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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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Predicting Cryptocurrency Prices Using Machine Learning: A Case Study on Bitcoin Alfarizi, Muhammad; Lestarini, Dinda
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.v9i6.11234

Abstract

The rapid growth of cryptocurrencies, particularly Bitcoin, has drawn significant attention from investors and researchers due to its extreme price volatility. However, predicting the price of Bitcoin against the Indonesian Rupiah (BTC/IDR) remains a major challenge, especially in emerging markets such as Indonesia. This study aims to conduct an empirical comparison among three deep learning models Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (CNN-1D) in forecasting Bitcoin prices based on historical data obtained from the Indodax platform for the period 2018–2025. The dataset consists of five main variables: opening price, highest price, lowest price, closing price, and trading volume. Prior to model training, preprocessing steps were conducted, including handling missing values using the forward fill method, normalization with MinMaxScaler, and constructing time series data with a 60-day look-back window. The models were trained using an 80% training and 20% testing data split, the Adam optimizer, Mean Squared Error (MSE) as the loss function, for 50 epochs with a batch size of 32. Evaluation was performed using five quantitative metrics: MSE, RMSE, MAE, MAPE, and R², along with validation techniques to prevent data leakage. The results indicate that the GRU model achieved the best performance, with a MAPE of 1.77% and an R² of 0.9916, outperforming LSTM (MAPE 3.90%) and CNN-1D (MAPE 6.17%). These findings suggest that GRU is computationally more efficient and better adapted to nonlinear temporal dependencies in highly volatile markets. This research contributes to the academic discourse on the application of deep learning for digital asset price forecasting and provides practical implications for investors and developers of financial predictive systems in Indonesia. Future studies are expected to explore hybrid models or multi-step forecasting approaches to enhance real-time predictive performance.
Comparing Machine Learning Models for Sentiment Analysis of Tokopedia Reviews Ulhaq, Afif Langgeng Dhiya; Suprayogi, Suprayogi
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.v9i6.11239

Abstract

This study presents a comparative evaluation of machine learning models for sentiment analysis on Tokopedia user reviews written in the Indonesian language. The objective is to assess the effectiveness of three algorithms—Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP)—in classifying customer sentiments extracted from Tokopedia reviews on Google Play Store. The dataset, collected between January and October 2025, consists of 10,236 unique entries after preprocessing, which included text cleaning, case folding, tokenization, stopword removal, normalization using a verified Indonesian word normalization dictionary, and optional stemming with the Sastrawi library. The reviews were divided into positive and negative categories based on rating polarity (4–5 stars as positive; 1–2 stars as negative).Each model was evaluated using both hold-out validation (80:20 split) and 5-fold cross-validation, employing metrics such as accuracy, precision, recall, and F1-score. Experimental results indicate that the SVM achieved the highest accuracy of 0.88, outperforming Random Forest (0.85) and MLP (0.83). These findings demonstrate that SVM performs more robustly on sparse TF-IDF vector features and is more resistant to noise within informal Indonesian expressions. The research further discusses the linguistic challenges inherent in Indonesian sentiment analysis, including code-mixing, abbreviations, and non-standard words, while proposing preprocessing strategies to mitigate them.The outcomes of this study contribute to enhancing the reliability of sentiment-based decision support systems in Indonesian e-commerce platforms. The methodological framework developed here can serve as a baseline for future work involving hybrid or deep-learning approaches such as LSTM or IndoBERT for improved contextual understanding.
Deep Learning-Based Detection of Online Gambling Promotion Spam in Indonesian YouTube Comments Ammar, Muhammad Zhafran; Putra, Ricky Eka; Yamasari, Yuni
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.v9i6.11240

Abstract

Online gambling promotion has increasingly penetrated social media platforms, with YouTube comments becoming a frequent target for spam-based advertising. Such activities not only violate platform policies but also expose users to harmful content. Addressing this issue requires automated detection systems capable of handling noisy, informal, and highly imbalanced text data. This study investigates the effectiveness of four recurrent neural architectures LSTM, GRU, BiLSTM, and BiGRU for detecting gambling promotion comments in Indonesian YouTube data. To address class imbalance, multiple experimental scenarios were explored, including the original distribution, undersampling, oversampling, and class weighting. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix analysis. The results show that bidirectional models outperformed their unidirectional counterparts, with BiGRU achieving the best overall performance. When combined with class weighting, BiGRU reached 98% accuracy, 0.83 F1-score, and 0.971 ROC-AUC, demonstrating a superior ability to detect minority-class instances. Oversampling improved recall substantially but increased false positives, while undersampling reduced accuracy; class weighting provided the most balanced performance across metrics. These findings confirm that BiGRU with class weighting offers the most practical balance between accuracy, recall, and computational efficiency, making it well-suited for real-time moderation systems. The study provides a strong foundation for future research on transformer-based architectures and cross-platform spam detection in Indonesian social media environments.
Optimization of Rice Field Irrigation Based on Fuzzy Logic and the Internet of Things Through Water Level Analysis Nasir, Hamida; Wardi, Wardi; Jalil, Abdul
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.v9i6.11244

Abstract

The low efficiency of conventional irrigation systems often results in water waste and decreased rice productivity. The research was carried out by designing an automatic monitoring and control system using a water level sensor, a Raspberry Pi Pico W microcontroller, a water pump, and a Blynk application as a real-time monitoring medium. Water level data is processed by fuzzy logic method to categorize low, normal, or high conditions, so that the system can adjust the water pump adaptively according to the needs of the land. The results of the study show that the integration of IoT and fuzzy logic is able to improve water use efficiency, maintain soil moisture at optimal conditions, and support better rice growth. The system has also been proven to be accurate in the classification of water conditions with a success rate above 90%. Thus, this research contributes to the development of smart agricultural technologies that can increase productivity while supporting sustainable agricultural practices.
Improving YOLO Performance with Advanced Data Augmentation for Soccer Object Detection Puspita, Rahayuning Febriyanti; Naufal, Muhammad; Al Zami, Farrikh
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.v9i6.11256

Abstract

This study developed an object detection system for soccer games using the YOLOv8m algorithm with four main classes: player, goalkeeper, referee, and ball. The dataset, consisting of 372 annotated images, exhibited class imbalance, with significantly fewer ball instances compared to players. The basic YOLOv8m architecture was used without internal modifications, but adjustments were made to the output layer and fine-tuning of the pre-trained weights to adapt to the new dataset. Two models were compared: one without and one with advanced augmentation techniques (mosaic, mixup, cutmix). The experimental results showed an increase in mAP@50 from 74.9% to 81.4% in the augmented model, with a statistically significant difference (p < 0.01). However, model performance still decreased under extreme conditions such as high occlusion, rapid movement, and uneven lighting. The combination of data augmentation, output layer adaptation, and fine-tuning proved effective in improving object detection accuracy and provided the basis for the development of a real-time artificial intelligence-based soccer match analysis system.
Multiclass Classification of Tomato Leaf Diseases Using GLCM, Color, and Shape Feature Extraction with Optimized XGBoost Laiskodat, Fransisko Andrade; 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.v9i6.11273

Abstract

Automatic classification of tomato leaf diseases is an essential component in advancing precision agriculture based on artificial intelligence. This study aims to develop a multiclass classification model for tomato leaf diseases by utilizing texture, color, and shape features, and employing an optimized XGBoost algorithm. The public PlantVillage dataset was used, with preprocessing stages including feature extraction, normalization, dimensionality reduction using PCA, and class balancing using SMOTE. The experimental results showed that the model successfully classified ten disease classes with a high accuracy of 97.63%, and both macro and weighted f1-scores of 0.98. These findings indicate that the combination of handcrafted features and XGBoost offers an effective, efficient, and applicable solution for plant disease diagnostic systems.
A Comparative Analysis of Character and Word-Based Tokenization for Kawi-Indonesian Neural Machine Translation Budaya, I Gede Bintang Arya; Yusadara, I Gede Putra Mas
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.v9i6.11283

Abstract

Preserving regional languages ​​is a strategic step in preserving cultural heritage while expanding access to knowledge across generations. One approach that can support this effort is the application of automatic translation technology to digitize and learn local language texts. This study compares two tokenization strategies, word-based and character-based on a Kawi–Indonesian translation model using the FLAN-T5-Small Transformer architecture. The dataset used consists of 4,987 preprocessed sentence pairs, trained for 10 epochs with a batch size of 8. Statistical analysis shows that Kawi texts have an average length of 39.6 characters (5.4 words) per sentence, while Indonesian texts have an average length of 54.9 characters (7.5 words). These findings suggest that Kawi sentences tend to be lexically dense, with low word repetition and high morphological variation, which can increase the learning complexity of the model. Evaluation using BLEU and METEOR metrics shows that the model with word-based tokenization achieved a BLEU score of 0.45 and a METEOR score of 0.05, while the character-based model achieved a BLEU score of 0.24 and a METEOR score of 0.04. Although the dataset size has increased compared to previous studies, these results indicate that the additional data is not sufficient to overcome the limitations of the semantic representation of the Kawi language. Therefore, this study serves as an initial baseline that can be further developed through subword tokenization approaches, dataset expansion, and training strategy optimization to improve the quality of local language translations in the future.
Comparison of Online Gambling Promotion Detection Performance Using DistilBERT and DeBERTa Models Pratama, Halim Meliana; Wijayakusuma, IGN Lanang; Widiastuti, Ratna Sari
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.v9i6.11293

Abstract

Online gambling promotions on social media have become a serious concern in Indonesia, where perpetrators use ambiguous and disguised language to evade detection. This study compares two transformer-based models, DistilBERT and DeBERTa, in detecting such content within Indonesian YouTube comments. Using a balanced dataset of 6,350 comments, both models were fine-tuned with optimized hyperparameters (learning rate 1e-5, batch size 32, 5 epochs) and evaluated through five-fold cross-validation. Results show that DeBERTa achieves superior performance with 99.84% accuracy and perfect recall, while DistilBERT achieves 99.29% accuracy. Error and linguistic analyses indicate that DeBERTa’s disentangled attention and Byte-Pair Encoding provide better understanding of non-standard and ambiguous language. Despite requiring higher computational cost, DeBERTa is ideal for high-accuracy applications, whereas DistilBERT remains suitable for real-time and resource-limited environments.
Prototype of Temperature, Humidity and Fire Detection Monitoring System in Rice Warehouse Based on ESP32 Microcontroller Anggraini, Dewi; Fitriani, Endah; Paramytha, Nina; Dasmen, Rahmat Novrianda
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.v9i6.11311

Abstract

Rice warehouses in Indonesia experience significant post-harvest losses, reported to reach 10–20% annually, primarily due to poor environmental control and fire incidents. This study develops and evaluates an Internet of Things (IoT)-based environmental monitoring prototype for rice warehouses, utilizing the ESP32 microcontroller, DHT22 temperature-humidity sensor, and a flame sensor. The ESP32 was chosen for its low power consumption and robust connectivity, while DHT22 and the flame sensor were selected for their balance of accuracy, sensitivity, and cost-effectiveness. System calibration employed a digital thermohygrometer and a standard flame detector to ensure measurement validity. Experimental tests were conducted in a controlled laboratory setting with three sensor points, simulating temperature variations of 28–45°C and humidity of 60–95%, together with 24-hour reliability tests and scenarios involving fire detection at a 30 cm distance. The system achieved sensor error margins within ±0.5°C for temperature and ±2% for humidity, with actuator response times of 1–3 seconds. Real-time Telegram notifications were successfully delivered within 2–3 seconds. The integration of multi-sensors, automated actuators, and instant notifications distinguishes the proposed system from conventional approaches and previous studies. While effective for small-to-medium scale warehouses, limitations remain in fire sensor coverage and dependence on internet connectivity. The system offers an adaptable, efficient, and reliable solution to minimize manual errors and improve rice warehouse management. Future work will address broader scalability, additional gas sensors, GSM communication, and cloud-based data logging for enhanced safety and analytics.
Optimized LSTM with TSCV for Forecasting Indonesian Bank Stocks Salsabila, Rizka Mars; Fahmi, Amiq; Al Zami, Farrikh
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.v9i6.11314

Abstract

Volatility in financial markets presents complex forecasting challenges for investors, particularly within emerging economies such as Indonesia. This study proposes an optimized Long Short-Term Memory (LSTM) model for forecasting the stock prices of five significant Indonesian banks: BBCA, BBRI, BMRI, BBNI, and BBTN, utilizing daily OHLCV data (Open, High, Low, Close, Volume) and technical indicators from 2020 to 2025. The dataset comprises over 6,000 daily records, segmented using a sliding window approach to preserve temporal structure and enhance learning efficiency. Concurrently, the model architecture comprising dual LSTM layers with dropout regularization was refined through systematic hyperparameter tuning to enhance predictive performance. Model evaluation employed 5-fold Time Series Cross-Validation (TSCV), a sequential validation technique that mitigates data leakage and explicitly overcomes the limitations of conventional k-fold methods by preserving chronological integrity. Performance metrics included MSE, RMSE, MAE, R², and MAPE. The experiment results demonstrate the model’s robustness in capturing long-term dependencies within financial time series. BBCA and BMRI achieved superior accuracy (R² > 0.95), with BBCA recording the lowest MAPE of 2.34%. Despite market fluctuations, the model maintained consistent reliability across all test folds. This study overcomes a methodological limitation by integrating LSTM with TSCV in expanding markets, offering actionable insights for investors, analysts, and policymakers, and serving as a reference for adaptive AI-based, more informed forecasting tools. Moreover, the proposed framework holds promise for broader application across other financial sectors and regional markets with similar volatility characteristics.

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