Garuda - Garba Rujukan Digital

Speaker Recognition in Content-based Image Retrieval for a High Degree of Accuracy Suhartono Suhartono; Fresy Nugroho; Muhammad Faisal; Muhammad Ainul Yaqin; Suyanta Suyanta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

The purpose of this research is to measure the speaker recognition accuracy in Content-Based Image Retrieval. To support research in speaker recognition accuracy, we use two approaches for recognition system: identification and verification, an identification using fuzzy Mamdani, a verification using Manhattan distance. The test results in this research. The best of distance mean is size 32x32. The best of the verification for distance rate is 965, and the speaker recognition system has a standard error of 5% and the system accuracy is 95%. From these results, we find that there is an increase in accuracy of almost 2.5%. This is due to a combination of two approaches so the system can add to the accuracy of speaker recognition.

Speaker Recognition in Content-based Image Retrieval for a High Degree of Accuracy Suhartono Suhartono; Fresy Nugroho; Muhammad Faisal; Muhammad Ainul Yaqin; Suyanta Suyanta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

The purpose of this research is to measure the speaker recognition accuracy in Content-Based Image Retrieval. To support research in speaker recognition accuracy, we use two approaches for recognition system: identification and verification, an identification using fuzzy Mamdani, a verification using Manhattan distance. The test results in this research. The best of distance mean is size 32x32. The best of the verification for distance rate is 965, and the speaker recognition system has a standard error of 5% and the system accuracy is 95%. From these results, we find that there is an increase in accuracy of almost 2.5%. This is due to a combination of two approaches so the system can add to the accuracy of speaker recognition.

Speaker Recognition in Content-based Image Retrieval for a High Degree of Accuracy Suhartono Suhartono; Fresy Nugroho; Muhammad Faisal; Muhammad Ainul Yaqin; Suyanta Suyanta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

The purpose of this research is to measure the speaker recognition accuracy in Content-Based Image Retrieval. To support research in speaker recognition accuracy, we use two approaches for recognition system: identification and verification, an identification using fuzzy Mamdani, a verification using Manhattan distance. The test results in this research. The best of distance mean is size 32x32. The best of the verification for distance rate is 965, and the speaker recognition system has a standard error of 5% and the system accuracy is 95%. From these results, we find that there is an increase in accuracy of almost 2.5%. This is due to a combination of two approaches so the system can add to the accuracy of speaker recognition.

Hyperparameter-Optimized Gradient Boosting for Daily Rainfall Prediction Using BMKG Meteorological Data in Malang Regency, Indonesia Mohamad Arif Abdul Syukur; Suhartono Suhartono; Mochamad Imamudin
Jambura Journal of Mathematics Vol 8, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i2.38554

Weather conditions significantly affect many aspects of modern life, including transportation, tourism, agriculture, and disaster risk management, particularly in relation to rainfall. Consequently, reliable meteorological information is essential for supporting daily decision-making, making rainfall prediction increasingly important. This study develops a daily rainfall prediction model using gradient boosting based on daily meteorological data obtained from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG). The dataset includes date, minimum, maximum, and average temperatures, relative humidity, sunshine duration, maximum and average wind speeds, and wind direction, with daily rainfall as the target variable. Four chronological train-test split scenarios were evaluated. The first scenario produced an RMSE of 13.97, an MAE of 7.96, and an R^2 value of 0.14. The second scenario yielded an RMSE of 12.81, an MAE of 8.72, and an R^2 value of 0.17. The third scenario achieved an RMSE of 12.21, an MAE of 7.70, and an R^2 value of 0.20, whereas the fourth scenario obtained an RMSE of 10.31, an MAE of 7.11, and an R^2 value of -0.27. Considering both prediction error and generalization capability, the third scenario was selected as the best-performing model. The main contribution of this study lies in demonstrating the effectiveness of hyperparameter optimization in improving the stability of rainfall prediction under complex tropical climatic conditions. Practically, the proposed model may support BMKG and regional policymakers in Malang Regency in hydrometeorological disaster mitigation and agricultural planning.

Title

Found 4 Documents
Search

Abstract

Abstract

Abstract

Abstract

Title Search

Found 4 Documents Search

Abstract

Abstract

Abstract

Abstract

Title

Found 4 Documents
Search