cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ronal Watrianthos
Contact Email
ronal.watrianthos@gmail.com
Phone
+6281263621335
Journal Mail Official
joseitjournal@gmail.com
Editorial Address
Professional Organization - Ikatan Ahli Informatika Indonesia (IAII) / Indonesian Informatics Experts Association Jalan Jati Padang Raya No. 41 Jati Padang Pasar Minggu 12540 South Jakarta - Indonesia http://iaii.or.id/
Location
Unknown,
Unknown
INDONESIA
Journal of Systems Engineering and Information Technology
Published by Organisasi Profesi IKATAN AHLI INFORMATIKA INDONESIA
ISSN : -     EISSN : 2829310X     DOI : https://doi.org/10.29207/joseit.*
Core Subject : Science,
Computer Science & IT
International Journal of Systems Engineering and Information Technology (JOSEIT) is an international journal published by Ikatan Ahli Informatika Indonesia (IAII / Association of Indonesian Informatics Experts). The research article submitted to this online journal will be peer-reviewed. The accepted research articles will be available online (free download) following the journal peer-reviewing process. The language used in this journal is English. JOSEIT is a peer-reviewed, blinded journal dedicated to publishing quality research results in Computers Engineering and Information Technology but is not limited implicitly. All journal articles can be read online for free without a subscription because all journals are open-access.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 2 Documents
 1 
Search results for , issue "vol. 3 no. 2 (2024)" : 2 Documents clear
Can Artificial Intelligence Fix Bad Environmental Regulation? Evidence from Green Total Factor Productivity in China Lihua Peng; Xingtong Lin; Qiuhan Luo
Journal of Systems Engineering and Information Technology (JOSEIT) Vol. 3 No. 2 (2024)
Publisher : Ikatan Ahli Informatika Indonesia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i2.8451

Abstract

Environmental regulation is often assumed to work the same way regardless of how it is designed — yet this study shows that command-and-control and market-incentive instruments pull green productivity in opposite directions, and that artificial intelligence (AI) does not treat them equally either. Drawing on panel data from 281 Chinese prefecture-level cities over 2012–2024, this study finds that command-and-control regulation, measured through text analysis of local government work reports, significantly suppresses green total factor productivity (GTFP) by raising compliance costs, while market-incentive regulation, measured as the ratio of pollutant discharge fee revenue to GDP, significantly promotes it by channeling price signals into innovation. GTFP is estimated using the SBM-ML index, which incorporates labor, capital, and energy inputs together with industrial pollution as an undesirable output. Interaction-term estimates further show that local AI development attenuates the negative effect of command-and-control regulation while amplifying the positive effect of market-incentive regulation — AI acts as a “buffer” for coercive regulation and an “amplifier” for market-based regulation, rather than a uniform productivity booster. The findings suggest that AI cannot simply substitute for well-designed environmental policy, but it can make both good and bad policy design matter less — or more — than they otherwise would.
Optimisation of Biotechnological Processes through a Combined Algorithm M Petrov
Journal of Systems Engineering and Information Technology (JOSEIT) Vol. 3 No. 2 (2024)
Publisher : Ikatan Ahli Informatika Indonesia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i2.8454

Abstract

Choosing a good initial point matters as much as the optimisation method itself when tuning a biotechnological process, yet the two most common approaches to getting there each carry a cost: fuzzy-set methods give a direct, transparent solution but scale poorly once the decision space grows large, while random-search methods scale well but do not on their own guarantee a well-chosen starting point. This paper develops a combined algorithm that pairs a random-search-with-back-step (RSBS) method for locating a good initial point with a fuzzy-sets-theory (FST) optimisation stage, so that the random search narrows the search region before the fuzzy method is asked to discretise it — directly addressing the scale limitation that otherwise limits fuzzy optimisation. The combined algorithm is applied to the initial-condition and feeding-rate optimal control problem for the fed-batch biotransformation of whey by a strain of Kluyveromyces marxianus var. lactis MC5 in a laboratory stirred-tank bioreactor, raising the optimisation.

Page 1 of 1 | Total Record : 2

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 5 No. 1 (2026) Vol 4 No 2 (2025) Vol 4 No 1 (2025) Vol. 3 No. 3 (2024) Vol 3 No 2 (2024) Vol. 3 No. 2 (2024) Vol 3 No 1 (2024) Vol 2 No 2 (2023) Vol 2 No 1 (2023) Vol. 2 No. 1 (2023) Vol 1 No 2 (2022) Vol 1 No 1 (2022) More Issue