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Anggraini Puspita Sari
UPN “Veteran” Jawa Timur
Computer Science & IT

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Optimasi Hiperparameter LSTM Menggunakan PSO untuk Peramalan Bawang Merah dan Bawang Putih Mutiq Anisa Tanjung; Anggraini Puspita Sari; Achmad Junaidi
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2569

Abstract

This research develops a shallot and garlic price prediction model using a Long Short-Term Memory (LSTM) network optimized through the Particle Swarm Optimization (PSO) method. Indonesia experiences an annual increase in demand for these two commodities. This research focuses on optimizing LSTM parameters, such as the number of units in each layer, learning rate, batch size, time step, and number of training epochs using PSO. Various trials were conducted with different PSO parameter settings and data partitioning scenarios to find the best configuration in predicting prices. The results show that the LSTM model optimized with PSO produces an RMSE value of 436,969 for shallots and 173,866 for garlic. In addition to RMSE, the Mean Absolute Percentage Error (MAPE) and R² metrics also show high prediction accuracy. The 90:10 data partitioning scenario showed the best evaluation results, indicating that more data improves the accuracy of the LSTM in learning price patterns. Scatter plots comparing predicted prices with actual prices show a good match, although there is some variation in certain price ranges. This study also highlights the effect of data partitioning on model performance. The LSTM-PSO approach proved effective in improving the accuracy of price predictions and has practical implications for farmers and policy makers in decision making. The model has the potential to be a decision support tool in the agribusiness sector, with the possibility of further development with external factors.
Classification of Feline Skin Diseases Based on Severity Using Type-2 Fuzzy Dhevi Puspitasari; Anggraini Puspita Sari; Firza Prima Aditiawan
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3027

Abstract

Cats are among the most popular pets due to their friendly nature and relatively easy care, yet limited health attention often makes them vulnerable to skin diseases. Clinical examinations at veterinary clinics provide accurate results but require considerable time and cost. This study develops an early detection system for feline skin diseases, defined as a computational tool to help owners recognize symptoms at an early stage prior to advanced clinical diagnosis. The system integrates Dempster–Shafer Theory (DST) for disease classification and Fuzzy Type-2 for severity classification, where severity is categorized into mild, moderate, or severe based on symptom intensity. Fuzzy Type-2 was selected over type-1 fuzzy logic due to its superior ability to manage uncertainty and linguistic variability in veterinary assessments. The hybrid approach combines decision tree-based questioning with DST to identify the most probable disease, followed by Fuzzy Type-2 to evaluate severity. Validation was conducted using 100 medical records from the Easy Pet Care Animal Clinic in Tulungagung. For DST-based disease classification, evaluation with a confusion matrix on 100 cases achieved 83% accuracy, 93% precision, 86% recall, and an F1-score of 89%, demonstrating strong statistical performance. For severity prediction using fuzzy type-2, testing on 20 cases resulted in 85% correct classifications. These findings confirm that integrating DST with Fuzzy Type-2 provides an effective and statistically validated model for decision support in feline dermatology. The system offers a low-cost, fast, and reliable screening method that accelerates decision-making and minimizes delays in responding to potentially severe cases
Air Quality Prediction using a BiLSTM-Based Approach for Sustainable Environmental Management Mohammad Lucky Kurniawan; Anggraini Puspita Sari; Eva Yulia Puspaningrum
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3286

Abstract

In cities, where particulate matter (PM) levels are particularly high, air pollution has become a major problem that endangers human health and the environment. Accurate PM₁₀ forecasting is essential for effective environmental management and early warning systems. However, conventional LSTM models, which learn temporal patterns in only one direction, often fail to capture complex long-term dependencies. To overcome this limitation, this study proposes a Bidirectional Long Short-Term Memory (BiLSTM) model that learns temporal patterns in both forward and backward directions to improve prediction accuracy. Based on data collected from the Satu Data Jakarta platform and the Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG) over the period January 2010–July 2023, the dataset used herein include daily PM₁₀ concentrations. Three steps were taken to prepare the data: normalizing the Z-score, smoothing the moving average, and linear interpolation. In order to find the best parameters, the BiLSTM model was trained with several configurations of the learning rate. Based on the results of the experiments, the BiLSTM performed best when trained with a learning rate of 0.001. This parameter was associated with a R² value of 0.929, an MAE of 2.283, an RMSE of 3.029, and a MAPE of 5.016%. According to these data, BiLSTM's bidirectional mechanism improves both predictive stability and temporal feature extraction, surpassing the performance of the traditional LSTM model. The outcomes demonstrate that employing a BiLSTM-oriented method yields highly consistent and accurate PM₁₀ predictions, which can strengthen long-term air quality assessment and support environmentally informed policymaking.
Co-Authors Achmad Junaidi Dhevi Puspitasari Eva Yulia Puspaningrum Firza Prima Aditiawan Mohammad Lucky Kurniawan Mutiq Anisa Tanjung