Pendidikan merupakan salah bidang yang merasakan perkembangan internet. Dunia Pendidikan dituntut untuk selalu menyesuaikan dengan perkembangan teknologi internet yang selalu berkembang khususnya dalam proses belajar mengajar.Teknologi informasi sudah mulai digunakan dilembaga pendidikan dalam rangka mendukung proses belajar mengajar baik sebagai media informasi atau sebagai alat pembelajaran. Lembaga Pendidikan berusaha untuk selalu menerapkan sistem teknologi internet di setiap pembelajaran yang berada di lingkungan Pendidikan. Dengan pemanfaatan teknologi, dapat meningkatkan performa, ketercapaian kompetensi dan memahami apa yang dipelajari oleh siswa dengan menggunakan rancangan pembelajaran yang baik dan keatif. Dalam pengabdian ini kami memberikan sosialisasi dan edukasi kepada siswa SMAN 1 Telukjambe mengenai Internet cerdas, sehat dan aman. Dari kegiatan yang telah dilaksanakan hasil evaluuasi menunjukan 35% dan 60% peserta mengatakan setuju dan sangat setuju secara berturut-turut bahwa sosialisasi ini bermanfaat bagi kegiatan pembelajaran.

Tempe is an average food from Indonesia, eaten in Indonesia. Even today, tempe is around the world, and vegans around the world use tempeh as a meat substitute. This study plans to work on the accuracy of tempe characterization by utilizing the three-element extraction technique and the choice tree arrangement strategy. This research uses a decision tree method with three texture features in its classification. The results obtained indicate that this method has the highest Gabor channel level, including extraction, which is 71% accuracy, the split proportion is 10;90 and the lowest is 60% with parted balance of 90:10. The most important level value of GCLM extraction precision is 86% with a split proportion of 90;10 and the lowest price level and 60% level with a split ratio of 10;90 for Wavelet including the highest extraction rate price is 77%. It can be said that from the extraction of three elements, GLCM is the element extraction with the highest value from Gabor and Wavelet, including extraction at a split proportion of 10:90 by 86%. The test shows the Featured Tree highlight designation. The extraction technique was superior to different strategies for interaction characterization of tempe development quality. In the next research, improve the accuracy performance so that it can reach 100% using the CNN deep learning method. Then you can also add Support Vector Machine (SVM) and Naive Bayes methods based on the GLCM Extraction feature.

The role of government health websites as a source of referrals and credible health information is very important, especially now that everything is digital. People use the internet and make health websites as the first step in finding health information, government policies related to health, and public health services. So it is very important to consider the user aspect in designing the appearance of an appropriate health website. This study utilizes the Kansei Engineering KEPack type 1 in analyzing various emotional factors related to the e-government website interface in the health sector. So that it can be found that the psychological emotional factors of users are important and become the main recommendations in the design of the website interface. We are focuses on user preferences for the e-government site interface of the Karawang District Health Office with the Kansei Engineering Type I approach. The Kansei Engineering study was conducted to analyze various emotional factors related to the user interface by comparing 5 specimens of e-Government sites in the health sector. A total of 20 kansei words were identified which were then processed using the multivariate statistical method Cronbach's Alpha (CA), Coefficient Correlation Analysis (CCA), Factor Analysis (FA). The result is that 4 kansei words have a high influence and successfully present a matrix of design element recommendations with 7 main elements and 45 sub-criteria for specific design elements.

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In today's competitive environment, having the best sequence of operations for production and distribution activities is a basic need for survival. As a result, one of the major challenges in fixed supply chain systems is unnecessary transportation costs and the inability to meet customer demand as quickly as possible. In order to meet these challenges, factories and mobile equipment have been considered in this study, and have recently been used in several industries, including pharmaceutical, chemical, and dairy. In the course of this study, a novel mathematical model was put forward for an integrated production and distribution scheduling problem taking into account some real-world features, focusing on reducing customer waiting time and also reducing production costs. A small-scale problem was resolved to check the model’s accuracy. The accuracy of the model is affirmed given the example and its solution acquired from GAMS software. The results of the study prove the effectiveness of this model in reducing customer waiting time and production costs and also demonstrate that the model has the capacity to be utilized by all organizations that produce and distribute perishable products, including dairy and pharmaceutical products, chemical compounds and masks during the Coronavirus pandemic.