Setiap manusia memiliki karakter yang berbeda antara satu dengan yang lainnya, salah satunya adalah karakteristik alami yang dimiliki oleh manusia yaitu wajah. Wajah manusia tentu saja memiliki ciri unik yang membedakan satu dengan lainnya, sehingga dapat dikenali oleh manusia lain maupun oleh suatu sistem yang memiliki kemampuan tersebut. Pengenalan wajah berkaitan erat dengan biometrik manusia, hal tersebut dikarenakan terdapat informasi unik yang terkandung di dalamnya. Teknologi pengenalan wajah dapat dimanfaatkan salah satunya pada sistem presensi kehadiran. Banyak metode yang digunakan pada proses pengenalan wajah, salah satunya dengan menggunakan algoritma eigenface. Eigenface berfungsi untuk menghitung eigenvalue dan eigenvector yang akan digunakan sebagai fitur dalam melakukan pengenalan wajah. Citra akan direpresentasikan dalam sebuah gabungan vektor yang dijadikan satu matriks tunggal. Dari matriks tunggal ini akan di ekstrasi suatu ciri utama yang membedakan antara citra wajah satu dengan citra wajah yang lainnya. Untuk dapat mengenali dan mengidentifikasi wajah seseorang maka pada penelitian ini diperlukan sebuah tools tambahan berupa web camera atau sering kita kenal dengan istilah WebCam dan aplikasi yang akan digunakan adalah Visual Studio 2012. Teknologi pengenalan wajah ini dapat dimanfaatkan oleh IT Telkom Purwokerto sebagai sistem presensi kehadiran mahasiswa. Salah satu hasil evaluasi perlunya pemanfaatan teknologi face recognition sebagai sistem presensi kehadiran mahasiswa dikarenakan belum optimalnya pemanfaatan sistem absensi berbasis RFID yang sebelumnya telah digunakan, berbagai permasalahan teknis yang dihadapi oleh sistem absensi tersebut mengakibatkan proses absensi kembali dilakukan secara manual menggunakan kertas absensi yang diberikan oleh Dosen. Kata kunci: Citra, Eigenface, Face recognition, Image Processing, C#, Sistem Absensi
每个人都有不同的性格特征，其中之一是人类的自然特征——面部。当然，人脸具有区分他人的独特特征，因此可以被他人或有这种能力的系统所识别。面部识别与人类的生物识别密切相关，这是因为它包含独特的信息。面部识别技术可以应用其中任何一种到出勤系统。许多用于面部识别的方法，其中一种使用了eigenface算法。Eigenface的功能是计算eigenth和eigenvector，这些将被用作面部识别的特征。图像将被代表在一个单一矩阵的向量中。从这个矩阵中提取一个主要的特征，将区分一个人的面部图像和另一个人的面部图像。为了识别和识别一个人的脸，在这项研究中，需要一个额外的网络摄像头工具，或者我们经常知道摄像头术语和应用程序将使用的是2012年的视觉工作室。这种面部识别技术可以被IT Purwokerto作为一种学生出勤系统利用。对面部识别技术利用需求的评估之一，其结果是，由于该系统还没有达到以前使用过的RFID缺席系统的优化利用，旷课系统面临的技术问题导致教师缺席论文手动完成。关键词:图像，Eigenface, Face recognition，图像处理，C#，缺席系统
{"title":"Penerapan Face Recognition Berbasis GUI Visual Studio 2012 Menggunakan Algoritma Eigenface dan Metode Pengembangan Waterfall Pada Sistem Absensi Mahasiswa IT Telkom Purwokerto","authors":"I. Fauzi, A. Junaidi, W. Saputra","doi":"10.20895/dinda.v2i1.264","DOIUrl":"https://doi.org/10.20895/dinda.v2i1.264","url":null,"abstract":"Setiap manusia memiliki karakter yang berbeda antara satu dengan yang lainnya, salah satunya adalah karakteristik alami yang dimiliki oleh manusia yaitu wajah. Wajah manusia tentu saja memiliki ciri unik yang membedakan satu dengan lainnya, sehingga dapat dikenali oleh manusia lain maupun oleh suatu sistem yang memiliki kemampuan tersebut. Pengenalan wajah berkaitan erat dengan biometrik manusia, hal tersebut dikarenakan terdapat informasi unik yang terkandung di dalamnya. Teknologi pengenalan wajah dapat dimanfaatkan salah satunya pada sistem presensi kehadiran. \u0000Banyak metode yang digunakan pada proses pengenalan wajah, salah satunya dengan menggunakan algoritma eigenface. Eigenface berfungsi untuk menghitung eigenvalue dan eigenvector yang akan digunakan sebagai fitur dalam melakukan pengenalan wajah. Citra akan direpresentasikan dalam sebuah gabungan vektor yang dijadikan satu matriks tunggal. Dari matriks tunggal ini akan di ekstrasi suatu ciri utama yang membedakan antara citra wajah satu dengan citra wajah yang lainnya. Untuk dapat mengenali dan mengidentifikasi wajah seseorang maka pada penelitian ini diperlukan sebuah tools tambahan berupa web camera atau sering kita kenal dengan istilah WebCam dan aplikasi yang akan digunakan adalah Visual Studio 2012. \u0000Teknologi pengenalan wajah ini dapat dimanfaatkan oleh IT Telkom Purwokerto sebagai sistem presensi kehadiran mahasiswa. Salah satu hasil evaluasi perlunya pemanfaatan teknologi face recognition sebagai sistem presensi kehadiran mahasiswa dikarenakan belum optimalnya pemanfaatan sistem absensi berbasis RFID yang sebelumnya telah digunakan, berbagai permasalahan teknis yang dihadapi oleh sistem absensi tersebut mengakibatkan proses absensi kembali dilakukan secara manual menggunakan kertas absensi yang diberikan oleh Dosen. \u0000 \u0000Kata kunci: Citra, Eigenface, Face recognition, Image Processing, C#, Sistem Absensi","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127164288","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}
Skin cancer is one of the most commonly diagnosed cancers worldwide, especially in the white population. One of the most dangerous skin diseases is melanoma cancer. Melanoma is a skin cancer that can develop in melanocytes, the skin pigment cells that produce melanin. Melanin is what absorbs ultraviolet rays and protects the skin from damage. Melanoma is a type of skin cancer that is rare and very dangerous, many laypeople have not been able to distinguish between ordinary moles and melanoma. Therefore, a study on the classification of melanoma skin cancer was carried out using the CNN method, where CNN was able to classify melanoma images. In CNN itself there is an architectural model, while the architecture used in this research is using conv2d layer, max pooling, flatten, dense, dropout, and using ReLu activation. The image size used in this architecture is 128x128, at the 50th epoch, an accuracy rate of 92.64% is obtained. It is hoped that this research can help the community in distinguishing normal moles and melanoma cancer.
{"title":"Klasifikasi Penyakit Kanker Kulit Menggunakan Metode Convolutional Neural Network (Studi Kasus: Melanoma)","authors":"Reynaldi Rio Saputro, A. Junaidi, W. Saputra","doi":"10.20895/dinda.v2i1.349","DOIUrl":"https://doi.org/10.20895/dinda.v2i1.349","url":null,"abstract":"Skin cancer is one of the most commonly diagnosed cancers worldwide, especially in the white population. One of the most dangerous skin diseases is melanoma cancer. Melanoma is a skin cancer that can develop in melanocytes, the skin pigment cells that produce melanin. Melanin is what absorbs ultraviolet rays and protects the skin from damage. Melanoma is a type of skin cancer that is rare and very dangerous, many laypeople have not been able to distinguish between ordinary moles and melanoma. Therefore, a study on the classification of melanoma skin cancer was carried out using the CNN method, where CNN was able to classify melanoma images. In CNN itself there is an architectural model, while the architecture used in this research is using conv2d layer, max pooling, flatten, dense, dropout, and using ReLu activation. The image size used in this architecture is 128x128, at the 50th epoch, an accuracy rate of 92.64% is obtained. It is hoped that this research can help the community in distinguishing normal moles and melanoma cancer.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116324793","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}
Rice (Oryza sativa) is a grain that comes in third place among all grains after corn and wheat. 80 percent of Indonesians eat rice as a staple diet, especially in Southeast Asian countries, but the International Rice Research Institute (IRRI) reports that farmers lose 37 percent of their rice crops each year owing to pests and illnesses. Based on this study, it is critical to investigate the detection of rice pests and illnesses. Using the Convolution Neural Network (CNN) technique, an automatic classification system to identify and predict plant illnesses has been developed. A study titled Classification of Rice Leaf Diseases was undertaken by the author. The CNN Algorithm is being used to help farmers learn how to combat rice leaf diseases. Bacterial leaf blight, Rice blast, and Rice tungro virus were among the rice leaf types classified in this study. There are 6000 datasets in all, with 80% of them being training data, 10% being validation data, and 10% being testing data. The accuracy of the results obtained for epochs 25, 50, 75, and 100 varies. The best training accuracy results come from epoch 100, which has a 98% accuracy rate, and testing using a confusion matrix has a 98% accuracy rate. In diagnosing rice leaf diseases, the Convolutional Neural Network (CNN) algorithm delivers great accuracy.
{"title":"Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network","authors":"M. Khoiruddin, A. Junaidi, W. Saputra","doi":"10.20895/dinda.v2i1.341","DOIUrl":"https://doi.org/10.20895/dinda.v2i1.341","url":null,"abstract":"Rice (Oryza sativa) is a grain that comes in third place among all grains after corn and wheat. 80 percent of Indonesians eat rice as a staple diet, especially in Southeast Asian countries, but the International Rice Research Institute (IRRI) reports that farmers lose 37 percent of their rice crops each year owing to pests and illnesses. Based on this study, it is critical to investigate the detection of rice pests and illnesses. Using the Convolution Neural Network (CNN) technique, an automatic classification system to identify and predict plant illnesses has been developed. A study titled Classification of Rice Leaf Diseases was undertaken by the author. The CNN Algorithm is being used to help farmers learn how to combat rice leaf diseases. Bacterial leaf blight, Rice blast, and Rice tungro virus were among the rice leaf types classified in this study. There are 6000 datasets in all, with 80% of them being training data, 10% being validation data, and 10% being testing data. The accuracy of the results obtained for epochs 25, 50, 75, and 100 varies. The best training accuracy results come from epoch 100, which has a 98% accuracy rate, and testing using a confusion matrix has a 98% accuracy rate. In diagnosing rice leaf diseases, the Convolutional Neural Network (CNN) algorithm delivers great accuracy.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116705130","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}
Sarkasme adalah penggunaan kata-kata pedas untuk menyakiti hati orang lain, berupa cemoohan atau ejekan kasar. Kata sarkasme diturunkan dari kata Yunani sarkasmos yang berarti “merobek-robek daging seperti anjing”, “menggigit bibir karena marah”, atau ”berbicara dengan kepahitan”. Sarkasme dapat bersifat ironis, atau tidak, tetapi yang pasti adalah bahwa gaya bahasa ini selalu akan menyakiti hati dan kurang enak didengar. Pada penelitian ini akan dibuat model klasifikasi untuk memprediksi sarkasme pada judul berita berbahasa inggris dikarenakan judul berita menggunakan kata baku dan tidak ada salah pengejaan kata, menjadikan judul berita sebuah dataset yang tepat untuk dilakukan deteksi sarkasme. Algoritme Bidirectional Long Short-Term Memory (BiLSTM) yang merupakan salah satu algoritme deep learning digunakan pada penelitian untuk membuat model klasifikasi. Model ini lalu dibandingkan dengan model algoritme Long Short-Term Memory (LSTM) untuk memvalidasi keunggulan dari algoritme BiLSTM daripada algoritme LSTM dasar. Didapatkan akurasi validasi dari model sebesar 82,55%, precision validasi sebesar 82,36%, recall validasi sebesar 79,53%, dan f1 score validasi sebesar 80,92%.
{"title":"Deteksi Sarkasme Pada Judul Berita Berbahasa Inggris Menggunakan Algoritme Bidirectional LSTM","authors":"Muhammad David Hilmawan","doi":"10.20895/dinda.v2i1.331","DOIUrl":"https://doi.org/10.20895/dinda.v2i1.331","url":null,"abstract":"Sarkasme adalah penggunaan kata-kata pedas untuk menyakiti hati orang lain, berupa cemoohan atau ejekan kasar. Kata sarkasme diturunkan dari kata Yunani sarkasmos yang berarti “merobek-robek daging seperti anjing”, “menggigit bibir karena marah”, atau ”berbicara dengan kepahitan”. Sarkasme dapat bersifat ironis, atau tidak, tetapi yang pasti adalah bahwa gaya bahasa ini selalu akan menyakiti hati dan kurang enak didengar. Pada penelitian ini akan dibuat model klasifikasi untuk memprediksi sarkasme pada judul berita berbahasa inggris dikarenakan judul berita menggunakan kata baku dan tidak ada salah pengejaan kata, menjadikan judul berita sebuah dataset yang tepat untuk dilakukan deteksi sarkasme. Algoritme Bidirectional Long Short-Term Memory (BiLSTM) yang merupakan salah satu algoritme deep learning digunakan pada penelitian untuk membuat model klasifikasi. Model ini lalu dibandingkan dengan model algoritme Long Short-Term Memory (LSTM) untuk memvalidasi keunggulan dari algoritme BiLSTM daripada algoritme LSTM dasar. Didapatkan akurasi validasi dari model sebesar 82,55%, precision validasi sebesar 82,36%, recall validasi sebesar 79,53%, dan f1 score validasi sebesar 80,92%.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127061059","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}
Annisa Nugraheni, R. Ramadhani, Amalia Beladinna Arifa, Agi Prasetiadi
Breast cancer is the second most common cause of death from cancer after lung cancer is in the first place. Breast cancer occurs when cells in breast tissue begin to grow uncontrollably and can disrupt existing healthy tissue. Therefore, there is a need for a classification to distinguish breast cancer patients and healthy people. Based on previous research, the Naïve Bayes and K-Nearest Neighbor algorithms are considered capable of classifying breast cancer. In the research process using the breast cancer dataset from the Breast Cancer Coimbra dataset in 2018 UCI Machine Learning Repository with a total of 116 data, while for the calculation of the feasibility of the method using the Confusion Matrix (Accuracy, Precision, and Recall) and the ROC-AUC curve. The purpose of this study is to compare the performance of the Naïve Bayes and K-Nearest Neighbor algorithms. In testing using the Naïve Bayes algorithm and the K-Nearest Neighbor algorithm, there are several test scenarios, namely, data testing before and after normalization, model testing based on a comparison of training data and testing data, model testing based on K values in K-Nearest Neighbors, and model testing. based on the selection of the strongest attribute with the Pearson correlation test. The results of this study indicate that the Naïve Bayes algorithm has the highest average accuracy of 69.12%, healthy precision 64.90%, pain precision 83%, healthy recall 88%, sick recall 61.11% and AUC 0.82 which is included in the good classification category. Meanwhile, the highest average results of the K-Nearest Neighbor algorithm are 76.83% for accuracy, 76% healthy precision, 80.21% pain precision, 74.18% for healthy recall, 80.81% sick recall and 0.91 AUC which is included in the excellent classification category.
乳腺癌是仅次于肺癌的第二大常见癌症死亡原因。当乳腺组织中的细胞开始不受控制地生长并破坏现有的健康组织时，就会发生乳腺癌。因此，有必要对乳腺癌患者和健康人进行分类。根据之前的研究，Naïve贝叶斯和k近邻算法被认为能够对乳腺癌进行分类。在研究过程中使用了来自2018年UCI机器学习存储库中乳腺癌科英布拉数据集的乳腺癌数据集共116个数据，同时使用混淆矩阵(Accuracy, Precision, and Recall)和ROC-AUC曲线来计算该方法的可行性。本研究的目的是比较Naïve贝叶斯和k -最近邻算法的性能。在使用Naïve贝叶斯算法和K近邻算法进行测试时，有几种测试场景，分别是归一化前后的数据测试、基于训练数据和测试数据对比的模型测试、基于K近邻中K值的模型测试、模型测试。通过Pearson相关检验选择最强属性。研究结果表明，Naïve贝叶斯算法的平均准确率最高，为69.12%，健康准确率为64.90%，疼痛准确率为83%，健康召回率为88%，疾病召回率为61.11%，AUC为0.82，属于良好分类类别。同时，k -最邻近算法的最高平均结果为准确率76.83%，健康准确率76%，疼痛准确率80.21%，健康召回率74.18%，疾病召回率80.81%，AUC 0.91，属于优秀分类类别。
{"title":"Perbandingan Performa Antara Algoritma Naive Bayes Dan K-Nearest Neighbour Pada Klasifikasi Kanker Payudara","authors":"Annisa Nugraheni, R. Ramadhani, Amalia Beladinna Arifa, Agi Prasetiadi","doi":"10.20895/dinda.v2i1.391","DOIUrl":"https://doi.org/10.20895/dinda.v2i1.391","url":null,"abstract":"Breast cancer is the second most common cause of death from cancer after lung cancer is in the first place. Breast cancer occurs when cells in breast tissue begin to grow uncontrollably and can disrupt existing healthy tissue. Therefore, there is a need for a classification to distinguish breast cancer patients and healthy people. Based on previous research, the Naïve Bayes and K-Nearest Neighbor algorithms are considered capable of classifying breast cancer. In the research process using the breast cancer dataset from the Breast Cancer Coimbra dataset in 2018 UCI Machine Learning Repository with a total of 116 data, while for the calculation of the feasibility of the method using the Confusion Matrix (Accuracy, Precision, and Recall) and the ROC-AUC curve. The purpose of this study is to compare the performance of the Naïve Bayes and K-Nearest Neighbor algorithms. In testing using the Naïve Bayes algorithm and the K-Nearest Neighbor algorithm, there are several test scenarios, namely, data testing before and after normalization, model testing based on a comparison of training data and testing data, model testing based on K values in K-Nearest Neighbors, and model testing. based on the selection of the strongest attribute with the Pearson correlation test. The results of this study indicate that the Naïve Bayes algorithm has the highest average accuracy of 69.12%, healthy precision 64.90%, pain precision 83%, healthy recall 88%, sick recall 61.11% and AUC 0.82 which is included in the good classification category. Meanwhile, the highest average results of the K-Nearest Neighbor algorithm are 76.83% for accuracy, 76% healthy precision, 80.21% pain precision, 74.18% for healthy recall, 80.81% sick recall and 0.91 AUC which is included in the excellent classification category.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133063160","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}
The Elderly is someone who has reached the age of 60 years, the main health problem in the elderly is nutritional problems. Nutritional status is a measurement that can assess food intake and the use of nutrients in the body. One of the assessments of nutritional status in the elderly uses anthropometry with the type of measurement of Body Mass Index (BMI). Determination of nutrition is an effort to increase Life Expectancy (UHH). Therefore, a study will be conducted on the classification of nutritional status in the elderly using the Learning Vector Quantization 3 (LVQ 3) method with seven inputs used, namely: gender, age, Bb, Tb, BMI, social status and disease history, and five results of status classification nutritional status, namely inferior nutritional status, poor nutritional status, normal nutritional status, obese nutritional status, and very obese nutritional status. The best parameters used in this study are: learning rate (α) = 0.2, learning rate reduction = 0.4, window (ɛ) = 0.4 and minimum learning rate = 0.001, epoch = 1, 5, 10, 50, 100, 200, 500, 1000 with a comparison of the distribution of training and testing data of 80:20 on a total of 599 data. Based on the test results, the number of epoch values affects the accuracy results. The highest accuracy obtained is 86.67%. The calculations using the confusion matrix in this algorithm are 87% accuracy, 83% precision, and 81% recall. The Learning Vector Quantization 3 (LVQ 3) method can use to classify nutritional status in the elderly.
{"title":"Klasifikasi Status Gizi Pada Lansia Menggunakan Learning Vector Quantization 3 (LVQ 3)","authors":"Khurun Ain Muzaqi, A. Junaidi, W. Saputra","doi":"10.20895/dinda.v2i1.272","DOIUrl":"https://doi.org/10.20895/dinda.v2i1.272","url":null,"abstract":"The Elderly is someone who has reached the age of 60 years, the main health problem in the elderly is nutritional problems. Nutritional status is a measurement that can assess food intake and the use of nutrients in the body. One of the assessments of nutritional status in the elderly uses anthropometry with the type of measurement of Body Mass Index (BMI). Determination of nutrition is an effort to increase Life Expectancy (UHH). Therefore, a study will be conducted on the classification of nutritional status in the elderly using the Learning Vector Quantization 3 (LVQ 3) method with seven inputs used, namely: gender, age, Bb, Tb, BMI, social status and disease history, and five results of status classification nutritional status, namely inferior nutritional status, poor nutritional status, normal nutritional status, obese nutritional status, and very obese nutritional status. The best parameters used in this study are: learning rate (α) = 0.2, learning rate reduction = 0.4, window (ɛ) = 0.4 and minimum learning rate = 0.001, epoch = 1, 5, 10, 50, 100, 200, 500, 1000 with a comparison of the distribution of training and testing data of 80:20 on a total of 599 data. Based on the test results, the number of epoch values affects the accuracy results. The highest accuracy obtained is 86.67%. The calculations using the confusion matrix in this algorithm are 87% accuracy, 83% precision, and 81% recall. The Learning Vector Quantization 3 (LVQ 3) method can use to classify nutritional status in the elderly.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114242034","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}
Logika fuzzy salah satu komponen pembentuk soft computing yang digunakan sebagai cara untuk memetakan masalah dari input ke output yang diharapkan. logika fuzzy memiliki beberapa kelebihan seperti mudah dimengerti karena memiliki konsep matematis yang sederhana, fleksibel untuk digunakan, terdapat toleransi pada data-data yang tidak tepat, mampu memodelkan fungsi-fungsi non-linear yang sangat kompleks, dapat menerapkan pengalaman pakar secara langsung tanpa proses pelatihan, dapat bekerja sama dengan teknik-teknik kendali secara konvensional, dan didasarkan pada bahasa alami. Logika fuzzy memiliki banyak peran di industri seperti bidang Kesehatan, Ilmu Ekonomi, Psikolog, dan Teknologi yang dapat membantu manusia dalam memecahkan suatu masalah dalam kehidupan. Dalam penerapan logika fuzzy terdapat beberapa proses, salah satunya yaitu sistem inferensi. Sistem inferensi merupakan kerangka komputasi yang didasarkan pada teori himpunan fuzzy, aturan fuzzy berbentuk IF-THEN, dan penalaran fuzzy. Manfaat dari inferensi fuzzy yaitu sebagai alat untuk mewakili pengetahuan yang berbeda tentang suatu masalah, serta untuk memodelkan interaksi. Dengan menggunakan metode penelitian studi literatur dari beberapa sumber, ditemukan banyak produk yang dikembangkan dari logika fuzzy seperti pengambilan keputusan, penentuan atau penilaian hasil, perangkat kendali jarak jauh, alat ukur, dan sistem pakar.
{"title":"Sistem Inferensi Fuzzy: Pengertian, Penerapan, dan Manfaatnya","authors":"Ummi Athiyah, Ade Eka Putri Handayani, Muhammad Yusril Aldean, Novantri Prasetya Putra, Rafi Ramadhani","doi":"10.20895/dinda.v1i2.201","DOIUrl":"https://doi.org/10.20895/dinda.v1i2.201","url":null,"abstract":"Logika fuzzy salah satu komponen pembentuk soft computing yang digunakan sebagai cara untuk memetakan masalah dari input ke output yang diharapkan. logika fuzzy memiliki beberapa kelebihan seperti mudah dimengerti karena memiliki konsep matematis yang sederhana, fleksibel untuk digunakan, terdapat toleransi pada data-data yang tidak tepat, mampu memodelkan fungsi-fungsi non-linear yang sangat kompleks, dapat menerapkan pengalaman pakar secara langsung tanpa proses pelatihan, dapat bekerja sama dengan teknik-teknik kendali secara konvensional, dan didasarkan pada bahasa alami. Logika fuzzy memiliki banyak peran di industri seperti bidang Kesehatan, Ilmu Ekonomi, Psikolog, dan Teknologi yang dapat membantu manusia dalam memecahkan suatu masalah dalam kehidupan. Dalam penerapan logika fuzzy terdapat beberapa proses, salah satunya yaitu sistem inferensi. Sistem inferensi merupakan kerangka komputasi yang didasarkan pada teori himpunan fuzzy, aturan fuzzy berbentuk IF-THEN, dan penalaran fuzzy. Manfaat dari inferensi fuzzy yaitu sebagai alat untuk mewakili pengetahuan yang berbeda tentang suatu masalah, serta untuk memodelkan interaksi. Dengan menggunakan metode penelitian studi literatur dari beberapa sumber, ditemukan banyak produk yang dikembangkan dari logika fuzzy seperti pengambilan keputusan, penentuan atau penilaian hasil, perangkat kendali jarak jauh, alat ukur, dan sistem pakar. \u0000 ","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122216868","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}
Hikmah Quddustiani, Ummi Athiyah, Made Riza Kartika, R. Hidayat, Luthfi Rakan Nabila
According to the Regulation of the Minister of National Education (Permendiknas) Number 22 of 2006, the determination of majors is carried out at the end of the second semester of class X and the implementation of Teaching and Learning Activities (KBM) according to the program majors starts in the first semester of class XI. In fact, there are still many high school students who are confused about choosing a major in a tertiary institution and this phenomenon can be seen in high school students. There are several majors in high school programs such as Natural Sciences (IPA), Social Sciences (IPS), and Language. This research is expected to be able to determine the majors according to the abilities of the new students for the process of selecting majors and provide recommendations that help students in determining the majors in SMA using the Fuzzy Tsukamoto method. The Tsukamoto method is an extension of monotonous reasoning. In the Tsukamoto method, each consequence of the IF-THEN rules must be represented by a fuzzy set with monotonous membership functions assisted by system design using the programming language PHP, HTML, Javascript, and MySQL database, resulting in a decision making system using the fuzzy method. tsukamoto which is a useful website for determining student majors so that it can lighten the work of the school in order to help speed up and make it easier for schools to make decisions in choosing majors.
{"title":"Penentuan Jurusan Siswa Sekolah Menengah Atas menggunakan Metode Fuzzy Tsukamoto","authors":"Hikmah Quddustiani, Ummi Athiyah, Made Riza Kartika, R. Hidayat, Luthfi Rakan Nabila","doi":"10.20895/dinda.v1i2.205","DOIUrl":"https://doi.org/10.20895/dinda.v1i2.205","url":null,"abstract":"According to the Regulation of the Minister of National Education (Permendiknas) Number 22 of 2006, the determination of majors is carried out at the end of the second semester of class X and the implementation of Teaching and Learning Activities (KBM) according to the program majors starts in the first semester of class XI. In fact, there are still many high school students who are confused about choosing a major in a tertiary institution and this phenomenon can be seen in high school students. There are several majors in high school programs such as Natural Sciences (IPA), Social Sciences (IPS), and Language. This research is expected to be able to determine the majors according to the abilities of the new students for the process of selecting majors and provide recommendations that help students in determining the majors in SMA using the Fuzzy Tsukamoto method. The Tsukamoto method is an extension of monotonous reasoning. In the Tsukamoto method, each consequence of the IF-THEN rules must be represented by a fuzzy set with monotonous membership functions assisted by system design using the programming language PHP, HTML, Javascript, and MySQL database, resulting in a decision making system using the fuzzy method. tsukamoto which is a useful website for determining student majors so that it can lighten the work of the school in order to help speed up and make it easier for schools to make decisions in choosing majors.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122510485","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}
Ummi Athiyah, Arnelka Hananta, Taufik Maulidi, Vico Meylana Eka Putra, Theodore Purba, Elisabeth Angeline Wilhelmina Bakowatun
Purwokerto City is a developing city located in the southwestern part of Central Java Province. Purwokerto is known as thecity of students in Central Java. It is not wrong if many newcomers choose to continue their studies at the favoriteuniversities in this city. One of the universities in this city is the Telkom Purwokerto Institute of Technology. With so manynewcomers who want to continue their studies in this city. Of course, you need a place to live like a boarding house. Eachboarding house has different facilities and also has varying prices, making it difficult for newcomers to choose the boardinghouse. So a decision support system is needed to help students of the Telkom Purwokerto Institute of Technology to make theright decision in predicting the price of a boarding house when choosing a boarding house according to the existing criteriaand funds using the Fuzzy Tsukamoto method.
普沃克托市是一个位于中爪哇省西南部的发展中城市。普沃基托是中爪哇省著名的学生之城。如果许多新来的人选择在这个城市最喜欢的大学继续学习，这并没有错。这个城市的一所大学是Telkom purokerto理工学院。有这么多新来的人想在这个城市继续学习。当然，你需要一个像寄宿公寓一样的地方住。每个寄宿公寓都有不同的设施，也有不同的价格，这使得新来者很难选择寄宿公寓。因此需要一个决策支持系统来帮助Telkom purokerto Institute of Technology的学生在选择寄宿公寓时，根据现有的标准和资金，使用模糊冢本方法来预测寄宿公寓的价格，从而做出正确的决策。
{"title":"Sistem Pendukung Keputusan Prediksi Harga Rumah Kost untuk Mahasiswa IT Telkom Purwokerto Menggunakan Metode Fuzzy Tsukamoto Berbasis Web","authors":"Ummi Athiyah, Arnelka Hananta, Taufik Maulidi, Vico Meylana Eka Putra, Theodore Purba, Elisabeth Angeline Wilhelmina Bakowatun","doi":"10.20895/dinda.v1i2.202","DOIUrl":"https://doi.org/10.20895/dinda.v1i2.202","url":null,"abstract":"Purwokerto City is a developing city located in the southwestern part of Central Java Province. Purwokerto is known as thecity of students in Central Java. It is not wrong if many newcomers choose to continue their studies at the favoriteuniversities in this city. One of the universities in this city is the Telkom Purwokerto Institute of Technology. With so manynewcomers who want to continue their studies in this city. Of course, you need a place to live like a boarding house. Eachboarding house has different facilities and also has varying prices, making it difficult for newcomers to choose the boardinghouse. So a decision support system is needed to help students of the Telkom Purwokerto Institute of Technology to make theright decision in predicting the price of a boarding house when choosing a boarding house according to the existing criteriaand funds using the Fuzzy Tsukamoto method.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129849017","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}
At this time, it is very difficult to introduce culture to students in school, and this is also involved to children do not recognizing their own culture. Many schools have so limited funds to go to museums or cultural performances, especially school which are far from the capital city. Therefore, it is necessary to make an android-based application using Virtual Reality. This writing describes about a method of designing and making Central Javanese cultural learning-application for elementary and secondary school students by utilizing technological developments, one of the fields is education. In the field of education, Virtual Reality can be used as a learning media which is able to make it more attractive. This Virtual Reality technology can be applied in regional cultural learning systems, one of this is the introduction of Central Javanese culture. The use of Virtual Reality technology is expected to be able to display objects in the form of musical instruments, traditional clothes, traditional houses, paintings and traditional weapons in virtual 3D using images which can used to be markers. This making of cultural learning application using Unity, Blender, and SketchUp. The development of this application uses the waterfall model where this method pays close attention to the design of the analysis, design, implementation and testing. With this research, it is hoped that it can help students in Central Java to get to know their culture. This application is specified for students specifically for elementary and secondary schools based on Android. This application is expected to be used as an interactive alternative media besides books, so it’s able to make students more interest on learning Central Javanese culture. This application will be made by using Unity and other assistive software and finally it will be refined with VR Box hardware to make it more real. Keywords: Virtual Reality, Unity, Budaya, Blender, SketchUp, Waterfall.
{"title":"Aplikasi Pengenalan Budaya Jawa Tengah menggunakan Virtual Reality Berbasis Android","authors":"Rudy Silaen, A. Junaidi, Ely Purnawati","doi":"10.20895/dinda.v1i2.230","DOIUrl":"https://doi.org/10.20895/dinda.v1i2.230","url":null,"abstract":"At this time, it is very difficult to introduce culture to students in school, and this is also involved to children do not recognizing their own culture. Many schools have so limited funds to go to museums or cultural performances, especially school which are far from the capital city. Therefore, it is necessary to make an android-based application using Virtual Reality. This writing describes about a method of designing and making Central Javanese cultural learning-application for elementary and secondary school students by utilizing technological developments, one of the fields is education. In the field of education, Virtual Reality can be used as a learning media which is able to make it more attractive. This Virtual Reality technology can be applied in regional cultural learning systems, one of this is the introduction of Central Javanese culture. The use of Virtual Reality technology is expected to be able to display objects in the form of musical instruments, traditional clothes, traditional houses, paintings and traditional weapons in virtual 3D using images which can used to be markers. This making of cultural learning application using Unity, Blender, and SketchUp. The development of this application uses the waterfall model where this method pays close attention to the design of the analysis, design, implementation and testing. With this research, it is hoped that it can help students in Central Java to get to know their culture. This application is specified for students specifically for elementary and secondary schools based on Android. This application is expected to be used as an interactive alternative media besides books, so it’s able to make students more interest on learning Central Javanese culture. This application will be made by using Unity and other assistive software and finally it will be refined with VR Box hardware to make it more real. \u0000Keywords: Virtual Reality, Unity, Budaya, Blender, SketchUp, Waterfall.","PeriodicalId":419119,"journal":{"name":"Journal of Dinda : Data Science, Information Technology, and Data Analytics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115375280","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}
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