Electronic Theses and Dissertation

Kelapa sawit (Elaeis guineensis Jacq) merupakan komoditas strategis yang memberikan
kontribusi signifikan terhadap perekonomian, khususnya di Indonesia. Keberhasilan produksi
sangat bergantung pada kualitas bibit, namun peredaran bibit palsu menjadi tantangan serius yang
dapat menurunkan produktivitas dan merugikan petani. Penelitian ini bertujuan mengembangkan
metode autentikasi bibit kelapa sawit menggunakan near-infrared spectroscopy (NIRS) yang cepat
dan akurat. Sebanyak 429 sampel daun dari varietas 540, Simalungun, Yangambi, dan bibit palsu
dianalisis pada rentang panjang gelombang 999-2500 nm. Hasil spektrum menunjukkan puncak
serapan khas yang berkaitan dengan kandungan air, selulosa, hemiselulosa, lignin, protein, dan
lipid. Analisis principal component analysis (PCA) menunjukkan bahwa komponen utama (PC1)
menjelaskan 99,5% variasi data, sedangkan total variasi yang dijelaskan mencapai 99,97%,
mengindikasikan efektivitas PCA dalam membedakan varietas. Klasifikasi dengan linear
discriminant analysis (LDA) menghasilkan akurasi 92,55% sebelum penghapusan outlier, dan
meningkat menjadi 99,76% setelah 17 outlier dihapus. Hasil ini menunjukkan bahwa kombinasi
NIRS, PCA, dan LDA merupakan metode yang efektif untuk autentikasi varietas bibit kelapa sawit
secara cepat dan akurat.

Oil palm (Elaeis guineensis Jacq) is a strategic plantation commodity that significantly contributes to the global economy, particularly in major producing countries such as Indonesia. The success of oil palm production heavily depends on the quality of the seedlings used. However, the circulation of counterfeit and uncertified seedlings poses a major challenge, potentially reducing productivity and causing economic losses for farmers. This study aims to develop a rapid and accurate authentication method for oil palm seedlings using near-infrared spectroscopy (NIRS). A total of 429 leaf samples from the 540, Simalungun, Yangambi, and counterfeit varieties were analyzed within the wavelength range of 999-2500 nm. The spectral results revealed distinct absorption peaks related to water, cellulose, hemicellulose, lignin, protein, and lipid contents. Principal component analysis (PCA) showed that the first principal component (PC1) explained 99.5% of the data variance, with a total variance explanation of 99.97%, indicating the effectiveness of PCA in distinguishing among the varieties. Classification using wide linear discriminant analysis (LDA) yielded an accuracy of 92.55% before outlier removal, which increased to 99.76% after removing 17 outliers. These findings demonstrate that the combination of NIRS, PCA, and LDA is an effective method for the rapid and accurate authentication of oil palm seedling varieties.