Generation Journal <p>Generation is a scientific journal that presents original articles about knowledge and research information or application of current research and development in the field of technology. Scope of Generation Journal in the field of Informatics Covers Big Data, Decision Support System, Network, Multimedia, Image, NLP This journal is a means of publication and event to share his research and development work in the field of technology. Loading articles in this journal are addressed to the editor's office. Complete information for article creation and article writing instructions is available in every issue. Incoming articles will go through the selection process of bestari partners and / or editors. Generation Journal published 2 times a year, in January and July. Generation Journal Registered at PDII LIPI with e-ISSN number:&nbsp;2549-2233 p-ISSN: 2580-4952. For practitioners, academics and students in the field of Informatics, Multimedia, and Electrical Engineering who want article research results and ideas published in this journal through online registration. If there is any Difficulty in Submit Journal Please Contact the contact person below Risky Aswi Ramadhani: 085736745010</p> Universitas Nusantara PGRI Kediri en-US Generation Journal 2580-4952 <p>Authors who publish with this journal agree to the following terms: <br>1. Copyright on any article is retained by the author(s).<br>2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal. <br>3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.<br>4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.<br>5. The article and any associated published material is distributed under the&nbsp;<a href="" rel="license">Creative Commons Attribution-ShareAlike 4.0 International License</a></p> Metode Fuzzy Mamdani Pada Sistem Pendukung Keputusan Penentuan Jumlah Petugas BPBD Takengon <p>To determine the number of officers at the Regional Disaster Management <br>Agency (BPBD), the results of the Fuzzy calculations 2020. In May there are 70 requests for officers, <br>while the number of existing officers is 109 and possibly 95. In June the requests for officers are 80, <br>while the existing officers are 90 and possibly 87. In July the requests for officers are 90 , while the <br>existing officers numbered 75 and possibly 72 were assigned. Until September the request for <br>officers was 40, the existing officers were 58 and possibly 41 were assigned. Tests were also carried <br>out using matlab tools as a form of implementation in order to get accurate results according to <br>with manual calculation of the Fuzzy mamdani method.</p> Budy Satria LM FAJAR Efitra Efitra Irman Efendi Copyright (c) 2021 Budy Budy Satria 2021-05-08 2021-05-08 5 2 48 58 10.29407/gj.v5i2.15638 Penerapan Artificial Intelligence untuk Kontrol Suhu dan Kelembapan pada Kandang Broiler berbasis Internet of Things <p><strong><em>Abstract – </em></strong><em>Broiler chickens or broiler chickens are one of the popular sources of nutrition in Indonesia. The production of broilers reaches 3.15 billion heads, with the most</em> production center<em> on Java’s island. The Covid-19 disaster that hit Indonesia caused broilers’ production to decrease due to the government’s social restrictions. To maximize production and reduce production efficiency, artificial Intelligent application innovations are carried out for temperature, humidity, and gas control in broiler chicken coops. Artificial Intelligent methods of developing machines can think like humans to help control and make decisions. This artificial Intelligent model uses a fuzzy logic</em> <em>&nbsp;Pulse Width Modulator (PWM)model. The device used for control utilizes Internet of Things technology with a microcontroller as its primary device and sensor as an environmental data reader. The microcontroller used is ESP32 which has been embedded with Wifi to facilitate the transmission of data to the server. To read the sensors’ environmental conditions used by temperature sensors, humidity uses DHT11 and ammonia gas using MQ2. Environment data is sent to the server, which is useful for the user monitoring the cage environment’s condition remotely and, if needed, can be controlled by using the application interface. In this research, the process of system development using waterfall method, namely needs analysis, design, implementation and testing. The system’s application results were tested using two models, namely, trying the sensor reading value compared to the weight on the hygrometer and observation of the reaction of chickens in the cage. The test results obtained the difference in value between the sensor and hygrometer can be tolerated and the chicken reaction following the system’s cooling status.</em></p> Adimas Ketut Nalendra Heri Priya Waspada Copyright (c) 2021 Adimas Ketut Nalendra, Heri Priya Waspada 2021-07-07 2021-07-07 5 2 59 68 10.29407/gj.v5i2.15706 Klasterisasi Kasus Kekerasan Terhadap Anak dan Perempuan Berdasarkan Algoritma K-Means <p><em>Violence is action or threats against themselves alone, a group of people or community </em><em>a group of&nbsp;people&nbsp;or&nbsp;community, loss&nbsp;psychologist, trauma,&nbsp;or&nbsp;deprivation of&nbsp;rights. District Karawang is on of the district that exist in the province of Jawa Barat. Violence that befell children and women in the area of Karawang bloom occurs, such as the lacj awareness of the victim to follow up cases that happened. The purpose of knowing the results of the cluster of cases of violence against children and women into three clusters are statterd in every sub-district in the District Karawang with category level of hardness low, medium or high in order that the government Karawang can provide treatment that is defferent and more targeted and focused on the results ot the analysis for each-each district. Data mining is the process of extracting data to obtain new information. In this study using CRIPS-DM methodology.Research is doing computation algorithm k-means clustering on the data of case of violence against children and women in 2016-2020. Results of testing using tools WEKA 3.8 earnded three cluster or the three categories of the level of violence that is cluster 0 there are 4 members who categorized the level of violence high, cluster 1 there are 2 members categorized the level of violence medium, and cluster 2 there are 24 members who categorized the level of violence low, the results of clustering is evaluated using equation testing purity measure, generate value purity 0,617, case that shows the cluster is quite good</em>.</p> noviya adawiyah Nina Sulistiyowati Mohamad Jajuli Copyright (c) 2021 noviya adawiyah, Nina Sulistiyowati, Mohamad Jajuli 2021-07-08 2021-07-08 5 2 69 80 10.29407/gj.v5i2.15995 Klasifikasi Pengguna Shopee Berdasarkan Promosi Menggunakan Naïve Bayes <p><em>Online shopping is a transaction of buying and selling goods or services through intermediary media, namely social networks. There has been a change in consumption patterns and the way people spend their money, which was originally conventional to switch to E-Commerce services due to several factors, namely the increasing public interest in online shopping due to the COVID-19 virus outbreak, and throughout 2019 E-Commerce users who made transactions reached 168.3 million people. . Based on iprice report data in 2020, Shopee is the most visited E-Commerce with a total of 129,320,800 visitors. Shopee is only a third party that provides a place to sell and payment facilities, therefore Shopee is not responsible for marketing the products sold. To attract consumers, sellers need attractive promotions. Therefore, research is needed to classify E-Commerce users. The purpose of this research is to classify E-Commerce users based on the promotion used using the Naïve Bayes algorithm with the Knowledge Discovery in Database (KDD) methodology. Nine test scenarios were carried out with cross validation which showed that the best performance was a test scenario using 3 folds which resulted in performance with an accuracy value of 88.73%, and with a kappa value of 0.451 which was included in the moderate category. Based on these results, the model generated by the Naïve Bayes algorithm is quite consistent.</em></p> Tania Fatiah Rahmadanti Mohamad Jajuli Intan Purnamasari Copyright (c) 2021 Tania Fatiah Rahmadanti Tania Fatiah Rahmadanti, Mohamad Jajuli, Intan Purnamasari 2021-06-28 2021-06-28 5 2 81 90 10.29407/gj.v5i2.15998 Sistem Pendukung Keputusan Menggunakan Certainty Factor Dalam Mendiagnosa Kategori Tingkat Demam Berdarah <p><em>Dengue Fever is still a serious health problem and a major problem in the health sector in various parts of the world and also in Indonesia. This disease is an infectious disease and can infect all ages because there are still many people who still lack awareness in maintaining cleanliness to anticipate transmission. Data from the Ministry of Health of the Republic of Indonesia (Kemenkes RI) Indonesia recorded the number of people infected with Dengue Fever disease as many as 95,893 throughout 2020 and as many as 661 people died from this disease. The method used is using the certainty factor method where if it is interpreted this method will be used to help make a decision to diagnose the category of Dengue Fever level with a level of confidence in the form of a percentage of how much someone is infected with Dengue Fever in the existing category. The results obtained in the calculation of certainty factor get a result in the category of grade 1 of 98%, grade 2 of 98%, grade 3 of 99%, grade 4 of 99%. These results are enough to prove that this certainty factor method has a fairly high percentage level result.</em></p> Achmad Rizaldi Apriade Voutama Susilawati Susilawati Copyright (c) 2021 Achmad Rizaldi 2021-07-11 2021-07-11 5 2 91 101 10.29407/gj.v5i2.16015 Perbandingan Algoritma SVM dan SVM Berbasis Particle Swarm Optimization Pada Klasifikasi Beras Mekongga <p><em>Rice is the most important staple food in Indonesia. There are various types of varieties available, one of them is Inpari Mekongga variety. In Karawang, Mekongga rice type is the most popular and superior compared to others. However, this type of rice is often mixed with the other types because there are too many varieties and various other problems. Classifying varieties of rice types can be done to identify the types of rice. The classification of rice varieties in this research is divided into 2 classes, Mekongga and not Mekongga. The method that used in this reserach is Support Vector Machine (SVM) and Particle Swarm Optimatizon (PSO). SVM method was chosen because it basically handles the classification of two classes. Meanwhile, PSO method used to optimize the accuracy level of the SVM method. Combination from the two methods is very well used in classification data because it can increase the level of accuracy better. The purpose of this reserach is compare the accuracy of the 2 methods that used. The results from research is mekongga rice classification with Support Vector Machine has accuracy value 46.67% and&nbsp; AUC value 0.475. Meanwhile, using Support Vector Machine based on Particle Swarm Optimization (PSO) can help improve the classification of this mekongga rice with accuracy value 70.83% and AUC value 0.671.</em></p> Emilia Ayu Wijayanti Tania Rahmadanti Ultach Enri Copyright (c) 2021 Emilia Ayu Wijayanti, Tania Rahmadanti, Ultach Enri 2021-07-21 2021-07-21 5 2 102 108 10.29407/gj.v5i2.16075 Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19 <p><em>Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.</em></p> Euis Saraswati Yuyun Umaidah Apriade Voutama Copyright (c) 2021 Euis Saraswati, Yuyun Umaidah, Apriade Voutama 2021-07-21 2021-07-21 5 2 109 118 10.29407/gj.v5i2.16125 GOOGLE CLASSROOM OPTIMIZATION AS ONLINE LEARNING INNOVATION DURING THE COVID-19 PANDEMIC <p><em>The Covid-19 pandemic has plagued all corners of the world, including Indonesia. Simultaneously, the Ministry of Education and Culture (SE No. 36962/MPK.A/HK/2020), responded that learning during the pandemic can be carried out with an online or remote system and work from home. The application of online learning is a form of innovation that can be done to condition the teaching and learning process in the hope of preventing the spread of the Covid-19 outbreak. One of the online learning applications that can be used to support the learning process during the Covid-19 pandemic is Google Classroom. This Google Classroom application is equipped with classroom features that can be created by educators and can be followed by all students. The features provided are complete, including uploading materials, videos, assignments/practices, and even discussion forums. Research data was taken by distributing online questionnaires using google form to 122 students. These students are those who have done online learning using Google classroom. The results showed that the optimization of the use of Google Classroom can be used to develop online learning with high results, which is 73.57%.</em></p> Purnomo Hadi Susilo M. Ghofar Rohman Copyright (c) 2021 Purnomo Hadi Susilo, M. Ghofar Rohman 2021-07-30 2021-07-30 5 2 10.29407/gj.v5i2.16117