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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Ilmiah Media Sisfo Journal of Dinda : Data Science, Information Technology, and Data Analytics

Apri Junaidi

Institut Teknologi Telkom Purwokerto

Author-ID : 2489116

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 14 Documents Claim Missing Document

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Articles

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Hasil Klasifikasi Algoritma Backpropagation dan K-Nearest Neighbor pada Cardiovascular Disease Nashrulloh Khoiruzzaman; Rima Dias Ramadhani; Apri Junaidi
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 1 No 1 (2021): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Cardiovascular disease adalah penyakit yang diakibatkan oleh kelainan yang terjadi pada organ jantung. Cardivascular disease dapat menyerang manusia dari usia muda hingga usia tua yang terdapat 13 faktor yang mempengaruhinya yaitu Age, Sex, Chest pain, Trestbps, Chol, Fbs, Restecg, Thalach, Exang, Oldpeak, Slope, Ca, dan Thal. Cardiovascular disease beragam jenisnya antara lain penyakit jantung koroner, gagal jantung, tekanan darah tinggi, tekanan darah rendah dan lain-lain. Oleh karena itu, penelitian ini memiliki tujuan untuk melakukan klasifikasi terhadap cardiovascular disease. Pada penelitian ini menggunakan algoritma backpropagation dan algoritma K-nearest neighbor. Langkah awal dilakukan adalah proses perhitungan euclidean distance pada K-NN untuk mencari jarak k terdekat untuk mendapatkan kategori berdasarkan frequensi terbanyak dari nilai k yang ditentukan dan mencari bobot baru untuk algoritma backpropagation untuk mendapatkan bobot baru yang digunakan untuk mendapatkan nilai yang sesuai dengan yang diharapkan. Pengujian sistem ini terdiri dari pengujian nilai akurasi dengan nilai K, pengujian K-fold X validation dan pengaruh hidden layer. Hasil dari Penelitian ini bahwa algoritma backpropagation menghasilkan nilai akurasi sebesar 64%, presisi sebesar 62%, recall sebesar 64% dan algoritma K-nearest neighbor menghasilkan nilai akurasi sebesar 66%, presisi sebesar 61% dan recall sebesar 66%. Pengaruh hidden layer terhadap algoritma backpropagation dalam mengklasifikasikan cardiovascular disease sangat besar hal ini sesuai dengan hasil dari penelitian yang telah dilakukan bahwa ketika jumlah hidden layer kecil, nilai yang dihasilkan juga kecil akan tetapi ketika jumlah hidden layernya tinggi nilai akurasinya bahkan menjadi rendah .

Perancangan Aplikasi Kamus Online Informatika-Indonesia Beserta Fungsinya Berbasis Web Menggunakan Metode Sequential Search Alfira Mahda Ramadini; Apri Junaidi; Fahrudin Mukti Wibowo
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 1 No 1 (2021): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

In this era of digital optimization, Information and Communication Technology is growing very rapidly. This is in line with the demands and human needs that have been actualized in various fields, such as knowledge and education. To support a learning process based on SCIENCE (Science and Technology) required several supporting aspects such as online dictionary that includes vocabulary and functions of words / terms in the FIELD of IT. One of the supporting aspects is an alternative tool in the form of media that facilitates every community in learning the terms in the field of informatics. According to pie chart data from questionnaires that the authors made, as much as 50 percent of the knowledge of the term informatics of the general public (lay people) can be said to be still very low. In contrast to the academic community such as IT Lecturers and IT Students who do master the field, although it does not close the possibility there are some IT Students who do not fully understand in their fields. The expected result of the authors in this study is the increasing knowledge, especially the general public (lay people) in studying science in the field of IT especially language and informatics terms through online disses. This Online Dictionary application will be designed website-based using sequential search method where sequential search (also called linear search) is the most simple search model performed on a data set. The Online Dictionary application will run by searching for vocabulary or terms in informatics according to the keywords they are looking for, making it easier for users to learn the language of informatics. Keywords: Website, Vocabulary, Dictionary Online, Informatics, IT, Sequential Search, Science and Technology

Aplikasi Pengenalan Budaya Jawa Tengah menggunakan Virtual Reality Berbasis Android Rudolf Dekha Silaen; Apri Junaidi; Ely Purnawati
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 1 No 2 (2021): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

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.

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

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

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.

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

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

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.

Implementasi Deep Learning Untuk Klasifikasi Citra Undertone Menggunakan Algoritma Convolutional Neural Network Rizka Fayyadhila; Apri Junaidi; Novian Adi Prasetyo
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 1 No 2 (2021): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

The beauty of Indonesian women is distinguished by skin color, facial structure, hair color and body posture. For women today trying to look beautiful is a must. The way to make yourself look beautiful can be tricked by using make-up. But it's not that easy to use make-up because the type of make-up is differentiated based on the basic skin color, this is the problem for women in using make-up. Undertone is the basic color of the skin, there are three types of undertones, namely warm, cool and neutral. By knowing the type of undertone, it will make it easier for women to use make-up, namely to determine the appropriate shade based on the type of undertone. For this reason, a modeling of undertone image classification was made using the Convolutional Neural Network algorithm. This algorithm is claimed to be the best algorithm for solving object recognition and detection problems. The wrist vein color image dataset is required. The dataset used is 30 data per class, then preprocessing is carried out by homogenizing the image size to 64x64 pixels, then augmentation is carried out on each image by rotating and zooming. At this stage, the dataset will be divided into 3000 images which are divided into 80% training data and 20% testing data. Then it is processed through the convolution and pooling process at the feature learning stage, then the fully connected layer and classification stage where the feature learning results will be used for the classification process based on subclasses. Produces accuracy and training model values reaching 98% with a loss value of 0.0214 and for accuracy from data validation it reaches 99% with a loss value of 0.0239 with model testing results of 99.5%.

Sistem Rekomendasi Desain Website Berdasarkan Tingkat Kemiripan Menggunakan Euclidean Distance Cahyani Ainun Awaliyah; Agi Prasetyadi; Apri Junaidi
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 2 (2022): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i2.543

The more internet users, the more interest in creating websites for certain purposes. To create a website that can attract visitors, a good website design is required. Website design is an important element in making a website, because the design of a website will create its own impression and image for website users. One of the technological developments is artificial intelligence, namely the Recommendation System which is a computer technology that is able to provide recommendations for the layman, in this study a website design recommendation system based on the Euclidean distance assessment resulted in a "good" SUS (System Usability Scale) grade C “good”.

Co-Authors Afandi Nur Aziz Thohari Agi Prasetyadi Alfira Mahda Ramadini Cahyani Ainun Awaliyah Condro Kartiko Ely Purnawati Ilham Fauzi Khurun Ain Muzaqi Lusiana Haryanti Mohtar Khoiruddin Muhammad Fathoni Hervi Hermawan Nashrulloh Khoiruzzaman Novanda Alim Setya Nugraha Novian Adi Prasetyo Nurul Isna Ganggalia Ramadhani, Rima Dias Reynaldi Rio Saputro Rizka Fayyadhila Rudolf Dekha Silaen Sang Dara Parameswari Sindhi Pradnya Nareswari Tri Ginanjar Laksana Wahyu Andi Saputra Wibowo, Fahrudin Mukti

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