Journal of Informatics, Electrical and Electronics Engineering

Pengembangan Platform Distribusi dan Penjualan Musik Digital Berbasis Web dengan Integrasi Payment Gateway untuk Mendukung Musisi Independen

Natasya Pritasandri Sipayung; Omega Rimba Gemilang — Sun, 21 Jun 2026 00:00:00 +0000

The growth of the digital music industry in Indonesia has increased the demand for music distribution platforms that not only support streaming services but also facilitate direct digital song sales for independent musicians. However, most existing platforms still lack integrated mechanisms for digital distribution, license management, and online payment processing within a single system. This study aims to develop a web-based digital music distribution and sales platform that integrates transaction management, digital file delivery, and a local Indonesian Payment Gateway. The system was developed using a client-server architecture based on RESTful API with MySQL/MariaDB support and Midtrans Payment Gateway integration to support various digital payment methods, including bank transfers, GoPay, OVO, QRIS, and credit cards. The software development process employed the Waterfall model due to its systematic stages and suitability for systems with clearly defined requirements from the initial analysis phase. System evaluation was conducted using Black-box testing on core functionalities, including user authentication, transaction management, payment integration, admin verification, and digital file distribution. The testing results indicate that all system functions operated according to requirements, with an average server response time below two seconds and Payment Gateway integration capable of processing transactions consistently and securely. This study demonstrates that the developed system can support a more structured and efficient digital music distribution ecosystem for independent musicians in Indonesia. The contribution of this research lies in presenting the first direct sales platform for digital music that technically integrates file-ownership-based sales, structured license management, a local Indonesian Payment Gateway (Midtrans), and an admin verification panel within a single unified system specifically designed for the Indonesian independent music ecosystem.

Analisis Pengembangan Startup SmartWaste AI Berbasis Internet of Things dan Artificial Intelligence Menggunakan Pendekatan Mixed Methods untuk Mendukung Smart City Berkelanjutan

Sri Titi Handayani, Eddy Soeryanto Soegoto, Tri Utomo Wiganarto — Tue, 30 Jun 2026 00:00:00 +0000

Population growth, urbanization, and economic activities have continuously increased the volume of municipal solid waste each year, while conventional waste management systems have not been able to cope with these growing challenges. Waste bin capacity monitoring is still largely conducted manually, resulting in delays in waste collection, waste accumulation, inefficient fleet utilization, and limited use of data to support decision-making. In addition, there is no integrated system that combines real-time monitoring, data analytics, and waste volume prediction to enable more effective waste management. This study aims to analyze the current condition of waste management, identify the potential application of the Internet of Things (IoT) and Artificial Intelligence (AI), and examine the development of the SmartWaste AI startup as an intelligent waste management solution to support sustainable Smart City initiatives. The research employed a mixed methods approach using an explanatory-descriptive design. Data were collected through in-depth interviews, observations, and documentation, and subsequently analyzed using the Miles, Huberman, and Saldaña interactive analysis model, complemented by a business feasibility analysis. The results indicate that the waste management system handles approximately 75 tons of waste per month, with major challenges including increasing waste volume, limited monitoring systems, and low community participation in waste segregation. The implementation of IoT has the potential to reduce waste collection delays by up to 50% and prevent approximately three waste accumulation incidents per month, while AI is capable of predicting waste volume with an accuracy exceeding 80%. The integration of these technologies through the SmartWaste AI startup is estimated to improve operational efficiency by 27.5%, reduce waste accumulation, accelerate service delivery, and support the realization of a cleaner, smarter, and more sustainable Smart City. Therefore, SmartWaste AI has the potential to become a strategic innovation in the digital transformation of waste management systems in Indonesia.

Sistem Pendukung Keputusan Alokasi Guru Berbasis Web Menggunakan Metode SAW dengan Pendekatan Bidang Keahlian

Ubaydillah Ubaydillah, Sefrika Entas — Tue, 30 Jun 2026 00:00:00 +0000

This study aims to develop a web-based Decision Support System to assist the allocation of teachers to subjects at Madrasah Ibtidaiyah Nurul Huda. The main problem addressed in this study is the manual teacher allocation process, which leads to subjectivity, mismatch of expertise, and imbalance in workload distribution. The system is designed to improve objectivity and efficiency in decision-making based on several criteria, namely educational background, competence, teaching experience, and teaching hour availability. The method applied in this study is the Simple Additive Weighting (SAW) method, a multi-criteria decision-making technique that calculates preference values based on predetermined criteria weights. The contribution of this study lies in the implementation of a specialization-based grouping approach in the SAW calculation process, resulting in rankings that are more relevant to subject requirements. The results show that in the general field, alternative A6 achieved the highest preference value of 0.948, while in the Islamic studies field, alternative A4 obtained a value of 0.958. The system is able to generate objective allocation recommendations and produces results consistent with manual calculations. Usability testing resulted in a score of 84.2%, which is categorized as very good, indicating that the system is feasible to support faster, more transparent, and efficient decision-making.