CLOUD-BASED PREDICTIVE MOBILE APPLICATION FOR ASSESSING HONEY PURITY FROM STINGLESS BEES
Honey bees have various types and characteristics, one of which is the stingless bee. This type has limitations in producing honey, the selling value of its nest is quite expensive, and the water content in the honey produced is relatively high. The high water content affects the shelf life of this...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Informatics Department, Engineering Faculty
2024-12-01
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| Series: | Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi |
| Subjects: | |
| Online Access: | http://kursorjournal.org/index.php/kursor/article/view/420 |
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| Summary: | Honey bees have various types and characteristics, one of which is the stingless bee. This type has limitations in producing honey, the selling value of its nest is quite expensive, and the water content in the honey produced is relatively high. The high water content affects the shelf life of this type of honey product, making it a challenge for honey farmers in marketing it. In addition, the dominant sour taste also makes its market increasingly limited or vulnerable to falsification of its purity by irresponsible producers. The use of spectrophotometers is increasingly developing in the food sector, especially in detecting the purity of a food product. The portable type of spectrophotometer also makes it easier to obtain spectrum data for a particular product. A simpler technique that is directly connected to a computer device allows it to be developed into a cloud-based application by providing minimal raw data processing (pre-processing). This study produces an android-based application and a simple cloud-based application architecture, which aims to facilitate the application of a honey purity prediction model from stingless bees. The Android-based application was successfully created by applying 'raw' spectrum data processing from the results of scanning a portable spectrophotometer, and data experiments with the SVM classification model produced an accuracy of 95%. The application of PCA techniques to cloud-based mobile application architecture results in efficient preprocessing of spectrum data.
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| ISSN: | 0216-0544 2301-6914 |