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All Journal bit-Tech Algor RUBINSTEIN
Indah Fenriana
Universitas Buddhi Dharma
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Journal : bit-Tech

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Decision Support System Best Cage Selection for Chicken Raising Pandika Andio Efendi; Riki Riki; Hartana Wijaya; Indah Fenriana
bit-Tech Vol. 4 No. 1 (2021): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v4i1.235

Abstract

Chicken farming is considered not to require the application of an information system, however, the application of a decision-making system can increase the chances of getting bigger profits. In one time raising chickens requires large capital and the right time when planning sales. Therefore, the selection of decisions is an important factor in an ongoing business. Companies often experience losses which, although not too large, but also occur sustainably will affect the company's finances. It is hoped that the chicken rearing decision support system can also assist in managing the data that comes into the company. Decision support systems will be used in business to help meet company targets and reduce risk. Electre stands for Elimination and Choice Translating Reality which is a method of determining the ranking order through pairwise comparisons between alternatives and the appropriate criteria. Standardization and the use of complete data in decision making can increase the percentage of profit and satisfaction which increases partner integrity
Smart Home Prototype with HC–05 Bluetooth and RFID Modules, Based on Microcontroller Indah Fenriana; Dicky Surya Dwi Putra; Bagus Dermawan; Yusuf Kurnia
bit-Tech Vol. 5 No. 2 (2022): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v5i2.564

Abstract

How is the performance of the Arduino Uno in processing smart home creation, as well as the implementation of the results of its classification into Android. This design uses various sensors, RFID for smart door locks and Bluetooth HC-05 for lights and fans. The benefit of making this prototype is to make it easier to turn on the lights and fans so you don't have to bother looking for light sockets or fans. Smart door lock using RFID is a technology used to identify opening doors. This design uses the Arduino Uno R3 device. This device can be remote with Android. The android application uses the MIT App Inventor software. This smart home design can make it easier for users to carry out activities at home where previously they could turn on and off the lights and fans in the living room, still manually by pressing the socket, now they can turn it on with a smart phone connected to the Bluetooth module via an application. Utilize Google's help on Android smartphones to control lights and fans using voice commands. In addition, the author also designed a smart door lock using RFID and matrix keypad, which previously opened and closed the door with a key, now only by attaching an RFID card or pressing a pin to open the door.
Design of Diabetes Prediction Application Using K-Nearest Neighbor Algorithm Alvin Gunawan; Indah Fenriana
bit-Tech Vol. 6 No. 2 (2023): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v6i2.939

Abstract

The development of diabetes continues to increase accompanied by an increase in unhealthy lifestyles with a high number of cases, making diabetes need to be continuously researched and developed to obtain useful information in terms of research related to diabetes. This study aims to predict diabetes using the K-Nearest Neighbor Algorithm and make a simulation of checking the disease and test the quality of the K-Nearest Neighbor Algorithm for diabetes and make comparisons with the Naïve Bayes algorithm. The K-Nearest Neighbor algorithm is the method used in this study because it has the advantage of being able to train data that is fast, simple, and easy to learn. The way this algorithm works is by calculating the distance between each row of training data and test data based on a predetermined K value. In the process of using the K-Nearest Neighbor, there is a Z-Score normalization stage which is used to adjust the values for each attribute of diabetes symptoms so that they have a range of values that are not too far away. Based on the results of the research and testing of the K-Nearest Neighbor that has been carried out, an accuracy of 97.12% is obtained and the Area Under Curve value is 0.872 which is included in the good classification category and these results have a greater accuracy value compared to previous studies on the same disease, namely Diabetes with the Naïve Bayes algorithm which produces the most optimal accuracy of 87.69%.
Co-Authors Alvin Gunawan Avandy Wijaya Bagus Dermawan billy jordan Calvin Chandra Desiyanna Lasut Dicky Surya Dwi Putra Dicky Surya Dwi Putra Edy Edy Ellysha Dwiyanthi Kusuma Ellysha Kusuma Gunawan Gunawan Hartana Wijaya Jodi Zakharia Jonathan Marcellino Pratama Lianny Wydiastuty Kusuma Pandika Andio Efendi Pulung Adi Nugroho Raditya Rimbawan RIKI RIKI, RIKI Rudy Arijanto Susanto Hariyanto Verga Tandika Yusuf Kurnia