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All Journal Estudiante Law Journal Bulletin of Chemical Reaction Engineering & Catalysis Journal of Engineering and Technological Sciences
Farishi, Salman
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice

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Synthesis of MgFe2O4 Nanoparticles and its Application for Photodegradation of Methylene Blue Sukandar, Dede; Adawiah, Adawiah; Rohman, Saeful; Fitria, Shella; Saridewi, Nanda; Farishi, Salman; Nurhasni, Nurhasni; Aziz, Isalmi; Azizah, Yulyani Nur
Bulletin of Chemical Reaction Engineering & Catalysis 2024: BCREC Volume 19 Issue 4 Year 2024 (December 2024)
Publisher : Masyarakat Katalis Indonesia - Indonesian Catalyst Society (MKICS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/bcrec.20222

Abstract

Methylene blue wastewater from the paper, clothing, and textile industries can adversely affect aquatic ecosystems if improperly treated. One method to treat methylene blue pollutants in sewage is through photocatalysis techniques using magnesium ferrite (MgFe2O4) nanoparticle-based semiconductors. The MgFe2O4 is effective for methylene blue degradation because it is stable in aqueous systems, inexpensive, and has good photocatalytic activity. This study aims to synthesize MgFe2O4 nanoparticles with pumpkin seed extract (Cucurbita moschata) as a capping agent through a hydrothermal method. Characterization results show that MgFe2O4 nanoparticles synthesized with the addition of 3 mL pumpkin seed extract have a crystal size of 3.87 nm, cubic spinel structure, average particle size of 29 nm, and band gap energy value of 1.94 eV. The MgFe2O4 nanoparticles produced optimum degradation efficiency under mercury lamp irradiation with a degradation capacity of 391.98 mg/g at pH 12. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).
Examining Copyright Protection for Open Source Software: Legal Implications in Indonesia Farishi, Salman; Ayu, Cica Kusmira Sagita
Estudiante Law Journal VOL. 7 No. 2 JUNE 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33756/eslaj.v7i2.31640

Abstract

The development of information technology in Indonesia has increasingly driven the use of Open Source Software (OSS), which enables open collaboration among developers worldwide. However, the implementation of copyright protection for OSS in Indonesia faces challenges, as the basic principle of copyright, which grants exclusive rights to creators, contradicts the spirit of sharing that lies at the core of OSS. This article aims to analyze the challenges faced in implementing copyright for OSS in Indonesia and explore how the adaptation of copyright regulations can support OSS development. This research employs a descriptive qualitative approach with document analysis and interviews as data collection methods. Data sources are derived from relevant legal literature on copyright, OSS licenses, and interviews with software developers and legal experts in Indonesia. The findings indicate that the mismatch between the exclusive nature of copyright principles and the collaborative spirit of OSS is the main challenge in legal regulation. Moreover, limited understanding of OSS licenses among software developers in Indonesia exacerbates the potential for copyright violations. Therefore, this study recommends the need for updates to the Copyright Law to better support the concept of open licensing and to provide better education for developers regarding the legal implications of using OSS. Strengthening the monitoring system is also necessary to ensure that OSS developers in Indonesia can continue to innovate safely while respecting copyright.
Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8 Chitraningrum, Nidya; Banowati, Lies; Herdiana, Dina; Mulyati, Budi; Sakti, Indra; Fudholi, Ahmad; Saputra, Huzair; Farishi, Salman; Muchtar, Kahlil; Andria, Agus
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2024.56.1.5

Abstract

Corn is one of the primary carbohydrate-rich food commodities in Southeast Asian countries, among which Indonesia. Corn production is highly dependent on the health of the corn plant. Infected plants will decrease corn plant productivity. Usually, corn farmers use conventional methods to control diseases in corn plants. Still, these methods are not effective and efficient because they require a long time and a lot of human labor. Deep learning-based plant disease detection has recently been used for early disease detection in agriculture. In this work, we used convolutional neural network algorithms, namely YOLO-v5 and YOLO-v8, to detect infected corn leaves in the public data set called ‘Corn Leaf Infection Data set’ from the Kaggle repository. We compared the mean average precision (mAP) of mAP 50 and mAP 50-95 between YOLO-v5 and YOLO-v8. YOLO-v8 showed better accuracy at an mAP 50 of 0.965 and an mAP 50-95 of 0.727. YOLO-v8 also showed a higher detection number of 12 detections than YOLO-v5 at 11 detections. Both YOLO algorithms required about 2.49 to 3.75 hours to detect the infected corn leaves. This all-trained model could be an effective solution for early disease detection in future corn plantations.
Co-Authors adawiah adawiah, adawiah Ahmad Fudholi Andria, Agus Ayu, Cica Kusmira Sagita Banowati, Lies Budi Mulyati, Budi Chitraningrum, Nidya Dede Sukandar Fitria, Shella Herdiana, Dina Indra Sakti Isalmi Aziz Muchtar, Kahlil Nanda Saridewi, Nanda Nurhasni Nurhasni Saeful Rohman Saputra, Huzair Yulyani Nur Azizah