Covid-19 is a global pandemic that drives many researchers to strive to look for its solution, especially in the field of health, medicine, and total countermeasures. Early screening with in-silico processes is crucial to minimize the search space of the potential drugs to cure a disease. This research aims to find potential drugs of covid-19 disease in the ZINC database to be further investigated through the in-vitro method. About 997.402.117 chemical compounds are searched about their similarity to some of the confirmed drugs to combat coronavirus. Sequential computation would take months to accomplish this task. The general programming graphic processing unit approach is used to implement a similarity comparison algorithm in parallel, in order to speed up the process. The result of this study shows the parallel algorithm implementation can speed up the computation process up to 55 times faster, and also that some of the chemical compounds have high similarity scores and can be found in nature

<span lang="EN-US">The exponentially increasing bioinformatics data raised a new problem: the computation time length. The amount of data that needs to be processed is not matched by an increase in hardware performance, so it burdens researchers on computation time, especially on drug-target interaction prediction, where the computational complexity is exponential. One of the focuses of high-performance computing research is the utilization of the graphics processing unit (GPU) to perform multiple computations in parallel. This study aims to see how well the GPU performs when used for deep learning problems to predict drug-target interactions. This study used the gold-standard data in drug-target interaction (DTI) and the coronavirus disease (COVID-19) dataset. The stages of this research are data acquisition, data preprocessing, model building, hyperparameter tuning, performance evaluation and COVID-19 dataset testing. The results of this study indicate that the use of GPU in deep learning models can speed up the training process by 100 times. In addition, the hyperparameter tuning process is also greatly helped by the presence of the GPU because it can make the process up to 55 times faster. When tested using the COVID-19 dataset, the model showed good performance with 76% accuracy, 74% F-measure and a speed-up value of 179.</span>

Juvenile delinquency is a phenomenon caused by many causes, and one of them is the lack of religion comprehension and its application in daily life. One way to instill a good religion comprehension in childhood is through an active learning approach, where students learn and play, and in this case using IDO Box: Islamic Dolanan. Active learning method is quite complex so that an effective approach and appropriate environment is needed, especially in a pandemic condition. The aim of this research are to know the appropriate media in implementing IDO Box and increasing the student's religion comprehension. This research was done in Taman Pendidikan Al Quran (TPA) Anugrah, Bojongsari, Depok City, West Java. Learning media that was used in this research are learning modules, website, learning videos, and webinars that were done online. Primary data was obtained by online interviews and questionnaires. IDO Box was implemented using The House Model that was created by Horovitz and Ohlsson-Corboz (2007). The House Model concept is divided into three parts that is vision mission purpose (roof), program indicators and milestones (middle), and IDO Box program and media (cornerstone). Overall the IDObox program consisted of 10 main indicators that has been completed 100% through the activities, i.e. Quran memorizing test, Islamic stories, online assignments, online quizzes, webinars, and also parents and teachers satisfaction survey. This accomplishment is supported by pretest, posttest, and learning improvement evaluation in terms of cognitive, affective, psychomotor, and learning outcomes.

Pengembalian investasi pendidikan di Indonesia menunjukkan angka yang konsisten dan semakin meningkat untuk jenjang pendidikan yang semakin tinggi. Namun jika melihat data, Angka Partisipasi Kasar (APK) di Indonesia mengalami penurunan pada jenjang pendidikan. Sehingga tujuan penelitian ini adalah untuk mengetahui hasil Return on Higher Education Investment (ROHEI) dan kelayakannya pada lulusan Fakultas Ekonomi dan Manajemen IPB sebagai perguruan tinggi negeri di wilayah Jabodetabek.  Jenis data yang digunakan dalam penelitian adalah data kuantitatif dan kualitatif melalui data primer dan sekunder. Metode penentuan dan penarikan sampel yang digunakan adalah nonprobability sampling dan purposive sampling. Jumlah sampel dalam penelitian ini sebanyak 150 orang. Metode analisis yang digunakan adalah analisis deskriptif dan pengembalian investasi. Analisis deskriptif digunakan untuk mengolah data pada karakteristik responden. Adapun analisis pengembalian investasi berupa benefit cost ratio (BCR), rate of return (ROR), cost benefit analysis (CBA), Payback Period (PP), net present value (NPV), dan internal rate of return (IRR) serta analisis loyalitas lulusan menggunakan net promoter score (NPS). Hasil penelitian ini menunjukkan, bahwa kelayakan investasi pendidikan dari gaji responden dinyatakan layak namun hasil penghitungan terkait nilai manfaat berdasarkan tabungan dinyatakan tidak layak. Loyalitas responden terhadap Fakultas Ekonomi dan Manajemen mayoritas berada dalam kategori passive. Kata Kunci: Investasi, Lulusan, Pendapatan Pendidikan, ROHEI, Tabungan

This study was purposed to overview on general application of Indonesia’s digital agriculture technology policies, implementation, and its relation to the Covid-19 pandemic. This study was undertaken through a systematic evidence evaluation complemented with an interactive map and thematic map of digital agriculture application. This study reported that the Government of Indonesia (GoI) has issued national initiatives and policies that support the implementation of digital techologies in food and agriculture sectors. However, a very limited number of both initiatives and policy has mainstreamed the Covid-19 pandemic. An interactive map of digital agriculture companies can be found at this link:https://agriculture40companies.gis.co.id/, and most of the companies are in form of farmers advisory, mechanization platforms, digital marketplace, e-commerce, traceability, food delivery, and peer-to-peer lending. These applications are mostly concentrated in Java island, and and have benefited digital technologies, such as IoT, blockchain, artifical intelligence, smart phone or android, mobile apps, GPS/GIS, and drone. Start-up companies have applied strategic measures to cope with the pandemic implications and some activities of the companies are suspended.