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Optimalisasi Energi Pada Lift Berdasarkan Gerak Vertikal pada Lift Menggunakan Hybrid Naive Bayes Prana Ihsanuddin, Adika; Sendari, Siti; Ari Elbaith Zaeni, Ilham; Afnan Habibi, M.; Arengga Wibowo, Danang
Jurnal JEETech Vol. 6 No. 2 (2025): Nomor 2 November
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v6i2.6203

Abstract

Penelitian ini bertujuan untuk mengoptimalkan penggunaan energi pada sistem lift berdasarkan gerak vertikal menggunakan algoritma Hybrid Naive Bayes. Proses optimalisasi didasarkan pada pengumpulan data dilakukan di Gedung B11 Fakultas Teknik Universitas Negeri Malang selama periode waktu tertentu, dalam upaya mengurangi konsumsi energi pada gedung bertingkat, efisiensi energi lift menjadi salah satu fokus utama. Dengan memanfaatkan data penggunaan lift yang meliputi pola pergerakan vertikal, waktu operasional, serta beban muatan, penelitian ini melakukan klasifikasi dan prediksi efisiensi energi. Algoritma Hybrid Naive Bayes dipilih karena kemampuannya dalam menangani ketidakpastian data serta keandalannya dalam klasifikasi, terutama saat dikombinasikan dengan metode optimisasi lainnya. Hasil prediksi efisiensi energi yang akurat juga memungkinkan manajemen gedung untuk menerapkan strategi operasional yang lebih hemat energi dan ramah lingkungan. Dengan demikian, penelitian ini diharapkan memberikan kontribusi signifikan dalam pengelolaan energi yang lebih efisien pada sistem lift di gedunggedung tinggi.
Metode Migrasi Lebah Madu Ratu untuk Meningkatkan Deteksi Fibrilasi Atrium dari Sinyal Detak Jantung Muhammad Hafiizh; Aripriharta Aripriharta; Ilham Ari Elbaith Zaeni
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1362

Abstract

Atrial Fibrillation (AF) is a common cardiac arrhythmia characterized by rapid and irregular electrical activity of the atrium. AF significantly increases the risk of ischemic stroke and mortality. With the increasing prevalence of cardiovascular risk factors, early detection of AF is crucial for effective intervention. Traditional electrocardiogram (ECG)-based detection methods face limitations, especially in asymptomatic patients or those with sporadic episodes of AF. This paper proposes a novel approach using the Queen Honey Bee Migration (QHBM) algorithm to detect AF from heartbeat signals. The dataset comprises both normal and AF heartbeat signals. The data undergoes preprocessing steps, including noise reduction and feature extraction. The system then classifies the signals using the QHBM algorithm. Key features such as heart rate variability (HRV), amplitude, and RR intervals are extracted for analysis. The QHBM algorithm achieved an accuracy of 95.2%, with a precision of 96.1%, a recall of 94%, and an F1 score of 95%. It outperformed traditional classifiers such as Random Forest, Support Vector Machine (SVM), and Naive Bayes across all performance metrics. In addition, QHBM demonstrated a superior ability to distinguish between normal sinus rhythm and AF, showing a significant improvement over the conventional method. Although the results are promising, challenges remain, including data imbalance and false positive and negative classifications. Oversampling techniques and further optimization of feature selection can enhance model performance. The QHBM algorithm presents a highly effective solution for automatic and real-time AF detection, offering a promising alternative to improve cardiac health monitoring systems.
Co-Authors A.N. Afandi Adam Rachmawan Adib Nur Sasongko Aditama Yudha Atmanegara Adjie Rosyidin Afifah Salim Afnan Habibi, M. Afrian, Ronny Agung Bella Putra Utama Aji Prasetya Wibawa Aji Wibawa Akhmad Afrizal Rizqi Amalia Sufa Andrew Nafalski Andy Hermawan Anggraeni Budiarti Anik N. Handayani Anik Nur Handayani Arengga Wibowo, Danang Arifin, Samsul Aripriharta - Aripriharta Aripriharta Arya Kusuma Wardhana Arya Tandy Hermawan Atmaja, Nimas Hadi Dessy Rif’a Anzani Dian Candra Lestari Dony Setiawan Dwiyanto, Felix Andika Dyah Lestari Eko Pambagyo Setyobudi Elmusyah, Hakkun Erinda, Hayyu Fahreza Al Rafi, Muhammad Alif Fanani, Erianto Faozan Fauzi, Rochmad Fawaidul Badri Febi Elvara Aprilia Felix Andika Dwiyanto Felix Andika Dwiyanto Ferdiansyah, Dodik Septian Ferdinand, Miftakhul Anggita Bima Fitriana Kurniawati Gunawan Gunawan Gunawan Gwinny Tirza Rarastri Hakkun Elmunsyah Hanny Prasetya Hariyadi Hari Putranto Harits Ar Rosyid Hartono, Nickolas Hendrawan, William Hartanto Hidayah Kariima Fithri Hsien-I Lin I Made Wirawan Irvan, Mhd Ismail, Amelia Ritahani Ivatus Sunaifah Kartika Kirana Kevin Raihan Khafit Zaman Kotaro Hirasawa Liliek Rahayu M. Adib Nursasongko Maftuh Ahnan Mahisha Laila Moh. Iqbal Ardiansyah Mohamad Iqbal Mokh Sholihul Hadi Muhammad Arrazy Muhammad Firmansyah Muhammad Hafiizh Muhammad Iqbal Akbar Muhammad Khusairi Osman Muhammad Rifai Muhammad Syauqi Muhammad Usman Mursyit, Mohammad Nafalski, Andrew Ningtyas, Yana Nurfadila, Piska Dwi Nusantar, Alrizal Akbar Nusantar Akbar Prana Ihsanuddin, Adika Puji Santoso Pundhi Yuliawati Rasidy, Ahmad Himawari Retno Indah Rokhmawati Ridwan Shalahuddin Rina Dewi Indahsari Riris Andriani Rizal Kholif Nurrohman Ronny Afrian Samsul Arifin Setumin, Samsul Setyorini Setyorini Shandy Darmawan Simbolon, Triyanti Siti Sendari Sugiono, Bhima Satria Rizki Sujito Sujito Suyono Suyono Syaad Patmanthara Syafaat, Mokhammad Tri Atmadji Sutikno Utama, Agung Bella Putra Wibisono, M. Nurwiseso Yandhika Surya Akbar Gumilang Yogi Dwi Mahandi Yosi Kristian Zafifatuz Zuhriyah