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All Journal ComEngApp : Computer Engineering and Applications Journal Teknika Jurnal Teliska Jurnal Ampere PROtek : Jurnal Ilmiah Teknik Elektro Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Teknik Informatika (JUTIF) Jurnal Profesi Insinyur Universitas Lampung Jurnal Pengabdian Masyarakat Bangsa Journal of Environment and Sustainability Education
Nyayu Latifah Husni, Nyayu Latifah
State Polytechnic of Sriwijaya
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The Application of the Adaptive Neuro Fuzzy Inference System (ANFIS) Method in Estimating State of Charge (SOC) and State of Health (SOH) of Lithium-Ion Batteries Muslimin, Selamat; Prihatini, Ekawati; Husni, Nyayu Latifah; Caesandra, Wahyu
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2357

Abstract

The increasing reliance on lithium-ion batteries (LIBs) for electric vehicles and portable electronics demands accurate monitoring of battery performance, particularly the State of Charge (SOC) and State of Health (SOH). Conventional estimation methods—such as Coulomb counting, Kalman filtering, and equivalent circuit modeling—face challenges under dynamic conditions due to drift and limited adaptability. Recent studies have explored machine learning and neuro-fuzzy approaches to enhance prediction accuracy, yet many lack integration of real-time hybrid learning or struggle with high estimation error in noisy data environments. This research aims to apply the Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate SOC and SOH using experimental data from a 48V lithium-ion battery. The novelty lies in combining voltage, current, and capacity data within a MATLAB-based ANFIS framework that employs a hybrid learning algorithm integrating backpropagation and Recursive Least Squares Estimation (RLSE). Training data for SOC estimation used charging voltage and current, while SOH estimation incorporated discharging data and capacity. Results show that ANFIS achieved high accuracy with RMSE of 0.1466 and MAE of 0.021 for SOC, and RMSE of 0.012 and MAE of 0.0017 for SOH. The estimated SOH was 33.61%, closely aligned with actual values. These findings confirm ANFIS as a robust and adaptive method for real-time battery diagnostics. Future work will explore multi-input hybrid models, the integration of IoT-based BMS telemetry, and testing across diverse battery chemistries to generalize the model's performance and extend its application in smart energy systems.
Robustness Testing of TrOCR for Multi-Condition Food Ingredient Labels Detected By YOLO Chairunnisa, Charina Mutiara; Husni, Nyayu Latifah; Kusumanto, RD.
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.37301

Abstract

This study aimed to develop an automatic text extraction system for ingredient labels by integrating YOLOv8 for object detection and a Transformer-based Optical Character Recognition (OCR) for text recognition. YOLOv8 was trained to detect and crop the label area in the image, while TrOCR was used to extract text from the cropped bounding box. The evaluation involved 16 sample image inputs under various conditions, including background color (Monochrome and RGB), languages (Bahasa Indonesia and English), and text formatting (single-line and multi-line). The results indicated that TrOCR performed well in single-line format, but struggled with multi-line format and longer text, even omitting words. Character and word error rates reached up to 100% for this complex layout. 
Pengembangan Model Deteksi Sampah Berbasis YOLOV8 Dan Evaluasi Performanya Dalam Sistem Monitoring Lingkungan Sungai Ramadhoni, M.; Husni, Nyayu Latifah; Muhammad Amri Yahya
Jurnal Profesi Insinyur Universitas Lampung Vol. 6 No. 2 (2025)
Publisher : Fakultas Teknik Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpi.v6n2.258

Abstract

Pencemaran sungai akibat sampah merupakan permasalahan lingkungan yang membutuhkan solusi berbasis teknologi. Penelitian ini bertujuan untuk mengembangkan model deteksi sampah di permukaan sungai menggunakan algoritma YOLOv8 serta mengevaluasi performanya dalam sistem monitoring lingkungan. Dataset citra sampah dilabeli menggunakan Roboflow dan dilatih menggunakan YOLOv8 di Google Colaboratory. Model diuji dengan parameter pelatihan sebanyak 50 epoch, ukuran citra 320 × 320 piksel, dan batch size 32. Hasil pelatihan menunjukkan nilai precision sebesar 0.894, recall 0.833, mAP50 sebesar 0.89, dan mAP50-95 sebesar 0.726. Evaluasi lanjutan melalui confusion matrix dan pengujian terhadap 20 citra acak menunjukkan model mampu mendeteksi objek sampah dengan akurasi dan stabilitas yang baik dalam berbagai kondisi citra. Dengan demikian, model ini dinilai layak untuk diimplementasikan sebagai bagian dari sistem monitoring lingkungan sungai secara otomatis dan real-time.
Co-Authors A. Rahman Abdurahman Abdurrahman Ade Silvia Handayani Aditya, M Rizky Vira Ahmad Taqwa Al Rasyid, Johansyah Alfarizal, Niksen Amperawan Amperawan, Amperawan Andika, Destra Angguna, Welan Mauli Asriyadi Asriyadi Caesandra, Wahyu Chairunnisa, Charina Mutiara DWI RAMADHANI Evelina Evelina Faisal Damsi, Faisal Faris, Fakhri Al Felia, Okta Ganza Handayani, Ade S Silvia Hetty Meileni Irawan Hadi Irdayanti, Yeni Irsyadi Yani Kinasih, Ayu Antika Sekar Kusumanto, Kusumanto Leni Novianti Marniati, Yessi Meranda, Arganda Moch Hikmal Abrar1 Muhammad Amri Yahya Muslimin, Selamat Niksen Alfarizal Nur ‘Azah Permatasari, Rosmalinda Plowerita, Sanyyah Pratama, Destra Andika Prihatini, Ekawati Putra, Yogie Dwi Rahman, M. Arief Ramadhan, M. Daffa Ramadhoni, M. Rasyad, Sabilal RD Kusumanto Rizkiyanti, Shally Rossi Passarella Sepulau, Ifroh Intan Siti Chodijah Siti Nurmaini Sitompul, Carlos R Sobri, M. Ulandari, Monica Vira, Rizky Wahyu Caesarendra