Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science JENDELA PENGETAHUAN INTERNAL (Information System Journal)
Novrian, Willi
Unknown Affiliation
Computer Science & IT Education Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Penerapan Deep Learning pada Pengolahan Data Citra dan Klasifikasi Udang Vaname Menggunakan Algoritma Convolutional Neural Network Astiti, Sarah; Nopriadi, Nopriadi; Novrian, Willi; Putra, Yusran Panca
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5418

Abstract

Deep learning-based shrimp image processing has become a rapidly growing research field in recent years. This technology aims to increase efficiency and accuracy in various applications related to the fishing and aquaculture industry, such as monitoring shrimp health, disease detection, species classification, and assessing the quality and quantity of harvested crops. Based on observations to date, fish sellers and buyers in the market have difficulty distinguishing vaname shrimp cultivated in tarpaulin ponds and earthen ponds. This research aims to apply deep learning techniques to determine the classification of Litopenaeus vannamei shrimp cultivation results in earthen ponds and tarpaulin ponds. To facilitate this research, the author uses a classification method by applying two Convolutional Neural Network (CNN) architectures, namely Visual Geometry Group-16 (VGG-16) and Residual Network-50 (ResNet-50). The dataset used in this research is 2,080 images per class of vannamei shrimp from two types of shrimp ponds. The results of this research are learning rates of 0.001 and 0.0001 on the Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (ADAM) optimizer to evaluate their effectiveness in model training. The VGG-16 and ResNet-50 models were trained with a learning rate parameter of 0.0001, taking advantage of the flexibility and reasonable control provided by the SGD optimizer. Lower learning rate values ​​were chosen to prevent overfitting and increase training stability. Model evaluation showed promising results, with both architectures achieving 100% accuracy in classifying vannamei shrimp from ground and tarpaulin ponds. The conclusion of this research is to highlight the superiority of using SGD with a learning rate of 0.0001 versus 0.001 on both architectures, then the significant impact of optimizer selection and learning rate on the effectiveness of model training in image classification tasks
Financial Feasibility Study and Development of Technical Specifications for Coffee Agroforestry for Community Forest Farmers with Revolving Fund Support for Productivity Optimization Oktoyoki, Hefri; Pratama, Benny; Putra, Yusran Panca; Novrian, Willi; Douni, James Byeker; Ansiska, Paisal
JENDELA PENGETAHUAN Vol 17 No 2 (2024): JENDELA PENGETAHUAN
Publisher : Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/jp17iss2pp239-251

Abstract

This research investigates coffee agroforestry systems' financial feasibility and technical specifications in community forests (HKm). Coffee agroforestry is acknowledged for its potential to enhance biodiversity, increase land productivity, maintain ecological balance, and provide economic benefits for small-scale farmers. The study employs a mixed-methods approach, utilizing in-depth interviews, field observations, and questionnaires to gather primary data while incorporating reports and literature as secondary data sources. The analysis reveals that the coffee agroforestry system in HKm demonstrates financial viability, with a positive NPV, a BCR exceeding 1, and an IRR of 33%. However, challenges such as insufficient technical knowledge and limited financial support persist as significant barriers. Furthermore, this research develops technical specifications, including selecting high-quality coffee seeds, shoot grafting techniques, fertilization, and pest management practices. These specifications are intended to serve as a practical guide for farmers adopting coffee agroforestry systems, enabling them to improve productivity and economic well-being. Moreover, the findings of this study provide a solid foundation for developing policies that encourage the widespread adoption of coffee agroforestry. As a result, this research makes notable contributions to both the scientific and practical aspects of coffee agroforestry for Community Forest farmers, paving the way for further research and formulating comprehensive strategies to address the challenges farmers face.
Penerapan Data Mining Clustering terhadap Perekonomian di Kelurahan Sawah Lebar Menggunakan Algoritma K-Means Ayi Purnama, Soni; Novrian, Willi; Ramadani, Niska
INTERNAL (Information System Journal) Vol. 7 No. 2 (2024)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v7i2.1191

Abstract

This study aims to analyze the economic conditions of the community in Sawah Lebar Village, Ratu Agung District, Bengkulu City, Bengkulu Province, using the data mining clustering method based on the K-Means algorithm. The K-Means algorithm is applied to group economic data of the community based on several variables, such as Employment Status, Home Ownership, Dependents, Monthly Income, Monthly Expenditure and Age of Head of Family. Through this clustering process, several groups (clusters) are produced that identify different economic patterns in the region, namely Cluster_0 (C0), Cluster_1 (C1) and Cluster_2 (C2). In order for the results of this study to be accurate, the data processing in this study uses the Rapidminer Studio Application. From the results of the data processing that has been carried out, it was found that for C0 there are 17 data, C1 there are 16 Data and C2 there are 17 Data. Then, for the recommendation of the community that is recommended to receive government assistance, it is the community that is in the lowest economic class, namely Cluster_1 (C1), Cluster_2 (C2) is the community with the highest economy, while the community in Cluster_0 (C0) is the middle economy.  
Co-Authors Ansiska, Paisal Ayi Purnama, Soni Douni, James Byeker Niska Ramadani, Niska Nopriadi Nopriadi Oktoyoki, Hefri Pratama, Benny Putra, Yusran Panca Sarah Astiti S.Kom., M.MT