Abstract:• Abstract: The skin is an elastic wrapping that protects the body from environmental influences, the skin is the organ that is located on the outside and limits it from the human environment. Skin diseases can be caused…
ed by fungi, viruses, germs, animal parasites, bacterial infections and others. To identify skin diseases, we usually have to see a doctor, but we still experience problems in dealing with disease identification. This is sometimes influenced by the community, sometimes they feel embarrassed to consult their skin disease to a doctor because the signs of skin disease have started to appear, consultation fees and drugs are relatively expensive. Current technological developments are able to process knowledge with artificial intelligence techniques. Due to the many symptoms of disease nowadays, it is necessary to make a system application with artificial intelligence that can diagnose skin diseases and provide solutions for skin diseases using one of the methods, namely the Naïve Bayes Classifier. Naïve Bayes is a simple classification algorithm where each attribute is independent and may contribute to the final decision. The goal is to produce an expert system website that helps the general public in diagnosing skin diseases and providing solutions for detected skin diseases. The results of this study concluded that based on the application of skin cancer diagnosis can display the results of skin cancer diagnosis decisions.
Keywords: expert system; naïve bayes; skin disease
Abstrak: Kulit merupakan pembungkus yang elastis yang melindungi tubuh dari pengaruh lingkungan, kulit merupakan organ tubuh yang terletak paling luar dan membatasinya dari lingkungan hidup manusia. Penyakit kulit dapat disebabkan oleh jamur, virus, kuman, parasit hewani, infeksi bakteri dan lain-lain. Mengidentifikasi penyakit kulit biasanya kita harus ke dokter, namun masih mengalami kendala dalam menangani pengidentifikasi penyakit hal itu terkadang dipengarui oleh masyarakat terkadang merasa malu untuk mengkonsultasikan penyakit kulitnya ke dokter karena tanda-tanda penyakit kulit sudah mulai tampak, biaya konsultasi dan obat yang tergolong mahal. Perkembangan teknologi saat ini mampu mengolah pengetahuan dengan teknik kecerdasan buatan. Karena banyaknya gejala penyakit pada masa sekarang ini perlu dibuat aplikasi sistem dengan kecerdasan buatan yang dapat mendiagnosa penyakit kulit dan memberikan solusi dari penyakit kulit dengan salah satu metode yaitu Naïve Bayes Classifier. Naïve bayes merupakan algoritma klasifikasi yang sederhana dimana setiap atribut bersifat berdiri sendiri dan memungkinkan berkontribusi terhadap keputusan akhir. Tujuannya adalah menghasilkan website sistem pakar yang dan membantu masyarakat luas dalam mendiagnosa penyakit kulit dan memberikan solusi dari penyakit kulit yang terdeteksi. Hasil dari penelitian ini menyimpulkan bahwa berdasarkan aplikasi diagnosa penyakit kanker kulit dapat menampilkan hasil keputusan diagnosa penyakit kanker kulit.
Kata kunci: Naïve Bayes; Penyakit Kulit; sistem pakar
Abstract:Keberlanjutan operasional sebuah camp bela diri sangat bergantung pada kualitas pengelolaan data anggota. Di BangRajan Muaythai Boxing, pencatatan kehadiran dan pemantauan sesi latihan masih bertumpu pada proses manual,…
yang kerap menghasilkan ketidakakuratan data serta membuka celah bagi peserta dengan paket habis untuk tetap berlatih tanpa terdeteksi. Penelitian ini merancang dan membangun sistem informasi manajemen member berbasis web sebagai solusi atas permasalahan tersebut. Pendekatan pengembangan dilakukan secara bertahap dengan pemodelan UML meliputi Activity Diagram, Use Case Diagram, Sequence Diagram, dan Entity Relationship Diagram. Stack teknologi yang digunakan mencakup Next.js pada sisi klien, NestJS pada sisi server, PostgreSQL sebagai basis data relasional, serta Better Auth untuk manajemen autentikasi. Sistem yang dihasilkan mengintegrasikan fitur absensi cerdas berbasis barcode, pengelolaan paket latihan, dashboard personal bagi member, panel administrasi lengkap, notifikasi kadaluarsa paket, dan manajemen pengumuman camp. Validasi dilakukan melalui Black Box Testing dengan empat skenario pengujian, seluruhnya menghasilkan keluaran yang sesuai ekspektasi. Penerapan sistem ini terbukti meningkatkan efisiensi operasional dan akurasi pencatatan data secara signifikan.
The operational sustainability of a martial arts camp depends greatly on the quality of member data management. At BangRajan Muaythai Boxing, attendance recording and session monitoring still rely on manual processes, which frequently lead to data inaccuracies and allow members with expired packages to continue training undetected. This study designs and develops a web-based member management information system to address these issues. The development follows a phased approach employing UML modeling including Activity Diagrams, Use Case Diagrams, Sequence Diagrams, and Entity Relationship Diagrams. The technology stack consists of Next.js on the client side, NestJS on the server side, PostgreSQL as the relational database, and Better Auth for authentication management. The resulting system integrates barcode-based smart attendance, training package management, a personal member dashboard, a comprehensive admin panel, package expiry notifications, and camp announcement management. Validation was conducted using Black Box Testing across four test scenarios, all yielding expected outputs. Implementation of this system demonstrably improves operational efficiency and data recording accuracy.
Abstract:This study aims to Test Wastewater Quality Using the Mpn (Most Probable Number) Method to Detect Total COLIFORM Bacteria. This study uses laboratory analysis, which aims to provide an overview of the microbiological quality…
ity of wastewater based on the presence and number of Coliform bacteria. The working principle of the Most Probable Number (MPN) method in detecting total Coliform bacteria in wastewater is carried out semi-quantitatively by detecting microbial growth through lactose fermentation which produces gas or turbidity in selective liquid media. The implementation procedure includes a presumptive test stage using Lactose Broth (LB) media, a confirmatory test with Brilliant Green Lactose Bile Broth (BGLB) media, and determining the final value by matching the combination of positive tubes with the standard MPN table. From the results of laboratory testing on three domestic wastewater samples (1224 AL, 1225 AL, and 1226 AL), it was found that all samples showed negative results with a combination of 0-0-0 tubes in the presumptive test. This indicates that the tested sample does not contain Total Coliform bacteria or has a value of less than 1000 MPN/100 mL, so that its microbiological quality is declared very good and has met the quality standard threshold set by the government through the Minister of Environment Regulation No. 5 of 2014 and PP RI No. 22 of 2021