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All Journal Pixel : Jurnal Ilmiah Komputer Grafis Infotech Journal Jurnal ICT : Information Communication & Technology Jurnal Informatika Teknologi dan Sains (Jinteks)
Edvin Ramadhan
Universitas Jenderal Achmad Yani
Computer Science & IT Control & Systems Engineering Education

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DETEKSI OBJEK DAN JENIS BURUNG MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR INCEPTION RESNET-V2 Prima Nugraha; Agus Komarudin; Edvin Ramadhan
INFOTECH journal Vol. 8 No. 2 (2022)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v8i2.2889

Abstract

Banyaknya spesies burung membuat kita kesulitan untuk mengenali jenis burung dan diperlukannya pemahaman yang lebih khususnya dalam bidang zoologi. mengenali spesies burung secara manual merupakan tugas berat, di perlukannya SDM yang besar untuk mengidentitifikasi spesies burung apalagi jumlah yang akan akan di identifikasi begitu banyak dan juga memakan banyak waktu. Pada penelitian ini membuat sebuah sistem yang dapat mengenali spesies burung menggunakan citra gambar secara otomatis dengan menggunakan salah satu Arsitektur dari Convolutional Neural Network yaitu Inception Resnet V2, sehingga data citra tersebut dapat diekstraksi kemudian dapat mengenali spesies dari jenis burung. Yang bertujuan untuk melakukan pemantauan satwa khususnya burung dengan mengidentifikasi spesies burung secara otomatis, kemudian diharapkan masyarakat dengan mudah untuk mengenali jenis burung dan juga meningkatkan kemampuan kita untuk mempelajari dan melestarikan ekosistem khususnya ekosistem burung.
Kajian Peningkatan Kualitas Ekstraksi Fitur Berdasarkan Pola Gerakan Mata Untuk Kepentingan Rekognisi Edvin Ramadhan; Eddie Putra
Jurnal ICT: Information Communication & Technology Vol. 23 No. 1 (2023): JICT-IKMI, Juli 2023
Publisher : LPPM STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research examines how to recognize objects in digital images with results that can be explained logically like the perspective of the human eye. Pattern recognition techniques using statistical methods cannot provide a logical description of the recognized objects, because this concept considers the features used as probability classes, so the essence of the way the human eye sees cannot be demonstrated. The syntactic method is also not able to provide a logical description of the object that is recognized according to the perspective of the human eye, this concept prioritizes low-level features for the recognition process. So, in this research, we examine several syntactic and statistical recognition methods that adapt some of the standard abilities of the human eye. Features such as lines, chain codes, and colors have been able to define objects in images, and approach human reasoning. Simple Human Eye Movement Analysis, can help us to detect the relationship between line, true color, and chain code to show the object unity. We hope that developing this approach will enrich the object pattern recognition method to be simpler and faster.
SISTEM KLASIFIKASI UNTUK MENENTUKAN TINGKAT STRESS MAHASISWA SECARA UMUM MENGGUNAKAN METODE K-NEAREST NEIGHBORS Sopwatun Anisa; Agus Komarudin; Edvin Ramadhan
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 3 (2024): EDISI 21
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i3.4317

Abstract

Stress is often the main challenge faced by students due to academic and social demands in the educational environment. Factors such as nervousness, inability to control oneself, worry, etc. are several stress triggers, all of which can have a negative impact on students' physical and mental health. This research aims to identify the level of stress experienced by students using the K-Nearest Neighbors (KNN) method and evaluate the accuracy of the results of this research. The KNN method is used to classify student stress levels based on similarity or closeness to other data in the dataset. By using data taken from the data.world site, the results of this research show that the KNN method is able to achieve an accuracy of 91.58%. In addition, the precision, recall, and f1-score values are 76.10%, 73.11%, and 74.17% respectively. This research makes an important contribution in understanding student stress levels and shows the effectiveness of the KNN method in classifying stress data. It is hoped that these results will help in the development of better strategies for managing and reducing stress among college students.
IMPLEMENTASI ASSOCIATION RULE MINING DALAM MENGANALISIS DATA PENJUALAN SEPATU MENGGUNAKAN ALGORITMA FP-GROWTH Zalfa Salsabila Muliawati; Wina Witanti; Edvin Ramadhan
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 3 (2024): EDISI 21
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i3.4335

Abstract

Association Rule dan algoritma fp-growth digunakan sebagai kerangka kerja analisis data untuk mengidentifikasi hubungan atau asosiasi antar variable. Hasil dari penelitian ini adalah Men's Apparel dan Women's Apparel kemungkinan membeli Women's Athletic Footwear dan Men's Athletic Footwear dan Women's Athletic Footwear kemungkinan membeli Men's Appare 100%, membeli Men's Apparel, maka kemungkinan membeli Women's Athletic Footwear dan membeli Women's Athletic Footwear kemungkinan membeli Men's Apparel 95.65%, membeli Men's Apparel dan Women's Street Footwear kemungkinan membeli Women's Athletic Footwear dan membeli Women's Athletic Footwear dan Women's Street Footwear kemungkinan membeli Men's Apparel 91.67%, membeli Men's Athletic Footwear kemungkinan membeli Men's Apparel dan Women's Athletic Footwear dan membeli Men's Apparel dan Men's Athletic Footwear kemungkinan membeli Women's Athletic Footwear 85.71%,  membeli Women's Apparel kemungkinan membeli Men's Apparel dan Women's Athletic Footwear dan  membeli Women's Apparel dan Women's Athletic Footwear kemungkinan membeli Men's Apparel 83.33%. Hasil tersebut terlihat bahwa terdapat kecenderungan pembelian satu jenis produk sering diikuti oleh pembelian produk terkait lainnya.
Sistem Rekomendasi Snack and Beverages Menggunakan Metode Item Based Collaboration Filtering M. Riza Alfarrel; Wina Witanti; Edvin Ramadhan
Pixel :Jurnal Ilmiah Komputer Grafis Vol. 18 No. 2 (2025): Pixel :Jurnal Ilmiah Komputer Grafis dan Ilmu Komputer
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v18i2.3319

Abstract

In today's digital era, recommendation systems have become an integral part of supporting consumer purchasing decisions, including in the food and beverage industry. This study aims to develop a product recommendation system for snacks and beverages using the item-based collaborative filtering method. This method was chosen due to its ability to handle large-scale user and product data, as well as its efficiency in providing relevant recommendations based on user consumption patterns. In this study, the system calculates the average user rating and implements   Cosine Similarity to measure the similarity between products, resulting in more accurate recommendations. The system also evaluates the accuracy of recommendations using the Mean Absolute Error (MAE) metric. Based on the results obtained, which is 0.285403 for the average error on 17 items, the developed recommendation system can improve consumers' shopping experience, help them find products that suit their tastes, and support the sales of snacks and beverages products in the market
Co-Authors Eddie Putra Komarudin, Agus M. Riza Alfarrel Prima Nugraha Sopwatun Anisa Wina Witanti Zalfa Salsabila Muliawati