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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

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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

Aplikasi Berbasis Web Deteksi Undertone Menggunakan Metode Agile Untuk Rekomendasi Makeup Sang Dara Parameswari; Novian Adi Prasetyo; Apri Junaidi
Jurnal Ilmiah Media Sisfo Vol 16 No 1 (2022): Jurnal Ilmiah Media Sisfo
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/mediasisfo.2022.16.1.1111

Berpenampilan menarik adalah keharus bagi kaum perempuan, agar wajah bisa terlihat indah dan menawan dibutuhkan sebuah makeup. Saat ini perempuan yang mengenal berbagai jenis makeup, kosmetik yang dibutuhkan dalam makeup seperti pensil alis, maskara, foundation, lipstik, bedak dan lain sebagainya. Pemilihan shade pada foundation masih menjadi kesulitan bagi para perempuan, hal tersebut dapat membuat warna kulit jadi terlihat berbeda sehinga membuat perempuan merasa insecure atau tidak percaya diri. Tiga jenis udertone yaitu warm, cool dan neutral. Cara manual untuk mengetahun jenis undertone untuk melihat warna urat nadi pada tangan, namun terkadang dengan melakukan cara manual perempuan masih kesulitan untuk menentukan jenis undertone yang dimiliki. Oleh karena itu, dengan semakin berkembangnya zaman, diperlukannya sebuah aplikasi untuk mempermudah perempuan dalam mengetahui jenis undertone dan mendapatkan rekomendasi makeup. Metode agile digunakan untuk pengembangan aplikasi karena berfokus pada penanganan perubahan sesuai kebutuhan pengguna dan kecepatan. Deteksi undertone diimplementasikan sebagai aplikasi berbasis website yang dibangun menggunakan metode Agile. Metode Agile berhasil diterapkan pada penelitian ini dengan hasil berupa aplikasi deteksi Undertone, keberhasilan tersebut diukur menggunakan pengujian blackbox dengan menunjukan hasil semua fitur berjalan dengan baik sesuai dengan kebutuhan pengguna.

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 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%.

Prototype Alat Pengendali Lampu dengan Perintah Suara menggunakan Arduino Uno Berbasis Web Nurul Isna Ganggalia; Apri Junaidi; Fahrudin Mukti Wibowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 3 (2019): Desember 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The use of electric power for lights often less considered, a lot of lights are on continuously even though it's not used. As a result, a lot of electricity is wasted. This motivated researchers to create innovations of creating a light control system. The light controller system is designed to simplify and benefit the user. For this reason, researchers make light controllers on the web use voice commands that can be done anywhere and anytime using the internet. Making a prototype of a light control system with voice commands utilizes speech to text on the Web Speech API that converts sound into text, then it will be processed into a command of light controllers by the Arduino Uno microcontroller. The researcher used the prototype development method, where through 3 stages starting from Listen to Customer, Design and Building, and Test Drive Evaluations. The testing results are Internet speed and noise level affect the success rate on the use of light control using sound. At 9.9 Mbps internet speed has a success rate of 86% with response time 2.01 second, while at internet speed 1.9 Mbps has a success rate of 65% with response time 2.50 second. At the noise level of 34.5 dB room has a success rate of 86% with response time 2.02 second, while the noise level of 62 dB has a success rate of 72% with response time 2.21 second.

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah Rima Dias Ramadhani; Afandi Nur Aziz Thohari; Condro Kartiko; Apri Junaidi; Tri Ginanjar Laksana; Novanda Alim Setya Nugraha
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

PERANCANGAN SISTEM PENGENALAN NADA ANGKLUNG MENGGUNAKAN DISCRETE FOURIER TRANSFORM Sindhi Pradnya Nareswari; Lusiana Haryanti; Muhammad Fathoni Hervi Hermawan; Apri Junaidi
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 4 No. 3 (2018)
Publisher : Universitas Widyatama

Angklung is one of Indonesia's traditional musical instruments that has become famous. This popularity certainly invites a lot of people's interest to learn angklung instruments. Online learning are one of the learning technique that is popular to be used. The problem is, not all people who use this learning technique understand the tone that is used in the video. Therefore, this study will create a breakthrough that can help these problems. The result wich is expected is this design can help people recognize tones and improve their skills in music

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 .

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

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