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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) IJID (International Journal on Informatics for Development) Internet of Things and Artificial Intelligence Journal
Riwanto, Yudha
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics

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Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method Riwanto, Yudha; Nuruzzaman, Muhammad Taufiq; Uyun, Shofwatul; Sugiantoro, Bambang
IJID (International Journal on Informatics for Development) Vol. 11 No. 2 (2022): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2022.3743

Abstract

High accuracy and speed in data search, which are aims at finding the best solution to a problem, are essential. This study examines the brute force method, genetic algorithm, and two proposed algorithms which are the development of the brute force algorithm and genetic algorithm, namely Multiple Crossover Genetic, and Genetics with increments values. Brute force is a method with a direct approach to solving a problem based on the formulation of the problem and the definition of the concepts involved. A genetic algorithm is a search algorithm that uses genetic evolution that occurs in living things as its basis. This research selected the case of determining the pin series by looking for a match between the target and the search result. To test the suitability of the method, 100-time tests were conducted for each algorithm. The results of this study indicated that brute force has the highest average generation rate of 737146.3469 and an average time of 1960.4296, and the latter algorithm gets the best score with an average generation rate of 36.78 and an average time of 0.0642.
Performance Analysis of Genetic Algorithms and KNN Using Several Different Datasets Riwanto, Yudha; Atika, Enda Putri
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.767

Abstract

This research aims to increase the accuracy of the classification of mango, corn, and potato leaf types using an approach involving feature selection with a genetic algorithm (Genetic Algorithm), classification with K-Nearest Neighbors (KNN), and image processing in the HSV color space (Hue, Saturation). , Value). The dataset used consists of more than 1500 image samples for each type of leaf, with a total of 10 tests carried out. The research process begins with processing leaf images in HSV color space to extract more representative color information. Next, a genetic algorithm is applied to select the most relevant features from the processed image. The selected features are then used as input for the KNN model in the classification process. The test results show that the proposed method can achieve a classification accuracy of 87,9%. This shows that the combination of image processing in the HSV color space, feature selection using a genetic algorithm, and classification with KNN can improve performance in recognizing leaf types. This research makes significant contributions to the field of image processing and classification and shows the potential of using genetic algorithms for feature selection in pattern recognition applications.
Performance Comparison Analysis on Weather Prediction using LSTM and TKAN Wardhana, Ajie Kusuma; Riwanto, Yudha; Rauf, Budi Wijaya
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.808

Abstract

The development of machine learning methods in the last few decades has shown great potential in various predictive applications, including in domains such as financial prediction, medical diagnosis, and big data analysis. One of the most widely used methods in prediction tasks is Long Short-Term Memory (LSTM). LSTM has become popular because of its ability to handle time series data by retaining relevant information in the long term and the ability to forget irrelevant information through the forget-gate mechanism. However, along with the development of technology and the need to improve accuracy and efficiency, new methods such as the Kolmogorov Arnold Network (KAN)  have emerged. KAN was then developed into the Temporal Kolmogorov Arnold Network (TKAN), which was designed to match or even surpass the performance of LSTM. The TKAN architecture has produced significant improvements in the management of both new and historical information. Because of this capability, TKAN can excel in multi-step predictions, demonstrating a clear advantage over conventional models such as LSTM and GRU, particularly in the context of long-term forecasting. This research goal is to give insight into the comparison of both the TKAN and LSTM models for weather prediction using model loss and mean absolute error evaluation (MAE). The model for both LSTM and TKAN achieved 0.09 and 0.11 for model loss and 0.08 and 0.96 for MAE.
Reduksi Dimensionalitas pada Klasifikasi Kualitas Air Sungai Menggunakan Algoritma Genetika dan Seleksi Fitur Berbasis Korelasi Riwanto, Yudha; Ningrum, Fauzia Anis Sekar
Jurnal Penelitian Pendidikan IPA Vol 11 No 9 (2025): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i9.11863

Abstract

Water quality monitoring is a crucial element in data-driven environmental management. This study aims to identify the most important parameters in river water quality classification through feature selection and machine learning approaches. Eleven physicochemical parameters were used as initial features, and two selection methods were applied: Genetic Algorithm (GA) and Spearman Rank Correlation (RS). Classification was performed using Radial Basis Function Support Vector Machine (RBF-SVM), with performance evaluation based on accuracy, F1 score, and recall. GA testing results identified influential parameters (pH, DHL, DO, BOD, COD, TSS, NO₂⁻-N), achieving an accuracy of 96.67% and an F1 score of 0.82. RS generated seven different features with an accuracy of 90.00% and an F1 score of 0.67. Both methods revealed five consistently significant features (DHL, BOD, COD, TSS, NO₂⁻-N), which are the influential features. The model without feature selection, despite producing high accuracy (93.33%), only achieved an F1 score of 0.48, indicating poor recognition of the minority class. These findings confirm that feature selection improves classification efficiency and capability. In conclusion, GA-based feature selection provides the most effective subset for water quality classification and supports the development of intelligent and cost-effective monitoring systems suitable for sensor-based field applications.
KU Band Proximity-Coupled Supply Based Microstrip Array Antenna for Microwave Imaging Applications Ningrum, Fauzia Anis Sekar; Riwanto, Yudha
Jurnal Penelitian Pendidikan IPA Vol 11 No 9 (2025): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i9.11991

Abstract

This research focuses on the design and simulation of a 4x1 microstrip array antenna with a proximity-coupled supply technique for Ku frequency band applications, especially in microwave imaging. The antenna is designed to operate in the frequency range 12 - 16 GHz, with a resonance frequency of 14 GHz, using a Duroid 5880 substrate which has a thickness of 3.15 mm and a relative permittivity of 2.2. Array configuration and proximity-coupled techniques are applied to improve impedance matching as well as expand bandwidth. Evaluation through simulation includes important parameters such as return loss, gain, and radiation patterns. The simulation results show a return loss of -26.46 dB at a frequency of 14 GHz, which shows high transmission efficiency with minimal reflections. The radiation patterns in the azimuthal and elevation planes show consistent directivity, with stable gain throughout the frequency range. These results confirm that the designed microstrip array antenna is suitable for microwave imaging applications in the Ku band. The antenna design in this research produces high efficiency, directional radiation, and minimal signal loss, so it is able to support accurate and detailed imaging.
Co-Authors Atika, Enda Putri Bambang Sugiantoro Ningrum, Fauzia Anis Sekar Nuruzzaman, Muhammad Taufiq Rauf, Budi Wijaya Shofwatul ‘Uyun Wardhana, Ajie Kusuma