological framework that integrates feature selection and data balancing techniques, providing interpretable sentiment classification insights for under-resourced language settings.
An Intelligent Web-Based Mental Health Management Platform with Rule-Based Music Therapy Recommendation Delimayanti, Mera Kartika; Wibowo, Harits Taqiy
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.v9i6.10799

Abstract

This research developed a web-based application for mental health management with an emotional music therapy recommendation feature using Rule-Based Filtering. The system is designed to help individuals recognize and manage emotional conditions caused by life pressures, work stress, and often overlooked psychological issues. A 2023 survey showed that 43% of respondents were concerned about mental health problems, followed by stress at 40%, while 43.8% of parents of teenagers managed their children’s mental health issues independently, 19.2% did not know where to seek help, and 15.4% believed the problems would improve on their own. The system analyzes daily emotional input and weekly PANAS questionnaires to classify moods (Positive, Negative, Mixed, Neutral) based on Positive Affect (PA) and Negative Affect (NA) scores, then recommends relevant music from the database. The technical implementation uses Laravel for the backend and Tailwind CSS for the frontend. Black Box Testing showed 100% functionality. User Acceptance Test (UAT) with 32 respondents resulted in UAT-J 90.25%, UAT-K 90.41%, UAT-R 89.18%, and UAT-A 91.24%. The System Usability Scale (SUS) reached an average score of 85 (very high), while the Net Promoter Score (NPS) was 59.37% (62.50% Promoters), indicating strong user satisfaction and loyalty. This research is expected to help individuals monitor emotional conditions and increase mental health awareness through an innovative music-based approach.
Performance Comparison of Multilayer Perceptron (MLP) and Random Forest for Early Detection of Cardiovascular Disease Setiawan, Dita Widayanti; Lapatta, Nouval Trezandy; Amriana, Amriana; Nugraha, Deny Wiria; Lamasitudju, Chairunnisa Ar.
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.v9i6.10826

Abstract

Cardiovascular disease is a disorder of the heart and blood vessels that can lead to heart attacks, strokes, and heart failure, so early detection is essential. This study compares Multilayer Perceptron (MLP) and Random Forest for risk classification in a Kaggle dataset containing 70,000 samples with balanced targets. Pre-processing included age conversion, outlier cleaning, standardization, and feature selection based on feature importance. Both models were optimized using RandomizedSearchCV and evaluated using accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, and k-fold cross-validation. The results show that the accuracy of MLP is 73.90% and Random Forest is 74.23% with an AUC of 0.80 for both. Random Forest is more stable across all folds and performs better on the negative class, while MLP is slightly more sensitive to the positive class. Independent t-test and Mann-Whitney U tests show p>0.05, indicating that the difference in performance is not significant. The most influential features were diastolic blood pressure, age, cholesterol, and systolic blood pressure. The non-clinical Streamlit prototype demonstrated the model's potential for education and initial decision support.
Classification For Determining Nutritional Status of Toddlers Using Random Forest Method at Tanah Pasir Primary Health Centre, North Aceh Sofyan Iryad, Indana; Qamal, Mukti; Razi, Ar
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.v9i6.10855

Abstract

The nutritional status of toddlers is a fundamental factor in supporting their growth and development, particularly during the golden period of 0–5 years of age. Malnutrition in toddlers can have detrimental effects on physical growth, cognitive development, and immune function. In Indonesia, child malnutrition remains a significant public health challenge, particularly in rural areas, necessitating improved nutritional surveillance systems at primary health centers. The manual assessment of nutritional status at community health centers (Puskesmas) often poses challenges in promptly identifying toddlers with undernutrition or severe malnutrition. This study aims to develop a toddler nutritional status classification system based on the Random Forest method to assist healthcare workers in determining nutritional status quickly and accurately. This study utilized a dataset of 2,612 toddler anthropometric records collected from Tanah Pasir Community Health Center, North Aceh, between November 2024 and January 2025. The dataset was split into training (2,090 records, 80%) and testing (522 records, 20%) sets using stratified random sampling. Key variables included age (0-60 months), body weight (kg), and body height (cm). Nutritional status categories were determined based on WHO Child Growth Standards using the weight-for-age (W/A), height-for-age (H/A), and weight-for-height (W/H) indices. The Random Forest method was chosen due to its ability to construct multiple decision trees through ensemble learning, resulting in more accurate predictions and better resistance to overfitting. The model was implemented with 100 trees and evaluated using standard classification metrics. The experimental results demonstrated that the system achieved strong classification performance, with an accuracy of 93%, precision of 95%, recall of 98%, and an F1-score of 96%. The high recall value is particularly significant in healthcare applications, ensuring minimal false negatives in detecting malnourished toddlers. The developed system facilitates healthcare workers in efficiently and systematically monitoring toddlers' nutritional status with consistent classification standards. Therefore, this system is expected to serve as a decision-support tool to improve community nutritional status at the community health center level, enabling early intervention for at-risk children.
Hyperparameter Optimization and Feature Selection Analysis on the XGBoost Model for Hepatitis C Infection Prediction Lefi, Nadia Martha; 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.v9i6.10876

Abstract

Hepatitis C is a liver disease that can progress to chronic conditions such as cirrhosis and liver cancer. Early detection is essential and can be supported through machine learning approaches. This study analyzes the effect of feature selection and hyperparameter tuning on the performance of the XGBoost model in classifying hepatitis C infection. The dataset, obtained from Kaggle, contains laboratory test attributes. The preprocessing stage involved handling missing values, encoding categorical variables, removing outlier classes, and normalizing data using StandardScaler. After stratified splitting, the training set was balanced using the SMOTE technique. Feature selection was carried out using the ANOVA F-score method, and hyperparameter tuning was performed using GridSearchCV. Three model scenarios were compared: baseline, with feature selection, and with combined feature selection and hyperparameter tuning. The evaluation results showed that the third model achieved the best performance with 96% accuracy, 79% precision, 81% recall, and a 78% F1-score, despite a slight decrease in the ROC AUC value. This approach has proven effective in improving model performance and is relevant for supporting more accurate hepatitis C diagnosis systems.
Programming Assessment in E-Learning through Rule-Based Automatic Question Generation with Large Language Models Saputro, Halim Teguh; Nurhasan, Usman; Nur Wijayaningrum, Vivi
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.v9i6.10901

Abstract

This study develops an evaluation instrument for Python programming using a Rule-Based Automatic Question Generation (AQG) system integrated with Large Language Models (LLMs), designed based on the Revised Bloom’s Taxonomy. The urgency of this research stems from the limitations of conventional programming evaluations, which are often time-consuming, less objective, and insufficiently aligned with cognitive learning levels. The proposed method applies assessment terms as rule-based constraints to guide LLM-generated questions, ensuring both pedagogical validity and structural consistency in JSON format. A total of 91 questions were produced, consisting of multiple-choice and coding items, which were then validated by three programming experts and tested on 32 vocational students. The findings indicate that the instrument achieved an overall validity of 77.66% (valid category), with the highest accuracy at the Apply (96.30%) and Create (100%) levels. The reliability test using Cronbach’s Alpha yielded 0.721, showing acceptable internal consistency. Item difficulty analysis revealed a strong dominance of easy questions (97.78%), with only 2.22% classified as moderate and none as difficult. Student performance also showed a fluctuating pattern: high in Remember (94.79%), Understand (95.83%), and Create (95.60%), but lower in Apply (86.11%), Analyze (90.97%), and Evaluate (87.15%). These results confirm that integrating Rule-Based AQG with LLMs can produce valid, reliable, and adaptive evaluation instruments that not only capture basic programming competencies but also partially address higher-order cognitive skills. This research contributes both practically by providing educators with an efficient tool for generating evaluation items and academically by enriching the growing body of literature on AI-assisted assessment in programming education.
Sentiment Analysis of Trending Topics on Social Media X Using Natural Language Processing and LSTM Rasmila, Rasmila; Saputri, Yosandra; Syaki, Firamon; Hadinata, Novri
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.v9i6.10931

Abstract

In today’s fast-paced digital era, trending news on Social Media X spreads rapidly, influences public opinion, and is often vulnerable to disinformation. This study analyzes netizens’ sentiment towards trending topics on Social Media X using Natural Language Processing (NLP) and a Long Short-Term Memory (LSTM) model. A dataset of 4483 comments was collected across 15 trending topics (Feb–Jun 2025). The preprocessing steps included cleansing, case folding, stopword removal, tokenization, and translation to handle bilingual data. Results show sentiment distribution: 35% positive, 36% negative, and 29% neutral. Model performance varied between 34%–67% accuracy, with precision, recall, and F1-scores indicating that topic sensitivity, language diversity, and data imbalance strongly influenced outcomes. This research contributes to text analytics by providing a baseline model for real-time trending news sentiment analysis in Indonesia, particularly under multilingual and noisy data conditions.

Page 61 of 70 | Total Record : 695

 59   60   61   62   63 

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