Consumers believe that restaurant reviews are very important when choosing a restaurant. Due to the fact that reviews have become one of the most effective ways to influence customer decisions, research that has been done on restaurant customer reviews is about sentiment analysis. Previous studies have only used sentiment analysis at the sentence or document level, while a better level uses Aspect-Based Sentiment Analysis (ABSA), or a type of aspect-based sentiment analysis. LSTM is a variant of RNN that stores long-term information in memory cells. Use of global max pooling to reduce output resolution features and prevent overfitting. In addition, the optimization method used by Adam Optimizer is an adaptive learning rate optimization algorithm specifically designed to train deep neural networks. This study aims to classify restaurant customer opinions based on aspects (food, place, service, and price) based on restaurant customer reviews on Indonesian-language TripAdvisor with LSTM and global max pooling for sentiment classification (negative, half negative, neutral, half positive, positive). The results of this study indicate that the ABSA in restaurant customer reviews for sentiment classification accuracy is 78.7% and the aspect category accuracy is 78%, both are interconnected and can help understand restaurant customer opinions on TripAdvisor.
{"title":"Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Restoran Menggunakan LSTM Dengan Adam Optimizer","authors":"Wardianto Wardianto, Farikhin Farikhin, Dinar Mutiara Kusumo Nugraheni","doi":"10.31328/jointecs.v8i2.4737","DOIUrl":"https://doi.org/10.31328/jointecs.v8i2.4737","url":null,"abstract":"Consumers believe that restaurant reviews are very important when choosing a restaurant. Due to the fact that reviews have become one of the most effective ways to influence customer decisions, research that has been done on restaurant customer reviews is about sentiment analysis. Previous studies have only used sentiment analysis at the sentence or document level, while a better level uses Aspect-Based Sentiment Analysis (ABSA), or a type of aspect-based sentiment analysis. LSTM is a variant of RNN that stores long-term information in memory cells. Use of global max pooling to reduce output resolution features and prevent overfitting. In addition, the optimization method used by Adam Optimizer is an adaptive learning rate optimization algorithm specifically designed to train deep neural networks. This study aims to classify restaurant customer opinions based on aspects (food, place, service, and price) based on restaurant customer reviews on Indonesian-language TripAdvisor with LSTM and global max pooling for sentiment classification (negative, half negative, neutral, half positive, positive). The results of this study indicate that the ABSA in restaurant customer reviews for sentiment classification accuracy is 78.7% and the aspect category accuracy is 78%, both are interconnected and can help understand restaurant customer opinions on TripAdvisor.","PeriodicalId":259537,"journal":{"name":"JOINTECS (Journal of Information Technology and Computer Science)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134589010","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}
Pub Date : 2023-07-01DOI: 10.31328/jointecs.v8i2.4747
Adi Ariyo Munandar, Farikhin Farikhin, C. Widodo
Google Play Store is where a wide variety of applications are available, whether paid or not. Google Play Store page is a place for application users to express opinions, reviews and ratings. Ruang Guru, Zenius and Quipper are available on the platform. Analysis was carried out using sentiment analysis and SVM algorithm. Data was obtained using data scraping techniques, using help of google-play-scraper library. Web scraping process is divided into 3 stages namely Fetching, Extraction, and Transformation. Data collected is 30,000 data which is divided into 10,000 data for each application. Research begins with data preprocessing stage which includes normalization, case folding, cleaning, tokenizing, and stopwords. then data is divided into 90% training data and 10% test data. Training data is labeled with values 1, 0, and -1. Value 1 means positive, value 0 means neutral and -1 means negative. Results of classification sentiment using SVM show that Ruang Guru has highest positive value compared to Zenius and Quipper. However, user response equally gives a positive value for application. Accuracy value of research shows that sentiment classification data with SVM has an average accuracy for Ruang Guru of 99%, Zenius of 96%, and Quipper of 82%.
Google Play Store提供各种各样的应用程序，无论是否付费。Google Play Store页面是应用程序用户表达意见、评论和评分的地方。Ruang Guru, Zenius和Quipper都可以在平台上使用。采用情感分析和支持向量机算法进行分析。使用数据抓取技术，借助于google-play-scraper库获得数据。Web抓取过程分为抓取、提取和转换3个阶段。收集的数据为30,000个数据，每个应用程序分为10,000个数据。研究从数据预处理阶段开始，包括规范化、案例折叠、清理、标记化和停止词。然后将数据分为90%的训练数据和10%的测试数据。训练数据被标记为值1,0和-1。值1表示积极，值0表示中性，-1表示消极。基于支持向量机的情感分类结果显示，与Zenius和Quipper相比，Ruang Guru具有最高的正面价值。然而，用户的反应同样为应用程序提供了积极的价值。研究的准确率值表明，使用SVM的情感分类数据，Ruang Guru平均准确率为99%，Zenius平均准确率为96%，Quipper平均准确率为82%。
{"title":"Sentimen Analisis Aplikasi Belajar Online Menggunakan Klasifikasi SVM","authors":"Adi Ariyo Munandar, Farikhin Farikhin, C. Widodo","doi":"10.31328/jointecs.v8i2.4747","DOIUrl":"https://doi.org/10.31328/jointecs.v8i2.4747","url":null,"abstract":"Google Play Store is where a wide variety of applications are available, whether paid or not. Google Play Store page is a place for application users to express opinions, reviews and ratings. Ruang Guru, Zenius and Quipper are available on the platform. Analysis was carried out using sentiment analysis and SVM algorithm. Data was obtained using data scraping techniques, using help of google-play-scraper library. Web scraping process is divided into 3 stages namely Fetching, Extraction, and Transformation. Data collected is 30,000 data which is divided into 10,000 data for each application. Research begins with data preprocessing stage which includes normalization, case folding, cleaning, tokenizing, and stopwords. then data is divided into 90% training data and 10% test data. Training data is labeled with values 1, 0, and -1. Value 1 means positive, value 0 means neutral and -1 means negative. Results of classification sentiment using SVM show that Ruang Guru has highest positive value compared to Zenius and Quipper. However, user response equally gives a positive value for application. Accuracy value of research shows that sentiment classification data with SVM has an average accuracy for Ruang Guru of 99%, Zenius of 96%, and Quipper of 82%.","PeriodicalId":259537,"journal":{"name":"JOINTECS (Journal of Information Technology and Computer Science)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129285374","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}
Pub Date : 2023-07-01DOI: 10.31328/jointecs.v8i2.4326
Eka Ardhianto, W. Handoko, Endang Lestariningsih, Felix Andreas Sutanto
Algoritma Vigenere merupakan model algoritma enkripsi yang sampai saat ini masih dikembangkan dalam bidang keamanan infromasi sampai saat ini. Salah satu aspek yang dipandang penting dalam bidang keamanan informasi adalah confidentiality. Permasalahan pencapaian confidentiality pesan atau informasi yang tinggi menjadi sesuatu yang kritis dalam bidang pengamanan informasi. Extended Vigenere dikenal sebagai evolusi Vigenere yang menggaplikasikan jumlah karakter set yang lebih luas. Salah satu pengembangan dalam algoritma Vigenere adalah dengan memodifikasi pembangkit kunci yang digunakan. Eksperimen ini bertujuan untuk melihat pengaruh confidentiality informasi terhadap penggunaan pembangkit kunci Blum Blum Shub (BBS) dan Bilangan Euler yang diaplikasikan pada Extended Vigenere. Metode pembangkit kunci BBS dan Bilangan Euler digunakan secara berurutan. Sebagai metrik pengukuran digunakan perhitungan entropi terhadap output Extended Vigenere. Hasil yang diperoleh ialah berupa peningkatan confidentiality informasi yang signifikan dengan nilai capaian entropi lebih dari 79% terhadap entropi optimum
{"title":"Adopsi Pembangkit Kunci Blum Blum Shub Dan Bilangan Euler Pada Algoritma Extended Vigenere","authors":"Eka Ardhianto, W. Handoko, Endang Lestariningsih, Felix Andreas Sutanto","doi":"10.31328/jointecs.v8i2.4326","DOIUrl":"https://doi.org/10.31328/jointecs.v8i2.4326","url":null,"abstract":"Algoritma Vigenere merupakan model algoritma enkripsi yang sampai saat ini masih dikembangkan dalam bidang keamanan infromasi sampai saat ini. Salah satu aspek yang dipandang penting dalam bidang keamanan informasi adalah confidentiality. Permasalahan pencapaian confidentiality pesan atau informasi yang tinggi menjadi sesuatu yang kritis dalam bidang pengamanan informasi. Extended Vigenere dikenal sebagai evolusi Vigenere yang menggaplikasikan jumlah karakter set yang lebih luas. Salah satu pengembangan dalam algoritma Vigenere adalah dengan memodifikasi pembangkit kunci yang digunakan. Eksperimen ini bertujuan untuk melihat pengaruh confidentiality informasi terhadap penggunaan pembangkit kunci Blum Blum Shub (BBS) dan Bilangan Euler yang diaplikasikan pada Extended Vigenere. Metode pembangkit kunci BBS dan Bilangan Euler digunakan secara berurutan. Sebagai metrik pengukuran digunakan perhitungan entropi terhadap output Extended Vigenere. Hasil yang diperoleh ialah berupa peningkatan confidentiality informasi yang signifikan dengan nilai capaian entropi lebih dari 79% terhadap entropi optimum","PeriodicalId":259537,"journal":{"name":"JOINTECS (Journal of Information Technology and Computer Science)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130534442","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}
Pub Date : 2023-07-01DOI: 10.31328/jointecs.v8i2.4562
Muhammad Furqan Rasyid, M. S. Mustafa, Andi Asvin Mahersatillah Suradi, M. Rizal, Mushaf Mushaf, Arham Arifin
Eye detection technology is used to recognize and analyze unique features of a person's eyes as a way to identify or authenticate their identity. This technology can be used in various applications such as pattern recognition, biometric systems, surveillance systems, and others. Most applications require precision in eye detection, so a fast and reliable eye detection method is needed. In this research, an eye detection method is proposed using the Python OpenCV and MediaPipe libraries, which offer better accuracy compared to existing solutions. Both libraries are implemented in the Python programming language, which is popular among software developers for its ability in object-oriented programming, easy data manipulation and processing, and availability of libraries and modules in various fields such as artificial intelligence. The system was tested using videos captured using a smartphone. Although the videos were captured under suboptimal conditions, such as imperfect lighting, testing was conducted on 56 videos that had relatively good quality and lasted about 5-10 seconds. The results obtained showed an accuracy rate of 100%. Additionally, the system can distinguish between open and closed eye conditions, which will facilitate further research in detecting eye blinks. In conclusion, the model created can detect eyes with a very high accuracy rate.
{"title":"Deteksi Mata di Video Smartphone Menggunakan Mediapipe Python","authors":"Muhammad Furqan Rasyid, M. S. Mustafa, Andi Asvin Mahersatillah Suradi, M. Rizal, Mushaf Mushaf, Arham Arifin","doi":"10.31328/jointecs.v8i2.4562","DOIUrl":"https://doi.org/10.31328/jointecs.v8i2.4562","url":null,"abstract":"Eye detection technology is used to recognize and analyze unique features of a person's eyes as a way to identify or authenticate their identity. This technology can be used in various applications such as pattern recognition, biometric systems, surveillance systems, and others. Most applications require precision in eye detection, so a fast and reliable eye detection method is needed. In this research, an eye detection method is proposed using the Python OpenCV and MediaPipe libraries, which offer better accuracy compared to existing solutions. Both libraries are implemented in the Python programming language, which is popular among software developers for its ability in object-oriented programming, easy data manipulation and processing, and availability of libraries and modules in various fields such as artificial intelligence. The system was tested using videos captured using a smartphone. Although the videos were captured under suboptimal conditions, such as imperfect lighting, testing was conducted on 56 videos that had relatively good quality and lasted about 5-10 seconds. The results obtained showed an accuracy rate of 100%. Additionally, the system can distinguish between open and closed eye conditions, which will facilitate further research in detecting eye blinks. In conclusion, the model created can detect eyes with a very high accuracy rate.","PeriodicalId":259537,"journal":{"name":"JOINTECS (Journal of Information Technology and Computer Science)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130428312","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}
Pub Date : 2023-07-01DOI: 10.31328/jointecs.v8i2.4405
Ghufron Zaida Muflih, Iis Nurlaeli, Ageng Restu Triyanto
Learning Management System is a website-based online learning media commonly used in universities. The quality of a system can be measured by the level of usability. System's usability is crucial for the level of acceptance and satisfaction of the users. Therefore it is necessary to evaluate and test whether the system used is by its usefulness
{"title":"Pengukuran Usability Pada Learning Management System UMNU Kebumen Menggunakan System Usability Scale","authors":"Ghufron Zaida Muflih, Iis Nurlaeli, Ageng Restu Triyanto","doi":"10.31328/jointecs.v8i2.4405","DOIUrl":"https://doi.org/10.31328/jointecs.v8i2.4405","url":null,"abstract":"Learning Management System is a website-based online learning media commonly used in universities. The quality of a system can be measured by the level of usability. System's usability is crucial for the level of acceptance and satisfaction of the users. Therefore it is necessary to evaluate and test whether the system used is by its usefulness","PeriodicalId":259537,"journal":{"name":"JOINTECS (Journal of Information Technology and Computer Science)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133095193","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}
Pub Date : 2023-06-13DOI: 10.31328/jointecs.v8i1.4456
Nabilah Sharfina, Nur Ghaniaviyanto Ramadhan
According to WHO
根据世卫组织
{"title":"Analisis SMOTE Pada Klasifikasi Hepatitis C Berbasis Random Forest dan Naïve Bayes","authors":"Nabilah Sharfina, Nur Ghaniaviyanto Ramadhan","doi":"10.31328/jointecs.v8i1.4456","DOIUrl":"https://doi.org/10.31328/jointecs.v8i1.4456","url":null,"abstract":"According to WHO","PeriodicalId":259537,"journal":{"name":"JOINTECS (Journal of Information Technology and Computer Science)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128945857","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}
Pub Date : 2023-06-13DOI: 10.31328/jointecs.v8i1.4633
A. Setiawan, Istiadi Istiadi, Gigih Priyandoko
Internet of things (IoT) sangat bermanfaat memberikan peran membantu aktivitas rumah tangga dalam kehidupan sehari-hari. Dengan kecanggihan yang disajikan oleh Internet of Things (IoT), memungkinkan IoT untuk melakukan pengontrolan dan pemantauan penggunaan listrik pada suatu lokasi dari jarak jauh tanpa menggunakan kabel yang dikontrol melalui smart phone yang kita miliki. Korsleting listrik banyak ditemukan di kota-kota besar yang dimana penggunaan listrik berlebih tanpa ada pengontrolan sehingga menimbulkan panas pada suatu perlengkapan elektronik yang mengakibatkan percikan api dan kebakaran rumah. Tujuan dalam penelitian ini yaitu mengembangkan teknologi smart home dalam mengendalikan dengan memanfaatkan smartphone android dan teknologi wifi. Hal ini juga membantu pengguna untuk mengendalikan perangkat smart home hanya dengan smartphone dan memanfaatkan teknologi wifi. Hasil dalam penelitian ini yaitu 223 volt ampere meter dengan arus 1 sebesar 0,03 dan arus 2 sebesar 3,29. Rata-rata waktu dalam penyusutan tegangan sebesar 1,66 detik. Dengan Smart Electric Terminal berbasis Internet of Things kita dapat melakukan pemantauan dan pengendalian penggunaan listrik di rumah kita. Microcontroller NodeMCU dan Arduino Nano dilengkapi dengan dua Sensor Arus ACS712 dan Sensor SCT013 dengan tambahan satu Sensor Tegangan ZMPT1018 memudahkan pengguna untuk mengatur dan memantau pergerakan aktivitas listrik di rumah. Tidak hanya itu Smart Electric Terminal dilengkapi dengan Modul Relay yang dimana dapat memutus arus listrik yang berlebih.
{"title":"Pengendali Dan Pemantau Arus Tegangan Pada Terminal Listrik Rumah Tangga Berbasis IoT","authors":"A. Setiawan, Istiadi Istiadi, Gigih Priyandoko","doi":"10.31328/jointecs.v8i1.4633","DOIUrl":"https://doi.org/10.31328/jointecs.v8i1.4633","url":null,"abstract":"Internet of things (IoT) sangat bermanfaat memberikan peran membantu aktivitas rumah tangga dalam kehidupan sehari-hari. Dengan kecanggihan yang disajikan oleh Internet of Things (IoT), memungkinkan IoT untuk melakukan pengontrolan dan pemantauan penggunaan listrik pada suatu lokasi dari jarak jauh tanpa menggunakan kabel yang dikontrol melalui smart phone yang kita miliki. Korsleting listrik banyak ditemukan di kota-kota besar yang dimana penggunaan listrik berlebih tanpa ada pengontrolan sehingga menimbulkan panas pada suatu perlengkapan elektronik yang mengakibatkan percikan api dan kebakaran rumah. Tujuan dalam penelitian ini yaitu mengembangkan teknologi smart home dalam mengendalikan dengan memanfaatkan smartphone android dan teknologi wifi. Hal ini juga membantu pengguna untuk mengendalikan perangkat smart home hanya dengan smartphone dan memanfaatkan teknologi wifi. Hasil dalam penelitian ini yaitu 223 volt ampere meter dengan arus 1 sebesar 0,03 dan arus 2 sebesar 3,29. Rata-rata waktu dalam penyusutan tegangan sebesar 1,66 detik. Dengan Smart Electric Terminal berbasis Internet of Things kita dapat melakukan pemantauan dan pengendalian penggunaan listrik di rumah kita. Microcontroller NodeMCU dan Arduino Nano dilengkapi dengan dua Sensor Arus ACS712 dan Sensor SCT013 dengan tambahan satu Sensor Tegangan ZMPT1018 memudahkan pengguna untuk mengatur dan memantau pergerakan aktivitas listrik di rumah. Tidak hanya itu Smart Electric Terminal dilengkapi dengan Modul Relay yang dimana dapat memutus arus listrik yang berlebih.","PeriodicalId":259537,"journal":{"name":"JOINTECS (Journal of Information Technology and Computer Science)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123489137","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}
Stakeholders widely use sentiment analysis in assessing sentiment towards an object. In this research, the object to be taken is sentiment analysis of political figures for the 2024 presidential candidate which is being widely discussed by netizens, especially on Twitter. The issues raised are regarding the performance measurement of an algorithm in classifying sentiments, some algorithms often need a higher level of accuracy. This study aims to improve performance measures from previous studies using the Naïve Bayes algorithm which has a fairly low level of accuracy, and in this study the SVM algorithm was used. This study takes Twitter data related to presidential candidates to see public opinion for each presidential candidate. The data taken was Twitter data with the keywords Ganjar, Anies, Prabowo totaling 8,959 data taken on October 17-25 2022. The results of the test concluded that the SVM algorithm has a performance measure or quite high accuracy compared to the Naïve Bayes algorithm in previous studies only of 73.86% while the SVM algorithm gets an average accuracy value of 98.61%, namely the Ganjar Pranowo dataset, then 98.81% precision, 99.79% recall. And for the proportion of sentiment, the positive sentiment obtained by Ganjar was higher than the other presidential candidates, namely 55%, Prabowo 30% and Anies 15%, while Anies' negative sentiment was 89% higher than Ganjar 8% and Prabowo 3%.
{"title":"Analisis Sentimen Calon Presiden 2024 Menggunakan Algoritma SVM Pada Media Sosial Twitter","authors":"Aprilia Putri Nardilasari, A. Hananto, Shofa Shofiah Hilabi, Tukino Tukino, Bayu Priyatna","doi":"10.31328/jointecs.v8i1.4265","DOIUrl":"https://doi.org/10.31328/jointecs.v8i1.4265","url":null,"abstract":"Stakeholders widely use sentiment analysis in assessing sentiment towards an object. In this research, the object to be taken is sentiment analysis of political figures for the 2024 presidential candidate which is being widely discussed by netizens, especially on Twitter. The issues raised are regarding the performance measurement of an algorithm in classifying sentiments, some algorithms often need a higher level of accuracy. This study aims to improve performance measures from previous studies using the Naïve Bayes algorithm which has a fairly low level of accuracy, and in this study the SVM algorithm was used. This study takes Twitter data related to presidential candidates to see public opinion for each presidential candidate. The data taken was Twitter data with the keywords Ganjar, Anies, Prabowo totaling 8,959 data taken on October 17-25 2022. The results of the test concluded that the SVM algorithm has a performance measure or quite high accuracy compared to the Naïve Bayes algorithm in previous studies only of 73.86% while the SVM algorithm gets an average accuracy value of 98.61%, namely the Ganjar Pranowo dataset, then 98.81% precision, 99.79% recall. And for the proportion of sentiment, the positive sentiment obtained by Ganjar was higher than the other presidential candidates, namely 55%, Prabowo 30% and Anies 15%, while Anies' negative sentiment was 89% higher than Ganjar 8% and Prabowo 3%.","PeriodicalId":259537,"journal":{"name":"JOINTECS (Journal of Information Technology and Computer Science)","volume":"327 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115657597","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}
Pub Date : 2023-03-19DOI: 10.31328/jointecs.v8i1.4231
Izin Ekspor, Impor Hasil, Pertanian Berbasis, Web Menggunakan, Algoritma ID3, Asmah Akhriana, Nurcholis Salman, Andi Irmayana, Abdul Rauf, Arini Fitramayanti, Gusti Made, Apriantama Nugraha, Balai Besar, Karantina Pertanian Makassar
Makassar Agricultural Quarantine Center is one of the Technical Implementation Units (UPT) of the Agricultural Quarantine Agency. So far, in the process of granting export/import permits, each file must go through a process of checking and evaluating where the process of checking the file takes quite a long time because it is done manually and there are many files to be processed because they cover all agricultural companies in South Sulawesi. This study aims to create an application for granting export/import permits for agricultural products by implementing the Iterative Dichotomizer Three (ID3) algorithm. Research methods for system development using UML including use case diagrams and class diagrams with functionality testing using the blackbox method and for feasibility testing and user satisfaction using the SUS (System Usability Scale) method. The results of this study are a web-based application for granting export/import permits for agricultural products that can facilitate checking and evaluating files in the process of granting export-import permits, can be used as a medium for companies in managing export-import permits for agricultural products so that they become more effective and efficient. Meanwhile, from the results of the calculation of the SUS value, a value of 79.75 is obtained where this value is included in the acceptable category with the adjective Ratings excellent for grade scale B, which means that this application is accepted and suitable for use by users with a good application rating.
{"title":"Izin Ekspor Impor Hasil Pertanian Berbasis Web Menggunakan Algoritma ID3","authors":"Izin Ekspor, Impor Hasil, Pertanian Berbasis, Web Menggunakan, Algoritma ID3, Asmah Akhriana, Nurcholis Salman, Andi Irmayana, Abdul Rauf, Arini Fitramayanti, Gusti Made, Apriantama Nugraha, Balai Besar, Karantina Pertanian Makassar","doi":"10.31328/jointecs.v8i1.4231","DOIUrl":"https://doi.org/10.31328/jointecs.v8i1.4231","url":null,"abstract":"Makassar Agricultural Quarantine Center is one of the Technical Implementation Units (UPT) of the Agricultural Quarantine Agency. So far, in the process of granting export/import permits, each file must go through a process of checking and evaluating where the process of checking the file takes quite a long time because it is done manually and there are many files to be processed because they cover all agricultural companies in South Sulawesi. This study aims to create an application for granting export/import permits for agricultural products by implementing the Iterative Dichotomizer Three (ID3) algorithm. Research methods for system development using UML including use case diagrams and class diagrams with functionality testing using the blackbox method and for feasibility testing and user satisfaction using the SUS (System Usability Scale) method. The results of this study are a web-based application for granting export/import permits for agricultural products that can facilitate checking and evaluating files in the process of granting export-import permits, can be used as a medium for companies in managing export-import permits for agricultural products so that they become more effective and efficient. Meanwhile, from the results of the calculation of the SUS value, a value of 79.75 is obtained where this value is included in the acceptable category with the adjective Ratings excellent for grade scale B, which means that this application is accepted and suitable for use by users with a good application rating.","PeriodicalId":259537,"journal":{"name":"JOINTECS (Journal of Information Technology and Computer Science)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115763426","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}
Pub Date : 2023-03-19DOI: 10.31328/jointecs.v8i1.4496
A. G. Sooai, P. A. Nani, N. M. R. Mamulak, Corazon Olivia Sianturi, Shine Crossifixio Sianturi, Alicia Herlin Mondolang
The use of artificial intelligence for the image recognition process has been carried out by many researchers. One of its fields is to recognize diseases of grape leaves. Modeling has been carried out using augmentation before support vector machine classification with kernel cubic, with 97.6% accuracy. Improved performance of image prediction accuracy through modeling can still be improved through various means. Some techniques that can be used include using feature selection, initial processing to find and discard outliers, or selecting classifier algorithms that are specifically able to handle datasets with certain characteristics. Another technique is to pass images in the feature extraction process to obtain models with relatively higher accuracy than previous studies. This study aims to improve the acquisition of accuracy figures using the help of the feature extraction process, as well as comparing the performance of several classifiers, namely k-Nearest Neighbor, Random Forest, Naïve Bayes, Neural Network, and Support Vector Machine. The method used starts from the feature extraction process utilizing the SqueezNet algorithm to obtain a dataset with a specific composition. Furthermore, the division of training and test data was carried out with a ratio of 60:40. Data training uses a variety of validated classifiers using 2-fold cross-validation. The data used is a secondary dataset of grape leaves, consisting of 7222 leaf images, divided into four validated classes from related studies. The results obtained outperformed the previous study, namely 98.1% on the Support Vector Machine classifier using linear kernels.
{"title":"Klasifikasi Citra Daun Anggur Menggunakan SVM Kernel Linear","authors":"A. G. Sooai, P. A. Nani, N. M. R. Mamulak, Corazon Olivia Sianturi, Shine Crossifixio Sianturi, Alicia Herlin Mondolang","doi":"10.31328/jointecs.v8i1.4496","DOIUrl":"https://doi.org/10.31328/jointecs.v8i1.4496","url":null,"abstract":"The use of artificial intelligence for the image recognition process has been carried out by many researchers. One of its fields is to recognize diseases of grape leaves. Modeling has been carried out using augmentation before support vector machine classification with kernel cubic, with 97.6% accuracy. Improved performance of image prediction accuracy through modeling can still be improved through various means. Some techniques that can be used include using feature selection, initial processing to find and discard outliers, or selecting classifier algorithms that are specifically able to handle datasets with certain characteristics. Another technique is to pass images in the feature extraction process to obtain models with relatively higher accuracy than previous studies. This study aims to improve the acquisition of accuracy figures using the help of the feature extraction process, as well as comparing the performance of several classifiers, namely k-Nearest Neighbor, Random Forest, Naïve Bayes, Neural Network, and Support Vector Machine. The method used starts from the feature extraction process utilizing the SqueezNet algorithm to obtain a dataset with a specific composition. Furthermore, the division of training and test data was carried out with a ratio of 60:40. Data training uses a variety of validated classifiers using 2-fold cross-validation. The data used is a secondary dataset of grape leaves, consisting of 7222 leaf images, divided into four validated classes from related studies. The results obtained outperformed the previous study, namely 98.1% on the Support Vector Machine classifier using linear kernels.","PeriodicalId":259537,"journal":{"name":"JOINTECS (Journal of Information Technology and Computer Science)","volume":"86 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125923326","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：