<p class="AbstractEnglish"><strong>Abstract:</strong> Fish eye color is an important attribute of fish quality. The change in eye color during the storage process correlates with freshness and has a direct effect on consumer perception. The process of changing the color of the fish eye can be analyzed using image processing. The purpose of this study was to obtain the best classification method for predicting fish freshness based on image processing in fish eyes. Three tuna fish were used in this study. The test was carried out for 20 hours with an eye image every 2 hours at room temperature. Fish eye image processing uses Matlab R.2017a software while the classification uses Weka 3.8 software. The image processing stages are taking fish eye image, segmenting ROI (region of interest), converting RGB image to grayscale, and feature extraction. Feature extraction used is the gray-level co-occurrence matrix (GLCM). The classification techniques used are artificial neural networks (ANN), k-neighborhood neighbors (k-NN), and support vector machines (SVM). The results showed the value using ANN = 0.53, k-NN = 0.83, and SVM = 0.69. Based on these results it can be determined that the best classification technique is to use the k-nearest neighbor (k-NN).</p><p class="AbstrakIndonesia"><strong>Abstrak:</strong> Warna mata ikan merupakan atribut penting pada kualitas ikan. Perubahan warna mata ikan selama proses penyimpanan berhubungan dengan tingkat kesegaran dan memiliki efek langsung pada persepsi konsumen. Proses perubahan warna mata ikan dapat dianalisis menggunakan pengolahan citra. Tujuan penelitian ini adalah mendapatkan metode klasifikasi terbaik untuk memprediksi kesegaran ikan berbasis pengolahan citra pada mata ikan. Tiga ekor ikan tuna digunakan dalam penelitian ini. Pengujian dilakukan selama 20 jam dengan pengambilan citra mata setiap 2 jam pada suhu ruang. Pengolahan citra mata ikan menggunakan software matlab R.2017a sedangkan pengklasifiannya menggunakan software Weka 3.8. Tahapan pengolahan citra meliputi pengambilan citra mata ikan, segmentasi ROI (<em>region of interest</em>), konversi citra RGB menjadi <em>grayscale</em>, dan ekstraksi fitur. Ekstraksi fitur yang digunakan yaitu <em>gray-level co-occurrence matrix</em> (GLCM).  Teknik klasifikasi yang digunakan yaitu, <em>artificial neural network</em> (ANN), <em>k-nearest neighbors</em> (k-NN), dan <em>support vector machine</em> (SVM). Hasil penelitian menunjukkan nilai korelasi menggunakan ANN = 0,53, k-NN = 0,83, dan SVM = 0,69. Berdasarkan hasil tersebut dapat disimpulkan teknik klasifikasi terbaik adalah menggunakan <em>k-nearest neighbors</em> (k-NN).</p>

Process production of floating fish feed in a society constrained by processing technology. The objective of this study was to observe effect of  condition process of twin screw extruders to the physical and chemical properties of produced of floating fish feed. Ingredients used involve fish meal, soybean meal, corn meal, and tapioca flour. The ingredients are mixed with 15, 20 and 25% water added to the total weight. Extrusion process condition conducted by several treatments i.e screw speed (540, 540, 600, 660 rpm) and barrel temperatures (80, 90, 100, 110, 120℃). In order to study, expansion ratio, unit density, floatability and hardness of feed were observed as physical parameters while chemical properties include moisture and protein content. Experimental result showed that addition 25 % water to the formula gives a good performance of expansion ratio, unit density and floatability than 15% and 20% moisture content. Higher of screw speed produces feed with higher ratio expansion, lower unit density, and higher floatability. The optimum of screw speed is 600 rpm.  Meanwhile increasing of barrel temperature caused reduction of unit density, and escalation floatability of feed. Best barrel temperature to meet the physical properties is 120℃. The produced feed contain protein 32.38-41.95% and moisture content 4.37-5.70%.

There is need for a detailed assessment of raw material quality during product development. This study focuses on the performance test of steamer cabinet for fish jelly production. The machine was designed in rectangular shape with 46 cm length, 45 cm width, and 110 cm height, comprising steam generator, burner chamber, wall, tray, and channel. In addition, eight baking sheets were vertically incorporated. Performance test was performed to determine temperature distribution of fish jelly/nugget and individual shelves. This process was conducted with/without load conditions, where parameters, including room cabinet and fish nugget temperatures, as well as fuel usage efficiency, were observed. Furthermore, temperature measurements were also recorded at the top (T1), middle (T2 and T3) and bottom (T4) of the sample device. The results showed the room temperature of each tray was extended to 100 °C under 40 minutes. However, no significant heat change was reported, indicating the steam from the generator was evenly distributed. Furthermore, fish nugget temperature was obtained at 80°C in 40 minutes, where at the top (T1), middle (T2 and T3) and bottom of the steamer cabinet, relatively similar occurrences were observed. The resulting vapour was steadily spread across the steam channel to individual shelves. Therefore, the fuel usage efficiency of the steamer cabinet was estimated at 67 %.

Penelitian disain pisau bowl cutter untuk pembuatan nugget ikan telah dilakukan. Penelitian ini bertujuan untuk mendapatkan bentuk dan jumlah pisau bowl cutter yang optimal untuk pembuatan nugget ikan. Penelitian dilakukan melalui beberapa tahapan yaitu penentuan kriteria disain, penentuan konsep disain, dan pembuatan alat dan uji kinerja. Pisau bowl cutter dibentuk dalam 3 buah disain yaitu tiga pisau lurus, tiga pisau melengkung dan enam pisau melengkung. Material pisau menggunakan stainless steel 304. Uji kinerja dilakukan dengan membuat adonan nugget ikan menggunakan bowl cutter selama 8, 12 dan 16 menit. Untuk mengetahui kualitas nugget ikan, parameter mutu yang diamati meliputi kadar air, tekstur, susut masak, water holding capacity (W HC) dan uji organoleptik. Biaya operasional listrik diketahui dengan mengukur konsumsi energi yang digunakan oleh motor listrik bowl cutter. Hasil penelitian menunjukkan bahwa disain bowl cutter terbaik terdiri dari tiga pisau melengkung dengan lama pengadonan 8 menit yang menghasilkan nugget dengan kadar air 54,2%, tekstur sebesar 12,6 N, susut masak 16,7%, W HC 32,9%, nilai organoleptik lebih dari 7 dan biaya operasional listrik sebesar Rp. 2.700,-/100 kg adonan.