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Rancang Bangun Alat Pengisi Saos Otomatis Berbasis Arduino UNO Muhammad Fauzi Alfikri; Arie Atwa Magriyanti; Danang Danang
Jurnal Penelitian Rumpun Ilmu Teknik Vol. 1 No. 2 (2022): Mei : Jurnal Penelitian Rumpun Ilmu Teknik
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1549.288 KB) | DOI: 10.55606/juprit.v1i2.580

Abstract

Mengisi saos ke dalam botol secara manual adalah pekerjaan yang cukup merepotkan. Proses produksi yang seharusnya bisa lebih efektif dan efisien menjadi sedikit terhambat. Keadaan ini menimbulkan kekhawatiran bagi perusahaan yang memerlukan proses produksi secara singkat dan akurat demi memenuhi kebutuhan konsumen agar tepat waktu. Hal ini membuat perusahaan yang bergerak di bidang F&B bernama JJCOW sedikit mengalami kewalahan. Perusahaan ini bergerak di bidang barbeque home service. Salah satu produk JJCOW yang populer adalah saos signature JJCOW. Kebutuhan konsumen akan produk ini cukup tinggi. Saat ini, JJCOW masih memproduksi saos signature ke dalam botol menggunakan cara manual sehingga membuat proses produksi sedikit terhambat dan memakan waktu yang cukup lama. Berdasarkan latar belakang di atas, penulis ingin memudahkan proses produksi di JJCOW dengan membuat alat pengisi saos otomatis berbasis Arduino UNO agar lebih efektif dan efisien. Untuk membuat alat ini, komponen yang diperlukan yaitu mikrokontroller Arduino UNO, kabel jumper, breadboard, sensor infrared, relay 1 channel, lcd 16x2, i2c 1206, push button, pompa diafragma 12v 2a, dan adaptor 12v 3a. Setelah melakukan uji coba, waktu yang diperlukan untuk pengisian satu botol saos sangat sedikit. Untuk pengisian satu botol saos, waktu yang diperlukan tidak sampai 2 detik.
Hybrid Subword–Character Representation for Robust Sentiment Classification on Multilingual and Code-Mixed Indonesian Text Danang Danang; Toni Wijanarko Adi Putra
Journal of Creative Student Research Vol. 2 No. 6 (2024): Desember : Journal of Creative Student Research
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsr-politama.v2i6.6153

Abstract

User-generated Indonesian text frequently exhibits code-mixing with English (“Indonglish”), informal spelling, elongation, and keyboard typos. These phenomena break subword tokeniza tion assumptions and may degrade multilingual Transformer performance in deployment. This paper studies a hybrid representation that fuses XLM-R sentence features with a character-level CharCNN branch designed to capture orthographic patterns and mitigate character noise. We evaluate (i) a standard XLM-R fine-tuning baseline, (ii) an ablation that removes the character branch (NusaX only), and (iii) the proposed hybrid model on two datasets: NusaX-Senti (12 regional languages) and Indonglish (Indonesian–English code-mixed sentiment). Beyond clean test performance, we introduce a controlled robustness protocol by injecting character-level perturbations with probability p=0.18 and measuring performance drop. Results show that the XLM-R baseline achieves the best clean Macro-F1 on both datasets, while the hybrid model substantially improves robustness on Indonglish by reducing Macro-F1 drop from 0.030 to 0.007 under noise. We analyze common error confusions and discuss when character-aware features help or harm across languages.
Analysis of EfficientNet-B0 with Sample Reweighting and Early-Learning Regularization for Food Recognition under Label Noise Danang Danang; Toni Wijanarko Adi Putra
Journal of Creative Student Research Vol. 3 No. 6 (2025): Journal of Creative Student Research
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsr-politama.v3i6.6154

Abstract

Food recognition systems are commonly developed under the assumption that training labels are fully accurate. In real-world applications, however, food image datasets frequently contain noisy annotations caused by incorrect user inputs, weak labeling mechanisms, or automated data collection processes. This study investigates the robustness of supervised food recognition under synthetic label noise using the Food-101 dataset Food-101. The research employs EfficientNet-B0 as a computationally efficient backbone model and compares conventional cross-entropy learning with a robust training approach that integrates two mechanisms: (1) small-loss sample reweighting to reduce the influence of potentially corrupted samples, and (2) an early-learning stopping strategy based on the memorization gap between noisy training accuracy and clean validation accuracy. Symmetric label noise levels of 20% and 40% are introduced only into the training data, while validation and testing datasets remain unaffected. Experimental results on a 20-class subset demonstrate that the proposed approach substantially improves clean test accuracy from 0.6476 to 0.8176 under 20% noise and from 0.5636 to 0.6928 under 40% noise. In addition, probability calibration performance measured by Expected Calibration Error (ECE) is improved from 0.1474 to 0.0813 at the 40% noise setting. Additional experiments on the complete 101-class dataset also reveal consistent performance improvements despite shorter training durations. The findings suggest that combining loss-aware sample weighting with memorization-aware early stopping can provide an efficient and practical solution for building robust and reliable food recognition models in noisy-label environments.
Co-Authors Ahmad Ashifuddin Aqham Amad Maijun Amad Maijun Amalia Septi Reslili Anderson, Evelyn Andik Susdiyanto Arie Atwa Magriyanti B Pujiati, Agnes Agnesita Basri Basri Beltsazaran Beltsazaran Brito Da Silva, Teodora Maria Fernandes Budi Hartono budi hartono Budi Hartono Budi Raharjo Dani Sasmoko Dani Sasmoko Duryono Duryono Eka Satria Wibawa Eko Siswanto Endang Swastuti Eni Dwifitri Astutiningtias Fatimah Indrawati Febri Adi Prasetya Febryantahanuji Febryantahanuji Fujiama Diapoldo Silalahi Gufron Gufron Gunawan Wibisono Guruh Aryotejo Guruh Aryotejo, Guruh Hendri Rasminto Hudha Pratna Safiyan Ilham Akhsani Iman Saufik Suasana Indra Ava Dianta Inti Englishtina Irlon Irlon Ismi Kusumaningroem, Ismi Joseph Teguh Santoso Kasih Purwantini Katon Abdul Fatah Kumoro, Dwi Ferdiyatmoko Cahya Maya Utami Dewi Minarida Nova Yuspita Moh Muthohir Mufadhol Mufadhol Muh Tofik Muhammad Agus Kurniawan Muhammad Fauzi Alfikri Muhammad Ryza Awwali , Sulartopo, Muhammad Ryza Awwali , Muhammad Sarjan Nandito Putra Prabawa Natalia, Elisa Ananda Ninda Lutfiani Nizar, Mochamad Fahrul Nur Cahyo Hendro Wibowo Nur Fauziah Nuris Dwi Setiawan Nusril Nusril Padjar Setyo Budi Priyadi Priyadi Putranti, Honorata Rarnawati Dwi Rashad Huseynaga Asgarov Ratnawati Dwi Putranti Honorata Ratri, Sanda Ramadhan Rawat, Bhupesh Rima Febriana Maryati Rizki, Soulenia Rahayu Ruli Supriati, Ruli S Siswanto Santoso, Nuke Puji Lestari Shobirin, Achmad Siti Kholifah Siti Nasekah Suprapti Suprapti Sutisna, Felix Suwardi Suwardi Syaidina, Musidiansyah Otto Tin Utami Toni Wijanarko Adi Putra Tony Winjanarko Adi Putra Vivi Kumalasari, Vivi YAN ILMAS PUIMERA Zaenal Mustofa Zaenal Mustofa