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All Journal Jurnal Abdimas Dedikasi Kesatuan Jurnal Informatika Kesatuan
Febri Damatraseta
Institut Bisnis dan Informatika Kesatuan
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PKM-Pendampingan Tehnik Pembuatan Konten Promosi Digital Bagi UMKM Kota Bogor Egi Adithia Pradana; Febri Damatraseta
Jurnal Abdimas Dedikasi Kesatuan Vol 2 No 2 (2021): JADKES Edisi Juli 2021
Publisher : LPPM Institut Bisnis dan Informatika Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jadkes.v2i2.1303

Abstract

Dalam membina dan mengembangkan UMKM di Kota Bogor, diperlukan wadah yang berfungsi sebagai pembinaan dan pelatihan yang dilakukan secara terus-menerus. Kegiatan Pengabdian Kepada Masyarakat (PKM) ini bertujuan agar UMKM yang bernaung dibawah LPPM Institut Bisnis dan Informatika Kesatuan dapat menambah kemampuan dan pengalaman dalam membuat konten digital terkait dengan promosi produk mereka. Dalam pelatihan ini menggunakan beberapa metode antara lain (1) ceramah untuk memaparkan pentingnya konten digital dalam promosi, (2) diskusi untuk mengetahui permasalahan yang dihadapi UMKM membuat konten digital, dan (3) simulasi sebagai sesi praktik sehingga diharapkan pelaku UMKM lebih memahami dan menguasai cara membuat konten dengan media digital sebagai sarana promosi bisnis. Kegiatan PkM-Pendampingan Tehnik Pembuatan Konten Promosi Digital Bagi Umkm Kota Bogor telah dilaksanakan dan berjalan dengan lancar tanpa hambatan berarti dan sesuai dengan tujuan yang ingin dicapai, serta mendapatkan hasil yang baik. 100 UMKM Juara Kota sebagai peserta pelatihan Bogor juga memberikan respon yang positif terhadap pelaksanaan pelatihan. Hal ini disebabkan mereka mendapatkan materi dari pelatihan ini sesuai dengan kebutuhan dan kegiatan pelatihan tidak monoton kegiatan pelatihan ini didukung sepenuhnya oleh Dinas UMKM Kota Bogor dengan bekerja sama dengan LPPM IBI Kesatuan sebagai penyelenggara kegiatan pelatihan, diharapkan kegiatan seperti ini akan terus ada secara kontinyu dan menghasilkan pelaku UMKM kota Bogor unggulan. Kata kunci: Media Digital, UMKM
Pelatihan Buzz Marketing Pada Sepatu Adidas Bambang Hengky Rainanto; Febri Damatraseta; Rafli Pradana
Jurnal Abdimas Dedikasi Kesatuan Vol 2 No 2 (2021): JADKES Edisi Juli 2021
Publisher : LPPM Institut Bisnis dan Informatika Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jadkes.v2i2.1305

Abstract

Buzz Marketing or word of mouth is an important attribute of Buzz Marketing. There are four attributes in Brand Equality from Brand Awareness , namely Association Brand ,, apartPrecieved Quality (perceived quality), Brand Loyalty (Brand Loyalty) and Other Proprientary Brand Assets (brand assets). other). There are four levels in Buzz Marketing, namely: Top Of Mind , Brand Recall , Brand Recognition , and Unware of Brand . In this review, it only focuses on "Overview of Buzz Marketing in Adidas Shoes", the purpose of this final project is to find out where Adidas is in thePyramid Buzz Marketing. Based on the research results show that Buzz Marketing has an important role in the introduction of adidas shoe products. Good marketing quality can foster high customer satisfaction. Keywords: Buzz Marketing, Top of Mind, Brand Recall, Brand Recognition Unaware of Brand
Real-time BISINDO Hand Gesture Detection and Recognition with Deep Learning CNN Febri Damatraseta; Rani Novariany; Muhammad Adlan Ridhani
Jurnal Informatika Kesatuan Vol 1 No 1 (2021): JIKES Edisi Agustus 2021
Publisher : LPPM IBI Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (213.887 KB) | DOI: 10.37641/jikes.v1i1.774

Abstract

BISINDO is one of Indonesian sign language, which do not have many facilities to implement. Because it can cause deaf people have difficulty to live their daily life. Therefore, this research tries to offer an recognition or translation system of the BISINDO alphabet into a text. The system is expected to help deaf people to communicate in two directions. In this study the problems encountered is small datasets. Therefore this research will do the testing of hand gesture recognition, by comparing two model CNN algorithms, that is LeNet-5 and Alexnet. This test will look for which classification technique is better if the dataset conditions in an amount that does not reach 1000 images in each class. After testing, the results found that the CNN technique on the Alexnet architectural model is better to used, this is because when doing the testing process by using still-image and Alexnet model data which has been released in training process, Alexnet model data gives greater prediction results that is equal to 76%. While the LeNet model is only able to predict with the percentage of 19%. When that Alexnet data model used on the system offered, only able to predict correcly by 60%. Keywords: Sign language, BISINDO, Computer Vision, Hand Gesture Recognition, Skin Segmentation, CIELab, Deep Learning, CNN.
Comparative Analysis Of Efficient Image Segmentation Technique For Text Recognition And Human Skin Recognition Septian Cahyadi; Febri Damatraseta; Lodryck Lodefikus S
Jurnal Informatika Kesatuan Vol 1 No 1 (2021): JIKES Edisi Agustus 2021
Publisher : LPPM IBI Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (205.573 KB) | DOI: 10.37641/jikes.v1i1.775

Abstract

Computer Vision and Pattern Recognition is one of the most interesting research subject on computer science, especially in case of reading or recognition of objects in realtime from the camera device. Object detection has wide range of segments, in this study we will try to find where the better methodologies for detecting a text and human skin. This study aims to develop a computer vision technology that will be used to help people with disabilities, especially illiterate (tuna aksara) and deaf (penyandang tuli) to recognize and learn the letters of the alphabet (A-Z). Based on our research, it is found that the best method and technique used for text recognition is Convolutional Neural Network with achievement accuracy reaches 93%, the next best achievement obtained OCR method, which reached 98% on the reading plate number. And also OCR method are 88% with stable image reading and good lighting conditions as well as the standard font type of a book. Meanwhile, best method and technique to detect human skin is by using Skin Color Segmentation: CIELab color space with accuracy of 96.87%. While the algorithm for classification using Convolutional Neural Network (CNN), the accuracy rate of 98% Key word: Computer Vision, Segmentation, Object Recognition, Text Recognition, Skin Color Detection, Motion Detection, Disability Application
Co-Authors Bambang Hengky Rainanto Egi Adithia Pradana Lodryck Lodefikus S Muhammad Adlan Ridhani Rafli Pradana Rani Novariany Septian Cahyadi