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Journal of Dinda : Data Science, Information Technology, and Data Analytics
Published by Institut Teknologi Telkom Purwokerto
ISSN : -     EISSN : 28098064     DOI : https://doi.org/10.20895/dinda
Core Subject : Science,
Computer Science & IT
Journal of Dinda : Data Science, Information Technology, and Data Analytics as a publication media for research results in the fields of Data Science, Information Technology, and Data Analytics, but not implicitly limited. Published 2 times a year in February and August. The journal is managed by the Data Engineering Research Group, Faculty of Informatics, Telkom Purwokerto Institute of Technology. Journal of Dinda is a medium for scientific studies resulting from research, thinking, and critical-analytic studies regarding Data Science, Informatics, and Information Technology. This journal is expected to be a place to foster enthusiasm in education, research, and community service which continues to develop into supporting references for academics. FOCUS AND SCOPE Journal of Dinda : Data Science, Information Technology, and Data Analytics receive scientific articles with the scope of research on: Machine Learning, Deep Learning, Artificial Intelligence, Databases, Statistics, Optimization, Natural Language Processing, Big Data and Cloud Computing, Bioinformatics, Computer Vision, Speech Processing, Information Theory and Models, Data Mining, Mathematical, Probabilistic and Statical Theories, Machine Learning Theories, Models and Systems, Social Science, Information Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 7 Documents
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Search results for , issue "Vol 2 No 1 (2022): February" : 7 Documents clear
Penerapan Face Recognition Berbasis GUI Visual Studio 2012 Menggunakan Algoritma Eigenface dan Metode Pengembangan Waterfall Pada Sistem Absensi Mahasiswa IT Telkom Purwokerto Ilham Fauzi; Apri Junaidi; Wahyu Andi Saputra
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.264

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. 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
Klasifikasi Status Gizi Pada Lansia Menggunakan Learning Vector Quantization 3 (LVQ 3) Khurun Ain Muzaqi; Apri Junaidi; Wahyu Andi Saputra
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.272

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.
Prediksi Harga Saham Bank Bri Menggunakan Algoritma Linear Regresion Sebagai Strategi Jual Beli Saham Janur Syah Putra; Rima Dias Ramadhani; Auliya Burhanuddin
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.273

Abstract

Shares are securities as proof of ownership of investors in a company. Stocks have a volatile nature, this makes stocks difficult to predict. Stock prediction is an effort to estimate the stock price, especially in the Bank Rakyat Indonesia company that will appear in the future, and to increase investors' profit opportunities in making investment decisions. During the COVID-19 pandemic, Bank BRI's shares experienced significant ups and downs in four months, which illustrates the sensitivity of the stock to an event. Therefore, it is important to predict stock prices to reduce the risk accepted by investors. The prediction itself requires time series data. Time series is data that is collected sequentially from time to time. The method used for time series data is Linear Regression because this method can handle time-series data. Based on these problems, stock prediction research will be conducted at the Bank Rakyat Indonesia company using the Linear Regression method. Bank Rakyat Indonesia share price data were obtained from the investing.com website from the period starting on January 1, 2008, to June 1, 2020. The data is processed starting from preprocessing to determine attributes, remove unnecessary attributes, and change the contents of the data type, then process split data to divide the dataset into training and test data. The attributes used in this study are Date and Price and the distribution of the data used is 60:40, 65:35, 70:30, 75:25, and 80:20. The best ratio is at 80:20 which produces train and test accuracy of 0.89 and 0.91, Then each training data and testing data are entered into the linear regression model for prediction. The error results from the predictions were calculated using MAPE and yielded a percentage of 13.751% for training data, 13.773% for test data, and 13.755% for overall data. The MAPE results indicate that the linear regression method can be used to predict the stock price of BRI Bank.
Deteksi Sarkasme Pada Judul Berita Berbahasa Inggris Menggunakan Algoritme Bidirectional LSTM Muhammad David Hilmawan
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.331

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%.
Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network Mohtar Khoiruddin; Apri Junaidi; Wahyu Andi Saputra
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.341

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.
Klasifikasi Penyakit Kanker Kulit Menggunakan Metode Convolutional Neural Network (Studi Kasus: Melanoma) Reynaldi Rio Saputro; Apri Junaidi; Wahyu Andi Saputra
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.349

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.
Perbandingan Performa Antara Algoritma Naive Bayes Dan K-Nearest Neighbour Pada Klasifikasi Kanker Payudara Annisa Nugraheni; Rima Dias Ramadhani; Amalia Beladinna Arifa; Agi Prasetiadi
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.391

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.

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