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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 Text Vectorization Methods for IMDB Movie Review Sentiment Analysis Using SVM Mulyawan, Rifqi; Naparin, Husni; Fatihia, Wifda Muna
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10372

Abstract

Sentiment Analysis is a scientific study in the field of Machine Learning that focuses on classifying opinions expressed in text. IMDb is a platform widely used to provide information and share viewpoints among moviegoers worldwide, where audience reactions often serve as a benchmark for a movie’s success. This research aims to classify positive and negative sentiments by applying and evaluating the effectiveness of Support Vector Machine (SVM) with four different feature representation methods: (a) Bag of Words (BoW), (b) TF-IDF, (c) Word2Vec, and (d) Doc2Vec. After preprocessing the textual data, each method was employed to extract features for model training. The experimental results demonstrate that the combination of SVM with Word2Vec achieved the best overall performance with an F1-Score of 0.8607 and an Accuracy of 0.8607, while also being the fastest in training time (75.0s). In comparison, BoW reached an F1-Score of 0.8219, TF-IDF achieved 0.8520, and Doc2Vec obtained 0.8440. These findings highlight that Word2Vec provides the most effective feature representation for sentiment classification using SVM in this study.
Comparative Analysis of LightGBM and Random Forest for Daily Bitcoin Closing Price Prediction with Ensemble Approach Nolejanduma, Dionisius Nusaca Redegnosis; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10589

Abstract

This study performs a comparative analysis of the LightGBM and Random Forest algorithms in predicting daily Bitcoin closing prices, with an exploration of an Ensemble approach for potential improvements in accuracy. A quantitative research design is employed, utilizing historical Bitcoin (BTC-USD) data from September 2015 to July 2025, enriched with various technical indicators. Data preprocessing, model training, and evaluation were carried out using Python in Google Colaboratory, with the dataset split into 80% for training and 20% for testing. Model performance was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the R-squared (R²) statistic, with statistical significance tests to ensure robust comparisons. A simple Linear Regression model was also included as a baseline. The findings reveal that Random Forest outperformed LightGBM, achieving an MAE of 11,599.74, an RMSE of 19,262.31, and an R² of 0.431, compared to LightGBM’s MAE of 12,285.42, RMSE of 19,995.04, and R² of 0.386. Although the Ensemble model showed slight improvements over LightGBM, it did not surpass Random Forest. The relatively low R² values across all models reflect the inherent volatility and difficulty in predicting Bitcoin prices. The study concludes that Random Forest demonstrates superior robustness for Bitcoin forecasting. Importantly, this work provides a novel empirical contribution by being one of the first to directly benchmark RF, LightGBM, and their Ensemble for Bitcoin prediction, highlighting that a simple averaging Ensemble does not guarantee superior performance. This finding provides a foundation for developing more refined Ensemble strategies tailored to high-volatility assets.
A Hybrid Framework Based on YOLOv8 and Vision Transformer for Multi-Class Detection and Classification of Coffee Fruit Maturity Levels Subki, Ahmad; M. Zulpahmi; Imran, Bahtiar
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10590

Abstract

Detection and classification of coffee cherries based on maturity levels present a significant challenge in agricultural product processing systems, primarily due to the high visual similarity among classes within a single bunch. This study aims to develop a multi-class detection and classification system for coffee cherries by integrating YOLOv8 and Vision Transformer (ViT) as a classification enhancer. The initial detection process is conducted using YOLOv8 to identify and automatically crop coffee cherry objects from bunch images. These cropped images are then re-classified using the Vision Transformer to improve prediction accuracy. The training process was carried out with a learning rate of 0.0001, a batch size of 16, and epoch variations of 50, 100, and 150. Evaluation results demonstrate that the integration of YOLOv8 and ViT significantly improves classification accuracy compared to using YOLOv8 alone. At 100 epochs, the YOLOv8+ViT model achieved an accuracy of 89.52%, a precision of 90.43%, and a recall of 89.52%, outperforming the standalone YOLOv8 model, which only reached an accuracy of 75.44%. These results indicate that the Vision Transformer effectively enhances classification performance, particularly for visually similar coffee cherry classes. The integration of these two methods offers a promising alternative solution for improving image-based multi-class classification in agriculture and other domains involving complex visual objects.
A Hybrid Data Science Framework for Forecasting Bitcoin Prices using Traditional and AI Models Hiskiawan, Puguh; William, Jovan; Tio Jansel, Louis Feliepe
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10631

Abstract

Bitcoin, a highly volatile and decentralized digital asset, presents considerable challenges for accurate price forecasting. This study proposes an applied data science framework that compares traditional statistical approaches with modern Artificial Intelligence (AI)-based models to predict Bitcoin’s daily closing price. Using BTC-USD historical data from January 2020 to December 2024, we converted prices into Indonesian Rupiah (IDR) to increase local relevance. Our forecasting horizon is 30 days, based on a 60-day lookback window. We evaluate six models: Linear Regression, ARIMA, and Prophet as traditional techniques, alongside Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks as AI approaches. All models were trained using lag-based or sequence-based time series features and evaluated using MAE, RMSE, R², MAPE, and SMAPE. Results show that AI models, particularly LSTM and XGBoost, offer better performance in capturing short-term non-linear dynamics compared to traditional models. LSTM provides high accuracy, though with greater computational demand, while XGBoost strikes a balance between speed and precision. Prophet and ARIMA remain effective for quick and interpretable forecasts but struggle with abrupt trend shift common in cryptocurrency markets. In addition to performance metrics, we include a robustness analysis based on median absolute error and outlier detection to assess model stability under extreme variations. Visual analytics—including forecast curves, error distributions, and uncertainty bounds—help interpret and communicate model behavior. This comprehensive evaluation offers practical insights for investors, analysts, and fintech practitioners, and the pipeline can be extended to other volatile assets.
Indonesian Food Classification Using Deep Feature Extraction and Ensemble Learning for Dietary Assessment Kardawi, Muhammad Yusuf; Saragih, Frederic Morado; Rahadianti, Laksmita; Arymurthy, Aniati Murni
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10643

Abstract

Food is a cornerstone of culture, shaping traditions and reflecting regional identities. However, understanding the nutritional content of diverse cuisines can be challenging due to the vast array of ingredients and the similarities in appearance across different dishes. While food provides essential nutrients for the body, excessive and unbalanced consumption can harm health. Overeating, particularly high-calorie and fatty foods, can lead to an accumulation of excess calories and fat, increasing the risk of obesity and related health issues such as diabetes and heart disease. This paper introduces a novel ensemble learning approach with a dictionary that contains food nutrition content for addressing this challenge, specifically on Padang cuisine, a rich culinary tradition from West Sumatera, Indonesia. By leveraging a dataset of nine Padang dishes, the system employs image enhancement techniques and combines deep feature extraction and machine learning algorithms to classify food items accurately. Then, depending on the classification results, the system evaluates the nutritional content and creates a dietary evaluation report that includes the amount of protein, fat, calories, and carbs. The model is evaluated using different evaluation metrics and achieving a state-of-the-art accuracy of 85.56%, significantly outperforming standard baseline models. Based on the findings, the suggested approach can efficiently classify different Padang dishes and produce dietary assessments, enabling personalised nutritional recommendations to provide clear information on a balanced diet to enhance physical and overall wellness.
Integration of Multi-Modal Sensors in Aquaponic Farming for IoT-Ready Based on ESP32 and Raspberry Pi Hybrid Platform Risal, Muhammad; Wahyuningsih, Pujianti; Haerani, Nining; Mikolas, Muhammad; Lewa, Muhammad Iqbal
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10644

Abstract

This study aims to design and implement a smart agriculture system that integrates multi-modal sensors with an aquaponic farming platform, utilizing Raspberry Pi and ESP32 microcontrollers. The integration approach adopts embedded system-based control to connect and coordinate all multi-modal sensor components within the smart aquaponic environment. The primary function of the multi-modal sensors is to acquire comprehensive environmental and operational data from the aquaponic system through instrumentation-based measurement techniques. These data are intended to be further integrated with Internet of Things (IoT) technology using ESP32 and Raspberry Pi as control units. In this study, the integration of the Raspberry Pi and ESP32 platforms demonstrates superior performance compared to a single platform, as it combines a microcontroller capable of reading analog sensor data and transmitting it to the Raspberry Pi, which subsequently functions as the central data processing unit. Experimental results confirm that all multi-modal sensor devices operate reliably when interfaced with the ESP32 and Raspberry Pi, producing accurate data streams that can be utilized in future implementations of IoT-based control systems.
The Influence of Trust and Habit on Users’ Intention to Keep Using Facebook Reels for Economic Purposes Soviana, Rita; Inan, Dedi I.; Juita, Ratna; Indra, Muhamad
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10661

Abstract

Social media now plays a crucial role in supporting economic activities. Facebook Reels, for instance, as a short video feature, offers economic opportunities for its users. However, understanding the factors that encourage continued usage remains limited. This study aims to explore the influence of trust and habit on users’ intention to keep using Facebook Reels for economic purposes (N=174 active users). We integrated the Technology Continuance Theory (TCT) applied Partial Least Squares Structural Equation Modelling (PLS-SEM) for the analysis. The results show that both factors significantly influence continuance intention (R² = 78.7%), although they do not directly affect user attitude. Attitude is influenced only by reciprocal benefits (R² = 62%). These findings indicate that in regions with limited digital infrastructure such as West Papua, the success of digital platforms strongly depends on building user trust and encouraging consistent usage habit. This study offers valuable insights for platform developers and policymakers in designing strategies to help users continue using the platform over time and support the growth of the local digital economy.
Comparing Different KNN Parameters Based on Woman Risk Factors to Predict the Cervical Cancer Saletia, Maria Claudia; Anshori, Mochammad; Haris, M Syauqi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10746

Abstract

Cervical cancer remains a major cause of mortality among women, particularly in low-resource regions where access to conventional screening is limited. Early detection through predictive modeling offers a low-cost and non-invasive alternative to clinical diagnostics. This study aims to evaluate the effectiveness of the k-Nearest Neighbors algorithm for predicting cervical cancer risk using behavioral and psychosocial attributes. The research utilized the publicly available Sobar cervical cancer behavioral dataset comprising 72 instances with 18 input features and a binary target label. Data preprocessing included removal of incomplete records, encoding of categorical variables, and normalization. The algorithm was tested across varying numbers of neighbors and distance metrics, with performance evaluated using 10-fold cross-validation and multiple classification metrics. The optimal configuration was achieved with three neighbors and the Manhattan distance metric, yielding an accuracy of 93.06%, sensitivity of 93.10%, specificity of 85.90%, precision of 93.10%, F1-score of 92.90%, and an area under the curve of 0.8952. This performance surpassed the reported baseline of a probabilistic classifier and demonstrated the algorithm’s capability to capture complex behavioral patterns associated with cervical cancer risk. These findings confirm the feasibility of applying optimized instance-based learning to behavioral data for early cancer risk assessment. The approach offers potential for integration into community health programs to support early detection and prevention strategies.
e Daeli, Barnabas Belieffain Fertility ; Sanjaya , Ucta Pradema
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10824

Abstract

Asthma prediction demands architectures capable of capturing multifactorial interactions among demographic, clinical, and environmental determinants. This study establishes Random Forest (RF) as the optimal solution through rigorous comparison with Logistic Regression (LR) and Support Vector Machines (SVM) on a 10,000-patient cohort. RF achieved performance: 99.55% accuracy, 100% precision, 98.19% recall, and exceptional stability (σ=0.0019 CV) surpassing SVM by 6.86% recall, preventing 167 missed diagnoses per 10,000 cases. Hereditary factors dominated feature importance (Gini=0.20), generating 18.7% greater node purity reduction than BMI, while the paradoxical "No Allergies" signal (3.726) revealed non-atopic phenotypes. Critically, sparse linear correlations (94% |r|<0.02) contrasted with RF’s capture of nonlinear thresholds like sedentarism (2.243) > smoking impact. Clinical implementation requires: (1) threshold calibration (θ=0.3) achieving >99% recall, (2) monthly false-negative audits mitigating 24.33% prevalence skew, and (3) dimensionality reduction eliminating 3.256  features. RF’s capacity to resolve hereditary-environmental interactions establishes a new paradigm for asthma risk stratification.
Comparative Analysis Transfer Learning Models for Early Detection of Pneumonia using Chest X-ray Images Rida, Rachmasari Annisa; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10857

Abstract

Pneumonia is a serious respiratory disease that continues to be a major worldwide health issue, especially in nations that are struggling with limited medical resources. Early and accurate detection is essential to improve patient outcomes and reducing the rate of death. This study compares the performance of two deep learning architectures, DenseNet121 and ResNet50, using transfer learning for pneumonia detection from chest X-ray images. The dataset consists 5,856 images with two classes, NORMAL and PNEUMONIA, split into training 60%, validation 20%, and testing 20%. Pretrained ImageNet weights were used as fixed feature extractors, with a custom classification layers. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. On the internal test set, DenseNet121 achieved 92% accuracy, with precision 0.79, recall 0.94, and F1-score 0.86 for NORMAL class, and precision 0.98, recall 0.91, and F1-score 0.94 for PNEUMONIA class. ResNet50 reached 81% accuracy, with precision 0.61, recall 0.80, and F1-score 0.70 for NORMAL class, and precision 0.92, recall 0.81, and F1-score 0.86 for PNEUMONIA class. External testing on an independent set of 200 images (100 images per class) yielded 98% accuracy for DenseNet121 and 85% for ResNet50. These results show that DenseNet121 provides better overall performance and lower false-negative risk for pneumonia cases, highlight the potential of DenseNet121 as a foundation for AI-assisted diagnostic tools in clinical practice.

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