Judul: Analisis Morfometrika Geometris pada Variasi Bentuk Kantong antar Jenis Nepenthes L. (Nepenthaceae).
Abstrak: Bentuk kantong antar spesies Nepenthes bervariasi, sehingga menjadi salah satu karakter penting untuk identifikasi. Namun, deskripsi karakter bentuk kantong secara verbal dapat bersifat subyektif dan ditafsirkan berbeda sehingga dapat menyebabkan ketidaktepatan dalam identifikasi spesies. Variasi bentuk kantong dapat dijelaskan secara lebih obyektif dan tepat melalui analisis kuantitatif. Penelitian ini bertujuan menggunakan pendekatan morfometrika geometris untuk menganalisis secara kuantitatif variasi bentuk kantong antar spesies, antar hybrid dengan tetuannya, dan antar specimen herbarium dengan specimen segar, serta membangun sistem identifikasi Nepenthes menggunakan machine learning. Penelitian ini menggunakan 252 gambar kantong Nepenthes dari 5 spesies alami (N. gracilis, N. mirabilis, N. reinwardtiana, N. bicalcarata, dan N. rafflesiana) dan 2 spesies hibrid (N. mirabilis x N. rafflesiana dan N. x hookeriana) yang diambil dari Kalimantan Tengah dan Kalimantan Barat. Gambar tersebut menunjukkan bentuk kantong dari spesimen hidup pada tiga sudut pandang berbeda, yaitu atas, depan, dan samping. Analisis outline berbasis Fourier, yaitu elliptical Fourier analysis, dilakukan dengan mengubah geometri outline (gambar bentuk kantong) menjadi variabel kuantitatif (matriks x,y koordinat) yang kemudian ditransformasikan ke bentuk elliptical Fourier coefficient. Data hasil analisis berupa matriks elliptical Fourier coefficients kemudian dieksplor menggunakan principal component analysis (PCA) dan multivariate analysis of variance (MANOVA). Hasil PCA dan MANOVA digunakan untuk mengevaluasi variasi bentuk kantong antar spesies, kemiripan antara hybrid dengan tetuanya, dan perbandingan antara specimen herbarium dengan specimen segar. Algoritma linear discriminant analysis (LDA) yang dipadukan dengan leave-one-out cross-validation (LOOCV) digunakan untuk membangun sistem identifikasi otomatis. Semua analisis menggunakan Momocs Package pada R Statistical Software. Hasil Scatterplot PCA (PC1 dan PC2) menunjukkan bentuk kantong mengelompok berdasarkan spesies, meskipun ada sedikit overlapping antar spesies. Bentuk samping dan depan kantong berbeda antar spesies dengan nilai MANOVA (p<0.05), kecuali bentuk samping kantong N. mirabilis x N. rafflesiana dan N. reindwardtiana (p>0.05). Sementara bentuk penutup kantong (bagian atas) beberapa spesies tidak berbeda (p>0.05). Hasil analisis hibrid menunjukkan bahwa N. mirabilis x N. rafflesiana memiliki bentuk intermediate dengan kedua tetuanya (N. mirabilis dan N. rafflesiana). Sementara itu, bentuk kantong Nepenthes pada specimen herbarium berbeda dengan gambar yamg diambil dari tanaman hidup. Sistem identifikasi otomatis menggunakan algoritma LDA dengan LOOCV menunjukkan bahwa sistem identifikasi yang dikembangkan berdasarkan bentuk bagian depan kantong memiliki tingkat akurasi (90.5%) lebih tinggi dari bentuk bagian samping (84.5%), dan bentuk tutup kantong (85.7%).
Keyword: Analisis outline, linear discriminant analysis

Judul: Geometric morphometrics analysis of the pitcher shapes variation among the species of Nepenthes L. (Nepenthaceae)
Abstrak: Nepenthes pitcher shapes are diverse and important for identifying the species. However, species dentification often found difficulties in understanding the lexical description of this pitcher characteristic. Modern morphometrics encompasses an array of mathematical approaches that turn shapes into quantitative variables. This study aims to analyze quantitatively the variation of pitcher shapes among Nepenthes species, the similarity between hybrids with its parents, and the comparison between the images from herbarium materials with the images from living plants using geometric morphometrics. And to development of an automated identification system using machine learning algorithms. Geometric morphometrics analysis was conducted in three main steps: feature extraction using elliptical Fourier (EF) analysis for obtaining the quantitative data from the outline images, statistical analysis for evaluating the variation of pitcher shapes among seven Nepenthes species, the similarity of hybrids with its parents, the differences between herbarium materials and fresh materials, and building an automated identification system using machine learning algorithms (linear discriminant analysis (LDA) with leave-one-out cross-validation (LOOCV). A total of 252 images of living pitchers, taken from three different perspectives, top view, front view, and side view were used in this study. We used five species (N. gracilis, N. mirabilis, N. reinwardtiana, N. bicalcarata, and N. rafflesiana) and two hybrids (N. mirabilis x N. rafflesiana and N. x hookeriana) from Central and West Kalimantan. This study shows that geometric morphometrics, with an outline analysis using Elliptical Fourier Transform, capable of distinguishing Nepenthes species and offer a powerful approach to provide an objective description of pitcher shape. The scatter plot of the PCA shows that the shapes are separated and grouped based on species, even there is still little overlap among species. Analysis Multivariate of variance (MANOVA) shows that all of the pitcher shapes among species on the side and front view are distinct with a value of MANOVA (p <0.05), except N. mirabilis x N. rafflesiana and N. reindwardtiana on the side view (p> 0.05). At the same time, the lid (top view) of some species is not different (p> 0.05). The hybrids analysis shows that N. mirabilis x N. rafflesiana has an intermediate pitcher shape between its parents (N. mirabilis and N. rafflesiana). Meanwhile, the images from herbarium materials are entirely different from the images from living plants. An automated identification system using LDA with LOOCV achieves an accuracy of identification as high as 84.5% (side view), 90.5% (front view), and 85.7% (top view). The current analysis shows that the front view is the best character that can be us as a taxonomical feature. The system developed in this study is able to classify the Nepenthes species according to pitcher shape with acceptable accuracy.
Keyword: Automated identification system, linear discriminant analysis, machine learning, Nepenthes, outline analysis, pitcher shapes

Judul: Wire electroplating sludge sebagai katalis pembakaran batu bara
Abstrak: A fuel additive (combustion catalyst) can improve the combustion efficiency by promoting complete oxidation of fuel. Combustion catalyst in this research was composed of wire electroplating sludge dispersed in a mixture of vegetable oil, water, and surfactant. Four types of catalyst suspensions were prepared, namely A (2% sludge); B (3% sludge); C (2% sludge, 3% oxalyc acid); and D (3% sludge, 3% oxalyc acid). Addition of oxalyc acid in catalyst suspension C and D showed significant effects on combustion efficiency. Each suspension was applied in coal having caloric value of 5235.09 cal/g. The concentration of catalyst suspension in coal was about ±0.1% w/w. The results of activity test by using differential thermal analyzer showed that the energy efficiency from catalyst A was 62.21%, B: 97.19%, C: 77.96%, and D: 97.54%. Catalyst D (3% sludge, 3% oxalyc acid) is the best catalyst suspension that can be used as coal combustion catalyst.
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