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All Journal Jurnal Riset Informatika International Journal Of Computer, Network Security and Information System (IJCONSIST) Journal of Information Systems and Technology Research
Mohammad Idhom
UPN Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Complex-Valued Neural Network And Fuzzy Inference System For Image Diagnosis Of Rice Leaf Diseases Mutiara Irmadhani; Wahyu Syaifullah JS; Mohammad Idhom
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.370

Abstract

Rice serves as a crucial food crop and holds significant importance in Indonesia's agricultural sector. so the health of rice leaves determines the productivity of the crop. Serious problems such as crop failure often occur due to leaf disease attacks caused by pests or unfavorable climatic factors. Controlling these diseases requires proper knowledge so as not to cause negative impacts on the ecosystem due to misdiagnosis. This research develops a Complex-Valued Neural Network (CVNN) and Fuzzy Inference System (FIS) based method to identify the type of disease and determine its severity. CVNN was used to classify leaf images based on detected visual traits, while FIS analyzed the relationship between these traits and disease severity using fuzzy rules constructed from expert data or input. The results show that CVNN provides superior performance compared to CNN, CVNN model with an accuracy of 92%, where all classes produce high and balanced. While the CNN model also provides satisfactory results with an accuracy of 89%, although there is still an imbalance in some classes. The results of the FIS model on the image The severity of the image of rice leaf disease is the most high category in the leaf blast class is the highest of all classes. The combination of CVNN and FIS model proves that this hybrid approach is effective to support diagnosis, so it can help farmers in making early and precise decisions.
Forecasting USD to Rupiah Exchange Rate with the Fuzzy Time Series Singh Approach Reghina Ajeng Santika; Aviolla Terza Damaliana; Mohammad Idhom
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1238

Abstract

The exchange rate plays a crucial role in determining a country's economic stability, especially for countries like Indonesia that rely heavily on international trade. In recent years, the fluctuations in global currency values have intensified, particularly after the trade war between the United States and China began in 2018. These fluctuations have significantly impacted the exchange rate between the Indonesian Rupiah and the US Dollar, which in turn affects the competitiveness of Indonesian exports, increases the cost of imports, and influences key economic decisions made by investors, importers, and exporters. The problem of this research lies in the challenge of predicting exchange rate movements amidst economic uncertainty and currency volatility.  This study aims to address this problem by forecasting the exchange rate of the Indonesian Rupiah against the US Dollar using the Fuzzy Time Series Singh method. This method is chosen due to its ability to capture complex data patterns with high accuracy and simpler computational requirements. The primary objective of the research is to evaluate the effectiveness and accuracy of the Fuzzy Time Series Singh method in predicting the exchange rate of the Rupiah against the US Dollar. The results of this study show that the forecasting model achieved an accuracy rate with a MAPE value of less than 10%, indicating that the method can provide highly reliable predictions, which can assist economic actors in making better-informed decisions in the face of currency volatility.
Comparative Analysis of Deep Learning Models for Wind Speed Prediction Using LSTM, TCN and RBFNN Firly Setya Wardani; Mohammad Idhom; Aviolla Terza Damaliana
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1298

Abstract

Wind speed forecasting plays a vital role in various sectors, including renewable energy management and disaster preparedness for extreme weather events. Accurate prediction models are essential to support decision-making processes, especially in regions with dynamic seasonal patterns. This study compares the performance of three time series prediction models Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Radial Basis Function Neural Network (RBFNN) for forecasting daily wind speed. The dataset consists of historical wind speed data that underwent multiple preprocessing steps, including seasonal-based missing value imputation, stationarity testing, supervised transformation, normalization, and hyperparameter tuning to optimize model performance. The models were evaluated using four standard regression metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-Squared (R²), and Mean Absolute Percentage Error (MAPE). The results show that the TCN model outperformed the others, achieving an MAE of 1.117, RMSE of 1.524, R² of 0.120, and MAPE of 20.95%. The LSTM model ranked second with competitive performance, while the RBFNN model produced consistent but slightly lower accuracy. The findings highlight the superiority of TCN in capturing complex sequential and seasonal patterns in wind speed data. The unique contribution of this research lies in integrating seasonal-based preprocessing with a comparative evaluation of three advanced models under varying conditions, including extreme weather scenarios. This study serves as a foundation for developing more accurate and reliable wind speed forecasting systems to support renewable energy planning and enhance disaster risk mitigation strategies.
Image Synthesis for Sperm Dataset Augmentation using WGAN-GP Hajjar Ayu Cahyani Kuswardhani; I Gede Susrama Mas Diyasa; Mohammad Idhom
IJCONSIST JOURNALS Vol 5 No 2 (2024): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i2.146

Abstract

This research explores the efficacy and limitations of applying a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to generate synthetic human sperm microscopy images for data augmentation. We assessed the WGAN-GP's performance on a complex, heterogeneous dataset where images contained multiple object types. Despite achieving stable training convergence, the model's output quality was suboptimal, as evidenced by a high Fréchet Inception Distance (FID) score of 134 and qualitative signs of partial mode collapse. The generator struggled to capture the complete morphological diversity of the sperm cells. A second experiment using a dataset pre-sorted into distinct classes (Normal, Abnormal, Non-Sperm) yielded a marked improvement. This approach led to substantially lower FID scores (59.19, 74.92, and 83.56) and exhibited more robust training dynamics. Our findings underscore a critical conclusion: the success of WGAN-GP in this domain is fundamentally tied to the simplicity of the data distribution. We recommend that future efforts leverage class-conditioned models, simplified data structures, and refined generator architectures to achieve high-precision augmentation for medical imaging tasks.
Co-Authors Aviolla Terza Damaliana Firly Setya Wardani Hajjar Ayu Cahyani Kuswardhani I Gede Susrama Mas Diyasa Mutiara Irmadhani Reghina Ajeng Santika Wahyu Syaifullah JS