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COVID-19 is a disease caused by coronavirus 2 (SARS-CoV-2). This virus belongs to the group of viruses that infect the respiratory system. Furthermore, the rapid rate of spread has made several countries implement a policy of implementing a lockdown to prevent the spread of this virus. In Indonesia, the government implemented the policy of "Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM)" to suppress the spread of this virus. Based on data from the Task Force for the Acceleration of Handling COVID-19 of the Republic of Indonesia, the number of confirmed positive cases as of August 6, 2021 is 3,568,331 people with a death toll of 102,375. The existence of the COVID-19 vaccine is currently under threat, this is due to the emergence of new variants of the COVID-19 virus. The RT-PCR method as the main standard used throughout the world in detecting this virus has a fairly high specificity, which is around 95 percent, which is a manual process that can only be done by health workers. In addition, this test takes a long time and the number of testing facilities is limited. The presence of X-ray scanning machines in hospitals can be used to increase the availability of COVID-19 testing facilities. The thoracic x-ray image generated by the scanner can be used to detect the virus easily, quickly and precisely. In this study, a website-based system was designed to detect the COVID-19 virus in thoracic x-ray images using a convolutional neural network algorithm. The results obtained show that this system is able to classify chest x-ray images into three classes, namely COVID-19, Viral Pneumonia, and normal. The accuracy value obtained is 89.6% and the F1 value is 87.9%.

Sentra HKI adalah sistem informasi yang dikembangkan oleh Puslit Hak Kekayaan Intelektual dan Sertifikasi Produk LPPM Universitas Jenderal Soedirman untuk mengarsipkan dokumen HKI secara digital seperti Paten, Indikasi Geografis, Cipta, Merek, PVT (Perlindungan Varietas Tanaman), Rahasia Dagang, DTLST (Desain Tata Letak Sikuit Terpadu), dan Desain Industri. Sentra HKI juga digunakan untuk mengelola pengajuan HKI baru dan memantau status HKI yang saat ini dimiliki oleh LPPM UNSOED. Kondisi saat ini Sentra HKI berjalan tidak sesuai dengan fungsinya, menu selain Paten dan Cipta tidak bisa diakses, selain itu menu paten dan cipta tidak mendukung pengarsipan dokumen berdasarkan riwayatnya karena setiap melakukan pembaharuan dokumen, dokumen lama akan terhapus dengan dokumen baru. Hal ini juga terlihat pada struktur tampilan layar menu paten dan cipta yang juga tidak mendukung pengarsipan dokumen berdasarkan riwayat dan diikuti dengan struktur basis data yang juga tidak mendukung pengarsipan dokumen berbasis riwayat. Dengan melalui pengembangan aplikasi berupa desain ulang baik struktur basis data dan struktur tampilan yang mendukung pengarsipan dokumen berbasis riwayat Sentra HKI kembali mampu mengelola pengajuan HKI baru dan memantau status HKI yang dikelola oleh LPPM UNSOED.

Recently, the development of data in the digital world has produced new challenges. The challenge is increasing number and complexity of data and variations in data (structured and unstructured). Conventional database management system (RDBMS) is not able to answer these challenges effectively because in RDBMS uses a related structured table approach to store information. Currently present, a database management system called NoSQL. This NoSQL system was introduced to provide quick scalability (amount and complexity) and unstructured data. In this study, we present the performance analysis of RDBMS and NoSQL database management systems in the case of increasing the number of data. We use the simple php page to test database systems, experiments are done with use four data sets of different sizes from academic information systems. The database NoSQL approach is used by Redis and the SQL database approach is used by MySQL. The results show that the noSql database has a execution time better than the MySQL database, there is an increase in the speed of the execution time by 87.58% in the create operation, 85.53% in the update operation, and in the delete operation by 86.40% and while in the read operation the increase in execution time speed is obtained by 57.09%, so that on average the Redis database has a show work better than the MySQL database at 79.15%.. Keywords : MySql Database, Redis Database, PHP Programming Abstrak Baru-baru ini, perkembangan data di dunia digital telah menghasilkan tantangan baru. Tantangannya adalah meningkatnya jumlah dan kompleksitas data dan variasi data (terstruktur dan tidak terstruktur). Sistem manajemen basis data konvensional (RDBMS) tidak dapat menjawab tantangan ini secara efektif karena dalam hal ini RDBMS menggunakan pendekatan tabel terstruktur sebagai konsep dalam menyimpan informasi. Saat ini, telah hadir sistem manajemen basis data yang disebut NoSQL, Sistem NoSQL memiliki kemampuan menangani data yang tidak terstruktur yang jumlah dan kompleksitas terus meningkat. Dalam penelitian ini, kami menyajikan hasil analisis perbandingan unjuk kerja Sistem Manajemen Basis Data konvensional (RDBMS) dengan NoSQL pada kemampuannya dalam menangani pertumbuhan data yang terus meningkat. Metode yang digunakan dalam melakukan analisis perbandingan unjuk kerja RDBMS dengan NoSQL adalah waktu eksekusi yang dibutuhkan untuk melakukan operasi dasar pada sistem basis data yaitu create, read, delete dan update. RDMBS yang digunakan pada penelitian kali ini adalah MySQL sedangkan pada sistem NoSQL digunakan oleh Redis Database. Hasil penelitian menunjukkan bahwa Redis Database memiliki waktu eksekusi yang lebih baik daripada MySQL Database, terdapat peningkatan kecepatan waktu eksekusi tersebut sebesar 87.58% pada operasi create, 85.53% pada operasi update, dan pada operasi delete sebesar 86.40% dan sedangkan pada operasi read penigkatan kecepatan waktu eksekusi yang diperoleh sebesar 57.09%, sehingga secara rata-rata database Redis memiliki unjuk kerja yang lebih baik dari database MySQL yaitu sebesar 79.15%. Kata Kunci : MySql Database, Redis Database, Pemgoramaman PHP

Photovoltaic (PV) systems can be connected to the utility grid to ensure the reliability and continuity of the electrical energy supply. Although the output of the PV modules and many electrical loads are direct current (DC), most grid connected PV systems use alternating current (AC) grid through the inverter. This study presents an analysis of DC microgrid PV system connected to PLN utility grid using controlled rectifier. The controlled rectifier circuit uses a thyristor which can be controlled at its ignition angle to regulate the output voltage and current supplied from utility. The proposed PV system configuration simulations are performed using PSIM software. The system supplies resistive loads in the form of DC lights. Simulations are carried out with variations in load resistance and the thytistor ignition angle. The simulation results show the rectifier circuit has a voltage ripple of 1.57 V (6.47%). While the efficiency of the system under various loading conditions and ignition angle varies between 95.08%–97.72%. The highest system efficiency is obtained under high thyristor ignition conditions.