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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Karinov Jurnal Kajian Matematika dan Aplikasinya Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi)
Denis Eka Cahyani
Universitas Negeri Malang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Hybrid deep learning for estimation of state-of-health in lithium-ion batteries Cahyani, Denis Eka; Gumilar, Langlang; Afandi, Arif Nur; Wibawa, Aji Prasetya; Junoh, Ahmad Kadri
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp995-1006

Abstract

Lithium-ion (li-ion) batteries have a high energy density and a long cycle life. Lithium-ion batteries have a finite lifespan, and their energy storage capacity diminishes with use. In order to properly plan battery maintenance, the state of health (SoH) of lithium-ion batteries is crucial. This study aims to combine two deep learning techniques (hybrid deep learning), namely convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), for SoH estimation in li-ion batteries. This study contrasts hybrid deep learning methods to single deep learning models so that the most suitable model for accurately measuring the SoH in lithium-ion batteries can be determined. In comparison to other methodologies, CNN-BiLSTM yields the best results. The CNN-BiLSTM algorithm yields RMSE, mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) in the following order: 0.00916, 0.000084, 0.0048, and 0.00603. This indicates that CNN-BiLSTM, as a hybrid deep learning model, is able to calculate the approximate capacity of the lithium-ion battery more accurately than other methods.
SISTEM BACK UP ENERGI DAN WEBSITE DIGITAL MARKETING KLINIK ISTANA SEHAT ABADI MALANG Achmad Safi’i; Dityo Kreshna Argeshwara; Langlang Gumilar; Denis Eka Cahyani; Ira Kumalasari; Dito Valentino; M. Farrel Akbar Firzatullah
Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2024): PROSIDING SEMINAR NASIONAL PENGABDIAN KEPADA MASYARAKAT - SNPPM2024
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Negeri Jakarta

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Abstract

Abstract Istana Sehat Abadi Clinic is an economically productive business where on average this clinic can serve 15 patients with 6 effective working days and 2 shifts every day. So that during one month the average number of patients is 360 patients with different complaints. Many patients are satisfied with the services of the clinic. In the early stages of the service, visits and discussions were carried out on partner problems. From the discussions carried out, there are problems experienced by partners, namely administrative activities are often disrupted due to frequent power outages and patients who seek treatment are only patients around the clinic and regular patients. Then idea generation, conceptualisation and implementation of applicable solutions and handover of service results to partners were carried out. After that, evaluation and monitoring have been carried out for 1 month where the energy back up system works well and the website can increase the interest of new patients between 1 to 4 patients per day to come to the Clinic. Then continuous monitoring and evaluation will be carried out to determine the usefulness of the service activities that have been implemented to partners. Keywords: energy back up system, marketing, service, website, clinic Abstrak Klinik Istana Sehat Abadi merupakan usaha produktif secara ekonomi yang mana rata-rata klinik ini dapat melayani pasien dengan jumlah 15 pasien dengan 6 hari kerja efektif serta 2 shift setiap harinya. Sehingga selama satu bulan jumlah pasien rata-rata adalah 360 pasien dengan keluhan yang berbeda beda. Banyak pasien puas dengan pelayanan dari klinik. Pada tahap awal pengabdian dilakukan kunjungan dan diskusi terhadap permasalahan mitra. Dari diskusi yang dilakukan terdapat masalah yang dialami pada mitra yaitu kegiatan administrasi sering terganggu karena sering terjadi pemadaman listrik serta pasien yang berobat hanya pasien disekitar klinik dan pasien langganan. Lalu dilakukan penggalian ide, pembuatan konsep serta implementasi dari solusi yang dapat diterapkan serta serah terima hasil pengabdian pada mitra. Setelah itu telah dilakukan evaluasi dan monitoring selama 1 bulan dimana sistem back up energi bekerja dengan baik dan website dapat menambah ketertarikan pasien baru antara 1 sampai 4 pasien per hari untuk datang ke Klinik. Kemudian akan dilakukan monitoring dan evaluasi secara berkelanjutan untuk mengetahui kebermanfaatan kegiatan pengabdian yang telah diimplementasikan pada mitra. Kata Kunci: sistem back up energi, pemasaran, pelayanan, website, klinik
Systematic review of a lightweight convolutional neural network architectures on edge devices Abu Talib, Muhammad Abbas; Setumin, Samsul; Abu Bakar, Siti Juliana; Che Ani, Adi Izhar; Cahyani, Denis Eka
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp339-352

Abstract

A lightweight convolutional neural network (CNN) has become one of the major studies in machine learning field to optimize its potential for employing it on the resource-constrained devices. However, a benchmark for fair comparison is still missing and thus, this paper aims to identify the recent studies regarding the lightweight CNN architectures including the types of CNN, its applications, edge devices usage, evaluation types and matrices, and performance comparison. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) framework was used as the main approach to collect and interpret the literature. In the process, 37 papers were identified as meeting the criteria for lightweight CNNs aimed at image classification or regression tasks. Of these, only 20 studies explored the use of these models on edge devices. To conclude, MobileNet appeared as the most used architecture, while the types of CNN focused on image classification for the general-purpose application. Following that, the NVIDIA Jetson Nano was the most utilized edge device in recent research. Additionally, performance evaluation commonly included measures like accuracy and time, along with metrics such as recall, precision, F1-Score, and other similar indicators. Finally, the average accuracy for performance comparison can serve as threshold value for future research in this scope of study.
Pembangunan Ontology Berbasis Metode Methontology Untuk Domain Tuberculosis Triyoga, Khavid Wasi; Cahyani, Denis Eka; Sihwi, Sari Widya
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 3 No. 1 (2019): PROSIDING SEMNAS INOTEK Ke-III Tahun 2019
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v3i1.512

Abstract

Aplikasi knowledge base untuk penyakit tuberculosis seperti repository telah banyak dikembangkan. Pada pengaplikasiannya, metode yang digunakan kebanyakan menggunakan metode konvensional yang memiliki banyak keterbatasan dalam representasi pengetahuan. Ontology adalah ide baru dalam penerapan pengetahuan yang lebih jelas dan kompleks dibandingkan menggunakan metode konvensional. Selain itu ontology juga memiliki kelebihan dalam semantic basic query dibandingan dengan metode konvensional. Hasil dari penelitian ini adalah sebuah ontology yang mampu menjadi dasar penerapan knowledge penyakit tuberculosis.
Co-Authors A.N. Afandi Abu Bakar, Siti Juliana Abu Talib, Muhammad Abbas Achmad Safi’i Afrizal Doewes Aji Prasetya Wibawa Anjar Dwi Hariadi Bambang Harjito, Bambang Che Ani, Adi Izhar Desi Rahmadani Didik Dwi Prasetya Dito Valentino Dityo Kreshna Argeshwara Faisal Farris Setyawan Hariadi, Anjar Dwi Ira Kumalasari Irene Patasik Ito Wasito Junoh, Ahmad Kadri Langlang Gumilar Lucky Tri O M. Farrel Akbar Firzatullah Mahmuddin Yunus Nafalski, Andrew Samsul Setumin Sari Widya Sihwi, Sari Widya Setumin, Samsul Setyawan, Faisal Farris Triyoga, Khavid Wasi Winda Mustikaningtyas