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All Journal Sriwijaya Bioscientia Jurnal Dehasen Mengabdi Jurnal Komputer
Sari, Nely Puspita
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Immunology & microbiology Medicine & Pharmacology Social Sciences

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Pengujian bakteri patogen pada Ikan Patin (Pangasius sp) yang dilalu-lintaskan di Stasiun Karantina Ikan Pengendalian Mutu dan Keamanan Hasil Perikanan Palembang Elfachmi, Elfachmi; Sari, Nely Puspita
Sriwijaya Bioscientia Vol 4 No 1 (2023)
Publisher : Biology Department, Faculty of Mathematics and Natural Sciences, Sriwijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24233/sribios.4.1.2023.357

Abstract

Stasiun Karantina Ikan Pengendalian Mutu dan Keamanan Hasil Perikanan Palembang. Penelitian bertujuan untuk mengetahui berbagai jenis bakteri patogen yang berbahaya yang dapat mengkontaminasi produk ikan patin yang dilalu lintaskan distasiun karantina ikan pengendalian mutu dan keamanan hasil perikanan Palembang. Pengujian Bakteri dilakukan dengan melakukan pemurnian, uji dasar, dan uji biokimia, kemudian diidentifikasi jenis bakteri yang terdapat pada ikan patin tersebut. Data hasil pengamatan dianalisis menggunakan buku panduan Bergey’s Manual of Determinative Bacteriology dengan melihat jenis spesies yang didapatkan melalui uji dasar dan uji biokimia sesuai prosedur yang dilakukan. Hasil menunjukkan Bakteri Penyebab penyakit pada ikan patin disebabkan oleh bakteri Edwardsiella ictaluri pada organ ginjal, hati dan limfa dan bakteri tersebut masuk dalam pemantauan HPIK. Ikan betutu yang diperiksa dalam keadaan sehat tidak terdapat bakteri dikarenakan dilakukan pengujian secara aseptik dan dapat dikirim dan tidak melakukan pengujian lanjutan.
Pendampingan Penyusunan Laporan Laba Rugi Berdasarkan SAK EMKM & Koperasi Pada Usaha Mie Ayam & Bakso Solo Legowo Susanti, Neri; Soleh, Ahmad; Sari, Nely Puspita; Sari, Meisie Novia; Panjaitan, Klara Mega Utari
Jurnal Dehasen Mengabdi Vol 1 No 1 (2022): Maret-Agustus
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (905.735 KB)

Abstract

One of the drivers of the Indonesian economy is the MSME business. This MSME business is a business that has been tested in various economic conditions. The problem is that many MSME businesses have not made financial reports. One of them is the Legowo Chicken Noodle and Meatball business which is located in Sukamerindu, Bengkulu city. Financial reports have not been considered as important reports in developing their business so that financial records are only based on sales experience, are not regular and tend to be based on estimates of the feelings of the business owner. As a result, it is difficult for business owners to determine how much the production costs of selling chicken noodles and meatballs will be and it is difficult to determine the profit or loss from the number of bowls of chicken noodles and meatballs sold. Based on these problems, this community service (PKM) aims to help legowo chicken and meatball businesses in making financial reports, especially profit and loss financial statements in accordance with SAK EMKM standards. The method used in this PKM is the method of counseling or lectures, creating discussion groups, and continuing with assistance in the practice of preparing profit and loss financial statements based on SAK EMKM from transactions that have occurred. The results of this PKM have a positive impact on the legowo chicken noodle and meatball business, where after this training, the legowo chicken noodle and meatball business owner is able to make a profit and loss financial report in accordance with SAK EMKM standards. With the making of this profit and loss financial report, the owner of the legowo chicken noodle and meatball business is able to record business books better and is able to design their business finances to be even better in the future.
Edukasi dan pemanfaatan Aplikasi Buku kas Dalam pengelolaan keuangan pada Usaha UMKM Faeyza Laundry di Kota bengkulu Fitriano, Yun; Sari, Nely Puspita; Amelya, Tifani Rika; Lutfiani, Irma
Jurnal Dehasen Mengabdi Vol 2 No 2 (2023): September-Februari
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jdm.v2i2.4453

Abstract

Micro, Small and Medium Enterprises (MSMEs) are one of the most numerous types of businesses in Indonesia. One of them is the Faeyza Laundry Business which is located on Jalan Sumatra Sukamerindu, Bengkulu city, the Faeyza Laundry business is one of the MSMEs engaged in the laundry of clothes. Based on the results of a previous survey with Faeyza Laundry business owners, it is known that this Laundry business has not prepared financial reports, this is due to a lack of knowledge and information about financial reports. Then the recording of financial reports in the Faeyza Laundry business is also still very simple, by manually recording incoming and outgoing cash every day. Based on these problems, we aim to help business owners by providing education and training regarding the use of the Cash Book Application in managing their finances.
Analisis Performa Algoritma CNN dalam Klasifikasi Citra Medis Berbasis Deep Learning Sari, Nely Puspita
Jurnal Komputer Vol 2 No 2 (2024): Januari-Juni
Publisher : CV. Generasi Insan Rafflesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70963/jk.v2i2.113

Abstract

The advancement of artificial intelligence technologies, particularly in the field of deep learning, has driven the application of Convolutional Neural Network (CNN) algorithms in various domains, including medical image classification. This study aims to analyze the performance of CNN in classifying medical images associated with different diseases using a standard CNN architecture. The dataset utilized consists of labeled X-ray and MRI images based on medical diagnoses. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to assess how effectively the model recognizes complex visual patterns. The results demonstrate that CNN achieves high accuracy in identifying objects within medical images, with an average F1-score exceeding 90% on selected datasets. These findings suggest that CNN has significant potential to support automated and efficient medical diagnosis, although further clinical validation is necessary for real-world implementation.
Co-Authors Ahmad Soleh Amelya, Tifani Rika Elfachmi, Elfachmi Fitriano, Yun Lutfiani, Irma Neri Susanti Panjaitan, Klara Mega Utari Sari, Meisie Novia