cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Henderi
Contact Email
indexsasi@apji.org
Phone
+6282226535471
Journal Mail Official
indexsasi@apji.org
Editorial Address
Jl. Radin Inten II no.53 A. RT 7/RW 14, Duren Sawit, Kec. Duren Sawit, Kota Jakarta Timur, DKI Jakarta, 13440
Location
Unknown,
Unknown
INDONESIA
Software Engineering in Computing Systems
Published by Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia
ISSN : -     EISSN : 31240062     DOI : 10.66472
Core Subject :
Software Engineering in Computing Systems is a peer-reviewed academic journal that aims to advance research in software engineering practices and methodologies for the development of reliable, secure, and scalable computing systems, covering topics such as software architecture and system design, software development methodologies, software testing, verification and validation, DevOps and continuous integration and deployment, software engineering for embedded and real-time systems, secure and dependable software systems, as well as software maintenance and evolution. The journal is published in February, May, August, and November.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "vol. 1 no. 2 (2026): may: software engineering in computing systems" : 5 Documents clear
Success Factors of Government Digital Applications in Public Service Delivery: A Systematic Literature Review Rifqi Fahrudin; Zainal Arifin Hasibuan; Bobby Kurniawan; Sri Supatmi
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.395

Abstract

The rapid development of digital government applications has significantly transformed public service delivery; however, their success remains inconsistent due to the complexity of multiple influencing factors. Many government digital systems experience low adoption, usability challenges, and limited impact on service quality, indicating the need for a comprehensive understanding of the determinants of success. This study aims to identify and synthesize the critical success factors of government digital applications in public service delivery. To achieve this objective, a systematic literature review (SLR) was conducted using the Scopus database, applying a predefined search strategy and PRISMA-based screening process. From an initial set of 176 articles, 44 relevant studies were selected and analyzed using a coding framework to classify success factors into four dimensions: technological, organizational, user, and governance. The results show that digital government success is inherently multidimensional, with user-related factors such as trust, usability, and satisfaction emerging as the most dominant, while technological factors function as enabling components and organizational and governance factors ensure sustainability and effectiveness. Furthermore, the findings reveal significant research gaps, particularly the lack of integrated frameworks and the fragmented treatment of success factors in existing studies. This study concludes by proposing an integrated classification framework that provides a comprehensive understanding of digital government success and offers practical guidance for policymakers in designing more effective and sustainable digital public services.
The Integration Of Non-Academic Variables In Student Risk Assessment: A Conceptual Framework Hani Irmayanti; Eddy Soeryanto Soegoto; Hidayat Hidayat; Rio Yunanto; Zainal Arifin Hasibuan; Sri Supatmi
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.435

Abstract

Students’ success in completing their studies on time is a vital indicator of the quality of higher education management in Indonesia. However, high dropout rates pose a major challenge, often caused by institutions’ failure to detect warning signs of academic failure in a timely manner. The main issue lies in the current evaluation approach, which is reactive and limited to conventional academic indicators such as the Grade Point Average (GPA), thereby neglecting the psychosocial factors that influence performance. This study aims to develop a more comprehensive conceptual framework for the early detection of academic failure risk by integrating academic and non-academic dimensions. The methodology used is adapted from the Design Science Research Methodology (DSRM), focusing on the stages from problem identification to the design of the model artifact. The proposed approach is a hybrid model that combines traditional academic variables with non-academic variables, including psychological stress levels, self-efficacy, and social support. The design results indicate that this framework is capable of identifying “latent pressure” as a leading indicator of failure before a decline in academic performance occurs. The synthesis of this study confirms that the integration of non-academic variables enhances the model’s transparency and provides a more meaningful and targeted interpretation of risk factors. In conclusion, this framework provides a theoretical foundation for educational institutions to transition from reactive evaluation to a system of personalized, proactive interventions. The implementation of this model is expected to improve student retention through earlier and more targeted risk mitigation.
Global Trends and Framework Development of AI and IoT Integrated Waste Automation for Emerging Economies Sri Erina Damayanti; Brian Damastu Ridho Hutama; Popon Dauni; Jack Febrian Rusli
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.444

Abstract

Waste management in Indonesia faces extreme regional disparities, ranging from critical waste accumulation zones and circular economy transition zones to specific material deficit zones. The primary problem lies in the inability of conventional systems to process heterogeneous waste efficiently, which leads to the failure of sustainable environmental conservation. An intelligent solution is required to integrate physical technology with an adaptive policy evaluation system. This research develops a systematic framework for the development and evaluation of waste processing automation technology. The research stages begin with a Bibliometric Analysis and Systematic Literature Review (SLR) using metadata from Scopus and Web of Science to identify global trends via VOSviewer. Furthermore, this study integrates AI and IoT as primary instruments for nature conservation. Through the processing of large data volumes (Big Data) from IoT sensors, AI (such as Multi-Criteria Decision Making) performs predictive analysis to automatically evaluate three regional conditions. AI plays a crucial role in determining corrective actions, including optimizing the use of oxy-hydrogen (HHO) fuel in incinerators to suppress emissions and managing cross-regional waste logistics, thereby ensuring natural resources are preserved through precise and low-pollution waste elimination processes. This research generates intelligent governance patterns and actionable insights to guide system users, particularly local governments and industrial managers, in implementing appropriate waste processing technologies. This solution provides automated operational guidance that ensures energy efficiency and economic sustainability while maintaining ecosystem preservation through standardized waste processing based on the specific regional characteristics in Indonesia.
Implementation and Development of Information Material Summarization using Web-based Speech-to-Text and Artificial Intelligence Fahriza Ilmal Haq
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.508

Abstract

A implementation or discussion summary material information using speech to text and artificial intelligence web- based . From a summarization process there is a gap due to the provider information or speaker give A information However Still Not yet recorded or accept something information in a way accurate . At the summary stage needed recording from speaker Then entered to in stage transmission using the ASR model. After do stage transmission to be continued with AI authorization stage from the result text transmission to the summary process use artificial intelligence technology . The results provided by artificial intelligence there is A results the resulting summary For needs information.
Design and Development of an Android-Based Diagnostic System for Supra X Motorcycle Engine Damage Using Fuzzy Tsukamoto and Scrum Methodology Rayhan Noor Fathurrachman
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.539

Abstract

Recently, Indonesian online news media have frequently reported cases of repair shops providing inaccurate motorcycle damage diagnoses to gain higher profits. This practice disadvantages users, especially those with limited technical knowledge of their vehicles. Such diagnostic errors often lead to repair costs that do not align with the actual severity of the damage. To address this issue, a motorcycle damage diagnosis application based on the Fuzzy Tsukamoto method was developed, allowing users to identify their vehicle's condition based on observed symptoms. The Fuzzy Tsukamoto method was selected for its ability to handle data uncertainty and generate specific damage severity levels. The system receives symptoms as input from the user, which are then processed through fuzzy rules to determine the type and severity of the malfunction. This application was developed using Flutter for the user interface and Laravel as the backend for data processing. Through this application, it is expected that users can obtain an initial overview of their motorcycle's damage, enabling them to make more informed and accurate decisions.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems Vol. 1 No. 1 (2026): February: Software Engineering in Computing Systems