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All Journal International Journal of Electrical and Computer Engineering Jurnal Perangkat Nuklir Prima Aplikasi dan Rekayasa dalam Bidang Iptek Nuklir Jurnal Teknologi Sistem Informasi dan Aplikasi KOMMAS: Jurnal Pengabdian Kepada Masyarakat LOGIC : Jurnal Ilmu Komputer dan Pendidikan AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan
Atang Susila
Pusat Rekayasa Dan Perangkat Nuklir - Badan Tenaga Nuklir Nasional
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Other

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Implementasi Metode Topsis pada Sistem Pendukung Keputusan Pemilihan Produk Skincare untuk Jenis Kulit Sensitif di Klinik Estetiderma Cinere Azizah, Nur Nadiatul; Susila, Atang
AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan Vol. 3 No. 2 (2025): Jurnal AI dan SPK : Jurnal Artificial Inteligent dan Sistem Penunjang Keputusan
Publisher : CV. Shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Industri skincare terus berkembang seiring meningkatnya kesadaran akan pentingnya menjaga kesehatan kulit, terutama bagi individu dengan kulit sensitif. Klinik Estetiderma Cinere menghadapi kendala dalam memberikan rekomendasi produk karena konsultasi manual cukup memakan waktu, bersifat subjektif, dan kurang efisien bagi pelanggan online. Penelitian ini mengimplementasikan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) pada Sistem Pendukung Keputusan (SPK) untuk membantu pelanggan memilih produk skincare khusus untuk kulit sensitif secara objektif dan efisien. Sistem dirancang berdasarkan kriteria seperti kandungan bahan aktif, pH, tingkat iritasi, kelembapan, tekstur dan formulasi, serta harga. Hasil pengujian menunjukkan bahwa sistem berbasis TOPSIS terbukti efisien dengan tingkat efisiensi 86,04% menurut pelanggan dan 86,19% menurut pihak klinik. Hasil rekomendasi produk yang diberikan sistem terbukti akurat dengan rata – rata kesamaan isi Top-3 sebesar 95,6% dan kesesuaian isi+urutan 82,2% untuk produk moisturizer (krim pelembab), serta 91,1% dan 81,1% untuk produk serum. Dengan tingkat akurasi di atas 80%, sistem ini layak digunakan sebagai alat bantu pengambilan keputusan dalam pemilihan produk skincare untuk kulit sensitif secara objektif, efisien, dan berbasis data.
Tiny machine learning with convolutional neural network for intelligent radiation monitoring in nuclear installation Istofa, Istofa; Kusuma, Gina; Ningsih, Firliyani Rahmatia; Triyanto, Joko; Susila, I Putu; Susila, Atang
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp404-413

Abstract

This study focuses on developing an intelligent radiation monitoring system capable of operating on a low-power single-board computer (Raspberry Pi) for deployment in remote monitoring stations within nuclear facility environments. The proposed system utilizes a radionuclide identification method based on tiny machine learning (TinyML) with a convolutional neural network (CNN) architecture. The radionuclide dataset was acquired through measurements of standard radiation sources, with variations in distance, exposure time, and combinations of multiple sources-including Cs-137, Co-60, Cs-134, and Eu-152. The radiation intensity data from detector measurements were structured into a response matrix and subsequently converted into a grayscale image dataset for model training. Keras is used to design and train machine learning models, while Tensor Flow Lite is used to model size reduction. Experimental results demonstrate that the developed model achieves an accuracy of 99.338% for Keras model trained on computer and 84.568% after deployment on the Raspberry Pi. Furthermore, this study successfully designed and embedded the TinyML model into an environment radiation monitoring system at the PUSPIPTEK nuclear installation.
Co-Authors Ahmad Rifai Ari Satmoko Ari Satmoko Azizah, Nur Nadiatul Bayu, Nur Firmansyah, Rafli I Putu Susila Ibnu Hibban, Muhammad Istofa Istofa Kristiyanti Kristiyanti Kusuma, Gina Maulana, Ahmad Fajar Nano, Karno Ningsih, Firliyani Rahmatia Romadhon Romadhon Sahrul Bahri, Dede Sanda Sanda Sigit Bachtiar Supron Tri Harjanto Triyanto, Joko