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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
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Comparison of Support Vector Machine and Decision Tree Algorithm Performance with Undersampling Approach in Predicting Heart Disease Based on Lifestyle Febriyanti, Gusti Ayu Putu; Baita, Anna
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8941

Abstract

Heart disease is one of the leading causes of death in the world with risk factors such as atherosclerosis, high blood pressure, and smoking. Early diagnosis is essential to reduce mortality and improve patients' quality of life. This study evaluates the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Decision Tree (DT), in predicting heart disease risk by applying undersampling techniques to handle data imbalance. The K-fold cross-validation method with K=10 and hyperparameter tuning were applied to obtain the optimal performance of both models. The results showed that SVM without undersampling achieved 92% accuracy, while with undersampling the accuracy decreased to 76%. DT without undersampling has 91% accuracy, while with undersampling the accuracy reaches 75%. The undersampling technique successfully improved the balance in recognizing minority classes, although it reduced the overall accuracy. This finding confirms that SVM is more reliable in predicting heart disease in datasets with unbalanced class distribution.
Aspect-Based Sentiment Analysis with LDA and IndoBERT Algorithm on Mental Health App: Riliv Aryanti, Firda Ayu Dwi; Luthfiarta, Ardytha; Soeroso, Dennis Adiwinata Irwan
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8958

Abstract

Indonesia's mental health crisis in 2024 is increasing along with the high growth of internet users. Thus, high internet usage provides an opportunity for mobile applications including Riliv, a popular mental health application in Indonesia to become the most complete solution for overthinking, anxiety, and depression. This research aims to analyze the sentiment correlation of aspects based on App Store and Play Store reviews through scraping to effectively expose Riliv’s user satisfaction knowledge to developers using topic labeling with Latent Dirichlet Allocation (LDA) and sentiment labeling using Bidirectional Encoder Representations from Transformers (BERT) indobenchmark/indobert-base-p1 model on Aspect-Based Sentiment Analysis (ABSA). This study used 3068 reviews from September 2015 to December 2024. The main results obtained were 1) Identified the sentiment that positive is highest in 2020, neutral is highest in 2020, and negative is highest in 2018. 2) Identified 4 main aspects of the Riliv application: Access Support, Counseling Services, Meditation Features, and User Interface with LDA. 3) The majority distribution was negative on User Interface, neutral on Counseling Services, and positive on Meditation Features. 4) The effectiveness of IndoBERT compared to the non-transformer baseline algorithm. 5) The most optimal results were obtained with 70% training, 10% validation, and 20% testing, resulting in 95% accuracy.
Detection of Political Hoax News Using Fine-Tuning IndoBERT Jocelynne, Charlotte; Wijayakusuma, IGN Lanang; Harini, Luh Putu Ida
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8989

Abstract

Indonesia has experienced a surge in the spread of political hoax news, posing a potential threat to democratic and social stability. This study aims to develop a model for detecting political hoax news in the Indonesian language using IndoBERT, a language model optimized for Indonesian text. The dataset was sourced from Kaggle and comprises 20,928 factual news articles and 2,251 hoax news articles from major Indonesian media outlets, including CNN, Kompas, Tempo, and Turnbackhoax. The imbalance between factual and hoax news articles was addressed through undersampling, resulting in 1,302 samples for each class. The research stages include data collection, preprocessing, IndoBERT model training, and model evaluation. Results indicate that fine-tuning IndoBERT can detect political hoax news with an accuracy of 94.1% and an ROC AUC of 0.991, demonstrating high performance in accuracy and generalization capability. This research is expected to contribute to minimizing the spread of political hoax news in Indonesia and enhance media literacy among the public.
Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm Performance with Random Undersampling Technique to Predict Gestational Diabetes Mellitus Risk Damayanti, Annisa; Baita, Anna
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9009

Abstract

Gestational Diabetes Mellitus (GDM) is a condition of glucose intolerance that develops during pregnancy until the birth process, which is characterized by an abnormal increase in blood sugar levels. Accurate early diagnosis is very important to provide information that can accelerate the treatment process and reduce complications in the mother and baby. One of the machine learning methods that can be used to predict GDM is the Support Vector Machine (SVM) algorithm and the Random Forest (RF) algorithm. This study aims to compare, and evaluate GDM disease prediction models using the SVM and RF algorithms by balancing the target data using the Random Undersampling Technique. The approach using the random undersampling technique managed to increase accuracy by 18% from the accuracy before using the random undersampling technique. The SVM model in this study also uses hyperparameter tuning with kernel parameters, C (cost), and gamma, while the RF model uses Scoring Metrix and four other parameters, namely N_estimators, max_depth, min_samples_split, and min_samples_leaf. The best parameter search process is carried out using GridSearchCV on both models. The results of the study showed that the SVM classification model with random undersampling technique and hyperparameter tuning with K-Fold achieved an average accuracy of 100% with precision, recall, f1-score values also reaching 100%, with the Best Parameter Kernel Linear, C value = 0.1 and gamma value = 0.001 reaching the highest accuracy of 1.0, with a ROC-AUC value of 99% indicating very good prediction performance. While the RF model showed an accuracy result of 99%, tuning was also carried out using the appropriate parameters resulting in the same accuracy of 99%, with a ROC-AUC value of 99% as well. From both models, it shows that the SVM and RF algorithms have very good prediction performance in predicting DMG, but the SVM algorithm can predict DMG better than RF because the number of prediction errors is lower. 
Breast Cancer Detection using Decision Tree and Random Forest Kaunang, Fergie Joanda; Hakim, Bhustomy; Fraderic, Fedelis; Hartono, Sherren; Mulyanto, Andrew Kristanto
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9073

Abstract

Cancer is one of the most challenging diseases to cure and is a chronic condition that contributes significantly to global mortality. With advancements in artificial intelligence (AI) technology, AI-integrated systems can provide quick and accurate diagnoses based on collected medical data. By leveraging machine learning techniques, this study aims to compare the performance of two models using the Decision Tree (DT) and Random Forest (RF) algorithms on routine blood test data. The research process involves data preprocessing techniques such as handling missing values, detecting outliers, and feature selection, followed by applying the bootstrap aggregating technique to enhance model performance. Feature selection is used to identify the most significant features in the data that contribute to cancer detection. Using the KBest feature selection technique, the study found that the features age, BMI, leptin, adiponectin, and MCP-1 had the highest correlation with the target variable. The resulting models were evaluated to compare the performance of each algorithm. The evaluation results showed that the RF algorithm outperformed DT, achieving an accuracy of 89.65% on the processed dataset using the bootstrap technique, compared to DT's accuracy of 80.17%. Additionally, the RF algorithm demonstrated superior metric values, including a precision of 91.66% and an F1-score of 87.12%. This study concludes that the RF algorithm is more effective than DT for detecting cancer in limited datasets, especially when used with the bootstrap technique. The findings are expected to support the development of decision support systems in healthcare services for more accurate early cancer detection.
Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction Khairunisa, Mutiara; Putri, Desak Made Sidantya Amanda; Wijayakusuma, I Gusti Ngurah Lanang
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9076

Abstract

This study compares three machine learning methods—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree—for analyzing and predicting menstrual cycles. The dataset consists of 1,665 samples with 80 attributes encompassing information related to menstrual health. These methods were evaluated using accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics. The results show that LSTM achieved the highest accuracy (91.3%), followed by CNN (88.9%) and Decision Tree (85.2%). LSTM excelled in capturing complex temporal patterns in menstrual cycle data, while CNN effectively identified key patterns, and Decision Tree offered interpretability despite lower performance. This study concludes that LSTM is the most effective model for menstrual cycle prediction. The findings highlight the potential for improved accuracy in reproductive health tracking, with future research opportunities to incorporate additional variables such as hormonal history and lifestyle factors, as well as a focus on data privacy.
The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 Model Manurung, Ayub Michaelangelo; Santoso, Ilham; Subhiyakto, Egia Rosi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9100

Abstract

The skin, being the largest organ in the human body, plays a vital role in protecting against various external threats. However, cases of skin diseases are steadily rising across countries, making it a significant global health concern. Diagnosis often faces challenges due to symptom variations and low public awareness, highlighting the need for automated technology in skin disease detection. This study developed an automated classification system for skin diseases using EfficientNet-B1, capable of categorizing five skin conditions: Acne and Rosacea, Eczema, Melanoma Skin Cancer Nevi and Moles, Normal, Vitiligo, Psoriasis pictures Lichen Planus and related diseases, Seborrheic Keratoses and other Benign Tumors, Tinea Ringworm Candidiasis and other Fungal Infections. The system utilized 1.571 plus 1641 JPG digital images resized to 224 x 224 pixels, with 80% of the data allocated for training and 20% for testing. The trained model achieved a high accuracy of 99%, demonstrating the system's potential to support faster and more accurate diagnostic processes.
Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy Rahim, Abdul Mizwar A; Ridwan, Ahmad; Hartato, Bambang Pilu; Asharudin, Firman
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9125

Abstract

HIV/AIDS remains a significant global health challenge, requiring accurate predictive models for early detection and improved clinical decision-making. However, developing an effective predictive model faces challenges such as data imbalance and the presence of irrelevant features, which can compromise model accuracy. This study aims to enhance the performance of AIDS infection prediction models by integrating feature selection, data balancing, and machine learning classification techniques. Feature selection is conducted using Pearson Correlation, Mutual Information, and Chi-Square tests to retain only the most relevant features. Random Oversampling, SMOTE, and ADASYN are employed to address data imbalance and improve model robustness. Nine machine learning algorithms, including Decision Tree, Random Forest, XGBoost, LightGBM, Gradient Boosting, Support Vector Machine, AdaBoost, and Logistic Regression, are tested for classification. Performance evaluation using confusion matrix, precision, recall, F1-score, and AUC-ROC shows that tree-based models (Random Forest, Extra Trees, and XGBoost) achieve the best results, particularly in handling minority class predictions. The study concludes that combining feature selection, data balancing, and machine learning techniques significantly improves predictive performance, making it a valuable approach for early detection and clinical decision support in HIV/AIDS diagnosis. Future research may explore hyperparameter tuning and real-world clinical data integration to enhance practical applicability.
Sentiment Analysis Using LSTM Algorithm Regarding Grab Application Services in Indonesia Gunawan, Akbar Rikzy; Alfa Aziza, Rifda Faticha
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8696

Abstract

This study aims to analyze the sentiment of user reviews for the Grab Indonesia application using Long Short-Term Memory (LSTM) algorithms. Two variants of LSTM, namely Stacked LSTM and Bi-Directional LSTM, were compared to determine the most effective model in classifying user review sentiments. Both models were enhanced with Multi-Head Attention mechanisms to capture more complex contextual relationships in sequential data. The data used consists of 2,000 user reviews collected through scraping from the Google Play Store, with sentiment labels of positive and negative. Data preprocessing included labeling, case folding, stopword removal, tokenization, stemming, and the application of the SMOTE technique to address class imbalance. The results show that the Bi-Directional LSTM model achieved the highest validation accuracy of 87%, with an F1-score of 0.90 for the negative class and 0.82 for the positive class, while the Stacked LSTM recorded an accuracy of 84%, with an F1-score of 0.87 for the negative class and 0.78 for the positive class. Overall, the Bi-Directional LSTM demonstrated better performance in identifying both negative and positive sentiments, providing a good balance between precision and recall. This study proves that Bi-Directional LSTM with Multi-Head Attention can improve sentiment analysis performance on user reviews of digital applications, with potential applications in various other platforms.
Implementation and Evaluation of User-Centered Design in an Online New Student Admissions System for Early Childhood Education Zulvi, Mutia Sari; Najwa, Nina Fadilah; Ningsih, Nindi Lestia
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8735

Abstract

The new student admissions process is often conducted conventionally, requiring parents to visit educational institutions in person to submit physical documents. This approach tends to be inefficient, poses risks of document loss or damage, and slows down data analysis. Promotional methods relying on word-of-mouth and social media are also considered less optimal. To enhance efficiency and user experience, a web-based admissions system was developed using a User-Centered Design (UCD) approach. The UCD method was applied to understand the needs and characteristics of key users, including administrators, admissions committee members, school principals, and parents. The development process involved three iterations, covering user needs analysis, user experience-based interface design, testing, and repeated evaluations. The system includes features such as online registration, data management, a payment system, automated notifications, and geolocation to assist parents in estimating school distance. System evaluation using black-box testing and usability testing resulted in a System Usability Scale (SUS) score of 92.125, classified as "Excellent." These findings indicate that the UCD approach effectively produces systems that align with user needs and improve the efficiency of new student admissions.

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