Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal JSAI (Journal Scientific and Applied Informatics) Envirous Journal of Elektronik Sistem InformasI
Aziz, Faisal
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Public Health

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

Found 3 Documents
Search

 1 
Aplikasi Pemantauan Lalu Lintas Kapal di Perairan Laut dengan Menggunakan Metode Haversine Formula Ani, Nur; Catra Pratama, Suga; Aziz, Faisal; Fardiansyah, Dwiki
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.6443

Abstract

By leveraging spatial data, new approaches to vessel traffic safety risk assessment can be developed, enabling a better understanding of vessel trajectories and improving overall maritime safety in congested and hazardous aquatic environments. This study aims to design an application for monitoring ship movement traffic in marine waters by applying the Haversine Formula algorithm. This method is used for calculating the distance between the nearest ports from the position of the ship, this aims to facilitate decision making in case of emergencies. From the experimental results, the comparison of values with Google Maps gets an average of 10.3 with the smallest difference of 2 and the largest 34, this still indicates that the calculation of the estimated distance of the ship to the nearest port using the haversine formula in the ship traffic monitoring dashboard system has slightly better accuracy than using the Google Maps application. The design of the system prototype has been successfully carried out based on the design of use case diagrams, activity diagrams, and class diagrams as well as the design of the interface display, has modules including; traffic module, ship module, voyage module, history module, port module.
Implementation of Machine Learning for Disease Detection in Tomato Plants Using Convolutional Neural Networks Aziz, Faisal; Suryana, Nana
JESII: Journal of Elektronik Sistem InformasI Vol 2 No 2 (2024): Journal of Elektronik Sistem InformasI - JESII (DECEMBER)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v2i2.3580

Abstract

Diseases in tomato plants can be highly detrimental to tomato farmers, with common afflictions such as begomovirus, blight, and spider mites posing significant challenges. The implementation of machine learning offers a promising solution to address these issues and mitigate the financial losses caused by such diseases. This study aims to evaluate the effectiveness of machine learning in detecting plant diseases using Convolutional Neural Networks (CNN). The data used in this implementation was obtained from public datasets available on Kaggle and real-time data collected directly from tomato farms in Kadudampit, Sukabumi Regency. The Kaggle dataset contains 4,800 images categorized into three classes: begomovirus, blight, and spider mites. Meanwhile, the real-time dataset comprises 450 images, also divided into the same three classes. The performance of the machine learning model was tested using different datasets, with accuracy measured through a confusion matrix. The results showed that the machine learning model trained on the public dataset achieved the highest accuracy of 97%. The model trained on a combination of the public and real-time datasets achieved an accuracy of 94%, while the model trained solely on the real-time dataset achieved an accuracy of 80%. A machine learning model is considered effective if its accuracy exceeds 75%. Therefore, based on the three tests conducted, it can be concluded that the machine learning models demonstrated a good level of accuracy in detecting diseases in tomato plants
PENGARUH JUMLAH CACING TANAH (LUMBRICUS RUBELLUS) DAN WAKTU PENGOMPOSAN TERHADAP C/N RASIO VERMIKOMPOSTING DARI SLUDGE IPAL PT SURABAYA INDUSTRIAL ESTATE RUNGKUT (SIER) Aziz, Faisal; R., Naniek Ratni J.A.
Envirous Vol. 2 No. 1 (2021): Jurnal Envirous
Publisher : UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/envirous.v2i1.89

Abstract

Proses pengolahan limbah PT SIER, menghasilkan buangan sampingan berupa lumpur yang berasal dari proses pengolahan lumpur aktif. Diperlukan pengolahan tambahan untuk mereduksi bahan organik, salah satunya dengan proses pengomposan dengan metode vermikomposting dengan bantuan cacing tanah (Lumbricus rubellus). Penelitian ini bertujuan untuk mengetahui pengaruh jumlah cacing tanah (Lumbricus rubellus) terhadap C/N Rasio vermikomposting dari sludge IPAL PT Surabaya Industrial Estate Rungkut (SIER) dan mengetahui pengaruh waktu pengomposan terhadap C/N Rasio vermikomposting dari sludge IPAL PT Surabaya Industrial Estate Rungkut (SIER). Penelitian ini menggunakan ukuran reaktor dengan tinggi 20 cm, jenis cacing tanah (Lumbricus rubellus) dengan panjang 7-10 cm, serbuk gergaji kayu sebanyak 1,5 kg dan sludge sebanyak 5 kg dengan waktu sampling 7 hari, 14 hari, 21 hari, 28 hari, dan 32 hari. Hasil dari penelitian ini didapatkan bahwa pengomposan dengan menggunakan cacing tanah (Lumbricus rubellus) dapat menurunkan kandungan rasio C/N sebesar 14,89 pada reaktor 5 dengan jumlah cacing 30 ekor pada pengomposan hari ke 32 dan penurunan terendah 23,51 pada reaktor 1 dengan jumlah cacing 10 ekor dengan waktu pengomposan 32 hari.
Co-Authors Catra Pratama, Suga Fardiansyah, Dwiki Nana Suryana Nur Ani R., Naniek Ratni J.A.