Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning
Swiftlet nests are highly valued for their health and cosmetic benefits, with moisture content crucial in determining their quality. Traditional moisture measurement methods are often slow and can potentially damage the samples. This study introduces PRORESKA, an innovative system utilizing resistan...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-10-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5923 |
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| author | Ratu Anggriani Tangke Parung Hanna Arini Parhusip Suryasatriya Trihandaru |
| author_facet | Ratu Anggriani Tangke Parung Hanna Arini Parhusip Suryasatriya Trihandaru |
| author_sort | Ratu Anggriani Tangke Parung |
| collection | DOAJ |
| description | Swiftlet nests are highly valued for their health and cosmetic benefits, with moisture content crucial in determining their quality. Traditional moisture measurement methods are often slow and can potentially damage the samples. This study introduces PRORESKA, an innovative system utilizing resistance sensors and Machine Learning (ML) for non-destructive, and real-time moisture measurement. The system incorporates a voltage divider circuit to establish a correlation between resistance data and moisture content. Three mathematical models (linear, exponential, and modulated exponential) and a neural network were employed to predict moisture content. Validation tests conducted on paper and swiftlet nests indicated that the neural network model, enhanced through transfer learning, achieved superior accuracy. The results demonstrated a strong correlation between predicted and actual moisture content (R² = 0.9759), with the neural network model attaining a mean squared error (MSE) of 0.01. This method holds significant potential to improve the efficiency and cost-effectiveness of moisture measurement for swiftlet nests and similar applications. |
| format | Article |
| id | doaj-art-7454096cb5cd4ef284de678c7b119566 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-7454096cb5cd4ef284de678c7b1195662025-01-13T03:31:56ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-10-018567468010.29207/resti.v8i5.59235923Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine LearningRatu Anggriani Tangke Parung0Hanna Arini Parhusip1Suryasatriya Trihandaru2Satya Wacana Christian UniversitySatya Wacana Christian UniversitySatya Wacana Christian UniversitySwiftlet nests are highly valued for their health and cosmetic benefits, with moisture content crucial in determining their quality. Traditional moisture measurement methods are often slow and can potentially damage the samples. This study introduces PRORESKA, an innovative system utilizing resistance sensors and Machine Learning (ML) for non-destructive, and real-time moisture measurement. The system incorporates a voltage divider circuit to establish a correlation between resistance data and moisture content. Three mathematical models (linear, exponential, and modulated exponential) and a neural network were employed to predict moisture content. Validation tests conducted on paper and swiftlet nests indicated that the neural network model, enhanced through transfer learning, achieved superior accuracy. The results demonstrated a strong correlation between predicted and actual moisture content (R² = 0.9759), with the neural network model attaining a mean squared error (MSE) of 0.01. This method holds significant potential to improve the efficiency and cost-effectiveness of moisture measurement for swiftlet nests and similar applications.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5923swallow’s nestsmoisture contentiotmachine learningneural networkproreska |
| spellingShingle | Ratu Anggriani Tangke Parung Hanna Arini Parhusip Suryasatriya Trihandaru Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) swallow’s nests moisture content iot machine learning neural network proreska |
| title | Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning |
| title_full | Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning |
| title_fullStr | Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning |
| title_full_unstemmed | Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning |
| title_short | Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning |
| title_sort | prototype of swiftlet nest moisture content measurement using resistance sensor and machine learning |
| topic | swallow’s nests moisture content iot machine learning neural network proreska |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5923 |
| work_keys_str_mv | AT ratuanggrianitangkeparung prototypeofswiftletnestmoisturecontentmeasurementusingresistancesensorandmachinelearning AT hannaariniparhusip prototypeofswiftletnestmoisturecontentmeasurementusingresistancesensorandmachinelearning AT suryasatriyatrihandaru prototypeofswiftletnestmoisturecontentmeasurementusingresistancesensorandmachinelearning |