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Interactive Digital Storybook for Increasing Children Reading Interest of Indonesian Folklore Naufal, Mohammad Farid; Kusuma, Selvia F
Jurnal Informatika dan Multimedia Vol 8 No 1 (2016): Jurnal Volume 8, No 1 (2016)
Publisher : Teknik Informatika Politeknik Kediri

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â is very important for children but now children reading interest are decreasing, especially in Indonesian folklore. It happened because Indonesian folklore is written in traditional storybook version. If it continues to happen then children in the future will not know the histories of Indonesia. One way to increase children reading interest is introducing pictured storybook, but the traditional pictured storybook are not sufficient to increase children reading interest. In a growing era we can create a more interesting pictured storybook with implementing interactive digital storybook on android devices. Interactive means that children can interact to move the animated storybook characters. We assume that children interaction with interactive digital storybook can increase their enthusiasms in reading Indonesian folklore. To prove our hypothesis we conducted an experiment to 30 children. In the experiment, the children will read interactive digital storybook and traditional storybook after that we will see their responds. The result shows that interactive digital storybook can increase children reading interest of Indonesian folklore.

Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19 Mohammad Farid Naufal; Selvia Ferdiana Kusuma; Kevin Christian Tanus; Raynaldy Valentino Sukiwun; Joseph Kristiano; Jeremy Owen Lieyanto; Daniel Cristianindra R.
Teknika Vol 10 No 2 (2021): Juli 2021
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v10i2.331

Kondisi pandemi global Covid-19 yang muncul diakhir tahun 2019 telah menjadi permasalahan utama seluruh negara di dunia. Covid-19 merupakan virus yang menyerang organ paru-paru dan dapat mengakibatkan kematian. Pasien Covid-19 banyak yang telah dirawat di rumah sakit sehingga terdapat data citra chest X-ray paru-paru pasien yang terjangkit Covid-19. Saat ini sudah banyak peneltian yang melakukan klasifikasi citra chest X-ray menggunakan Convolutional Neural Network (CNN) untuk membedakan paru-paru sehat, terinfeksi covid-19, dan penyakit paru-paru lainnya, namun belum ada penelitian yang mencoba membandingkan performa algoritma CNN dan machine learning klasik seperti Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) untuk mengetahui gap performa dan waktu eksekusi yang dibutuhkan. Penelitian ini bertujuan untuk membandingkan performa dan waktu eksekusi algoritma klasifikasi K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan CNN untuk mendeteksi Covid-19 berdasarkan citra chest X-Ray. Berdasarkan hasil pengujian menggunakan 5 Cross Validation, CNN merupakan algoritma yang memiliki rata-rata performa terbaik yaitu akurasi 0,9591, precision 0,9592, recall 0,9591, dan F1 Score 0,959 dengan waktu eksekusi rata-rata sebesar 3102,562 detik.

Analisa Teknik Pembelajaran dan Pengajaran pada Universitas dan Industri Naufal, Mohammad Farid
Jurnal Informatika dan Multimedia Vol 10 No 2 (2018): Jurnal Volume 10, No.2 (2018)
Publisher : Teknik Informatika Politeknik Kediri

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Perkembangan teknologi yang sangat pesat mempengaruhi cara belajar terhadap sesuatu. Salah satunya yaitu belajar koding atau pemrograman. Pentingnya memperbarui cara dalam mempelajari dan mengajarkan koding merupakan sebuah solusi untuk memperbaiki sistem yang dinilai kurang dan sudah usang. Terdapat banyak sekali metode yang telah diterapkan oleh universitas dan perusahaan dalam mempelajari dan mengajarkan teknik pemrograman. Metode-metode yang digunakan seperti menggunakan via interactive gaming, online learning, melalui perkuliahan, mengerjakan project, dan lain-lain. Di dalam jurnal ini akan menjelaskan teknik-teknik dan metode pembelajaran dan pengajaran koding yang bermacam-macam. Selain itu, akan membandingkan metode satu dengan yang lainnya sehingga akan mengetahui teknik dan metode apa yang paling efektif digunakan sebagai metode pembelajaran dan pengajaran koding.  Sehingga metode pembelajaran dan pengajaran koding yang paling efektif adalah dengan menggunakan game interaktif berbentuk open source bernama pex4fun yang dapat diikuti oleh banyak pelajar dengan teknik pembelajaran yang beragam dan teknik penilaian yang modern.

Analisis Perbandingan Algoritma Klasifikasi MLP dan CNN pada Dataset American Sign Language Mohammad Farid Naufal; Sesilia Shania; Jessica Millenia; Stefan Axel; Juan Timothy Soebroto; Rizka Febrina P.; Mirella Mercifia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i3.3009

People who have hearing loss (deafness) or speech impairment (hearing impairment) usually use sign language to communicate. One of the most basic and flexible sign languages is the Alphabet Sign Language to spell out the words you want to pronounce. Sign language uses hand, finger, and face movements to speak the user's thoughts. However, for alphabetical sign language, facial expressions are not used but only gestures or symbols formed using fingers and hands. In fact, there are still many people who don't understand the meaning of sign language. The use of image classification can help people more easily learn and translate sign language. Image classification accuracy is the main problem in this case. This research conducted a comparison of image classification algorithms, namely Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP) to recognize American Sign Language (ASL) except the letters "J" and "Z" because movement is required for both. This is done to see the effect of the convolution and pooling stages on CNN on the resulting accuracy value and F1 Score in the ASL dataset. Based on the comparison, the use of CNN which begins with Gaussian Low Pass Filtering preprocessing gets the best accuracy of 96.93% and F1 Score 96.97%

Comparative Analysis of Image Classification Algorithms for Face Mask Detection Mohammad Farid Naufal; Selvia Ferdiana Kusuma; Zefanya Ardya Prayuska; Ang Alexander Yoshua; Yohanes Albert Lauwoto; Nicky Setyawan Dinata; David Sugiarto
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.56-66

Background: The COVID-19 pandemic remains a problem in 2021. Health protocols are needed to prevent the spread, including wearing a face mask. Enforcing people to wear face masks is tiring. AI can be used to classify images for face mask detection. There are a lot of image classification algorithm for face mask detection, but there are still no studies that compare their performance.Objective: This study aims to compare the classification algorithms of classical machine learning. They are k-nearest neighbors (KNN), support vector machine (SVM), and a widely used deep learning algorithm for image classification which is convolutional neural network (CNN) for face masks detection.Methods: This study uses 5 and 3 cross-validation for assessing the performance of KNN, SVM, and CNN in face mask detection.Results: CNN has the best average performance with the accuracy of 0.9683 and average execution time of 2,507.802 seconds for classifying 3,725 faces with mask and 3,828 faces without mask images.Conclusion: For a large amount of image data, KNN and SVM can be used as temporary algorithms in face mask detection due to their faster execution times. At the same time, CNN can be trained to form a classification model. In this case, it is advisable to use CNN for classification because it has better performance than KNN and SVM. In the future, the classification model can be implemented for automatic alert system to detect and warn people who are not wearing face masks.

Analisis Perbandingan Algoritma Klasifikasi MLP dan CNN pada Dataset American Sign Language Mohammad Farid Naufal; Sesilia Shania; Jessica Millenia; Stefan Axel; Juan Timothy Soebroto; Rizka Febrina P.; Mirella Mercifia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i3.3009

People who have hearing loss (deafness) or speech impairment (hearing impairment) usually use sign language to communicate. One of the most basic and flexible sign languages is the Alphabet Sign Language to spell out the words you want to pronounce. Sign language uses hand, finger, and face movements to speak the user's thoughts. However, for alphabetical sign language, facial expressions are not used but only gestures or symbols formed using fingers and hands. In fact, there are still many people who don't understand the meaning of sign language. The use of image classification can help people more easily learn and translate sign language. Image classification accuracy is the main problem in this case. This research conducted a comparison of image classification algorithms, namely Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP) to recognize American Sign Language (ASL) except the letters "J" and "Z" because movement is required for both. This is done to see the effect of the convolution and pooling stages on CNN on the resulting accuracy value and F1 Score in the ASL dataset. Based on the comparison, the use of CNN which begins with Gaussian Low Pass Filtering preprocessing gets the best accuracy of 96.93% and F1 Score 96.97%

Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19 Mohammad Farid Naufal; Selvia Ferdiana Kusuma; Kevin Christian Tanus; Raynaldy Valentino Sukiwun; Joseph Kristiano; Jeremy Owen Lieyanto; Daniel Cristianindra R.
Teknika Vol 10 No 2 (2021): Juli 2021
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v10i2.331

Kondisi pandemi global Covid-19 yang muncul diakhir tahun 2019 telah menjadi permasalahan utama seluruh negara di dunia. Covid-19 merupakan virus yang menyerang organ paru-paru dan dapat mengakibatkan kematian. Pasien Covid-19 banyak yang telah dirawat di rumah sakit sehingga terdapat data citra chest X-ray paru-paru pasien yang terjangkit Covid-19. Saat ini sudah banyak peneltian yang melakukan klasifikasi citra chest X-ray menggunakan Convolutional Neural Network (CNN) untuk membedakan paru-paru sehat, terinfeksi covid-19, dan penyakit paru-paru lainnya, namun belum ada penelitian yang mencoba membandingkan performa algoritma CNN dan machine learning klasik seperti Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) untuk mengetahui gap performa dan waktu eksekusi yang dibutuhkan. Penelitian ini bertujuan untuk membandingkan performa dan waktu eksekusi algoritma klasifikasi K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan CNN untuk mendeteksi Covid-19 berdasarkan citra chest X-Ray. Berdasarkan hasil pengujian menggunakan 5 Cross Validation, CNN merupakan algoritma yang memiliki rata-rata performa terbaik yaitu akurasi 0,9591, precision 0,9592, recall 0,9591, dan F1 Score 0,959 dengan waktu eksekusi rata-rata sebesar 3102,562 detik.

Analisis Pemilihan Supplier Pada Pengadaan Suku Cadang dengan Metode Analytic Hierarchy Process Mohammad Farid Naufal; Putu Aditya Riva Putra; Selvia Ferdiana Kusuma
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

PT. Bali Age is a company which engaged in freight forwarding service. Because of this, the company is using the trucks for carry out of their operational activities. Every truck always gets a routine maintenance at their garage, so they must provide the spare parts stock by themselves. The currently procurement of spare parts are still based on paper. By implementing the decision support in a new procurement system, it can provide a supplier recommendation for this company. This supplier recommendation which provides by system, are getting from the result of the comparation value from criteria priority calculation, using AHP method. The AHP method that implemented in this system, can also provide the final result of supplier recommendation comparison value with accurately.

PENGIDENTIFIKASIAN EXTRACT CLASS REFACTORING UNTUK MENINGKATKAN NILAI COHESION CLASS: SYSTEMATIC LITERATURE REVIEW Mohammad Farid Naufal
Telematika Vol 14, No 2 (2017): Edisi Oktober 2017
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v14i2.2100

Context: Cohesion merupakan faktor yang sangat diperhitungkan dalam menilai tingkat kualitas sebuah software yang menggunakan dasar Object Oriented Programming (OOP) dalam pengembangannya. Semakin besar nilai cohesion maka class tersebut semakin independen sehingga semakin mudah dilakukan maintenance pada saat software berevolusi. Dalam pengembangan OOP diharapkan memiliki nilai cohesion yang tinggi.Objective: Paper ini menggunakan studi literatur sistematis terkait pada salah satu teknik refactoring yaitu extract class yang merupakan suatu cara untuk meningkatkan nilai cohesion dari sebuah class dan class cohesion metric untuk menilai tingkat kompleksitas class.Method: Dalam paper ini akan dilakukan studi literatur secara sistematis pada dua database jurnal yang berkaitan dengan extract class dan class cohesion metric hingga tujuh tahun terakhir.Results: Literatur tentang extract class refactoring dan class cohesion metric yang telah ada saat ini diklasifikasikan dan dilakukan perbandingan dari masing-masing metode.Conclusion: Dari review studi literatur masih terdapat issue terkait berapa jumlah optimal class yang harus dibentuk dari extract class refactoring.

Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca Mohammad Farid Naufal
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8, No 2: April 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021824553

Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. Computer vision adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan self autonomous machine agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara real time. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari Machine Learning sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942. AbstractWeather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.

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