Judul: Fish Length Estimation of Free-Swimming Grouper in the Cage Culture Using De-haze Image Correction, and Deep Learning Methods
Abstrak: Pemantauan pertumbuhan kerapu secara teratur akan mendapatkan produksi ikan yang lebih efisien dan sukses dalam budidaya keramba. Penelitian ini menggunakan dua kelompok ikan kerapu hidup yang berbeda ukuran untuk mendapatkan gambaran pertumbuhannya. Tujuan penelitian ini yaitu mengaplikasikan algoritma koreksi gambar de-haze dan kecerdasan buatan (deep learning) untuk mengestimasi panjang ikan kerapu hidup di dalam KJA dari hasil perekaman kamera stereo UTS (Underwater Televisual System). Untuk memperkirakan panjang ikan kerapu hidup di dalam keramba, kami telah merekam video menggunakan kamera stereo yang merupakan bagian dari Underwater Televisual System (UTS). UTS dapat mengestimasi kedalaman jarak titik objek ke UTS dengan menggunakan dua kamera kiri dan kanan, sehingga dapat diperoleh persepsi 3D, dan penglihatan triangulasi sinar dari berbagai sudut pandang. Data pose yang dikumpulkan berjumlah 500 gambar kemudian dilakukan koreksi gambar de-haze, data preprocessing, dan diberi label. Panjang ikan kerapu diukur menggunakan algoritma SSD Mobilenet v3. Nilai training loss yang diperoleh 0.06%, accuracy 92.00%, precision 92,00%, recall 100%, dan F1-score 95.83%. Hasil pengukuran menunjukkan bahwa rata-rata panjang kedua kelompok ikan kerapu yang berbeda ukuran masing-masing adalah 12.65 ± 3.81 cm dan 26.00 ± 19.79 cm. Kesimpulannya, pengukuran panjang ikan kerapu hidup dapat dilakukan secara langsung dan akurat menggunakan kombinasi koreksi gambar de-haze dan SSD Mobilenet v3., Regular monitoring of the growth of the grouper will ensure the efficient and successful production of the fish in cage culture. In this study we used two different size of fish groups of free-swimming grouper to obtain its estimate length. The purpose of this study is to apply a de-haze image correction algorithm and artificial intelligence (deep learning algorithm) to estimate the length of free-swimming grouper in the KJA from the results of the UTS (Underwater Televisual System) stereo camera recording. To estimate the fish length of the free-swimming grouper in the cage, we have recorded a video using a stereo camera Underwater Televisual System (UTS). UTS can estimate the depth of a point object from the UTS using two cameras left and right, hence its 3D perception, and by triangulation of rays from multiple viewpoints. There were 500 images of pose collected and then dehaze to correct the image, preprocessed, and labeled. To measuring the length of the grouper, we used SSD Mobilenet v3 algorithm. The training loss value obtained is 0.06%, accuracy 92.00%, precision 92,00%, recall 100%, and F1-score 95.83%. The measurement results showed that the average length of the two groups different size of free-swimming grouper was 12.65 ± 3.81 cm and 26.00 ± 19.79 cm, respectively. In conclusion, the measurement of the free-swimming grouper length can be performed directly and accurately using combination of de-hazing image correction and SSD Mobilenet v3.
Keyword: grouper, free-swimming grouper length, camera underwater televisual system, SSD Mobilenet v3

Judul: Estimasi Panjang Karapas Udang dan Kecepatan Renang Udang Menggunakan Multiple Object Tracking
Abstrak: Udang merupakan salah satu komoditas perikanan budidaya yang penting di Indonesia dengan nilai ekonomi tinggi. Pada tahun 2017, produksi udang menurun akibat penyakit yang dapat dideteksi melalui pola tingkah laku seperti kecepatan renang dan pertumbuhan panjang karapas. Pemantauan dengan cara pengukuran manual membutuhkan waktu yang lama dan secara teknis sulit untuk rutin dilakukan petambak. Penelitian ini bertujuan untuk mengestimasi panjang karapas dan kecepatan renang udang serta melakukan tracking gerak udang sebagai dasar analisis tingkah laku. Metode yang digunakan merupakan kombinasi algoritma multiple objects tracking yaitu DeepSORT dengan YOLOv5. Proses penelitian meliputi pengumpulan dataset, pelabelan data, training dengan Teknik k-fold cross validation 5 iterasi, testing, evaluasi, tracking, serta perhitungan panjang karapas dan kecepatan renang udang. Hasil penelitian menunjukkan bahwa YOLOv5 mampu mendeteksi kepala udang dengan f1-score 81.148% dan DeepSORT berhasil melakukan tracking pada video udang. Metode ini efektif untuk estimasi panjang karapas dan kecepatan renang udang., Shrimp is a crucial aquaculture commodity in Indonesia with significant economic value. In 2017, shrimp production declined due to diseases identified through behavioral patterns such as swimming speed and carapace length growth. Manual monitoring is time-consuming and technically challenging for farmers to perform regularly. This study aims to estimate shrimp carapace length and swimming speed, and to track shrimp movement as a basis for behavioral analysis. The method employs a combination of multiple objects tracking algorithms, specifically DeepSORT with YOLOv5. The research process involves dataset collection, data labeling, training using the k-fold cross-validation technique with 5 iterations, testing, evaluation, tracking, and calculation of shrimp carapace length and swimming speed. The findings reveal that YOLOv5 achieves an F1-score of 81.148% in detecting shrimp heads, and DeepSORT successfully tracks shrimp in videos. This approach proves effective in estimating shrimp carapace length and swimming speed.
Keyword: shrimp behavior, shrimp carapace length, shrimp swimming speed, DeepSORT, k-fold cross validation

Judul: Sintesis 1-(2-hidroksifenil)-3-fenilpropana-1,3-dion dari o-hidroksiasetofenon dan benzoil klorida
Abstrak: Flavonoid is the most abundant naturaly-occuring polyphenolic compound which has C6-C3-C6 carbon skeleton. Flavone is a group of flavonoid with wide bioactivity spectrum. It is widely distributed in higher plants. Its bioactivities include antioxidant, HIV-1 proteinase inhibitor, strong and selective apoptotic inducer in human colon cell, and ligand for BDZ-R receptor. One of the important and widely applicable flavones synthesis route is through 1-(2-hydroxyphenyl)-3- phenylpropane-1,3-dione (1,3-diketone) acid-catalyzed oxidative cyclization. In this study, 1,3-diketone has been successfully synthesized via four-step route. In the first step, phenol was acetylated with 74–95% yields. Subsequently, phenyl acetate was converted to o-hydroxyacetophenone (o-HAP) with 34% yield via AlCl3-catalyzed Fries rearrangement. Benzoylation of o-HAP followed by Baker- Venkataraman rearrangement of the ester product using KOH in pyridine finally produced 1,3-diketone. The third and fourth steps yield were 27–44% and 82– 85%, respectively. All products in this study have been characterized spectroscopically. Melting point characterization were also conducted to the solid products.
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