Maxwell, Clarissa (2026) Analisis Kuantitatif Hubungan Komposisi Kimia Terhadap Warna Fly Ash Berbasis Citra Digital. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Fly ash merupakan material pozzolan sisa hasil pembakaran batu bara yang memiliki karakteristik fisik dan kimia yang sangat bervariasi sehingga pengujian X-Ray Fluorescence (XRF) diperlukan untuk mengetahui komposisi kimianya. Namun, pengujian tersebut relatif mahal dan memerlukan waktu analisis yang lama. Penelitian ini bertujuan mengembangkan metode karakterisasi awal yang cepat, ekonomis, dan aplikatif melalui analisis kuantitatif hubungan komposisi kimia fly ash dengan parameter warna citra digital. Sebanyak 54 sampel fly ash dari pembangkit listrik dengan boiler Pulverized Coal Combustion (PCC) di enam negara (Indonesia, Australia, Jepang, Spanyol, Kanada, dan Thailand) dianalisis melalui pengujian XRF, kehalusan, pH, berat jenis, dan karakterisasi pasta geopolimer. Sistem geopolimer menggunakan rasio fly ash terhadap alkali aktivator sebesar 65:35 dan rasio Na2SiO3/NaOH sebesar 2, kemudian karakteristik pasta geopolimer diuji melalui pengujian slump, setting time, dan kuat tekan umur 7 dan 28 hari. Citra permukaan direkam menggunakan mikroskop digital Dino-Lite Premier AM4113T pada perbesaran 250×, kemudian nilai Red, Green, dan Blue (RGB) diekstraksi menggunakan perangkat lunak ImageJ. Hasil analisis menunjukkan bahwa Fe2O3 memiliki hubungan paling kuat dengan parameter RGB. Model estimasi Fe2O3 menghasilkan nilai R2 tertinggi dan nilai RMSE terkecil. Nilai Red menunjukkan hubungan tidak langsung dengan berat jenis fly ash kelas F, melalui kadar Fe2O3.
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Fly ash is a pozzolanic material generated from coal combustion that exhibits substantial variations in its physical and chemical properties. Therefore, X-Ray Fluorescence (XRF) analysis is essential for determining its chemical composition. However, XRF testing is relatively expensive and requires considerable analysis time. This study aims to develop a rapid, cost-effective, and practical preliminary characterization method by quantitatively investigating the relationship between the chemical composition of fly ash and the color parameters of digital images. A total of 54 fly ash samples collected from Pulverized Coal Combustion (PCC) power plants across six countries (Indonesia, Australia, Japan, Spain, Canada, and Thailand) were characterized through XRF analysis, fineness testing, pH measurement, specific gravity testing and geopolymer paste characterization. The geopolymer system was prepared using a fly ash to alkali activator ratio of 65:35 and Na2SiO3/NaOH ratio of 2. The resulting geopolymer paste was evaluated through slump, setting time tests and compressive strength tests at 7 and 28 days. Surface images were captured using a Dino-Lite Premier AM4113T digital microscope at 250× magnification and the Red, Green, and Blue (RGB) color values were extracted using ImageJ software. The results demonstrated that Fe2O3 exhibited the strongest correlation with the RGB color parameters. The Fe2O3 prediction model achieved the highest R2 value and the lowest RMSE value. The Red parameter exhibited an indirect relationship with the specific gravity of class F fly ash through its Fe2O3 content.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Citra Digital, Fly Ash, Geopolimer, Komposisi Kimia, RGB, Chemical Composition, Digital Image, Fly Ash, Geopolymer, RGB |
| Subjects: | Q Science > Q Science (General) > Q180.55.M38 Mathematical models T Technology > TA Engineering (General). Civil engineering (General) T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing--Digital techniques. Image analysis--Data processing. T Technology > TA Engineering (General). Civil engineering (General) > TA418.16 Materials--Testing. |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Civil Engineering > 22201-(S1) Undergraduate Thesis |
| Depositing User: | Clarissa Maxwell |
| Date Deposited: | 28 Jul 2026 09:08 |
| Last Modified: | 28 Jul 2026 09:08 |
| URI: | http://repository.its.ac.id/id/eprint/138742 |
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