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PENGARUH EKSTRAK SEMANGKA MERAH (Citrullus vulgaris) KUALITAS SPERMATOZOA MENCIT (Mus musculus) YANG DIPAPAR ASAP ROKOK Effectivity of Red Watermelon Extract (Citrullus vulgaris) on The Abmormalties Morphology, Motility, and Concentration of Sperm Mice (Musmusculus) Which Have Been Exposed to Cigarette Smoke Ahmad ikhwan; Hamdan Hamdan; Rosmaidar Rosmaidar
JURNAL ILMIAH MAHASISWA VETERINER Vol 4, No 1 (2019): NOVEMBER-JANUARI
Publisher : JURNAL ILMIAH MAHASISWA VETERINER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21157/jim vet..v4i1.3460

Abstract

ABSTRAKPenelitian ini bertujuan mengetahui pengaruh ekstrak semangka merah dalam meminimalkan kerusakan spermatozoa mencit setelah dipapar asap rokok dengan dosis yang tepat. Penelitian ini mengikuti rancangan acak lengkap pola searah menggunakan 12 ekor mencit jantan (Mus muscullus) yang terbagi menjadi 4 kelompok. Kelompok perlakuan dibagi atas kelompok kontrol negatif diberi aquadest 0,5 ml, kelompok kontrol positif diberi paparan asap rokok dan aquadest 0,5 ml, kelompok perlakuan I diberi paparan asap rokok dan diberi ekstrak semangka merah dosis 22 mg/mencit dan kelompok perlakuan II diberi paparan asap rokok dan diberi ekstrak semangka merah dosis 44 mg/mencit. Pemaparan asap rokok dan pemberian ekstrak semangka merah dilakukan selama 30 hari. Parameter yang diamati adalah konsentrasi, morfologi abnormal, dan motilitas spermatozoa. Data dianalisis dengan menggunakan analisis varian. Hasil Rata-rata (+SD) konsentrasi spermatozoa kelompok KN adalah 3,3 ± 1,6 × 106 mm3;  kelompok KP adalah 2,6 ± 0,5 × 106 mm3;  kelompok K1 adalah 2,0 ± 1,0 × 106 mm3; dan kelompok K2 adalah 3,1 ± 1,4 × 106 mm3, rata-rata (+SD) motilitas spermatozoa kelompok KN adalah 73,33 ± 5,77 %; kelompok KP adalah 40,00 ± 36,06 %; kelompok K1 adalah 21,67 ± 10,41 %; dan K2 adalah 53,33 ± 15,28 %, dan rata-rata (±SD) morfologi spermatozoa kelompok KN adalah 0,67 ± 1,15 %; kelompok KP adalah 6,00 ± 1,73 %; kelompok K1 adalah 2,33 ± 0,58 %; dan  kelompok K2 adalah 3,33 ± 0.58 %. Disimpulkan bahwa paparan asap rokok pemberian ekstrak semangka merah (Citrulus vulgaris) terhadap mencit (Mus musculus) yang dipapar asap rokok tidak berpengaruh terhadap peningkatan konsentrasi dan motilitas spermatozoa mencit (p0,05), namun berpengaruh terhadap menurunkan jumlah morfologi abnormalitas spermatozoa sangat signifikan (p0,01), sehingga didapat dosis optimum ekstrak semangka merah 44 mg/ekor mencit. Kata Kunci: Semangka merah, asap rokok, motilitas , konsentrasi, morfologi abnormal,spermatozoa. ABSTRACTThe aim of this research was to investigate the effect of watermelon (Citrullus vulgaris) extractioncan to minimize affect smoke in mice (Mus muscullus) spermatozoa with the optimum dose. This study followed direct complete randomized design by using 12 males mice which divided into 4 groups. The treatment group is divided to negative control group which given 0,5 ml of aquadest, the positive control group which exposed to cigarette smoke and 0,5 ml of aquadest. The treatment group I was exposed to cigarette smoke and given with watermelon extraction dose 22 mg/kg bw mice. The treatment group II was exposed to cigarette smoke and given with watermelon extraction dose 44 mg/kg bw mice. Exposure to cigarette smoke and red watermelon extract were conducted for 30 days. Parameters in observing is concentration, abnormal morofologi, and motility in mice. Data were analyzed using analysis of variance with SPSS for Windows 16.0. It The results of Mean (± SD) concentration of spermatozoa KN group was 3.3 ± 1.6 × 106 mm 3; KP group was 2.6 ± 0.5 × 106 mm 3; K1 group was 2.0 ± 1.0 × 106 mm 3; and K2 group was 3.1 ± 1.4 × 106 mm3, the mean (± SD) motility KN group was 73.33 ± 5.77%; KP group was 40.00 ± 36.06%; K1 group was 21.67 ± 10.41%; and K2 is 53.33 ± 15.28%, and the average (± SD) morphology of spermatozoa KN group was 0.67 ± 1.15%; KP group was 6.00 ± 1.73%; K1 group was 2:33 ± 12:58%; and K2 group is 3.33 ± 12:58%. It can be concluded, that the exposure of cigarette smoke with crude extract (rough extract) to the mice exposed to cigarette smoke was not moderate to the increase of mice spermatozoa concentration and motility (p 0.05), but to decrease the morphology of spermatozoa abnormalities significantly (P 0.01), in order to get the optimum dose of red watermelon extract 44m /mice. Keywords: red watermelon, cigarette smoke, motilty, consentration, abnormal morpholgy, sperm
Analisis Performansi Pendekatan Machine Learning pada Deteksi Penyakit Daun Tanaman Kopi Yodhi Yuniarthe; Rosyana Fitria Purnomo; Hilda Dwi Yunita; Fatimah Fahurian; Ahmad Ikhwan
Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU) Vol. 5 No. 1 (2025): SEMNASTEKMU
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/p2t2nm71

Abstract

Abstract. Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.   Keywords: Coffee Classification, Image Processing, Machine Learning, Plant Disease Detection.  
Analisis Performansi Pendekatan Machine Learning Pada Deteksi Penyakit Daun Tanaman Kopi Purnomo, Rosyana Fitria; Yodhi Yuniarthe; Hilda Dwi Yunita; Fatimah Fahurian; Ahmad Ikhwan
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 2 (2025): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i2.3302

Abstract

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.