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All Journal Journal of Computer Networks, Architecture and High Performance Computing International Journal of Information Engineering and Science IPSSJ
Andre Leto
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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PROTOTYPE OF INTERNET OF THINGS (IOT) IMPLEMENTATION IN WASTE MANAGEMENT TO SUPPORT SMART CITY MONITORING WITH ANDROID-BASED MOBILE APPLICATION USING FORWARD CHAINING METHOD Mohammad, Bawazir Fadhil; Dody Pintarko; Farhans, Muhammad Izzudin; Andre Leto; Ninis Herawati; Dwi Arman Prasetya; Anggraini Puspita Sari
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.5977

Abstract

Efficient waste management is one of the main challenges in supporting the implementation of the smart city concept. This research aims to develop a prototype of an Internet of Things (IoT)-based waste management system capable of monitoring the condition of waste bins in real-time through an Android-based mobile application. The system uses the forward chaining method to perform inference processes in decision making, such as identifying the status of the bin (empty, almost full, or full) based on integrated sensor data. The results show that the system is able to detect the volume of waste with high accuracy, send automatic notifications to operators or users when the bin reaches a certain condition, and provide practical solutions to optimise the waste collection process. With these features, the system not only improves operational efficiency but also supports cost reduction and environmental impact. The resulting prototype is expected to be the first step in the application of IoT technology in urban waste management to support the realisation of smart cities.
Serverless Computing: Kajian Literatur untuk Memahami Arsitektur dan Implikasinya di Era Cloud Computing Andre Leto
Integrative Perspectives of Social and Science Journal Vol. 2 No. 2 April (2025): Integrative Perspectives of Social and Science Journal
Publisher : PT Wahana Global Education

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Abstract

Serverless computing merupakan paradigma komputasi awan yang semakin diminati karena kemampuannya dalam menyederhanakan proses pengembangan aplikasi dengan menghilangkan kebutuhan untuk mengelola infrastruktur server secara langsung. Dengan model Function-as-a-Service (FaaS) dan pendekatan event-driven, serverless computing memungkinkan pengembang untuk fokus pada penulisan kode fungsional dan membayar hanya untuk eksekusi aktual, bukan penyediaan server secara berkelanjutan. Kajian ini bertujuan untuk memahami landasan teori serverless computing melalui telaah pustaka dari lima belas jurnal terindeks SINTA dan internasional yang relevan. Hasil kajian menunjukkan bahwa serverless computing telah digunakan secara luas dalam berbagai bidang seperti sistem informasi desa, e-commerce, aplikasi mobile kesehatan, serta implementasi green computing. Selain keunggulannya dalam efisiensi biaya dan skalabilitas otomatis, tantangan utama dalam penerapannya mencakup isu keamanan, latensi (cold start), serta keterikatan terhadap penyedia layanan (vendor lock-in). Dengan demikian, perlu pengembangan strategi desain arsitektur yang adaptif serta dukungan teknis untuk mitigasi risiko tersebut. Kajian ini diharapkan menjadi referensi awal bagi peneliti dan pengembang yang ingin mengimplementasikan pendekatan serverless dalam skala nasional maupun internasional, serta sebagai pijakan bagi studi lebih lanjut dalam pengembangan teknologi cloud yang berkelanjutan dan aman.
Customer Data Management Analysis for Customer Segmentation Using K-Means Clustering Method Andre Leto; Reza Aminullah; Ani Dijah Rahajoe
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.345

Abstract

This study aims to examine customer segmentation through K-Means clustering from a customer data management perspective, emphasizing the interpretive value of analytical results rather than solely their computational outcomes. The research addresses a critical issue in contemporary data-driven organizations, where customer analytics is often reduced to technical modeling without sufficient translation into managerial insights. To respond to this gap, the study adopts a qualitative interpretive approach embedded within a quantitative clustering process, positioning clustering as part of a broader information management cycle. The empirical analysis is based on the Mall Customers Dataset obtained from Kaggle, consisting of 200 customer records with numerical attributes representing age, annual income, and spending score. Quantitative processing using K-Means clustering was employed to identify customer segments, while qualitative interpretation was applied to analyze the managerial meaning of each cluster. Data interpretation was supported by analytical documentation, visualization outputs, and reflective analysis of cluster characteristics. The findings reveal four distinct customer segments with different behavioral and economic profiles, each carrying specific strategic implications for customer relationship management and marketing decision-making. The study demonstrates that the primary value of clustering lies not merely in segment formation, but in its ability to transform raw customer data into actionable managerial knowledge. In conclusion, this research contributes to customer analytics literature by integrating data mining techniques with qualitative interpretation, offering a more human-centered and decision-oriented framework for customer data management. Future research is encouraged to extend this approach using organizational case studies or participatory decision-making contexts.
Co-Authors Anggraini Puspita Sari Ani Dijah Rahajoe Dody Pintarko Dwi Arman Prasetya Farhans, Muhammad Izzudin Mohammad, Bawazir Fadhil Ninis Herawati Reza Aminullah