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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Jurnal Pendidikan Usia Dini Jurnal Keuangan dan Bisnis J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Management Studies and Entrepreneurship Journal (MSEJ) Andragogi : Jurnal Ilmiah Pendidikan Agama Islam Humantech : Jurnal Ilmiah Multidisiplin Indonesia Transekonomika : Akuntansi, Bisnis dan Keuangan Jurnal Keuangan dan Bisnis Jurnal Agroindustri Pangan Jurnal Informatika Ekonomi Bisnis JAIMO
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Journal : JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi)

 1 
PENGENALAN WAJAH PEGAWAI KANTOR DENGAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK BERBASIS ANDROID Harry Chandra; Lina
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 10 No. 2 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i2.22530

Abstract

Face is one of the elements used to identify identity between humans. The purpose of making this thesis is as a basic basis for developing an attendance system and making artificial intelligence that can identify humans through their faces. How to do data processing, the data taken comes from a video of office employees which lasts approximately 10 seconds. To make a program that can recognize the faces of office employees, the Convolutional Neural Network (CNN) method is used which will be trained to be able to distinguish each unique feature on the face to distinguish and recognize humans specifically. In performing facial recognition, office employees can provide input in the form of facial photos of office employees who have been trained and use the camera on a smartphone to perform face recognition directly. The faces of office employees used as targets for this CNN training came from Pt Eternal Indonesia, Faculty of Information Technology, Tarumanagara University, and Kekar ​​Clinic. The output of the application is the accuracy of each photo of the office employee's face given. The results of the confusion matrix test show that the trained model has an accuracy of 80.39%, a precision of 80%, a recall of 80%, and an f1-score of 80%.
Klasifikasi Kekuatan Struktur Beton Menggunakan Convolutional Neural Networks Johan Hartanto; Lina
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 10 No. 2 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i2.22543

Abstract

Concrete is one of the most important elements in building a building construction. Concrete is widely used because it has advantages compared to other construction materials. In addition, the development of concrete construction has increased rapidly compared to other constructions, especially in the way of making concrete to the technology and use of materials used. In its development, materials will increase so that experiments in the laboratory make the costs swell. Therefore, a research is proposed which is intended to help researchers as well as to provide a comparison of the use of the model used. The method used to classify will use the CNN model by producing output that will display the class categories on the variables that have been inputted. The test results on training data resulted in an accuracy of 86.04% and testing on test or validation data was 82.14% on the Adam optimizer and 83.25% on training data and 80.35% on test or validation data on RMSprop. After determining the model to be used, it is continued with the use of K-fold validation.
PENGENALAN AKTIVITAS MANUSIA DI SUPERMARKET DENGAN METODE LONG SHORT TERM MEMORY Kristian Davidson Runtu; Lina
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 10 No. 2 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i2.22552

Abstract

Since a long time ago, supermarkets have become people's destinations for shopping for various things such as food, cooking ingredients, cleaning products and others. Supermarkets are known for their very large and crowded places, making it difficult to monitor. Therefore, supermarkets need a system to help monitoring. With the development of technology, monitoring systems are increasingly advanced and one of the results of these technological developments is a system for recognizing human activities. By using OpenPose to obtain human skeleton data on the image and using the Long Short Term Memory method to perform recognition, testing of the training data was carried out so as to produce a precision value of 99%, recall 99%, and f1-score 99%. And real-time testing using a camera resulted in an accuracy value of 73% for the picking class, 87% for the standing class and 81% for the walking class.
PENGENALAN OBJEK MENGGUNAKAN METODE SINGLE SHOT MULTIBOX DETECTOR PADA BAHAN SEMBAKO Henry Tanujaya; Lina
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24067

Abstract

Bahan sembako adalah singkatan dari sembilan bahan pokok yang artinya diperlukan oleh masyarakat secara umum sebagai kebutuhan sehari – hari. Bahan sembako sangat beragam jenisnya seperti minyak, beras, susu, dan masih banyak lagi. Bahan sembako biasanya dapat ditemui di supermarket, toko eceran, maupun warung kecil. Supermarket, toko eceran, dan warung kecil menjadi penyedia banyak barang dan salah satunya bahan sembako untuk dibeli oleh masyarakat umum. Penyedia yang sangat memiliki banyak kebutuhan jenis bahan sembako biasanya terdapat di supermarket. Untuk supermarket dan toko eceran biasanya memiliki data stok barang masing – masing agar mengetahui jumlah barang mereka di rak penjualan. Pengecekan stok barang juga dilakukan untuk mengetahui tanggal kedaluwarsa, kualitas barang, dan lainnya. Metode Single Shot Multibox Detector sudah banyak digunakan untuk pengenalan objek atau pengenalan objek seperti aplikasi pengenalan benda, makhluk hidup, makanan, bahkan pengenalan wajah sekalipun. Kelebihan metode ini adalah kecepatan dan keamanan yang tidak kalah bagus dengan metode lain seperti YOLO dan Fast R-CNN. Jika dibandingkan, metode SSD dapat jauh lebih tinggi keakuratannya dan kecepatan proses pengenalan objek.
Identifikasi Jumlah Manusia Dalam Kerumunan Menggunakan Convolutional Neural Network Fernando; Lina
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24079

Abstract

Public space is a place that generally used by the community in order to meet their needs and where crowds are usually formed. In a crowd, the number of people can be the first indicator in an anomaly in where the more number of people exists in a crowd, the more supervision is needed on the crowd to prevent chaos or other things that are not desirable in public spaces. The need for a crowd-counting is certainly needed to facilitate supervision and also the awareness of people in crowds. This research meant to develop a system that can identifies a crowd based on the number of people that exist in the crowd and also give the number of people as an output. The system applied the Convolutional Neural Network (CNN) algorithm. The CNN model is trained using a labeled crowd dataset with a total of 4372 crowd photos. The CNN works as a regression model that will count the number of people from the feature extracted from the image. The evalution shows the Mean Absolute Error value achieved is 55.1176 in the test data.
PENGENALAN AKTIVITAS MANUSIA PADA SUPERMARKET MENGGUNAKAN OPENPOSE DAN CNN Lina; Alvian Wijaya
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 12 No. 2 (2024): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v12i2.31558

Abstract

Human activity recognition is a dynamic area within artificial intelligence. It involves identifying human actions during everyday tasks such as standing, sitting, and walking. One application of this technology is in supermarkets, where it can analyze consumer behavior or function as a surveillance tool to prevent theft. This particular study utilizes OpenPose and Convolutional Neural Networks (CNN) with a custom-collected dataset. The program detects human skeleton shapes from camera footage and classifies these shapes using CNN with the ResNet50 model, subsequently displaying the identified activities. The classified activities include standing, walking, picking up items, looking at items, and pushing a trolley. The testing results indicate a training accuracy of 99.76% and a validation accuracy of 96.52%, along with an accuracy score of 96.52%, a precision of 96.59%, a recall of 96.525, and an F1-score of 96.53%.
Co-Authors Alvian Wijaya Angelino, Kevin Angesty, Viviana Arilaha, Muhammad Asril Arum Chuandra, Fendy Dadan Suryana Ewaldo, Davin Farida Mayar Fendy Cuandra Fernando Gallen GP, Mahendra Ryansa Harry Chandra Hasugian, Chandiny HENDRI WIJAYA Henry Tanujaya Iqomatin, Durrotul Jenny Johan Hartanto Juli Meliza Khairma , Fitriana Sari Kristian Davidson Runtu Marliana Meliza, Juli Michael Setiawan, Michael Muniya Alteza Pandiangan, Lady Avilla Pansing, Marcelina Helen Penny Rahmawaty Pratama, Rheza Riyanto, Agustinus Sari, Novi Engla Satria, Sri Sitopu, Juwita Dearni Theresia Widyastuti Tondy Winarti Zega, Thea Putri Amabel