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All Journal Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Agus Sigit Sumarsono
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science

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PENERAPAN METODE NEURAL NETWORK DENGAN STRUKTUR BACKPROPAGATION UNTUK MEMPREDIKSI KEBUTUHAN STOK PADA TOKO UMKM PERLENGKAPAN BAYI BABYQU Dadang Iskandar Mulyana; YkhSanur; FikriYadi; Sahroni; Agus Sigit Sumarsono
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.131

Abstract

At this time Machine Learning especially Deep Learning is developing very quickly in any field, especially in the fields of business, transactions, stock predictions, sales, and stocks and the like. Machine Learning has become a mainstay for facilitating and helping work. At the BabyQu store, the decisions used to carry out stock inventory still use the manual method, and there are several problems that arise including the occurrence of excess stock which makes other storage areas used for excess stock, especially if there are products that have an expiration date, and if there is a shortage of stock, problems will occur that will make consumers who need products or goods go to other places and several other problems that will arise which will cause losses to BabyQu stores, it is necessary to create a system or software that aims to assist BabyQu stores in forecasting stock availability product to overcomesome of these problems. This research was made using the Artificial Neural Network (ANN) model design and method using Backpropagation as the algorithm because this algorithm can reduce the percentage of errors. The results we got in this study using the 3-3-1 model obtained an accuracy rate of 85.72%, using 550 iterations of epochs, and taking approximately 11.1 seconds.
OPTIMASI KLASIFIKASI DETEKSI SENJATA API PADA ANIMASI BLACK LAGOON DENGAN ALGORITMA DEEPSORT DAN KALMAN FILTER Dadang Iskandar Mulyana; Agus Sigit Sumarsono
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.424

Abstract

As technology develops, especially in the field of artificial intelligence, many new systems and programs are created with artificial intelligence, one of which is the field of computer vision, namely the detection of objects ranging from transportation, humans, masks, animals, and even firearms detection systems. However, this system still has deficiencies in the level of accuracy when complex conditions such as lighting and others. Currently firearms detection systems use datasets in the form of photos of real weapons, rarely or even no one uses other datasets such as games or animations to determine the performance of the detection system. This study aims to determine the performance of the firearms detection and classification system by experimenting with objects from an animation with a public dataset from the website www.imfdb.org/Black_Lagoon as well as several scenes from other animations and also an animation entitled Black Lagoon, then optimization is carried out.with the DeepSORT algorithm and Kalman Filter assisted by the YOLOv3 detection system, several percentage values were obtained such as a mAP value of 82.13%, 81.19% precision, and 78.53% recall in the training dataset using 100 iterations of epochs, and in the training model got a loss value of 8.27% and 6.67% for the val_loss value using 50 iterations of epochs.
PENERAPAN METODE NEURAL NETWORK DENGAN STRUKTUR BACKPROPAGATION UNTUK MEMPREDIKSI KEBUTUHAN STOK PADA TOKO UMKM PERLENGKAPAN BAYI BABYQU Dadang Iskandar Mulyana; YkhSanur; FikriYadi; Sahroni; Agus Sigit Sumarsono
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.131

Abstract

At this time Machine Learning especially Deep Learning is developing very quickly in any field, especially in the fields of business, transactions, stock predictions, sales, and stocks and the like. Machine Learning has become a mainstay for facilitating and helping work. At the BabyQu store, the decisions used to carry out stock inventory still use the manual method, and there are several problems that arise including the occurrence of excess stock which makes other storage areas used for excess stock, especially if there are products that have an expiration date, and if there is a shortage of stock, problems will occur that will make consumers who need products or goods go to other places and several other problems that will arise which will cause losses to BabyQu stores, it is necessary to create a system or software that aims to assist BabyQu stores in forecasting stock availability product to overcomesome of these problems. This research was made using the Artificial Neural Network (ANN) model design and method using Backpropagation as the algorithm because this algorithm can reduce the percentage of errors. The results we got in this study using the 3-3-1 model obtained an accuracy rate of 85.72%, using 550 iterations of epochs, and taking approximately 11.1 seconds.
OPTIMASI KLASIFIKASI DETEKSI SENJATA API PADA ANIMASI BLACK LAGOON DENGAN ALGORITMA DEEPSORT DAN KALMAN FILTER Dadang Iskandar Mulyana; Agus Sigit Sumarsono
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.424

Abstract

As technology develops, especially in the field of artificial intelligence, many new systems and programs are created with artificial intelligence, one of which is the field of computer vision, namely the detection of objects ranging from transportation, humans, masks, animals, and even firearms detection systems. However, this system still has deficiencies in the level of accuracy when complex conditions such as lighting and others. Currently firearms detection systems use datasets in the form of photos of real weapons, rarely or even no one uses other datasets such as games or animations to determine the performance of the detection system. This study aims to determine the performance of the firearms detection and classification system by experimenting with objects from an animation with a public dataset from the website www.imfdb.org/Black_Lagoon as well as several scenes from other animations and also an animation entitled Black Lagoon, then optimization is carried out.with the DeepSORT algorithm and Kalman Filter assisted by the YOLOv3 detection system, several percentage values were obtained such as a mAP value of 82.13%, 81.19% precision, and 78.53% recall in the training dataset using 100 iterations of epochs, and in the training model got a loss value of 8.27% and 6.67% for the val_loss value using 50 iterations of epochs.
Co-Authors Dadang Iskandar Mulyana` FikriYadi Sahroni YkhSanur