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All Journal JOISIE (Journal Of Information Systems And Informatics Engineering) INTI Nusa Mandiri Journal of Education Research Knowbase : International Journal of Knowledge in Database Intellect : Indonesian Journal of Learning and Technological Innovation CSRID Journal of Informatics and Advanced Computing
Chan, Fajri Rinaldi
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Humanities Computer Science & IT Education Electrical & Electronics Engineering Mathematics Social Sciences

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Perancangan Sistem Pendeteksi Emosional Siswa Menggunakan Algoritma CNN untuk Mengukur Tingkat Pengelolaan Kelas Chan, Fajri Rinaldi; Annas, Firdaus; Yuspita, Yulifda Elin; Darmawati, Gusnita
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 3 No. 2 (2024): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57255/intellect.v3i02.295

Abstract

This Papers is motivated by a problem at SMAN 5 Bukittinggi shows that teachers often have difficulty analyzing the emotional state of their students. This is based on the difficulty of teachers to control and classify the emotional state of each student while learning, which has an impact on the effectiveness of classroom management. The developed system is designed to automatically detect students' emotions, so that teachers can understand students' emotional conditions and adjust teaching methods. The research method used is Research and Development (R&D) which includes the stages of needs identification, design, development, testing, implementation, and evaluation. The data used are 28,700 facial images taken from the Kaggle platform and used to train CNN models in classifying seven emotions: angry, disgusted, afraid, happy, sad, surprised, and neutral. The system was tested at SMA Negeri 5 Bukittinggi involving teachers and students. The results showed that the developed system was valid (score 0.88), practical (score 0.62), and effective (score 0.83) in detecting students' emotions and helping classroom management. The contributions of this research include innovating the use of AI in education, providing technological solutions for teachers to better understand students' emotional states, and improving the interaction and quality of classroom management. Overall, the system is expected to help improve the quality of learning by providing teachers with more sophisticated and responsive tools to support effective classroom management. Abstrak Artikel ini dilatar belakangi dari suatu permasalahan di SMAN 5 Bukittinggi menunjukkan bahwa guru sering mengalami kesulitan mengenai analisa emosional dari siswanya. ini didasari oleh guru susah untuk mengontrol dan mengklasisifikasikan emosional dari setiap siswa saat belajar, yang berdampak pada efektivitas pengelolaan kelas. Sistem yang dikembangkan dirancang untuk mendeteksi emosi siswa secara otomatis, sehingga guru dapat memahami kondisi emosional siswa dan menyesuaikan metode pengajaran. Metode penelitian yang digunakan adalah Research and Development (R&D) yang meliputi tahapan identifikasi kebutuhan, desain, pengembangan, pengujian, implementasi, dan evaluasi. Data yang digunakan berupa 28.700 citra wajah yang diambil dari platform Kaggle dan digunakan untuk melatih model CNN dalam mengklasifikasikan tujuh emosi: marah, jijik, takut, senang, sedih, terkejut, dan netral. Sistem diuji di SMA Negeri 5 Bukittinggi dengan melibatkan guru dan siswa. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan valid (skor 0,88), praktis (skor 0,62), dan efektif (skor 0,83) dalam mendeteksi emosi siswa dan membantu pengelolaan kelas. Kontribusi penelitian ini mencakup inovasi penggunaan AI dalam pendidikan, memberikan solusi teknologi bagi guru untuk lebih memahami kondisi emosional siswa, serta meningkatkan interaksi dan kualitas pengelolaan kelas. Secara keseluruhan, sistem ini diharapkan dapat membantu meningkatkan kualitas pembelajaran dengan memberikan guru alat yang lebih canggih dan responsif untuk mendukung pengelolaan kelas yang efektif.
Penerapan Image Processing untuk Identifikasi Jenis Pisang Emas dan Pisang Kapas Menggunakan Metode K-Means Clustering Yanti, Rahma; Chan, Fajri Rinaldi; Ramadhanu, Agung
Journal of Education Research Vol. 5 No. 4 (2024)
Publisher : Perkumpulan Pengelola Jurnal PAUD Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37985/jer.v5i4.1698

Abstract

Indonesia dikenal sebagai penghasil sumber pangan salah satunya pisang. Ada dua varietas pisang, yaitu pisang emas (Musa acuminata) dan pisang kapas (Musa balbisiana). Sering kali kita kesulitan dalam membedakan kedua jenis pisang secara visual, terutama ketika tidak dapat membedakan jenis pisang tersebut, sering menyebabkan kebingungan bagi konsumen dan pelaku usaha. Tujuan utama dari penelitian ini adalah mengembangkan sistem klasifikasi otomatis berbasis pengolahan citra yang mampu membedakan kedua jenis pisang tersebut. Metode yang diterapkan meliputi pengolahan citra dengan konversi dari ruang warna RGB ke LAB untuk memisahkan kecerahan dan warna. Proses segmentasi dilakukan menggunakan K-Means untuk mengelompokkan piksel berdasarkan kesamaan warna, diikuti dengan ekstraksi fitur geometris dan tekstur seperti Eccentricity, Energy, dan Homogeneity. Data yang digunakan mencakup 30 citra untuk pelatihan dan 10 citra untuk pengujian. Hasil penelitian menunjukkan bahwa metode ini berhasil mengklasifikasikan pisang emas dan pisang kapas dengan tingkat akurasi 97%, di mana 29 dari 30 citra diidentifikasi dengan benar. metode K-Means Clustering terbukti efektif dan akurat dalam membedakan kedua varietas pisang berdasarkan ciri fisik yang dihasilkan dari citra digital.
Implementation of Convolutional Neural Networks (CNN) in An Emotion Detection System for Measuring Learning Concentration Levels Chan, Fajri Rinaldi; Firdaus Annas; Yulifda Elin Yuspita; Gusnita Darmawati
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 1 (2024): June 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i1.8429

Abstract

Technological advancements have had a significant impact on the education sector, including the application of Convolutional Neural Networks (CNN) for facial image analysis. This research aims to implement CNN to measure students' learning concentration levels. The FER2013 dataset, which includes seven emotion classifications and comprises 28,709 images for training data, is used as the database. The data is processed through rescaling and augmentation to prepare the CNN model. The model consists of several convolutional layers, pooling layers, and fully connected layers designed to extract crucial features from facial images. Evaluation results demonstrate a very high accuracy of 94.95% on training data, indicating that the model effectively recognizes complex patterns within the data. Although there is a higher loss value of 157% and a decreased accuracy of 62.75% on validation data, this suggests that the model possesses a strong foundational capability and can still be improved through further adjustments. With high accuracy in training and promising validation results, the model shows substantial potential for real-world application, where it can assist teachers in understanding students' emotional responses in real-time. The implementation of CNN aids educators in comprehending students' emotional responses and adapting their teaching methods more effectively, thereby creating a more conducive learning environment and enhancing students' academic and social development. These findings also open opportunities for further research to improve the performance and generalization of the model on unseen data, making this technology an increasingly reliable tool in education
IMPLEMENTASI HYBRID INTELLIGENCE SYSTEM UNTUK KLASIFIKASI BIJI-BIJIAN DENGAN ALGORITMA PCA DAN KNN Chan, Fajri Rinaldi; Ramadhanu, Agung
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6397

Abstract

Food security has become a pressing global issue with the increasing population and food consumption needs. Red kidney beans, peanuts, and sunflower seeds play a crucial role in meeting the nutritional needs of society and serving as raw materials for various industries. This study aims to develop a seed classification system based on the Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) algorithms. The system is designed to recognize three types of seeds—red kidney beans, peanuts, and sunflower seeds—to improve the efficiency and accuracy of the classification process compared to manual methods. The dataset consists of 58 seed image samples, divided into training data (48 samples) and test data (10 samples). The research stages include image preprocessing (cropping, background removal, and thresholding segmentation), feature extraction using PCA to reduce data dimensionality, and classification with KNN based on Euclidean distance. A value of K=3 is used in the KNN algorithm to determine the proximity between data points. The test results show a classification accuracy of 90%, with 9 out of 10 test data correctly classified. PCA successfully simplified high-dimensional data into two main components without significant information loss, while KNN demonstrated strong capability in distinguishing the three types of seeds. This research contributes to the development of an AI-based automatic classification system for the food industry, with broader potential applications in high-dimensional data processing across various fields.
Perancangan Aplikasi Pengelolaan Gudang Berbasis Android Menggunakan Android Studio Chan, Fajri Rinaldi; Harni Dusri; Muhammad Ramadani; Hanifah; Liza Efriyanti
Journal of Informatics and Advanced Computing (JIAC) Vol 3 No 2 (2022): Journal of Informatics and Advanced Computing (JIAC)
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/jiac.v3i2.4522

Abstract

Gudang merupakan salah satu bagian penting dalam suatu perusahaan, karena merupakan tempat penyimpanan produk yang akan dijual. Penanganan gudang yang efektif sangat diperlukan untuk memudahkan proses pengelolaan dan meningkatkan efisiensi. Salah satu solusi yang dapat digunakan adalah dengan membuat aplikasi pengelolaan gudang berbasis Android menggunakan Android Studio. Aplikasi ini dirancang untuk membantu dalam pengelolaan gudang dengan menyediakan fitur-fitur seperti penerimaan dan pengiriman barang, stok barang, dan laporan gudang. Aplikasi ini juga memiliki kemudahan dalam penggunaannya karena dapat diakses melalui perangkat Android sehingga memudahkan dalam pengelolaan gudang. Hasil dari penelitian ini adalah aplikasi pengelolaan gudang berbasis Android yang efektif dan mudah digunakan.
Segmentation and Classification of Vitamin C Content in Red Chili Pepper Images Using the Linear Discriminant Analysis (LDA) Method: Segmentation and Classification of Vitamin C Content in Red Chili Pepper Images Using the Linear Discriminant Analysis (LDA) Method Ramadhanu, Agung; Chan, Fajri Rinaldi; Yasmin, Nabilla; Negoro, Wahyu Saptha; Mardison, Mardison; Hendri, Halifia
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.149-162

Abstract

The vitamin C content in red chili peppers plays a crucial role in meeting nutritional needs, particularly in free nutritious lunch programs. Red chili peppers are one of the essential sources of vitamin C in daily consumption. However, vitamin C content in chilies can degrade due to storage and drying processes. This study develops a segmentation and classification method for vitamin C content in red chili pepper images using Linear Discriminant Analysis (LDA) as a faster and more efficient alternative to conventional laboratory methods. The dataset consists of 100 red chili images categorized into fresh and dried chilies. The analysis process includes preprocessing, feature extraction of color and texture (RGB, HSV, GLCM), dimensionality reduction, and classification using LDA. Experimental results show that this method achieves 99% accuracy on training data and 97% on test data, demonstrating that digital image processing can serve as a non-destructive approach for food quality estimation. This approach has the potential to be applied in food quality monitoring within the food industry and public nutrition programs.
PENINGKATAN METODE MEDIAN FILTER UNTUK IDENTIFIKASI DAN AKURASI JENIS PISANG EMAS DAN PISANG KAPAS Chan, Fajri Rinaldi; Yanti, Rahma; Ramadhanu, Agung
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol 8 No 2 (2024)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v8i2.4767

Abstract

Pengembangan teknologi dalam bidang pertanian telah membawa dampak signifikan, terutama dalam proses identifikasi dan klasifikasi hasil pertanian. Salah satu inovasi yang berpotensi meningkatkan efisiensi ini adalah teknologi pengolahan citra digital. Pisang, sebagai komoditas pertanian yang penting di Indonesia, memerlukan akurasi tinggi dalam klasifikasi, khususnya dalam membedakan jenis-jenis seperti Pisang Emas dan Pisang Kapas yang memiliki karakteristik visual mirip. Untuk itu, penelitian ini fokus pada peningkatan metode pengolahan citra untuk membedakan kedua jenis pisang tersebut. Metode yang digunakan adalah Median Filter, yang efektif mengurangi noise pada citra, namun terbukti kurang akurat dalam kasus dengan kemiripan visual tinggi. Penelitian ini bertujuan untuk mengembangkan dan menguji metode Median Filter yang ditingkatkan untuk meningkatkan akurasi dalam identifikasi jenis Pisang Emas dan Pisang Kapas. Hasil penelitian menunjukkan bahwa dengan peningkatan tersebut, tingkat akurasi identifikasi meningkat secara signifikan, mencapai 98% pada 35 citra yang diuji. Temuan ini membuka potensi untuk penerapan teknologi pengolahan citra dalam sistem klasifikasi otomatis di sektor pertanian, terutama dalam memastikan kualitas dan efisiensi distribusi produk pertanian.
Implementation of Convolutional Neural Networks (CNN) in An Emotion Detection System for Measuring Learning Concentration Levels Chan, Fajri Rinaldi; Firdaus Annas; Yulifda Elin Yuspita; Gusnita Darmawati
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 1 (2024): June 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i1.8429

Abstract

Technological advancements have had a significant impact on the education sector, including the application of Convolutional Neural Networks (CNN) for facial image analysis. This research aims to implement CNN to measure students' learning concentration levels. The FER2013 dataset, which includes seven emotion classifications and comprises 28,709 images for training data, is used as the database. The data is processed through rescaling and augmentation to prepare the CNN model. The model consists of several convolutional layers, pooling layers, and fully connected layers designed to extract crucial features from facial images. Evaluation results demonstrate a very high accuracy of 94.95% on training data, indicating that the model effectively recognizes complex patterns within the data. Although there is a higher loss value of 157% and a decreased accuracy of 62.75% on validation data, this suggests that the model possesses a strong foundational capability and can still be improved through further adjustments. With high accuracy in training and promising validation results, the model shows substantial potential for real-world application, where it can assist teachers in understanding students' emotional responses in real-time. The implementation of CNN aids educators in comprehending students' emotional responses and adapting their teaching methods more effectively, thereby creating a more conducive learning environment and enhancing students' academic and social development. These findings also open opportunities for further research to improve the performance and generalization of the model on unseen data, making this technology an increasingly reliable tool in education
Penerapan Image Processing untuk Identifikasi Jenis Pisang Emas dan Pisang Kapas Menggunakan Metode K-Means Clustering Yanti, Rahma; Chan, Fajri Rinaldi; Ramadhanu, Agung
Journal of Education Research Vol. 5 No. 4 (2024)
Publisher : Perkumpulan Pengelola Jurnal PAUD Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37985/jer.v5i4.1698

Abstract

Indonesia dikenal sebagai penghasil sumber pangan salah satunya pisang. Ada dua varietas pisang, yaitu pisang emas (Musa acuminata) dan pisang kapas (Musa balbisiana). Sering kali kita kesulitan dalam membedakan kedua jenis pisang secara visual, terutama ketika tidak dapat membedakan jenis pisang tersebut, sering menyebabkan kebingungan bagi konsumen dan pelaku usaha. Tujuan utama dari penelitian ini adalah mengembangkan sistem klasifikasi otomatis berbasis pengolahan citra yang mampu membedakan kedua jenis pisang tersebut. Metode yang diterapkan meliputi pengolahan citra dengan konversi dari ruang warna RGB ke LAB untuk memisahkan kecerahan dan warna. Proses segmentasi dilakukan menggunakan K-Means untuk mengelompokkan piksel berdasarkan kesamaan warna, diikuti dengan ekstraksi fitur geometris dan tekstur seperti Eccentricity, Energy, dan Homogeneity. Data yang digunakan mencakup 30 citra untuk pelatihan dan 10 citra untuk pengujian. Hasil penelitian menunjukkan bahwa metode ini berhasil mengklasifikasikan pisang emas dan pisang kapas dengan tingkat akurasi 97%, di mana 29 dari 30 citra diidentifikasi dengan benar. metode K-Means Clustering terbukti efektif dan akurat dalam membedakan kedua varietas pisang berdasarkan ciri fisik yang dihasilkan dari citra digital.
Co-Authors Annas, Firdaus Darmawati, Gusnita Firdaus Annas Gusnita Darmawati Halifia Hendri Hanifah Harni Dusri Liza Efriyanti, liza Mardison Mardison Muhammad Ramadani Negoro, Wahyu Saptha Ramadhanu, Agung Yanti, Rahma Yasmin, Nabilla Yulifda Elin Yuspita