Vertical farming is a new agricultural system which aims to utilize the limited access to land, especially in big cities. Vertical agriculture is the answer to meet the challenges posed by land and water shortages, including urban agriculture with limited access to land and water. This research study uses the Preferred Reporting for Systematic Review and Meta-analysis (PRISMA) item as one of the literary approaches. PRISMA is one way to check the validity of articles for a literature review or a systematic review resulting from this paper. One of the aims of this study is to review a survey of scientific literature related to vertical farming published in the last six years. Artificial intelligence with machine learning, deep learning, and the Internet of Things (IoT) in supporting precision agriculture has been optimally utilized, especially in its application to vertical farming. The results of this study provide information regarding all of the challenges and technological trends in the area of vertical agriculture, as well as exploring future opportunities.

The process of analyzing and classifying comment data done by reading and sorting one by one negative comments and classifying them one by one using Ms. Excel not effective if the data to be processed in large quantities. Therefore, this study aims to apply sentiment analysis on comment data using K-Nearest Neighbor (KNN) method. The comment data used is the comments of the participants of the training on Udiklat Jakarta filled by each participant who followed the training. Furthermore, the comment data is processed by pre-processing, weighting the word using Term Frequency-Invers Document Frequency, calculating the similarity level between the training data and test data with cosine similarity. The process of applying sentiment analysis is done to determine whether the comment is positive or negative. Furthermore, these comments will be classified into four categories, namely: instructors, materials, facilities and infrastructure. The results of this study resulted in a system that can classify comment data automatically with an accuracy of 94.23%

Embedded System Practicum Module for Increase Student Comprehension of Microcontroller

Smart taxi security system design with internet of things (IoT)

This research Producer Electric meter Appliance model base on smart card kWh (kilo Watt hour). The card power meter Is use to calculate the prepaid electricity meter using the 20-digit voucher number as a charging medium. Utilizing a smart card, the token number in the prepaid electricity meter can help reduce the error rate when entering the token number, the model destination is created. Hardware design and with apps to detect how much energy is used to store basic data to help view history of usage. Using smart cards and microcontrollers in apps that come from a usage history in the Pulse usage category will help users know their usage in efficient, normal, and wasteful categories. The results of this research is a meter model with credit card for charging pulses, and data processing applications as devices that can be used for various electricity consumption. The information system is used as a model validation with pulse input that provides information about the use of electrical energy and as a determinant to save the time and time required to make optimal energy

The purpose of this study is to make an electric pulses top-up model with a smart card as an alternative to fill in the digit number from the voucher into the prepaid electricity meter which is currently done manually. Using a smart card in a prepaid electricity meter can reduce the error rate when entering a token number. This application can also detect how much energy is used, store in the database, and can determine the category of pulses use in the kWh meter, as well as help users know the use of electricity that is classified as saving, normal, and wasteful. The system was designed with Vb.net and MySql programs. The results are an electric pulse top-up model based on a smart card for prepaid electricity meter, and an application that stores a history of the use of electrical energy used in determining the electricity usage category.Tujuan penelitian ini adalah membuat model pengisian pulsa listrik dengan smart card (kartu pintar) sebagai alternatif pengisian nomor digit ke dalam meteran listrik prabayar yang saat ini dilakukan secara manual. Pemanfaatan smart card sebagai pengganti nomor token dalam meteran listrik prabayar dapat membantu mengurangi tingkat kesalahan pada saat memasukkan nomor token. Aplikasi ini juga dapat mendeteksi berapa besar energi yang terpakai, menyimpan dalam database, dan dapat menentukan kategori pemakaian pulsa pada kWh listrik, serta membantu pengguna mengetahui pemakaian listrik yang tergolong dalam hemat, normal dan boros. Sistem dirancang dengan program Vb.net dan MySql. Hasil yang didapatkan berupa model pengisian pulsa pada meteran listrik prabayar berbasis kartu pintar dan aplikasi untuk menyimpan riwayat pemakaian energi listrik yang digunakan dalam penentuan kategori pemakaian listrik.