SINTECH (Science and Information Technology) Journal最新文献

Support Vector Machine For Hoax Detection 支持向量机骗局检测

SINTECH (Science and Information Technology) Journal Pub Date : 2023-08-31 DOI: 10.31598/sintechjournal.v6i2.1366

Ni Wayan, Sumartini Saraswati, I. Putu, Krisna Suarendra Putra, Dewa Made, Krishna Muku, Gede Dana Pramitha

{"title":"Support Vector Machine For Hoax Detection","authors":"Ni Wayan, Sumartini Saraswati, I. Putu, Krisna Suarendra Putra, Dewa Made, Krishna Muku, Gede Dana Pramitha","doi":"10.31598/sintechjournal.v6i2.1366","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i2.1366","url":null,"abstract":"Along with the development of information technology, news media has also developed by presenting information online Along with the rapid development of online news, the spread of fake news information (hoaxes) is also increasing rapidly and widely. Hoax news is often spread intentionally for various purposes. Generally, hoax news aims to direct the reader's perception to believe in a bad perception of an event, character or even a company. The motivation is to invite readers to believe something that is not true with the aim of benefiting the news disseminator is something dangerous. This research aims to detect English-language hoaxes by applying the Support vector machine (SVM) algorithm. In this study, the data used are two data sources, namely English news datasets from Kaggle and English news taken from BBC. The results of this study show that the application of the SVM algorithm turns out to get good performance because the model is able to classify hoax news with an accuracy of 99.4% on Kaggle data while on the BBC news dataset the model gets an accuracy of 98.9%. This research also shows that the SVM method is proven to have good generalization properties. Where it is able to identify test data that is completely different from the training data.","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122483766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analisis Sentimen Program Mbkm Pada Media Sosial Twitter Menggunakan KNN Dan SMOTE

SINTECH (Science and Information Technology) Journal Pub Date : 2023-08-31 DOI: 10.31598/sintechjournal.v6i2.1372

Komang Pramayasa, Md Dendi Maysanjaya, Gusti Ayu, Agung Diatri Indradewi

{"title":"Analisis Sentimen Program Mbkm Pada Media Sosial Twitter Menggunakan KNN Dan SMOTE","authors":"Komang Pramayasa, Md Dendi Maysanjaya, Gusti Ayu, Agung Diatri Indradewi","doi":"10.31598/sintechjournal.v6i2.1372","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i2.1372","url":null,"abstract":"The Merdeka Belajar-Kampus Merdeka (MBKM) program is a relatively new program implemented in Indonesia since February 2020. Like a new program, the implementation of the MBKM program is also followed by various pro and con attitudes. Therefore, a sentiment analysis technique is needed to determine the public opinion towards the MBKM program. The purpose of this study is to determine the performance of the KNN method in performing sentiment classification optimized by the SMOTE method in overcoming the problem of unbalanced data and to determine the tendency of public sentiment towards the implementation of the MBKM program. Based on the research results, the KNN method optimized with the SMOTE method is proven to improve classification performance. From initially producing an accuracy value of 76.13%, precision of 76.03%, recall of 76.13% and f1-score of 76.01% there was an increase in accuracy value to 76.13%, precision to 76.03%, recall to 76.13%, and f1-score to 76.01%. In this study, it was found that community responses tended to be neutral towards the MBKM program. The community feels that the MBKM program is a program that can increase student experience. However, there are still program systems that are considered complicated and need to be evaluated.","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125948952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Klasifikasi Penyakit Antraknosa Pada Cabai Merah Teropong ”Inko Hot” Dengan Metode Convolutional Neural Network 红灯区炭疽热对“热仁科”望远镜的分类与神经对焦网络

SINTECH (Science and Information Technology) Journal Pub Date : 2023-08-31 DOI: 10.31598/sintechjournal.v6i2.1377

Donny Avianto, Ilmy Eka Handayani

{"title":"Klasifikasi Penyakit Antraknosa Pada Cabai Merah Teropong ”Inko Hot” Dengan Metode Convolutional Neural Network","authors":"Donny Avianto, Ilmy Eka Handayani","doi":"10.31598/sintechjournal.v6i2.1377","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i2.1377","url":null,"abstract":"The red chili variety \"inko hot\" is a type of red chili that has a high economic value. Unfortunately, these red chili plants are often infected with anthracnose disease, which results in significant losses for farmers. Anthracnose is one of the major diseases infecting chili plants, potentially resulting in crop failure and losses of up to 80%. The purpose of this study is to develop a classification system to identify anthracnose disease in red chili fruit, using Convolutional Neural Network (CNN) method. In this experiment, 1500 data were used, of which 80% were used as training data and 20% as validation data. The best results of this experiment produced a model with an accuracy of 97% and a loss rate of 6.45%, by applying the Nadam optimization algorithm and going through 50 iterations (epochs). The model showed good performance with a prediction accuracy rate of 83.33%. The development of this classification system has significant potential in providing efficient solutions to recognize diseases in chili plants. Through continuous development, this system can be a valuable tool for farmers to increase crop productivity and reduce the negative impact of disease attacks on red chili peppers and other crops.","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126458437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Berbasis CNN Untuk Pengenalan Pola Partial Discharge Isolasi Silicone Rubber 深度学习基础CNN Untuk Pengenalan Pola部分放电硅橡胶

SINTECH (Science and Information Technology) Journal Pub Date : 2023-08-31 DOI: 10.31598/sintechjournal.v6i2.1390

Ferlian Seftianto, Sukemi Sukemi, Zainuddin Nawawi

{"title":"Deep Learning Berbasis CNN Untuk Pengenalan Pola Partial Discharge Isolasi Silicone Rubber","authors":"Ferlian Seftianto, Sukemi Sukemi, Zainuddin Nawawi","doi":"10.31598/sintechjournal.v6i2.1390","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i2.1390","url":null,"abstract":"Partial discharge (PD) activity measurements have been carried out by selecting noise signals (de-noising) using Support Vector Machine (SVM)and then recognized using Convolutional Neural Network (CNN). CNN testing was carried out using various models such as activation methods: Sigmoid, Softmax, Relu, Tanh, Swish. Number of layers used is 1, 2, 3, 4 with filter sizes of 32, 64, 128, 256 and kernel sizes 3x3, 2x2, 1x1, 1x2, 1x3 in the MaxPooling and AveragePooling pooling methods. The results obtained, On sigmoid method the MaxPooling and AveragePooling with 1 layers having a low accuracy around 14.40% but the other layers configurations gets a high accuracy around 98.99% both has been done with or without de-noising. In Softmax activation method, MaxPooling pooling method has an accuracy around 84.94% and has de-noising 90.66%. The AveragePooling pooling method has an accuracy 65.25% and around 75.29% with de-noised. The result shows that SVM de-noising increases the accuracy around 11.12% in the Softmax activation method. In the Tanh, Relu, and Swish activation methods, a low level of accuracy is obtained with an average of 14.40%, and SVM de-noising doesn’t increase the accuracy, so CNN-based deep learning with SVM de-noising is more suitable using the Sigmoid and Softmax.","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129717085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pengembangan Sistem Business Intelligence Dalam Monitoring Performa Perusahaan Multi Company

SINTECH (Science and Information Technology) Journal Pub Date : 2023-08-31 DOI: 10.31598/sintechjournal.v6i2.1394

Dewa Gde, Deva Baskara Muku, I. Putu, Agung Bayupati, Anak Agung Gede Ngurah, Hary Susila

{"title":"Pengembangan Sistem Business Intelligence Dalam Monitoring Performa Perusahaan Multi Company","authors":"Dewa Gde, Deva Baskara Muku, I. Putu, Agung Bayupati, Anak Agung Gede Ngurah, Hary Susila","doi":"10.31598/sintechjournal.v6i2.1394","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i2.1394","url":null,"abstract":"Multi-company companies have challenges in managing subsidiary data into performance information of all subsidiaries in one window. This is due to the variation of data dimensions according to the business processes of each subsidiary. CV. XYZ is a holding company engaged in the food & beverage business. CV. XYZ currently manages three companies consisting of restaurants, catering, and tent and decoration rental. The problem faced by the company owner is the limited access to company performance in one information window. Each company has working papers in Excel format to record expenditure and income transactions. This article proposes the development of a website-based business intelligence system to overcome the problems of CV. XYZ. The purpose of developing a business intelligence system in this article is to provide access to the performance of each subsidiary in one website media. The business intelligence system is developed through the stages of data collection and analysis, data warehouse design, ETL process, and data visualization with Microsoft Power BI. The data warehouse design uses Kimball's nine-step method which produces a data warehouse with a star scheme. The developed Business Intelligence system was tested using the UAT method. The UAT test results show that the system development is following the company's needs as indicated by the UAT score of 92%","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"28 2 Suppl 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123437333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation Of A* Algorithm In A Great Elephant Game With Unity 2D 用Unity 2D实现大象游戏中的A*算法

SINTECH (Science and Information Technology) Journal Pub Date : 2023-08-31 DOI: 10.31598/sintechjournal.v6i2.1396

Ahmad Zuhdi, Imam Ahmad, Ade Dwi Putra

{"title":"Implementation Of A* Algorithm In A Great Elephant Game With Unity 2D","authors":"Ahmad Zuhdi, Imam Ahmad, Ade Dwi Putra","doi":"10.31598/sintechjournal.v6i2.1396","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i2.1396","url":null,"abstract":"Video game is a game play which related to interesting User Interface through a picture which processed and transmitted into a video, as it alive and moving. At early 2014, smartphone users were increased, affected to decreasing of computer users.Pathfinding is a route search method. Google maps is one example of an application which using pathfinding method. A* Algorithm through pathfinding method is the development of dijkstra algorithm using heuristic to produce best and optimum solution. So it is possible to discover fastest path without checking in every path. Therefore, the objective of this study is implementing A* algorithm in Great Elephant game. This algorithm can be implemented to the character for discovering nearest route and increase the score easily. Because of the route is in maze form, it will be time wasting to looking for any food. This algorithm help the character to not wasting time in looking for the food. The result study is a game using A* algorithm in the player game which can access in android smartphone. The test result is using ISO 25010 in functionality suitablity aspect and produced 100% success. \u0000 ","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116788103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimasi Parameter Support Vector Machine Dengan Algoritma Genetika Untuk Analisis Sentimen Pada Media Sosial Instagram

SINTECH (Science and Information Technology) Journal Pub Date : 2023-04-30 DOI: 10.31598/sintechjournal.v6i1.1245

Putu Dedy, Wiratama Darmawan, Gede Aditra Pradnyana, Ida Bagus, Nyoman Pascima

{"title":"Optimasi Parameter Support Vector Machine Dengan Algoritma Genetika Untuk Analisis Sentimen Pada Media Sosial Instagram","authors":"Putu Dedy, Wiratama Darmawan, Gede Aditra Pradnyana, Ida Bagus, Nyoman Pascima","doi":"10.31598/sintechjournal.v6i1.1245","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i1.1245","url":null,"abstract":"Social media is an online media that users use to interact with each other by expressing themselves by giving comments, and one example is Instagram. All the collected comments will form a public opinion. This opinion can be used with sentiment analysis to become information. The method commonly used to carry out sentiment analysis is classification using machine learning. One of the machine learning that is often used is the Support Vector Machine (SVM). However, on non-linear problems such as sentiment analysis, SVM requires the kernel to map vectors into high-dimensional spaces to solve non-linear problems. The problem faced in using the kernel is to choose the optimal parameters for the classification model to produce a good classification model. This study proposes a new approach to obtain optimal parameters for SVM using Genetic Algorithm (GA). This study designed an SVM-GA classification model from the data collection, processing, classification, and evaluation stages. The results showed that the best accuracy produced with parameters optimized with the genetic algorithm was 81.6%, or an increase of 2.4% from the SVM sentiment analysis method without GA optimization.","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130474107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network

SINTECH (Science and Information Technology) Journal Pub Date : 2023-04-30 DOI: 10.31598/sintechjournal.v6i1.1258

Khairunnisak Khairunnisak, Gilang Miftakhul Fahmi, Didit Suhartono

{"title":"Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network","authors":"Khairunnisak Khairunnisak, Gilang Miftakhul Fahmi, Didit Suhartono","doi":"10.31598/sintechjournal.v6i1.1258","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i1.1258","url":null,"abstract":"Abstract \u0000In the era of information technology, it is very important to protect data and information so that irresponsible parties do not misuse it. One technique for securing data is steganography. Steganography is a technique of hiding messages in a medium. One of the media for hiding messages is pictures. However, steganography techniques can still be detected by steganalysis techniques. Steganalysis is a technique for analyzing hidden messages in steganography. Therefore this study applies image processing techniques with the Generative Adversarial Network algorithm model, which aims to manipulate images so that steganalysis techniques cannot detect hidden messages. Proof of the results of applying the Generative Adversarial Network algorithm using a web-based application containing message hiding and extraction functions. The results obtained are that the Generative Adversarial Network algorithm can be applied to create mock objects, and images can revive based on training data which is a model for how the algorithm works. In addition, the results of testing the Generative Adversarial Network algorithm were successfully applied to image steganography which functions to prevent steganalysis techniques from trying to detect messages in images. Future research is expected to be able to select steganographic images other than the results from the training data model according to the original size chosen randomly according to the selection of the user.","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129773831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pengembangan Sistem Prediksi Bantuan Program Keluarga Harapan (PKH) Berbasis Machine Learning 基于eccl的基于希望的家庭学习计划的预测系统的发展

SINTECH (Science and Information Technology) Journal Pub Date : 2023-04-30 DOI: 10.31598/sintechjournal.v6i1.1297

Wayan Supriana, M. A. Raharja, I. Made, Satria Bimantara

{"title":"Pengembangan Sistem Prediksi Bantuan Program Keluarga Harapan (PKH) Berbasis Machine Learning","authors":"Wayan Supriana, M. A. Raharja, I. Made, Satria Bimantara","doi":"10.31598/sintechjournal.v6i1.1297","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i1.1297","url":null,"abstract":"The Family Hope Program (PKH) is a poverty alleviation program which is one of the government's strategies in reducing the poverty line. This program provides cash social assistance to poor families who are included in the list of beneficiary families with a focus on education and health. The purpose of implementing the PKH program is not only to reduce poverty and increase human resources but to break the poverty chain. The implementation of PKH in its realization experienced many obstacles that caused the program not to be on target, this was because the data verification process was not yet effective and was still carried out manually. A process is needed to digitize the distribution and realization of the family of hope program. Through this research, a system was developed that can predict the value of PKH beneficiary assistance. The system developed is based on machine learning with a prediction model using Artificial Neural Network (ANN) and Backpropagation learning algorithm. Parameters in the learning system using PKH assessment as many as 8 indicators from the data of PKH beneficiaries in Tabanan Regency. Based on the prediction model testing using two data treatments, namely with and without preprocessing data. Parameters treated with data on numeric attributes and categories provide optimal values with an R2 Score of 0.695824 with a number of hidden layers of 500 and a max epoch of 375","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122836230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analisa Prediksi Harga Emas Dengan Kemungkinan Terjadinya Resesi Menggunakan Metode SVR 使用SVR方法分析可能发生衰退的黄金价格预测

SINTECH (Science and Information Technology) Journal Pub Date : 2023-04-30 DOI: 10.31598/sintechjournal.v6i1.1329

Fevrierdo Nathaniel Shanahan Pradana, F. S. Papilaya

{"title":"Analisa Prediksi Harga Emas Dengan Kemungkinan Terjadinya Resesi Menggunakan Metode SVR","authors":"Fevrierdo Nathaniel Shanahan Pradana, F. S. Papilaya","doi":"10.31598/sintechjournal.v6i1.1329","DOIUrl":"https://doi.org/10.31598/sintechjournal.v6i1.1329","url":null,"abstract":"Gold is a resource that has a high value and has the advantage of a stable selling price. This can be proven by the choice of gold which is often used as a long-term investment tool. It can be seen that the impact of Covid-19 and the Russia-Ukraine war is considered to be the causes of recession that will affect the economy and end with the changes of gold selling price. This research was conducted on the basis of the large number of people who are now starting to be interested in investing in gold. However, this is quite a question for gold investors because of economic changes from the impact of Covid-19 and the Russia-Ukraine war. People are certainly worried, especially for those who have investments in the form of gold. The purpose of this research is to provide an analysis in the form of predictions of gold prices in 2023, an advice on managing gold in the future. The method used is the Support Vector Regression method using a polynomial kernel and supported by the Mean Absolute Percentage Error measurement. From the past research that has been done, the prediction results for gold prices in 2023 with an error value of 4.8% where this value is in the very good category. From this research, several suggestions are also given in managing gold during a recession","PeriodicalId":302091,"journal":{"name":"SINTECH (Science and Information Technology) Journal","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128960606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0