Implementing mobile learning in curriculum-based educational settings faces challenges related to... more Implementing mobile learning in curriculum-based educational settings faces challenges related to perceived ethical and learning issues. This study investigated the affordances of mobile technologies to support mathematics instruction by teachers. An exploratory study employing questionnaires and semi-structured interviews revealed that, while mathematics instruction can be augmented with mobile learning, the majority of schools in Indonesia have banned student use of mobile phones in classrooms. Teachers are concerned about the improper use of mobile phones that could impact their students' mental well-being and distract them from learning. Most teachers perceive mobile technologies to be disruptive and seem reluctant to use them for teaching delivery. However, teachers are eager to experiment with digital technologies within mathematics instruction. Our findings suggest infusing alternate technologies that fit better with the school's teaching and learning environment. This includes web-based applications that can run on different digital devices ranging from desktop computers, laptops, and tablets, to mobile phones. We propose setting up communities of practice for mathematics teachers to share their instructional repertoire on integrating digital technologies within the classroom.
Mathematical literacy plays an important role in supporting individuals to fulfil their professio... more Mathematical literacy plays an important role in supporting individuals to fulfil their professional roles in modern society. The affordances of mobile technologies as well as the emergence of new theories in mobile learning have the potential to promote mathematical literacy. However, implementation of mobile learning in Indonesian society faces challenges related to perceived ethical and learning issues in curriculum-based educational settings. This study aims to investigate the preparedness of teachers in integrating mathematics subject content with mobile technologies, especially in promoting mathematical literacy. An exploratory study has been conducted using mixed methods. Questionnaire survey and semi-structured interviews were conducted to understand teachers' knowledge in mathematical literacy and to identify opportunities and challenges for mobile learning within instruction. Findings indicate that teachers mostly do not know about mathematical literacy, indicating that the concept of mathematical literacy needs to be promoted. Further, most schools prohibit the use of mobile devices in classrooms as they are wary of inappropriate use of mobile devices which may harm students' mental health and distract them from learning. Study finds this to be the most common cause for teachers' reluctance in using mobile learning.
PERBANDINGAN PREDIKSI HARGA SAHAM DENGAN MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATI... more PERBANDINGAN PREDIKSI HARGA SAHAM DENGAN MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION DAN ARIMA ARIMA merupakan metode yang umum digunakan untuk memprediksi suatu data. Seiring dengan perkembangan ilmu, ada metode lain yang dapat digunakan memprediksi, salah satunya adalah JST backpropagation. Tulisan ini bertujuan mengetahui arsitektur jaringan syaraf tiruan yang optimal serta mengkomparasikan antara model ARIMA dan JST backpropagation. Metode yang digunakan adalah studi pustaka, perumusan masalah, pemecahan masalah, analisis data dengan bantuan GUI MATLAB dan Minitab, dan penarikan kesimpulan. Berdasarkan hasil simulasi data harga saham PT. Asuransi Bina Dana Arta (ABDA).Tbk dengan menggunakan JST backpropagation diperoleh nilai MSE dari proses training sebesar 0,000206 dan proses testing sebesar 0,00140. Arsitektur jaringan yang optimal adalah 1 neuron input layer, 1 neuron hidden layer dan 1 neuron input layer. Sedangkan model terbaik ARIMA, yaitu ARIMA (1,1,0) dengan nilai MSE sebesar 0,001145. Namun, karena selisih nilai MSE dari kedua metode tidak terlalu besar, maka kedua metode dapat digunakan untuk penelitian prediksi harga saham.
Jaringan Syaraf Tiruan (JST) merupakan salah satu teknik klasifikasi yang cukup handal dikarenaka... more Jaringan Syaraf Tiruan (JST) merupakan salah satu teknik klasifikasi yang cukup handal dikarenakan kemampuannya dalam memprediksi ataupun mengenali suatu citra.JST mampu belajar dari data yang dilatihkan kepadanya.Oleh karena itu, penelitian ini membahas tentang sistem pengenalan citra daging babi dalam upaya mengenali citra daging babidan citra daging sapi.Penelitian ini memanfaatkan sensitivitas dari JST backpropagation dan pengolahan citra digital dengan Principal Component Analysis(PCA) sebagai pengekstraksi ciri dari sebuah citra.Hasil pengujian menunjukkan bahwa dengan adanya variasi jumlah neuron hidden layerdan learning ratedalam arsitektur JST untuk mendapatkan arsitektur jaringan yang optimal. Dari hasil pengujian diperoleh recognition rate88,3 % dengan parameter arsitektur jaringan learning rate: 0,2, jumlah neuron hidden layer: 20, target eror: 0,001, dan jumlah epoh: 100.
Ijccs Indonesian Journal of Computing and Cybernetics Systems, Jan 31, 2011
In daily lives, especially in interpersonal communication, face often used for expression. Facial... more In daily lives, especially in interpersonal communication, face often used for expression. Facial expressions give information about the emotional state of the person. A facial expression is one of the behavioral characteristics. The components of a basic facial expression analysis system are face detection, face data extraction, and facial expression recognition. Fisherface method with backpropagation artificial neural network approach can be used for facial expression recognition. This method consists of two-stage process, namely PCA and LDA. PCA is used to reduce the dimension, while the LDA is used for features extraction of facial expressions. The system was tested with 2 databases namely JAFFE database and MUG database. The system correctly classified the expression with accuracy of 86.85%, and false positive 25 for image type I of JAFFE, for image type II of JAFFE 89.20% and false positive 15, for type III of JAFFE 87.79%, and false positive for 16. The image of MUG are 98.09%, and false positive 5.
Implementasi Jaringan Syaraf Tiruan Backpropagation Untuk Pengenalan Citra Digital Menggunakan ... more Implementasi Jaringan Syaraf Tiruan Backpropagation Untuk Pengenalan Citra Digital Menggunakan Deteksi Tepi
International Journal of Computer Applications, 2012
Facial expression plays significant role for human beings to communicate their emotions. Automati... more Facial expression plays significant role for human beings to communicate their emotions. Automatic facial expression analysis is a flourishing area of research in computer science, and it is also still a challenge. This paper discusses the application of a natural network based facial expression recognition using fisherface. Back propagation neural network is used as a classifier for classifying the expressions. For face portion segmentation and localization, integral projection method is used. The accuracy of system performance have evaluated on a public database "Japanese Female Facial Expression (JAFFE)". The experimental results show the effectiveness of our scheme. The best average recognition rate achieves 89.20%.
Implementing mobile learning in curriculum-based educational settings faces challenges related to... more Implementing mobile learning in curriculum-based educational settings faces challenges related to perceived ethical and learning issues. This study investigated the affordances of mobile technologies to support mathematics instruction by teachers. An exploratory study employing questionnaires and semi-structured interviews revealed that, while mathematics instruction can be augmented with mobile learning, the majority of schools in Indonesia have banned student use of mobile phones in classrooms. Teachers are concerned about the improper use of mobile phones that could impact their students' mental well-being and distract them from learning. Most teachers perceive mobile technologies to be disruptive and seem reluctant to use them for teaching delivery. However, teachers are eager to experiment with digital technologies within mathematics instruction. Our findings suggest infusing alternate technologies that fit better with the school's teaching and learning environment. This includes web-based applications that can run on different digital devices ranging from desktop computers, laptops, and tablets, to mobile phones. We propose setting up communities of practice for mathematics teachers to share their instructional repertoire on integrating digital technologies within the classroom.
Mathematical literacy plays an important role in supporting individuals to fulfil their professio... more Mathematical literacy plays an important role in supporting individuals to fulfil their professional roles in modern society. The affordances of mobile technologies as well as the emergence of new theories in mobile learning have the potential to promote mathematical literacy. However, implementation of mobile learning in Indonesian society faces challenges related to perceived ethical and learning issues in curriculum-based educational settings. This study aims to investigate the preparedness of teachers in integrating mathematics subject content with mobile technologies, especially in promoting mathematical literacy. An exploratory study has been conducted using mixed methods. Questionnaire survey and semi-structured interviews were conducted to understand teachers' knowledge in mathematical literacy and to identify opportunities and challenges for mobile learning within instruction. Findings indicate that teachers mostly do not know about mathematical literacy, indicating that the concept of mathematical literacy needs to be promoted. Further, most schools prohibit the use of mobile devices in classrooms as they are wary of inappropriate use of mobile devices which may harm students' mental health and distract them from learning. Study finds this to be the most common cause for teachers' reluctance in using mobile learning.
PERBANDINGAN PREDIKSI HARGA SAHAM DENGAN MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATI... more PERBANDINGAN PREDIKSI HARGA SAHAM DENGAN MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION DAN ARIMA ARIMA merupakan metode yang umum digunakan untuk memprediksi suatu data. Seiring dengan perkembangan ilmu, ada metode lain yang dapat digunakan memprediksi, salah satunya adalah JST backpropagation. Tulisan ini bertujuan mengetahui arsitektur jaringan syaraf tiruan yang optimal serta mengkomparasikan antara model ARIMA dan JST backpropagation. Metode yang digunakan adalah studi pustaka, perumusan masalah, pemecahan masalah, analisis data dengan bantuan GUI MATLAB dan Minitab, dan penarikan kesimpulan. Berdasarkan hasil simulasi data harga saham PT. Asuransi Bina Dana Arta (ABDA).Tbk dengan menggunakan JST backpropagation diperoleh nilai MSE dari proses training sebesar 0,000206 dan proses testing sebesar 0,00140. Arsitektur jaringan yang optimal adalah 1 neuron input layer, 1 neuron hidden layer dan 1 neuron input layer. Sedangkan model terbaik ARIMA, yaitu ARIMA (1,1,0) dengan nilai MSE sebesar 0,001145. Namun, karena selisih nilai MSE dari kedua metode tidak terlalu besar, maka kedua metode dapat digunakan untuk penelitian prediksi harga saham.
Jaringan Syaraf Tiruan (JST) merupakan salah satu teknik klasifikasi yang cukup handal dikarenaka... more Jaringan Syaraf Tiruan (JST) merupakan salah satu teknik klasifikasi yang cukup handal dikarenakan kemampuannya dalam memprediksi ataupun mengenali suatu citra.JST mampu belajar dari data yang dilatihkan kepadanya.Oleh karena itu, penelitian ini membahas tentang sistem pengenalan citra daging babi dalam upaya mengenali citra daging babidan citra daging sapi.Penelitian ini memanfaatkan sensitivitas dari JST backpropagation dan pengolahan citra digital dengan Principal Component Analysis(PCA) sebagai pengekstraksi ciri dari sebuah citra.Hasil pengujian menunjukkan bahwa dengan adanya variasi jumlah neuron hidden layerdan learning ratedalam arsitektur JST untuk mendapatkan arsitektur jaringan yang optimal. Dari hasil pengujian diperoleh recognition rate88,3 % dengan parameter arsitektur jaringan learning rate: 0,2, jumlah neuron hidden layer: 20, target eror: 0,001, dan jumlah epoh: 100.
Ijccs Indonesian Journal of Computing and Cybernetics Systems, Jan 31, 2011
In daily lives, especially in interpersonal communication, face often used for expression. Facial... more In daily lives, especially in interpersonal communication, face often used for expression. Facial expressions give information about the emotional state of the person. A facial expression is one of the behavioral characteristics. The components of a basic facial expression analysis system are face detection, face data extraction, and facial expression recognition. Fisherface method with backpropagation artificial neural network approach can be used for facial expression recognition. This method consists of two-stage process, namely PCA and LDA. PCA is used to reduce the dimension, while the LDA is used for features extraction of facial expressions. The system was tested with 2 databases namely JAFFE database and MUG database. The system correctly classified the expression with accuracy of 86.85%, and false positive 25 for image type I of JAFFE, for image type II of JAFFE 89.20% and false positive 15, for type III of JAFFE 87.79%, and false positive for 16. The image of MUG are 98.09%, and false positive 5.
Implementasi Jaringan Syaraf Tiruan Backpropagation Untuk Pengenalan Citra Digital Menggunakan ... more Implementasi Jaringan Syaraf Tiruan Backpropagation Untuk Pengenalan Citra Digital Menggunakan Deteksi Tepi
International Journal of Computer Applications, 2012
Facial expression plays significant role for human beings to communicate their emotions. Automati... more Facial expression plays significant role for human beings to communicate their emotions. Automatic facial expression analysis is a flourishing area of research in computer science, and it is also still a challenge. This paper discusses the application of a natural network based facial expression recognition using fisherface. Back propagation neural network is used as a classifier for classifying the expressions. For face portion segmentation and localization, integral projection method is used. The accuracy of system performance have evaluated on a public database "Japanese Female Facial Expression (JAFFE)". The experimental results show the effectiveness of our scheme. The best average recognition rate achieves 89.20%.
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