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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 805 Documents
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Comparison of LightGBM and CatBoost Algorithms for Diabetes Prediction Based on Clinical Data Latuconsina, Muhammad Sidik; 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.v10i1.12179

Abstract

Diabetes Mellitus presents a global health challenge necessitating accurate early detection to prevent fatal complications. However, clinical data often exhibit imbalanced class distributions, hindering standard prediction models from effectively detecting positive patients. This study aims to compare the performance of two modern Gradient Boosting algorithms, LightGBM and CatBoost, in predicting diabetes risk. Random Forest and Logistic Regression algorithms were included as baseline models to benchmark effectiveness. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during the training data preprocessing stage. The dataset was sourced from the Kaggle public repository (Diabetes Prediction Dataset), comprising 100,000 patient medical records with clinical attributes such as age, body mass index (BMI), and HbA1c levels. Performance evaluation utilized Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC) metrics. Experimental results demonstrated a tight competition, where LightGBM achieved the highest Accuracy of 97.16%. However, CatBoost demonstrated superior sensitivity (Recall) of 69.71% and the highest F1-Score of 80.48%. This makes CatBoost the most reliable model in minimizing False Negatives compared to LightGBM and Random Forest, whereas Logistic Regression showed the lowest performance. Furthermore, interpretability analysis using SHAP (SHapley Additive exPlanations) revealed that HbA1c and blood glucose levels were the most dominant features in detection, validating the model's alignment with clinical diagnosis. This study concludes that the CatBoost algorithm combined with SMOTE offers a more sensitive, transparent, and efficient diabetes prediction for medical screening.
Opinion Mining of Pedometer Application Reviews on Google Play Store Using Fine-Tuned IndoBERT-Base Primono, Anggi; Sanjaya, Ucta Pradema
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.v10i1.12184

Abstract

User reviews on the Google Play Store provide valuable insights into user satisfaction and application performance. However, manual analysis of these reviews is inefficient due to large data volume and the informal characteristics of the Indonesian language. This study proposes an opinion mining approach using a fine-tuned IndoBERT-Base model to classify user sentiments into three classes: positive, neutral, and negative. A total of 1,665 reviews of a Pedometer application were collected, with 1,636 reviews retained after preprocessing. The dataset was divided into training, validation, and test sets using stratified sampling to preserve class distribution. Experimental results show that the proposed model achieves an accuracy of 94.51% and a weighted F1-score of 0.93 on the test set. Despite strong overall performance, the results indicate that class imbalance significantly affects the classification of neutral and negative sentiments. Error analysis reveals that ambiguous expressions and limited samples in minority classes remain challenging for the model. This study demonstrates that fine-tuned IndoBERT-Base is effective for sentiment analysis of Indonesian mobile application reviews while highlighting the importance of addressing imbalanced data in opinion mining tasks.
Forecasting Export Values in West Sumatra Using Backpropagation Neural Network Rahmawati, Desi; Martha, Zamahsary
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.v10i1.12199

Abstract

Export value is an important indicator in supporting regional economic growth. However, its movement tends to be volatile and non-linear, making it difficult to forecast using conventional statistical methods such as ARIMA. This study aims to forecast the export value of West Sumatra Province using an Artificial Neural Network (ANN) with the Backpropagation algorithm. The data used consist of monthly export values from January 2006 to October 2025 obtained from Badan Pusat Statistik (BPS) of West Sumatra Province. The data were normalized and modified using the rolling window method, then divided into training and testing datasets. Several network architectures were evaluated through a trial-and-error process with variations in the number of neurons in the hidden layer. The best model was achieved with the BPNN(12,12,1) architecture, yielding a Mean Square Error (MSE) of 0.0236 and a Mean Absolute Percentage Error (MAPE) of 25.31%. The results indicate that the model is capable of capturing non-linear patterns and reasonably following the trend of the actual data. The selected model was then used to perform short-term forecasting of export values for the period from November 2025 to March 2026. The findings demonstrate that the Backpropagation Neural Network algorithm is effective for forecasting export values in West Sumatra Province. This study contributes theoretically by enriching the application of artificial intelligence in regional economic forecasting and practically by supporting data-driven policy formulation for export strategies in West Sumatra.
Classification Of Student Depression Using Support Vector Machine Modelling and Backward Elimination Sabar, Rohmat Abidin; Pajri, Afril Efan; Budiani, Jauhara Rana
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.v10i1.12203

Abstract

Depression among university students has become a serious mental health concern that can negatively affect academic performance and overall well-being. Early detection of Depression is essential to provide timely support and preventive interventions. This study proposes a machine learning approach to classify student Depression using a Support Vector Machine (SVM) combined with Backward Elimination (BE) for feature selection. The dataset used in this research was obtained from a public repository and consists of 502 student records with multiple psychological and demographic attributes. Data preprocessing included categorical encoding and Min–Max normalization, followed by an 80:20 split for training and testing. Experimental results show that the baseline SVM model achieved an accuracy of 0.9208, while the application of Backward Elimination improved the performance to 0.9604. In addition, precision, recall, and F1-score also showed notable improvements, indicating a reduction in misclassification, particularly for non-depressed students. These findings demonstrate that integrating feature selection with SVM can enhance classification performance and provide a more efficient model for supporting early Depression detection among university students.
Sentiment Analysis of the Free Nutritious Meal Program (MBG) on Social Media X (Twitter) Using K-Nearest Neighbor and Artificial Neural Network Hakim, Fernanda Amri; Prastya, Ifnu Wisma Dwi; Budiani, Jauhara Rana
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.v10i1.12205

Abstract

The Free Nutritious Meal Program (Makan Bergizi Gratis/MBG) is a national policy initiated by the Indonesian government to improve public nutritional status, particularly among children and vulnerable groups. Since its implementation, the program has generated extensive public discussion on social media, reflecting diverse opinions, support, and criticism. This study aims to analyze public sentiment toward the MBG program on social media X (Twitter) using machine learning-based text classification methods. A total of 9,038 Indonesian-language tweets were collected and processed through text preprocessing, semi-automatic sentiment labeling with manual validation, and feature extraction using the Term Frequency–Inverse Document Frequency (TF–IDF) method. Sentiments were classified into three categories: positive, neutral, and negative. The performance of K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), and ANN with class balancing using Synthetic Minority Over-Sampling Technique (ANN + SMOTE) was evaluated using accuracy, precision, recall, and F1-score metrics supported by confusion matrix analysis. The results indicate that the ANN + SMOTE model achieved the highest performance with an accuracy of 93.58%, outperforming ANN (92.59%) and KNN (86.28%). The sentiment distribution indicates that public opinion toward the MBG program is predominantly neutral (52.1%), followed by positive (40.0%) and negative (7.9%) sentiments. These findings suggest that while the MBG program is generally well received, negative sentiments provide important feedback related to program implementation and governance.

Page 81 of 81 | Total Record : 805

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