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International Journal of Computer and Information System (IJCIS)
Published by Institut Teknologi Bisnis AAS Indonesia
ISSN : -     EISSN : 27459659     DOI : https://doi.org/10.29040/ijcis
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
The aim of this journal is to publish quality articles dedicated to all aspects of the latest outstanding developments in the field of informatics engineering. Its scope encompasses the applications of (but are not limited to) : 1. Artificial Intelligence 2. Software Engineering 3. System Design Methodology 4. Data mining and Big Data 5. Human and Computer Interaction 6. Mobile Computing 7. Soft Computing 8. Animation 9. Multimedia and Image Processing 10. Parallel/Distributed Computing 11. Machine Learning 12. Computational Lingustics 13. Data Comunication 14. Networking
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 188 Documents
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Optimizing Capacity of a Hybrid Diesel-Solar PV-BESS on Nusa Penida Island Using a Load Following Approach Enggar Bowo Suasono, Oktavianus; Husnayain, Faiz
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.241

Abstract

Indonesia, as the world's largest archipelagic country, faces substantial challenges in achieving equitable energy access, particularly in remote regions. These areas are predominantly reliant on Diesel Power Plants (PLTD), which result in high operational costs, logistical complexities in fuel supply, and considerable carbon emissions. Despite these limitations, remote regions possess abundant renewable energy resources, particularly solar energy. However, the intermittency of solar generation due to weather fluctuations hampers its reliability as a primary energy source. To address these challenges, this study proposes the implementation of a hybrid energy system integrating Solar Photovoltaic (PV) systems and Battery Energy Storage Systems (BESS), supported by a load-following dispatch strategy and optimal capacity planning. The objective is to improve both the reliability and efficiency of the local power system. The study was conducted on Nusa Penida Island, specifically at the 20 kV Kutampi Substation, which is interconnected with the existing diesel power infrastructure. The methodology encompasses a comprehensive literature review, secondary data acquisition, manual system sizing, and simulation-based analysis. PV capacity potential was assessed using PVSyst software, while power flow and voltage simulations were performed for three operational scenarios: (i) existing diesel-only configuration (baseline), (ii) hybrid Diesel-PV-BESS configuration, and (iii) PV-BESS configuration without diesel generators. Power system simulations were carried out using a computer-based electrical analysis platform to evaluate the technical impact of integrating renewable energy into the local grid. Simulation results demonstrate that the integration of PV and BESS enhances voltage stability and ensures a more reliable energy supply. Furthermore, techno-economic analysis reveals that the hybrid Diesel-PV-BESS configuration yields the most favourable outcome, achieving a Levelized Cost of Energy (LCOE) of IDR 3,088 per kWh. These findings underscore the potential of hybrid renewable energy systems as a viable solution for sustainable energy development in remote island regions.
The Design of a Public Relations Services Information System for Creative Media State Polytechnic Using the Agile Development Method Yuliana, Citra Ayu; Kartika, Tipri Rose; Mawardi, Cholid
International Journal of Computer and Information System (IJCIS) Vol 6, No 3 (2025): IJCIS : Vol 6 - Issue 3 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i3.243

Abstract

Vocational colleges play a crucial role in producing a work-ready human resource with skills that align with industry demands through practice-based learning and direct application. In this context, effective communication between vocational colleges and prospective students becomes a critical aspect. One form of this communication is introducing vocational education through promotional and outreach activities conducted by the Polimedia Public Relations (Humas) department. The "Polimedia Open House" and "Polimedia Goes to School" are two main programs organized by Humas Polimedia, aiming to introduce the campus to prospective students and provide an overview of the educational system and facilities available. This research aims to design and develop an information system for registering for Public Relations services, specifically the Polimedia Open House and Polimedia Goes to School events. The system was built using the Laravel framework, applying the Software Development Life Cycle (SDLC) method with an Agile Development Cycle approach for subsequent implementation by schools wishing to visit Polimedia. The system development was conducted iteratively through the stages of planning, requirements gathering, design, development and testing, implementation, and review. This web-based information system was created using the PHP programming language and a MySQL database. The system is equipped with registration features for the Polimedia Open House and Polimedia Goes to School activities, a gallery of event documentation, registration status tracking, and contact information. With this system, it is expected to simplify the management of event information for the Public Relations department and attract more schools to visit.
Being Songwriter and Singer Using Suno as AI Music Generator: Creating English Songs for Young Learners in Teaching Vocabularies Fitria, Tira Nur
International Journal of Computer and Information System (IJCIS) Vol 6, No 3 (2025): IJCIS : Vol 6 - Issue 3 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i3.230

Abstract

This research simulates the use of Suno AI (https://suno.com/) in creating English songs for young learners in teaching vocabulary. This research is descriptive qualitative. The analysis shows that Suno offers AI-driven solutions encompassing song creation, sound processing, and data analysis, known for efficiently producing lifelike songs blending vocals with instruments or entirely instrumental compositions. Teachers utilize Suno AI to create English songs aimed at teaching children vocabulary, benefiting from its user-friendly tools regardless of musical expertise. This approach enhances vocabulary acquisition in engaging classroom settings, fostering creativity and emotional connections to learning materials. Teachers can customize songs to suit students' preferences and educational needs, promoting motivation and creative skills development. Using Suno AI involves visiting their website, registering, selecting a music genre and vocabulary theme (e.g., colors, numbers), and customizing generated melodies and lyrics to accommodate varying proficiency levels. The platform supports easy song creation, editing, and sharing, facilitating effective vocabulary learning through enjoyable musical experiences integrated into daily classroom activities. This innovative use of technology supports educators in enhancing language acquisition through engaging and accessible musical content, exemplified by the creation of an English children's song titled "Colors of the Rainbow." This song vividly teaches color vocabulary through descriptive lyrics and lively melodies, complemented by a "children's lively" music style that enhances its cheerful and educational impact on young learners. With Suno AI, English teachers can assume the roles of songwriters and musicians in education by utilizing its capabilities to create engaging songs tailored for teaching basic English vocabulary. Suno AI allows teachers to organize lyrics around new words, phrases, or language concepts they aim to teach, fostering a dynamic learning experience that seamlessly integrates language acquisition with musical expression. This innovative use of technology enhances educational engagement through enjoyable and effective teaching methods.Keywords: AI Music Generator, English song, Suno AI, vocabularies
Implementation of a Web-Based Malware Analysis System With Random Forest Integration Fauzan, Muhammad; Mawardi, Cholid; Asgawanti, Eka Desy
International Journal of Computer and Information System (IJCIS) Vol 6, No 3 (2025): IJCIS : Vol 6 - Issue 3 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i3.242

Abstract

With the rapid advancement of digital technology, the threat of malware has become increasingly prevalent and sophisticated, posing significant risks to both individuals and organizations. Despite the growing need for robust protection, many existing malware analysis tools are overly complex, often requiring advanced technical knowledge, which makes them less accessible to general users. To address this gap, this study proposes the development of a web-based malware analysis system that is both powerful and user-friendly. The system is built using the Streamlit framework, which allows for the creation of interactive and responsive web applications with minimal overhead. At its core, the system integrates a Machine Learning model based on the Random Forest algorithm, chosen for its high accuracy and robustness in classification tasks, particularly in distinguishing between benign and malicious files. For in-depth file analysis, the system connects to the MetaDefender API, which scans submitted files using multiple antivirus engines and provides comprehensive threat intelligence data. To further enhance accessibility, especially for users without a technical background, the GPT API is integrated to automatically generate simplified interpretations of complex scan results, explaining the findings in natural language. The system displays results using graphical visualizations, making it easier for users to comprehend potential threats without needing to interpret raw data or technical jargon. This visual and interactive approach supports real-time decision-making and improves user experience. The methodology employed in this research is quantitative, focusing on the evaluation of the system’s performance and the effectiveness of the Random Forest model in accurately classifying malware. Key performance metrics such as accuracy, precision, recall, and F1-score are used in the analysis. Overall, this system offers several competitive advantages: enhanced accessibility, improved ease of use, and simplified result interpretation compared to traditional malware analysis tools. The research contributes to the broader field of cybersecurity by providing a more practical and user-friendly solution for malware detection, thereby helping to raise awareness and improve protective measures against digital threats.
An Analysis of the Implementation of Artificial Intelligence (AI) in School-Based Learning Muqorobin, Muqorobin
International Journal of Computer and Information System (IJCIS) Vol 6, No 3 (2025): IJCIS : Vol 6 - Issue 3 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i3.238

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly reshaped various sectors, including education. In school-based learning environments, AI is being increasingly adopted to support diverse pedagogical and administrative functions. This study investigates the implementation of AI technologies in primary and secondary schools, with a focus on their impact on teaching practices, student engagement, and institutional management. Through a mixed-methods approach combining systematic literature review and qualitative interviews with educators across four countries (Indonesia, India, Finland, and the United States), this paper provides a nuanced analysis of how AI tools—such as intelligent tutoring systems, predictive analytics platforms, natural language processing (NLP), and automated assessment systems—are being deployed in classrooms. The results demonstrate that AI contributes positively to personalized learning experiences, enhances the efficiency of assessment and feedback mechanisms, and aids in streamlining school administration. However, the study also highlights persistent challenges, including disparities in infrastructure, ethical dilemmas related to data privacy and algorithmic bias, as well as a lack of comprehensive teacher training in AI integration. The research emphasizes the importance of human-centered AI design that supports—not supplants—teachers, and calls for inclusive policy frameworks that ensure equitable access and ethical use of AI in education. Recommendations include targeted professional development, stakeholder collaboration, and the incorporation of ethical guidelines in the deployment of AI systems in schools. This study contributes to the growing body of knowledge on AI in education and offers practical insights for policymakers, educators, and researchers aiming to harness AI's full potential while mitigating its risks.
The Impact of Website Interactivity on Users’ Speed in Finding Information: Evidence from Indonesia’s Top 5 Universities Setyadi, Agung Teguh; Mufid, Mohammad Robihul; Alia, Putri Ariatna; Fahruddin, Agus; Kriswibowo, Rony
International Journal of Computer and Information System (IJCIS) Vol 6, No 3 (2025): IJCIS : Vol 6 - Issue 3 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i3.245

Abstract

University websites serve as primary sources of information for prospective students and the general public. This study aims to examine the relationship between website interactivity and the efficiency of information retrieval at five of the top ten Indonesian universities according to the QS World University Rankings 2025: ITB, UGM, IPB, ITS, and UI. A total of 30 participants from various universities in Surabaya were asked to complete three types of information search tasks on two different university websites. The time taken to complete each task was recorded using a stopwatch. After completing the tasks, participants completed a questionnaire evaluating their perceptions of the websites' interactivity and ease of use. A Two-Way ANOVA revealed significant effects of university website, task type, and their interaction on task completion time. The findings highlight the crucial role of website structure and interactivity in enhancing users’ efficiency when searching for information. A significant interaction effect was found between website interactivity and task completion time (F = 4.395, p < .05).
The Prosumer's Role as a Driver of Energy Transition: A Study of Individual Power Producers in Jakarta, Indonesia Noviyanto, Harfiyan -; Sobirin, Ridwan -
International Journal of Computer and Information System (IJCIS) Vol 6, No 3 (2025): IJCIS : Vol 6 - Issue 3 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i3.244

Abstract

The transition to a sustainable energy system is an urgent priority at both global and national levels, aligning with Indonesia's target of achieving Net Zero Emissions (NZE) by 2060. Within this transition, the phenomenon of the "prosumer"—an individual who not only consumes but also produces energy, primarily through Rooftop Solar Power Plants (PLTS Atap)—has emerged. This study aims to conduct an in-depth analysis of the strategic role of prosumers within the metropolitan context of Jakarta using a techno-social approach. The methodology integrates a techno-economic assessment with social, policy, and user behavior dimensions. A primary focus is placed on the significant regulatory shift from the Ministry of Energy and Mineral Resources (MEMR) Regulation No. 26/2021, which featured a net-metering scheme, to MEMR Regulation No. 2/2024, which eliminates compensation for energy exported to the grid. This change fundamentally alters the prosumer's position from an active contributor of grid energy to a participant focused on optimizing self-consumption. Consequently, the integration of supporting technologies such as Battery Energy Storage Systems (BESS) and Home Energy Management Systems (HEMS) becomes critical to achieving system economic viability. The key finding of this study is that the success of prosumers as transition agents is highly dependent on the stability and supportiveness of public policy. Without consistent and favorable regulations, the strategic potential of prosumers to accelerate energy decentralization in Indonesia is at risk of remaining underdeveloped. Keywords: prosumer, energy transition, rooftop solar PV, techno-social, energy policy.
Swarm Intelligence Framework using Hybrid ACO–PSO for Lecture Scheduling in Higher Education Hidayat, Rahmad; Sri Lestari, Ninik; Sukirno, Sukirno; Rosmalina, Rosmalina; YS, Herawati; Ramady, Givy Devira; Suhana, Asep; Willa Permatasari, Raden; Sukandi, Ganjar Kurniawan; Afiyah, Salamatul; Aca, Rukman; Subawi, Handoko
International Journal of Computer and Information System (IJCIS) Vol 6, No 3 (2025): IJCIS : Vol 6 - Issue 3 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i3.252

Abstract

Complex combinatorial optimization problems that must meet various hard constraints and soft constraints occur in lecture scheduling. A feasible and high-quality schedule in limited computing time is often difficult to produce using conventional methods. In this study, a hybrid optimization model is proposed that combines Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), the aim of which is to improve solution quality and convergence speed. In this model, ACO builds solutions based on pheromone intensity and heuristic information, while PSO is used to dynamically adjust ACO parameters through learning from individual and global search experiences. The model is implemented using MATLAB R2023b and tested on real data involving 10 courses, 4 classrooms, and 6 time slots per day. The ACO+PSO approach is significantly able to reduce the penalty value. This approach reflects better fulfillment of constraints and is the result of experiments obtained. Compared to pure ACO, the hybrid method shows more consistent and stable performance in various trials. Visualization of parameter convergence also strengthens the effectiveness of this hybrid approach in finding the optimal parameter configuration. This research contributes to the development of an intelligent lecture scheduling system that is adaptive and aligned with institutional policies.
Evaluating Machine Learning Algorithms for Predictive Modeling of Large-scale Event Attendance Nugroho, Deni Kurnianto; Fauzy, Marwan Noor; Hidayat, Kardilah Rohmat
International Journal of Computer and Information System (IJCIS) Vol 6, No 3 (2025): IJCIS : Vol 6 - Issue 3 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i3.249

Abstract

Predicting attendance at large-scale public events is a critical task to support better resource planning, logistics, and safety management. This study investigates the performance of various machine learning models in forecasting event attendance using metadata features such as event type, venue, location, date, and duration. The dataset comprises over 19526 event records obtained from a U.S. government open data repository, covering multiple years and diverse event categories. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). Among the models tested, ensemble methods particularly Gradient Boosting Regressor and XGBoost outperformed others, achieving the lowest MAE (61.37 and 59.52, respectively) and the highest R² values (0.22 and 0.15). These results suggest superior generalization capability in capturing complex nonlinear patterns in the data. In contrast, linear models and simpler non-parametric methods such as Decision Trees and K-Nearest Neighbors (KNN) exhibited relatively weaker predictive accuracy, with R² scores close to or below 0.14. While the R² values indicate that metadata alone provides a limited view of attendance dynamics, the relatively low MAE across models implies that reasonable point predictions are still achievable. These findings highlight the potential of ensemble-based methods for baseline forecasting tasks. Furthermore, the study underscores the importance of incorporating richer feature sets such as pricing, weather, promotional activity, and social sentiment for future model improvement. This research provides a foundational benchmark for data-driven attendance forecasting and offers practical implications for event organizers seeking scalable, automated prediction tools to support strategic planning.
Uncovering WhatsApp Fraud Modus Operandi Through Digital Artifact Analysis and Cyber Kill Chain Mapping Ramadhani, Erika
International Journal of Computer and Information System (IJCIS) Vol 6, No 3 (2025): IJCIS : Vol 6 - Issue 3 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i3.254

Abstract

WhatsApp fraud has emerged as a significant cybercrime threat, exploiting the platform’s wide user base through social engineering and malware-based attacks. This study investigates a WhatsApp fraud case by analyzing digital artifacts to uncover the perpetrator’s modus operandi and provide structured guidance for law enforcement. Using the Digital Forensics for Incident Response (D4I) Framework in conjunction with Cyber Kill Chain (CKC) mapping, five key artifacts were identified and evaluated quantitatively based on their strength of evidence (v) and reliability (r). The results show that the malicious APK and source code containing a Telegram bot token constitute primary evidence with the highest probative value, while the Manifest.xml file and hidden background application serve as supporting evidence, and contextual indicators such as sender information provide limited legal weight. These findings highlight the importance of differentiating artifacts by evidentiary significance and demonstrate the value of the proposed scoring methodology. The study has limitations, as it is based on a simulated case and relies partly on expert judgment in scoring criteria. Future research should apply the approach to other platforms and fraud scenarios, and explore automation to enhance objectivity and scalability. Beyond its academic contributions, the study offers a structured rubric for prioritizing evidence and emphasizes the need for standardized evaluation frameworks in digital forensic policy and practice, ultimately strengthening the legal robustness and societal trust in digital investigations.

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