Judul: Identification of Citrus Ripeness Level  Using Digital Image Processing and Extreme Learning Machine
Abstrak: Buah jeruk dikenal dan digemari karena rasanya yang manis dan segar serta kandungan nutrisinya yang baik untuk kesehatan tubuh. Tingkat kematangan buah jeruk menjadi faktor penting yang mempengaruhi kualitas dan nilai jualnya. Petani kebun mahir untuk menentukan kematangan buah jeruk yang siap panen, namun mereka sering menghadapi masalah saat menyortir buah tersebut setelah panen karena keterbatasan fisik yang dapat membuat proses pengelompokan kurang efektif dan efisien. Dalam penelitian ini, digunakan metode Extreme Learning Machine (ELM) untuk melakukan klasifikasi kematangan dan memprediksi nilai acidity, Total Padatan Terlarut (TPT), dan kekerasan buah jeruk, dengan tujuan dapat mengidentifikasi tingkat kematangan buah jeruk secara cepat dan akurat. Dalam proses pengolahan citra, dilakukan ekstraksi fitur menggunakan Red Green Blue (RGB), Hue Saturation Value (HSV), dan Gray Level Co-occurrence Matrix (GLCM). Penggunaan model ELM untuk klasifikasi dengan fitur RGB, HSV, dan semua fitur memberikan akurasi klasifikasi yang sama, yaitu 100% dan 98% pada fitur GLCM. Penggunaan model ELM dengan semua fitur dalam melakukan prediksi nilai acidity, TPT, dan kekerasan memberikan performa model terbaik dengan nilai Root Mean Square Error (RMSE) 0,19 dan koefisien determinasi (R²) 0,90 untuk acidity, RMSE 0,61 dan R² 0,79 untuk TPT, serta RMSE 0,75 dan R² 0,77 untuk kekerasan pada data Training, dan performa model pada data Testing menghasilkan nilai RMSE 0,50 dan R² 0,74 untuk acidity, RMSE 0,69 dan R² 0,80 untuk TPT, serta RMSE 0,90 dan R² 0,77 untuk kekerasan. Berdasarkan hasil pengujian model ELM, jumlah hidden node dan pemilihan fitur berpengaruh terhadap akurasi serta performa model., Citrus fruits are known and loved for their sweet and refreshing taste, as well as their beneficial nutritional content for the body. The level of maturity of citrus fruits is an important factor that affects their quality and market value. Skilled orchard farmers are able to determine the maturity of citrus fruits that are ready for harvest, but they often face challenges when sorting the fruits after harvest due to physical limitations that can make the grouping process less effective and efficient. In this study, the Extreme Learning Machine (ELM) method was used to classify maturity and predict the values of acidity levels, Soluble Solid Content (SSC), and fruit hardness of citrus fruits, with the aim of identifying citrus fruit maturity levels quickly and accurately. In the image processing process, feature extraction was carried out using Red Green Blue (RGB), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM). The use of the ELM model for classification with RGB, HSV, and all features yielded the same classification accuracy of 100% and 98% for GLCM features. The use of the ELM model with all features in predicting the acidity, SSC, and hardness values yielded the best model performance with a Root Mean Square Error (RMSE) value of 0,19 and a coefficient of determination (R²) value of 0,90 for acidity, an RMSE value of 0,61 and R² value of 0,79 for SSC, and an RMSE value of 0,75 and R² value of 0,77 for hardness on the Training data. The model performance on the Testing data yielded an RMSE value of 0,50 and R² value of
Keyword: classification, extreme learning machine, image, prediction, ripeness

Judul: Classification of Cherry Tomato Grade Based on Color Using Image Processing
Abstrak: Tomat cherry biasanya dijual dalam kemasan retail dengan ukuran dan warna yang hampir seragam, oleh karena itu perlu digolongkan ke dalam kelas mutu tertentu sebelum dikemas. Salah satu parameter mutu yang sering digunakan karena mudah adalah tingkat kematangan buah yang diwakili oleh warna kulit buah, namun pekerjaan ini dalam waktu lama cukup melelahkan pekerja sehingga dapat menurunkan konsistensi pemutuan. Metode pengolahan citra dan machine learning dapat digunakan untuk melakukan klasifikasi mutu tomat cherry untuk menggantikan pekerjaan manual. Tujuan dari penelitian ini adalah mempelajari mengolah informasi warna pada tomat cherry menggunakan pengolahan citra serta membangun, menguji, dan menentukan akurasi model klasifikasi mutu tomat cherry menggunakan machine learning. Citra tomat diambil dari berbagai sisi yaitu sisi atas, bawah, kanan dan kiri. Fitur yang diekstrak dan digunakan dalam pembuatan model adalah color moment dan algoritma machine learning yang digunakan adalah Support Vector Machine (SVM). Model dengan akurasi tertinggi didapatkan bila menggunakan data citra buah tomat dari sisi bawah dan kanan yaitu sebesar 98,81%. Sedangkan, model dengan akurasi terendah didapatkan menggunakan data citra buah tomat dari sisi atas yaitu sebesar 96,43%., Cherry tomatoes are usually sold in retail packaging with almost uniform sizes and colors, therefore they need to be classified into a certain quality class before being packaged. One of the quality parameters that is often used is fruit ripeness level indicated by the color of the fruit skin, but working for a long time is quite tiring for workers so that it can reduce consistency in grading. Image processing methods and machine learning can be used to classify the quality of cherry tomatoes to replace manual work. The purpose of this research is to study the color information on cherry tomatoes using image processing and to build, test, and determine the accuracy of model for the quality classification of cherry tomatoes using machine learning. Tomato images are taken from various sides, namely the top, bottom, right and left sides. The features extracted and used in the model development are color moments and the machine learning algorithm used is Support Vector Machine (SVM). The model with the highest accuracy is obtained when using tomato image data form the bottom and right sides, which was 98.81%. Meanwhile, the model with the lowest accuracy was obtained using tomato image data from the top side, which was 96.43%.
Keyword: Cherry tomatoes, color, image processing, quality, SVM

Judul: Sintesis Paduan Ti-6Al-4Nb sebagai Matrial Implan Pengganti Komponen Tibial
Abstrak: Synthesis of Ti-6Al-4Nb alloy was done by melting using Tri Arc Melting Furnace. Each sample was remelted five times to ensure the homogenity. Aluminum and niobium addition were expected to stabilize α phase and β phase until the αβ-titanium alloy was resulted. Yet based on the experiment, it was α-titanium alloy resulted. α-titanium alloy was a Hexagonal Close Packed (HCP) crystalline structure which would be cracked if deformation non-uniaxial happens. The presence of oxygen in pure titanium significantly increased the hardness of titanium. The addition of aluminum was also able to increase the hardness of titanium alloy because aluminum more stabilize the α phase in the α-titanium alloy. The corrosion ressistance of titanium could be increased by adding aluminum and niobium. Titanium coating with HA/chitosan composite was also proved in decreasing the corrosion rate because it barried direct interaction between titanium alloy and corrotion solution.
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