Fenomena Perilaku Bullying pada Remaja di Yogyakarta

Latar Belakang: Perilaku bullying yang dilakukan oleh remaja di Indonesia masih menjadisalah satu masalah yang belum teratasi. Tingginya angka kejadian bullying pada remaja memberikan dampak negatif bagi remaja seperti gangguan konsentrasi belajar, penurunan prestasi akademik, harga diri rendah, depresi, bahkan sampai keinginan remaja untuk bunuh diri.Tujuan Penelitian: Penelitian ini bertujuan untuk mengidentifikasi perilaku bullying pada remaja di Yogyakarta.Metodologi: Penelitianan ini menggunakan metode kualitatif pendekatan fenomenologi. Pengambilan data dilakukan dengan wawancara mendalam dan observasi. Partisipan dalam penelitian berjumalah 14 orang yang terdiri dari orang tua, remaja, dan guru yang ditentukan dengan purposive sampling. Keabsahan data dilakukan menggunakan triangulasi metode, sumber, dan peer debriefing. Analisis data menggunakan open code 4.02.Hasil: Hasil penelitian menunjukkan ada beberapa jenis bullying yang dilakukan remaja di Yogyakarta diantaranya bullying verbal seperti mengejek dan memberikan julukan tidak baik kepada teman. Bullying fisik seperti memukul, menendang, menjambak dan mencubit, kemudian ada juga bullying relasional seperti mengucilkan, mengintimidasi, dan mempermalukan teman di sekolah, serta cyberbullying seperti berkomentar kasar pada media sosial, mengupload foto, dan mengupdate instastory. Perilaku bullying pada remaja tersebut dipengaruhi oleh beberapa faktor seperti ajakan teman, keadaan lingkungan di sekitar remaja, riwayat bullying, pengaruh media elektronik dan karakter sasaran serta pelaku bullying.Kesimpulan: Perilaku bullying pada remaja memberikan dampak negatif baik pada pelaku maupun korbannya sehingga membutuhkan perhatian lebih baik bagi pemerintah, sekolah maupun orang tua. Dalam penelitian ini menemukan berbagai faktor yang mempengaruhi perilaku bullying pada remaja, namun belum bisa mengetahui faktor apa yang paling dominan sehingga diperlukan penelitian dengan menggunakan metode lain untuk mengindentifikasi hal tersebut.Kata Kunci: Perilaku Bullying, Remaja, Yogyakart

Urinary Tract Infection Bacteria Classification: Artificial Intelligence-based Medical Application

Urinary tract infection (UTI) is a type of health disorder, an infection in the urinary glands mainly caused by bacteria. Currently, conventional early detection methods that have been established involve rapid dipstick strip test and urine culture analysis, which have suboptimal accuracy and effectiveness. Several retrospective studies regarding UTI bacteria classification have shown promising results, but still have limitations regarding prediction accuracy and technical simplicity. This study aims to implement a method based on artificial intelligence (AI) in classifying images of bacteria that causes UTIs. Eight artificial intelligence methods based on deep neural networks were used in the study; the models were evaluated and compared based on the prediction's effectiveness and accuracy. This study also seeks to create the easiest method of classifying bacteria causing UTIs using a computer-based application with the best obtained AI-based model. The best training results using an intelligent approach placed DenseNet201 as the method with the highest accuracy (83.99%). Then, the output model was used as a knowledge reference for the designed computer-based application. Real-time prediction results will appear in the application window

Rapid Test of Pneumonia Cells: An Alternative Simple Application

In this pandemic period, pneumonia is often found in various cases. In many cases, COVID-19 has an adverse effect on people with pneumonia. Early detection of pneumonia can help health institutions map pneumonia in the community. However, pneumonia detection still uses conventional methods and takes a long time. This study detects pneumonia bacteria consisting of Acinetobacter baumannii and Pseudomonas aeruginosa. This study uses the DIBaS database, which consists of several bacterial images. This database is used to compare two classes, namely pneumonia and non-pneumonia. Detection is carried out using an artificial intelligence approach using the DenseNet121 and DenseNet169 methods. This study also uses the Genetic Algorithm optimization method to increase the accuracy of detecting pneumonia bacterial cells. The Genetic Algorithm provides random values for the last two DenseNet121 and DenseNet169 training layers. As a result, the accuracy of the DenseNet121 and DenseNet169 methods reached 95% and 96.7%, respectively. The optimization method intervention gave an increase of 5.2% and 3.4% over the original method, respectively. The best model results from this method are used as a reference model in making applications for the rapid detection of pneumonia with an average speed of accuracy reaching 4.25s. This computer-based application provides promising results for the future to be applied to the broader community