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All Journal International Journal of Advances in Applied Sciences Tekno : Jurnal Teknologi Elektro dan Kejuruan Jurnal Visi Ilmu Pendidikan TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Harmonia: Journal of Research and Education International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System ILKOM Jurnal Ilmiah JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Journal of Electronics, Electromedical Engineering, and Medical Informatics Mobile and Forensics International Journal of Visual and Performing Arts Journal of Robotics and Control (JRC) ILKOMNIKA: Journal of Computer Science and Applied Informatics Sains, Aplikasi, Komputasi dan Teknologi Informasi JPP (Jurnal Pendidikan dan Pembelajaran) Frontier Energy System and Power Engineering Science in Information Technology Letters International Journal of Robotics and Control Systems Jurnal Pengabdian Kepada Masyarakat Kaisa: Jurnal Pendidikan dan Pembelajaran ALINIER: Journal of Artificial Intelligence & Applications Fidelity : Jurnal Teknik Elektro SinarFe7 Jurnal Inovasi Teknologi dan Edukasi Teknik Jurnal INFOTEL Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia Jurnal JEETech
Zaeni, Ilham Ari Elbaith
Universitas Negeri Malang
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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.
Deep Learning Convolutional Neural Networks on Multi Label Image Classification of Torajanese Buffalo Ramadhan, Aslan Poetra; Handayani, Anik Nur; Zaeni, Ilham Ari Elbaith
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2905.162-169

Abstract

Convolutional Neural Networks (CNNs) represent the primary methodology in the advancement of intelligent systems and technologies. The capacity to transition from prediction to categorization establishes CNNs as the primary benchmark in the advancement of deep artificial intelligence. This study use CNN implementation to categorize photos of Torajanese buffalo. The Torajanese buffalo is a distinctive animal species belonging to the Bos bubalis family, integral to the lives and culture of the Torajanese people residing in northern South Sulawesi. This species is integral to the culture, deeply intertwined with several traditional practices of the community. This renders the species distinctive for more investigation. The distinctiveness of the buffalo's style, coloration, and form differentiates them from one another. This study use Convolutional Neural Networks (CNNs) as the primary method to categorize Torajanese buffalo species using head photos and markers derived from local knowledge. This research employs InceptionV3, DenseNet, and Xception as primary architectures, each with variations corresponding to 10, 50, and 100 epochs, therefore enhancing the study. The findings of this investigation indicate that the InceptionV3 architecture has commendable performance across both versions, achieving an average AUC-ROC score of 0.96, although with increased execution time. Nonetheless, the DenseNet architecture demonstrates superior performance in its optimal configuration, achieving flawless accuracy; nonetheless, it incurs the most processing time for the frontal view of the Torajanese buffalo head test case.
OPTIMALISASI PRODUKTIVITAS DAN KUALITAS BASRENG BERBASIS AUTOMATIC OIL DRAINER MACHINE TECHNOLOGY PADA UMKM DELLA MUDA Soenar Soekopitojo; Sujito, Sujito; Ilham Ari Elbaith Zaeni; Dyah Lestari; Revanza Akiella Jihan Putra
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 6 No 1 (2026): Mei 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v6i1.5268

Abstract

Basreng (fried meatballs) is one of popular Sundanese snacks that is highly favored by the Indonesian people. Its savory flavor, crunchy texture, and long shelf life make it a favorite snack choice among various groups. In Madiredo Village, Pujon District, Malang Regency, there is a micro, small, and medium enterprise (MSME) called Della Muda, led by Atik Muda. This MSME has a unique innovation in producing basreng made from vegetables, with a production capacity of up to 30 kilograms per batch. However, in practice, the business faces challenges during the oil-draining stage, which is still done manually. This process not only takes a considerable amount of time but can also affect the final quality of the product, such as its texture and shelf life.As a solution to this problem, appropriate technology in the form of an Automatic Oil Drainer Machine is applied. This technology works on the principle of centrifugal force, which can speed up and optimize the oil-draining process from fried products. The machine is designed using food-grade stainless steel material, which is rust-resistant, easy to clean, and safe for use in both household and small-scale industries. The implementation of this machine is expected to make the production process more efficient and improve product quality. This will certainly have a positive impact on increasing the productivity of Della Muda MSME, maintaining the consistency of taste and texture of basreng, and indirectly supporting the economic growth of the surrounding community through the strengthening of local businesses based on technological innovation.
Optimalisasi Energi Pada Lift Berdasarkan Gerak Vertikal pada Lift Menggunakan Hybrid Naive Bayes Adika Prana Ihsanuddin; Siti Sendari; Ilham Ari Elbaith Zaeni; M. Afnan Habibi; Danang Arengga Wibowo
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.
Co-Authors A.N. Afandi Adam Rachmawan Adib Nur Sasongko Adika Prana Ihsanuddin 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 Danang Arengga Wibowo 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 M. Afnan Habibi 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 Ramadhan, Aslan Poetra Rasidy, Ahmad Himawari Retno Indah Rokhmawati Revanza Akiella Jihan Putra Ridwan Shalahuddin Rina Dewi Indahsari Riris Andriani Rizal Kholif Nurrohman Ronny Afrian Samsul Arifin Setumin, Samsul Setyorini Setyorini Shandy Darmawan Simbolon, Triyanti Siti Sendari Soenar Soekopitojo 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