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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Teknoinfo Journal of Artificial Intelligence and Digital Business
Hardandrito, Awan Gumilang
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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PERANCANGAN PLATFORM CROWDSOURCING UNTUK KETERAMPILAN ADAT DAN KEBUDAYAAN MENGGUNAKAN MODEL RAD Atisina, Supardi; Fachrurrohman, Rozi Arfin; Fahrurrozi, Muhammad; Hardandrito, Awan Gumilang; Sumarsono, Sumarsono
Jurnal Teknoinfo Vol 19, No 1 (2025): January 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v19i1.4349

Abstract

Keterampilan adat dan kebudayaan merupakan faktor penting sebagai aset sebuah daerah yang dapat dijaga dan wariskan secara turun temurun. Namun masih minimnya keterlibatan masyarakat dalam memberikan pembelajaran secara terbuka tentang keterampilan adat dan kebudayaan sehingga adat dan kebudayaan. Penelitian ini memiliki tujuan untuk merancang sebuah platform crowdsourcing berbasis website yang dapat mengajarkan dan mewariskan adat dan kebudayaan kepada masyarakat. Perancangan platform crowdsourcing berbasis website menggunakan metode RAD. Metode RAD adalah pendekatan pengembangan perangkat lunak yang memprioritaskan siklus pengembangan yang cepat dan iteratif. Metode ini terdapat 3 tahap yaitu perencanaan kebutuhan, desain dan implementasi. Hasil penelitian ini  adalah peneliti mendapatkan hasil implementasi sesuai dengan perencanaan kebutuhan yang sudah ditetapkan yaitu kebutuhan fungsional dan kebutuhan nonfungsional. 5 kebutuhan fungsional adalah halaman Home, About, Course, Pages, Contact dan halaman pencarian. Kebutuhan nonfungsional juga dapat terealisasi yaitu keamanan, kinerja dan ketersediaan. Kesimpulannya adalah bahwa perancangan Platform Crowdsourcing berbasis website dapat berjalan dengan baik pada tiga tahap dari metode RAD.
Rancang Bangun Sistem Informasi Penerimaan Karyawan Pada PT. Doo Seung Global Berbasis Web Hardandrito, Awan Gumilang; Suhardoyo, Suhardoyo
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.663

Abstract

The development of information technology in the business world is getting tighter. With the support of quality human resources, it will support company management to be able to survive in its activities. The key to success in getting quality human resources lies in the employee recruitment stage. So it is necessary to have a better employee recruitment management system. Like PT. Doo Seung Global as a company engaged in the manufacture of knitted materials such as sweaters, cardigans, scarves and vests, the employee recruitment process still uses a manual system. This causes problems, including damaged or lost applicant data and takes a lot of time in the hiring process, and the availability of updated data is not guaranteed. To provide a solution, the authors designed a web-based employee recruitment information system, the authors used the waterfall model software development method to design an information system and used Unified Modeling Language (UML) diagrams, a website-based Employee Recruitment Information System at PT. Doo Seung Global can help the employee recruitment process become faster, precise and accurate in order to get employee candidates who meet the criteria
High Precision Deep Learning Model for Road Damage Classification using Transfer Learning Ghofur, Muhammad Abdul; Murdifin, Murdifin; Hardandrito, Awan Gumilang; 'Uyun, Shofwatul
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5707

Abstract

Roads are critical infrastructure that frequently experience damage, directly impacting transportation safety and efficiency. Manual road damage inspection is time-consuming and resource-intensive, highlighting the need for automated, image-based approaches. This study compares two Convolutional Neural Network (CNN) architectures—MobileNetV2 with transfer learning and a custom-built CNN—for classifying road surface damage severity. The dataset consists of 1,800 road surface images evenly distributed across three categories: good, minor damage, and severe damage. All images were normalized, augmented, and resized, followed by evaluation using 5-Fold Cross-Validation to ensure robust performance. Experimental results show that MobileNetV2 achieved an accuracy of 98%, outperforming the custom CNN, which achieved 89%. These findings demonstrate the effectiveness of transfer learning in improving classification accuracy with limited data and highlight the potential of MobileNetV2 for efficient, real-time road damage detection systems that can be integrated into intelligent infrastructure monitoring solutions.
Co-Authors Atisina, Supardi Fachrurrohman, Rozi Arfin Muh. Fahrurrozi MUHAMMAD ABDUL GHOFUR Murdifin, Murdifin Shofwatul ‘Uyun Suhardoyo Sumarsono Sumarsono