Judul: Pencitraan Ultrasonografi dan Digital Radiografi Ikan Mas (Cyprinus carpio)
Abstrak: Ultrasonografi merupakan sebuah teknologi pencitraan non-invasif untuk organ dalam tubuh dengan menggunakan gelombang suara. Teknik pencitraan noninvasif lainnya dapat menggunakan digital radiografi yang memanfaatkan sinar-X lalu diolah dan sisimpan dalam komputer. Penelitian ini bertujuan menilai citra otot, tulang, dan organ dalam serta reproduksi secara non-invasif menggunakan ultrasonografi dan digital radiografi pada ikan mas (Cyprinus carpio). Ultrasonografi dilakukan dengan posisi longitudinal dan transversal dengan mode Brightness serta color Doppler sedangkan digital radiografi dilakukan pada posisi ikan dorsoventral dan lateral. Digital radiografi ikan mas memperlihatkan bagian tulang dari kepala, sirip pektoral, sirip dorsal, sirip pelvis, sirip anal, tulang vertebra hingga ekor dengan citra radiopaque. Organ dalam teramati gelembung renang dengan citra radiolucent, jantung, usus, hati, testis dan ovarium teramati citra radiopaque. Ultrasonografi ikan mas memperlihatkan citra hiperekoik organ pencernaan dan reproduksi, ketebalan otot serta kecepatan aliran darah pada ventrikel., Ultrasonography is a non-invasive imaging technology used for assessing internal organs using sound waves. Other non-invasive imaging techniques can use digital radiography that uses x-rays to be processed and stored in a computer. This study aims to assess the muscles, bones, and images of internal and reproduction organs non-invasively using ultrasonography and digital radiography of carp (Cyprinus carpio). Ultrasonography was performed in longitudinal and transverse views with Brightness and color Doppler modes, while digital radiography was performed in the dorsoventral and lateral positions of the fish. Digital radiography of carp showed bony parts from the head, pectoral fins, dorsal fins, pelvic fins, anal fins, the vertebrae to the tail with radiopaque opacity. Internal organs were observed swim bladder with radioluscent opacity, and heart, intestines, liver, testes, ovaries with radiopaque opacity. Ultrasonography in Brightness mode of carp showed digestive organs, reproductive organs and muscle thickness. Color Doppler mode showed blood flow in the ventricle.
Keyword: digital radiografi, ikan mas, ultrasonografi

Judul: Detection of Motile Aeromonas Septicemia (MAS) Disease in Common Carp Using Deep Learning Methods
Abstrak: Ikan mas (Cyprinus carpio) adalah komoditas perikanan air tawar dengan nilai ekonomis tinggi sehingga banyak dibudidayakan. Namun, budidaya ikan mas rentan terhadap penyakit, seperti Motile Aeromonas Septicemia (MAS), yang dapat menyebabkan kematian. Penyakit MAS dapat dilihat secara kasat mata, namun pembudidaya sering menghadapi kendala pengawasan yang kontinu. Penelitian ini menerapkan algoritma YOLOv8 dan DeepSORT untuk mendeteksi ikan mas dan luka akibat infeksi bakteri A. hydrophila secara otomatis, serta mendapatkan data luasan luka dan kecepatan renang ikan. Training dilakukan menggunakan dataset dua label yaitu bagian luka ikan terinfeksi dan seluruh tubuh ikan dengan epoch yang berturut-turut sebesar 3000 dan 1000. Hasil training didapatkan akurasi berlabel luka mencapai 91,49% pada konsentrasi penyakit 107 cfu/ml dan 88,68% pada konsentrasi penyakit 108 cfu/ml, sedangkan akurasi berlabel ikan mencapai 96,61% pada konsentrasi penyakit 107 cfu/ml dan 93,44% pada konsentrasi penyakit 108 cfu/ml. Koefisien luas luka hasil prediksi terhadap hasil sebenarnya mendekati 1 yang menunjukkan kedekatan nilai kedua variabel, sedangkan estimasi kecepatan renang ikan yang didapatkan menunjukkan perilaku ikan yang lemas akibat penyakit MAS. Hasil menunjukkan bahwa kinerja model baik dalam mendeteksi ikan, luas luka, dan estimasi kecepatan renang namun model belum bisa mendeteksi penyembuhan pada ikan berdasarkan diameter luka dan kecepatan renang ikan yang dianalisis., Common carp (Cyprinus carpio) is a freshwater fisheries commodity with high economic value, so it is widely cultivated. However, common carp farming is susceptible to diseases, such as Motile Aeromonas Septicemia (MAS), which can lead to mortality. MAS is visually detectable, but continuous monitoring challenges arise for farmers. This research employs the YOLOv8 and DeepSORT algorithms to automatically detect common carp and wounds caused by A. hydrophila bacterial infection, obtaining wound area and swimming speed data. Training on a dataset with two labels: infected fish wound areas and the entire fish body, using consecutive epochs of 3000 and 1000. The training results show wound label accuracy reached 91,49% for disease concentration 107 cfu/ml and 88,68% for disease concentration 108 cfu/ml, while the accuracy for fish label reached 96,61% for concentration 107 cfu/ml and 93,44% for concentration 108 cfu/ml. The coefficient value between predicted and actual wound areas approximates 1, indicating the proximity of the two variables. The estimate for fish swimming speed reflects lethargic behavior due to MAS. The results demonstrate that the model is effective in detecting fish, wound areas, and swimming speed, but the model can’t detect fish healing based on analyzed wound diameter and fish swimming speed.
Keyword: DeepSORT, Common carp, Motile Aeromonas Septicemia, YOLOv8

Judul: The Exploration of Discrete Logarithm over Finite Field GF(3m)
Abstrak: The security of many public~key algorithms is based on the problem of finding discrete logarithms. The generalized discrete logarithm problem is the following: given a finite cyclic group G of order n, a generator a of G, and an element fl E G, find the integer x, 0 ~ x ~ n - 1, such that 0: = fl. Algorithm for discrete logarithm problem focused on Menezes et al. (1997) that consist of exhaustive search algorithm, the baby-step giant-step algorithm, Pollard's rho algorithm, Pohlig-Hellman algorithm, and index-calculus algorithm. These algorithms are explorated to be used in discrete logarithm problem over finite field GF(3m ). The exploration also produces some algorithms, i.e. naif negative algorithm, baby-step mother-step algorithm, baby-step mother free-step algorithm, and baby-step free-step algorithm. All algorithms implemented using Maple 11. The Pohlig-Hellman and baby-step giant-step algorithms are efficient enough to be used in discrete logarithm problem over finite field GF(3m ) for m < 20.
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