Muhammad Teguh Prananto, . (2025) PREDIKSI COFFEE BREWING LEVEL BERBASIS DATA SPEKTROSKOPI MENGGUNAKAN DEEP LEARNING. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
![[img]](http://repository.upnvj.ac.id/style/images/fileicons/text.png) |
Text
Abstrak.pdf Download (226kB) |
|
Text
Awal.pdf Download (1MB) |
|
Text
Bab 1.pdf Restricted to Repository UPNVJ Only Download (308kB) |
|
Text
Bab 2.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
Bab 3.pdf Restricted to Repository UPNVJ Only Download (451kB) |
|
Text
Bab 4.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
Bab 5.pdf Download (229kB) |
|
Text
Daftar Pustaka.pdf Download (244kB) |
|
Text
Riwayat Hidup.pdf Restricted to Repository UPNVJ Only Download (123kB) |
|
Text
Lampiran.pdf Restricted to Repository UPNVJ Only Download (942kB) |
|
Text
Hasil Plagiarisme.pdf Restricted to Repository staff only Download (19MB) |
|
Text
Artikel KI.pdf Restricted to Repository staff only Download (898kB) |
Abstract
The consistency of coffee flavor is a crucial factor for coffee enthusiasts, necessitating a method to measure the coffee brewing level according to the brewing chart to ensure standardized brewing quality. This study utilizes the AS7265X spectroscopy sensor to obtain coffee characteristic data based on the generated spectrum, which is then used in deep learning modeling with the Convolutional neural Network (CNN) algorithm to classify coffee brewing levels into five different categories. A total of 150 data samples were used for model training and testing. The initial model achieved an average accuracy of 97%, which improved to 100% after hyperparameter tuning using the Search method. However, this tuning process introduced a trade-off in runtime, increasing execution time from 15 seconds to 1 minute and 43 seconds. The research conducted is expected to be able to contribute to ensuring the quality of coffee brewing and become an opportunity for other research that applies similar technology and algorithms.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110511036] [Pembimbing 1: Ridwan Raafi’udin] [Pembimbing 2: Muhammad Adrezo] [Penguji 1: Musthofa Galih Pradana] [Penguji 2: Nurul Afifah Arifuddin] |
| Uncontrolled Keywords: | Classification/Prediction, Coffee Brewing Level, Spectroscopy Data, Convolutional Neural Network (CNN), AS7265X Sensor. |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | MUHAMMAD TEGUH PRANANTO |
| Date Deposited: | 05 Aug 2025 06:40 |
| Last Modified: | 05 Aug 2025 06:40 |
| URI: | http://repository.upnvj.ac.id/id/eprint/36799 |
Actions (login required)
 |
View Item |