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Journal : MULTINETICS

Implementasi Enkripsi Dekripsi Paket Data pada Rancang Bangun Smart Home Menggunakan Protokol MQTT Sari, Risna; -, Ayu Rosyida Zain; Marta Surya Cakraningrat
MULTINETICS Vol. 8 No. 2 (2022): MULTINETICS Nopember (2022)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v8i2.4110

Abstract

Internet of Things (IoT) technology can be applied in many ways, one of which is home automation or Smart Home. There are several sensors and actuator modules in the Smart Home system such as movement sensors (PIR) to detect the presence of living things around the house, radio wave frequency-based authentication sensors (RFID) to ensure that only registered residents can enter the house, actuator servo actuator (SG90) which is used to open the door, and actuator switch (Relay) to automatically turn off the lights. All activities at home that apply the Smart Home concept can be done automatically without touching it directly by using a special application called SUSAH v2, which is an Android-based application created using MIT App Inventor. What is still a concern is the lack of the Smart Home system where the network usInternet of Things (IoT) technology can be applied in many ways, one of which is home automation or Smart Home. There are several sensors and actuator modules in the Smart Home system such as movement sensors (PIR) to detect the presence of living things around the house, radio wave frequency-based authentication sensors (RFID) to ensure that only registered residents can enter the house, actuator servo actuator (SG90) which is used to open the door, and actuator switch (Relay) to automatically turn off the lights. All activities at home that apply the Smart Home concept can be done automatically without touching it directly by using a special application called SUSAH v2, which is an Android-based application created using MIT App Inventor. What is still a concern is the lack of the Smart Home system where the network used is still a LAN and can only be controlled if the user is on the same network as the Smart Home system. Therefore, this research objective is to developing, testing, and implementing security using cryptographic methods and the integration of the Antares Platform based on the MQTT protocol to be able to make the Smart Home system accessible on a WAN anywhere and anytime, taking into account the security of the data information sent. To create an IoT-based Smart Home system that is more efficient and remains safe when used.ed is still a LAN and can only be controlled if the user is on the same network as the Smart Home system. Therefore, this research objective is to developing, testing, and implementing security using cryptographic methods and the integration of the Antares Platform based on the MQTT protocol to be able to make the Smart Home system accessible on a WAN anywhere and anytime, taking into account the security of the data information sent. To create an IoT-based Smart Home system that is more efficient and remains safe when used.
IMPLEMENTASI ALGORITMA DECISION TREE DENGAN FITUR SELEKSI WEIGHT BY INFORMATION GAIN Ali, Euis Oktavianti; Agustin, Maria; Sari, Risna
MULTINETICS Vol. 9 No. 2 (2023): MULTINETICS Nopember (2023)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v9i2.5715

Abstract

This paper aims to apply the weight selection feature by considering the Gain Ratio value in the decision tree algorithm in classifying student academic scores. We determine the feature selection from the gain ratio based on the split value information to reduce the feature's (attribute) bias value. The highest Gain Ratio' value will be the root of the branching in the tree in which becomes a determining feature (attribute) of student graduation. We use 82 data which are divide into two classes called a pass and a not pass. From the data, we know that the attribute ip smt 7 got the highest gain ratio value with 0.581. On the other hand, the multimedia introduction attribute got the lowest gain ratio value with 0.070. The calculation model using cross-validation with a value of k = 5 resulted in optimal performance. The resulting accuracy is 79.19% and AUC 0.778 using the decision tree algorithm. The threshold value of the gain ratio used is 1.00 so that four attributes are not used in this paper. feature selection using weights with information gain ratio will select the attribute selection process to be built in the model.