Pengkajian Stok Ikan Cakalang (Katsuwonus Pelamis) Di Perairan Selat Makassar

Dinamika populasi ikan cakalang (Katsuwonus pelamis, Linnaeus) di perairan Selat Makassar yang merupakan bagian Wilayah Pengelolaan Perikanan Republik Indonesia 713 (WPP RI 713) di Provinsi Sulawesi Selatan dan Barat telah dilakukan dengan mengumpulkan sampel sejumlah 20.707 ekor ikan cakalang yang tertangkap dengan alat tangkap purse sSeine dan hand line di Kabupaten Barru dan Majene. Pengambilan sampel dilakukan pada bulan Mei – Oktober 2014 di sentra pendaratan ikan. Ukuran frekuensi panjang cagak diukur dan menduga parameter panjang cagak asimtot (L∞), koefisien pertumbuhan (K), koefisien kematian total (Z), koefisien kematian alami (M), koefisien kematian penangkapan (F), laju eksploitasi (E) dan potensi hasil tangkapan per rekrut relatif (Y\u27/R) dengan menggunakan alat bantu perangkat lunak FISAT-II. Hasil dugaan parameter pertumbuhan von Bertalanffy dengan metode Response Surface pada ELEFAN-I yaitu L∞ = 107,0 cm dan K = 0,8 per tahun. Laju mortalitas total diduga dengan menggunakan analisis kurva hasil tangkapan yaitu Z = 4,47 per tahun. Kematian alami diduga dengan rumus empiris Pauly diperoleh nilai dugaan M = 1,1 per tahun. Kematian karena penangkapan (F) sebesar 3,37 per tahun memberikan hasil dugaan laju eksploitasi (E) sebesar 0,75. Dugaan model hasil-per-rekruit relatif Beverton dan Holt menunjukkan bahwa tingkat eksploitasi telah memperlihatkan lebih tangkap sebesar 26,5% dari nilai E-optimumnya

Prediksi Penyakit Jantung Koroner (PJK) Berdasarkan Faktor Risiko Menggunakan Jaringan Syaraf Tiruan Backpropagation

Karena penyakit jantung koroner mempunyai angka kematian dan kesakitan yang tinggi, maka perludiketahui faktor-faktor risiko yang dapat meyebabkan penyakit jantung koroner ini. Prediksi Penyakit Jantungkoroner ini menggunakan metode pengenalan pola dari data catatan rekam medis penderita penyakit jantungkoroner yang dirawat di Instalasi Rawat Inap Unit Penyakit Dalam RSUP Dr. Sardjito Yogyakarta dan orangsehat yang melakukan General Check-up Unit Penyakit Dalam dan Poliklinik General Check-up Geriatri UnitPenyakit Dalam RSUP Dr. Sardito Yogyakarta dengan menggunakan metode Jaringan Syaraf TiruanBackpropagation (JST-BP). Berdasarkan data rekam medis penderita penyakit jantung koroner dan orang sehattersebut akan dilakukan pelatihan terhadap jaringan syaraf tiruan backpropagation ini, sehingga jaringansyaraf tiruan ini mampu mengenali polanya. Terdapat 9 faktor risiko penyebab timbulnya penyakit jantungkoroner yang akan dilatih agar dapat dikenali polanya.Setelah dilatih, jaringan syaraf tiruan ini akan diuji dengan 9 faktor risiko sebagai masukan yangdisimulasikan dengan Matlab 7.0.4. Dalam penelitian ini telah diujikan 9 faktor risiko penderita penyakitjantung koroner dan orang sehat. Dari hasil pengujian, metode JST-BP dapat mengenali pola-pola faktor risikopenyakit jantung koroner sebesar 80%

Keragaan Biologi Populasi Ikan Cakalang (Katsuwonus Pelamis) Yang Tertangkap Dengan Purse Seine Pada Musim Timur Di Perairan Laut Flores

Biological performance of skipjack tuna has been done in the Flores Sea during the southeast monsoon (June - August, 2013). The objective of the research was to analyze biological performance of skipjack tuna captured by purse seine: including size composition,age classes, growth rate, food habit, gonad maturity and suitable length for capture. The data for length structure and age class, the growth rate, food habit, gonad maturity, and volumetric fecundity estimation were analyzed using Bhattacharya, Von Bertalanffy, propendance index, histological and volumentric methods, respectively. The results showed that (1) the size structure and age class were different according to the fishing ground location and the fishing season, (2) the average size of fishes captured in the western part of Flores Sea was greater than the eastern one, (3) the average length of fishes catched by purse seine without fish aggregation device (FAD, rumpon) was longer than that of with FAD, (4) the growth rate of skipjack tuna was slow where the growth coefficient was less than 0,5 per year, and the asymptotic length was 106,0 cm FL, (5) the skipjack tuna achieved the mature stage at 45 cm and at 50 cm length for female and male, respectively, and ready to spawn at 55 cm and 60 cm for male and female , respectively, (6) it was more than 80 % of fishes captured by fishermen in the Flores Sea which were not yet suitable size for fishing

STUDI KONDISI DAN POTENSI MENJADIKAN SULAWESI SELATAN SEBAGAI STOCK CENTRE DAN DISTRIBUTION CENTRE IKAN KE KAWASAN BARAT INDONESIA

Penelitian ini bertujuan untuk mengakselerasi potensi perikanan tangkap dan budidaya serta mengidentifikasi peluang dan merancang strategi sehingga Sulawesi Selatan dapat menjadi stock centre dan distribution centre untuk memenuhi kebutuhan ikan di Kawasan Barat Indonesia secara berkelanjutan. Metode yang digunakan untuk mencapai tujuan penelitian melalui deep interview, pengamatan (observation), penggunaan kuisioner, focus group disscusion (FGD) serta participatory Rural Appraisal (PRA) dengan stakeholder dan Kajian Pustaka (literature review) ??? statistik perdagangan dan perikanan. Sementara rancangan desain strategi dilakukan melalui pendekatan analisis SWOT (Strength, Weakness, Opportunity, Threat), dan Analisis Hirarki Proses (AHP) yang dilengkapi dengan peta-peta dan skema desain. Hasil yang didapatkan adalah jenis ikan yang dapat didistribusikan ke kawasan barat Indonesia khususnya Pulau Jawa adalah ikan pelagis kecil dengan masing-masing jenis seperti ikan selar (Selaroides leptolepis), tembang (Sardinella fimbriata), kembung (Rastrelliger brachysoma), banyar (Rastreliiger kanugurta) dan layang (Decapterus macrosoma). Sementara potensi budidaya, ikan bandeng masih dapat terandalkan. Sistim rantai dingin dalam kegiatan produksi sampai pasca produksi perikanan tangkap dan budidaya masih mengandalkan teknologi pendinginan bukan teknologi pembekuan sehingga aktivitas mikroba masih terjadi yang dapat menyebabkan pembusukan ikan. Kelembagaan dan jalur pemasaran perikanan tangkap serta budidaya masih bersifat patronase (patron-klien). Tingkat konsumsi ikan termasuk kategori tinggi rata-rata mencapai 40???45 kg/kapita dengan pilihan jenis ikan tuna, tongkol, cakalang dan ikan yang dibudidayakan khususnya ikan bandeng

Guiding Principles for the Conduct of Violence Study of Healthcare Workers and System (ViSHWaS): Insights from a Global Survey

Background Globally many studies have reported on violence faced by healthcare workers. However, there is still a lack of homogeneous data to give us a concrete understanding of the present scenario on a global scale. Conducting a global survey required a robust team organization structure, unique dissemination strategies accounting for the regional limitations, and continual networking to maintain and propagate the pool of survey collaborators and responders. This study aims to describe the strategies that helped carry out a global survey- based study, the lessons learned, and recommendations for future studies.   Methods This cross-sectional survey-based study was based on methodology of the “Hub and Spoke” model with the core team and sub-groups about different regions and managing country leads. The study was conducted across eight weeks from 6th June 2022 to 8th August 2022. The key steps included team organization, strategy formulation for survey dissemination and data collection, launching the project on social media, and conducting a post-survey amongst the collaborators. The Core Team convened weekly via video conference platforms to discuss the modus operandi, including the responsibilities of team members in communicating with HCWs from each country; strategies for data extraction and analysis. A standard message was created for the survey in English, which was spread via text, audio and video messages; the message was tailored according to the target region and population. The language barrier was managed by creating an audio translation or shifting to “an interviewer-administered” questionnaire. Call for leads and collaborators was organized through social media platforms and incentivized by proposing collaborative authorship   Results A core team of 11 members from 7 countries was assembled, which expanded to 40 country leads from around 110 countries. We also amassed more than 75 regional collaborators who worked to provide feedback and spread the message. The “Violence Study of Healthcare Workers and Systems” (VISHWAS) amassed 5500 responses across the world. A weekly alternating trend in the number of survey responses was observed for eight weeks. Guiding principles garnered through this collaborative project include focusing on 1. Effective team organization, 2. Ensuring external validation of survey tool, 3. Personalized communication, 4. Global networking, 5. Timely communication for maintaining momentum, and 6. Addressing regional limitations. The post-survey analysis showed that WhatsApp messaging was the most common modality used for survey dissemination, followed by in-person meetings and text messaging. The successful techniques were noted to be 1. Direct communication with respondents, 2. Regular progress updates, 3. Responsiveness for regional and country lead’s needs  4. Timely troubleshooting. The most common barriers for the respondents were limitations in language proficiency, technical fallouts, lack of compliance with, and difficulty understanding the questionnaire.   Conclusion In this global survey-based study of more than 5500 responses from over 110 countries, valuable lessons in team management, survey dissemination, and addressing barriers to collaborative research. We thereby recommend incorporating the guiding principles from this study to design future surveys on a global scale

CNN Classification of Malaria Parasites in Digital Microscope Images Using Python on Raspberry Pi

Malaria disease occurs because the plasmodium parasite infects human red blood cells spread by female Anopheles mosquitoes and then causes health problems such as blood deficiency and even death. The gold standard of malaria diagnosis is to use laboratory microscopy examination of the patient's red blood cell samples to distinguish between microscope images of parasitic and non-parasitic red blood cells. However, diagnosing malaria through microscope observation is time-consuming, subjective, and tiring for health workers. So, a malaria classification system was designed using the Convolutional Neural Network (CNN) method to distinguish parasitic and non-parasitic red blood cell images. The CNN model is trained using training data and also tested using test data. Then, the CNN training model is embedded on a Raspberry Pi equipped with a Graphical User Interface to facilitate observer interaction through the LCD screen on this digital and portable microscope. The CNN classification rate achieved an accuracy value of 97.88% using the database image and 98.76% using the digital microscope acquisition image. The CNN classification system of malaria parasites designed on a Raspberry Pi-based digital and portable microscope is expected to improve the diagnosis of malaria and reduce the infection rate of malaria patients, especially in various remote areas in Indonesia

Mikroskop Digital, Otomatis, dan Portabel berbasis Raspberry Pi dengan Catu Daya DC

Mikroskop cahaya dapat digunakan untuk melakukan magnifikasi dan melihat objek mikroskopis seperti bakteri, virus, atau sel untuk tujuan diagnosis suatu penyakit. Dalam melakukan pengamatan berdasarkan jumlah sampel yang banyak, ahli laboratorium rentan terhadap kelelahan, kesalahan, dan subjektivitas pengamatan. Oleh karena itu, kamera digital, komputer mini raspberry pi, layar liquid crystal display (LCD), motor stepper, catu daya dan bahasa pemrograman python digunakan sebagai komponen pendukung mikroskop cahaya untuk mengakusisi sampel laboratorium yang diamati dalam format digital sehingga dapat dilakukan operasi pengolahan citra pada raspberry pi dan ditampilkan hasilnya di layar LCD. Sedangkan motor stepper dan driver motor digunakan untuk menggeser meja sampel secara otomatis. Semua proses ini dilakukan dengan menggunakan bahasa pemrograman python. Hal ini dilakukan dengan tujuan untuk mengurangi beban pengamat dalam melihat dan menggeser slide sampel sebanyak 100 kali. Sistem akuisisi citra digital dan penggeseran meja sampel otomatis ini dirancang pada mikroskop cahaya portabel mampu meng-capture sampel pada kaca preparat menjadi citra digital, menggeser kaca preparat 100 kali, dan dilengkapi dengan catu daya 220 Volt sehingga dapat digunakan di fasilitas kesehatan manapun di Indonesia. Mikroskop digital, otomatis, dan portabel berbasis platform komputer mini raspberry pi ini dapat digunakan untuk melakukan pemeriksaan laboratorium berbagai penyakit seperti tuberculosis, malaria, atau leukimia dengan efisien dan efektif sehingga dapat mempermudah proses diagnosis penyakit dan dapat meningkatkan layanan kesehatan di berbagai daerah di Indonesia dan berkontribusi dalam penurunan dan eliminasi berbagai penyakit

A novel end-to-end deep convolutional neural network based skin lesion classification framework

Background: Skin diseases are reported to contribute 1.79\% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency. Objective: Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions. Methods: We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analysed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance. Results: The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet. Conclusions: To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilised in real-time

A novel end-to-end deep convolutional neural network based skin lesion classification framework

Background:Skin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency.Objective:Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions.Methods:We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analysed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance.Results:The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet.Conclusions:To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilised in real-time

Perception regarding needle stick and sharp injuries among clinical year medical students

Introduction: Medical students are exposed to needle stick and sharp injuries (NSSIs) in their everyday routine whether in handling sharp medical instruments or contacting with patients. Thus, it is important to assess the perception of medical students regarding needle stick and sharp injuries, as it is an important feedback to medical school on the effectiveness of their teaching in reducing NSSIs. Objective: To determine the level of perception regarding NSSIs among clinical year’s medical students in a public university, and its association with the knowledge an attitude towards NSSIs. Materials and Methods: A cross sectional study was conducted among 320 clinical year’s medical students from a local public university in Malaysia. The respondents were selected according to the year of study by using stratified random sampling. The study was conducted in May and June 2011 for a period of two months. Self-administrated questionnaires were used in the study. The questionnaires were developed by researchers, and were pre-tested among nursing students in one of the local hospital. Results: Response rate was 93.8%, in which 300 out of 320 respondents were participating in the study. The result showed that 51% of respondents attained good level of perception regarding NSSIs. Meanwhile 53.3% achieved good level of attitude and 55.3% of the respondents attained good knowledge level towards NSSIs. The 5th year medical students got the highest level of perception (21%) on NSSIs as compared to 4th year (10%) and 3rd year (19.6%).There is significant association between level of perception and year of study (P= 0.002) as well as between level of perception and level of knowledge (P = 0.001) and attitude (P ˂ 0.001). Conclusion: Overall there were high proportion of clinical year medical students are having inappropriate perception, unsatisfactory knowledge and inappropriate attitude toward NSSIs. Knowledge and attitude regarding NSSIs showed significant association with perception toward NSSIs