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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknik Elektro Lembaran Ilmu Kependidikan Media Ilmu Keolahragaan Indonesia Rekayasa: Jurnal Penerapan Teknologi dan Pembelajaran Sainteknol : Jurnal Sains dan Teknologi ELINVO (Electronics, Informatics, and Vocational Education) CESS (Journal of Computer Engineering, System and Science) Edu Komputika Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) KOMPUTIKA - Jurnal Sistem Komputer Jurnal Ilmiah Permas: Jurnal Ilmiah STIKES Kendal Cyber Security dan Forensik Digital (CSFD) Jurnal Teknik Informatika (JUTIF) Jurnal Energi Baru dan Terbarukan Electron: Jurnal Ilmiah Teknik Elektro Jurnal Rekayasa elektrika Jurnal Pengabdian Dinamika Majalah Ilmiah Teknologi Elektro
Feddy Setio Pribadi
Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Nursing Public Health Social Sciences Veterinary

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Pengembangan Intelligent Electrocardiograph Portable untuk Pemantauan Detak Jantung: Systematic Literature Review Hardi, Septian Akbar Noor Wahyu; Aviando, Rizqi; Pribadi, Feddy Setio; Aprilianto, Rizky Ajie
CESS (Journal of Computer Engineering, System and Science) Vol. 9 No. 2 (2024): July 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i2.59003

Abstract

Kesehatan jantung menjadi faktor penting yang harus diperhatikan, terutama pada orang yang melakukan aktivitas fisik tinggi, seperti atlet. Untuk meningkatkan identifikasi dini penyakit jantung dan mengurangi bahaya kematian mendadak, perangkat elektrokardiogram (EKG) cerdas portabel telah banyak diusulkan untuk mendeteksi aktivitas jantung secara real-time. Penelitian ini bertujuan untuk memberikan informasi tentang klasifikasi sinyal jantung dengan memanfaatkan Filter Infinite Impulse Response (IIR) untuk menghilangkan noise sinyal dan Random Forest yang berguna untuk mengkategorikan masalah jantung secara cepat dan akurat. Referensi yang dirujuk, dipetakan berdasarkan sistematic literature review menggunakan metode preferred reporting items for systematic reviews and meta-analyses (PRISMA). Berdasarkan hasil ulasan yang telah dilakukan, terbukti EKG portable dengan filter IIR terbukti mampu membersihkan sinyal yang didukung dengan algoritma Random Forest untuk klasifikasi sehingga menghasilkan tingkat akurasi yang baik.
Soybean Collect Recommender Based on Distance and Productivity Cluster Using K-means Clustering and Simple Addictive Weighting Method Ningtyas, Mega Wahyu; Pribadi, Feddy Setio
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 1 (2023): Mei 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i1.53208

Abstract

Soybeans are an essential agricultural product that is one of the primary food sources in Indonesia, such as tempeh, tofu, soy milk, soy sauce, and other preparations. However, production yields, harvested land area, and soybean productivity in each district or city in Central Java Province vary widely. Differences in soybean productivity in each area are due to production factors such as area, use of fertilizers, seeds, and labor. This study tries to provide recommendations for soybean harvesting based on the distance and productivity of an area using K-means clustering and the simple addictive weighting method. In the Central Java Province, 35 regions will be divided into four clusters: the first with high productivity, the second with medium productivity; the third with low productivity; and the fourth with very low productivity. Additionally, based on the fourth cluster clustering results, it will be advised to take soybeans from other clusters by taking the closest distance and cluster members into account. According to the research, four clusters have formed: the first has five members, the second has fourteen, the third has nine, and the fourth has seven. The fourth cluster, which consists of seven members who do not grow soybeans, is advised to buy soybeans from the following regions: Kendal Regency, Klaten Regency, Magelang Regency, Batang Regency, and Brebes Regency.
Review on Impact of Artificial Intelligent on Efficiency and Productivity in Industrial Automation Fawwaz, Ega Nur; Setianingsih, Lita Dwi; Prabantara, Satria Krisna; Fawwaz, Fatahillah Nabil; Hidayat, Dwi Alvin; Aprilianto, Rizky Ajie; Pribadi, Feddy Setio
Majalah Ilmiah Teknologi Elektro Vol 24 No 1 (2025): ( Januari - Juni ) Majalah Ilmiah Teknologi Elektro
Publisher : Study Program of Magister Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.205.v24i01.P09

Abstract

As Industry 4.0 technologies evolve, the application of Artificial Intelligence (AI) in the manufacturing sector has become a major factor in improving operational efficiency, optimizing production processes, and reducing costs, enabling predictive analytics, data-driven maintenance, and automation of tasks that previously required human intervention. This study conducts a systematic literature review (SLR) on various AI methods applied in industrial automation, evaluates the effectiveness of their implementation, and identifies key challenges in their adoption. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Population,   Intervention, Comparison, Outcome, Context (PICOC) approaches are adopted. The sources used to search the literature included four electronic databases, comprising ScienceDirect, Taylor & Francis, Scopus, and Emerald Insight, resulting in 33 selected articles. The result shows that AI contributes significantly to improving production efficiency, but it still faces challenges in system integration, implementation costs, and workforce readiness. This study provides a comprehensive overview of the effectiveness of AI implementation in industrial automation and the challenges that need to be overcome to optimize competitiveness and production efficiency
THE ROLE OF BIG DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE IN BUSINESS STRATEGY: A SYSTEMATIC REVIEW Rahmeisi, Nazli; Sachroni, Mu'alfi Fahrul Fanani; Andyanto, Yehezkiel Nesta; Aprilianto, Rizky Ajie; Pribadi, Feddy Setio
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.14909

Abstract

The accelerating digital transformation across industries has intensified the need for datadriven approaches in strategic business management. This study conducts a Systematic Literature Review (SLR) to examine how Big Data Analytics (BDA) and Artificial Intelligence (AI) influence business strategy formulation, risk management, and organizational competitiveness. Guided by the PICOC framework and PRISMA 2020 protocol, 27 peer-reviewed journal articles published between 2020 and 2024 were analyzed through thematic synthesis and bibliometric visualization using VOSviewer. The results indicate that BDA and AI enhance strategic decision-making, operational efficiency, and risk mitigation through predictive insights and real-time analytics. However, their strategic integration remains limited due to socio-technical challenges such as inadequate analytical capability, weak data governance, and organizational resistance. The review highlights that the true strategic value of BDA and AI emerges when these technologies are embedded within long-term strategic planning, data governance, and sustainability frameworks, rather than treated merely as operational tools. This study contributes to strategic management literature by synthesizing cross-sectoral evidence and offering insights into how data-driven intelligence fosters long-term competitiveness and sustainable business transformation.
Co-Authors Adhistya Erna Permanasari Adiastoro, Mahendra Agus Suryanto Akbar, Dawam Muhammad Akbar, Dawam Muhammad Al Kindi, Bilal Alam, Mohammad Mahruf Alfian Nur Rahmad, Alfian Nur Alkindi, Muhammad Faizal Ananda, Briska Putra Andriano, Niko Andriyani, Ririn Andyanto, Yehezkiel Nesta Anggraini Mulwinda Aprilianto, Rizky Ajie Arundaya, Adil Arvina Rizqi Nurul’aini Ati Zuhrotal Afifah, Ning Imas Aviando, Rizqi Awang Harizka, Awang Djuniadi , Djuniadi Djuniadi Djuniadi Eko Supraptono Fahreza, Rizqy Achmad Faqih, Faiq Mananul Fawwaz, Ega Nur Fawwaz, Fatahillah Nabil Febriantika, Febriantika Hardi, Septian Akbar Noor Wahyu Hesti Wahyu Nugroho, Hesti Wahyu Hidayat, Dwi Alvin Hirroe Wijaya Ani Kesuma, Hirroe Wijaya Ani Iqbal Iqbal Irwan Irwan Karnisih, Karnisih Kitnas Dian Purwitasari, Kitnas Dian Ningtyas, Mega Wahyu Nurul'aini, Arvina Rizqi Octavianti Paramita Paramita, Octavianti Pongoh, Arthur Gregorius Prabantara, Satria Krisna Prima Astuti Handayani Putri, Melinia Rahmahani Rahmeisi, Nazli Rizky Ajie Aprilianto Rusiyanto Rusiyanto, Rusiyanto Sachroni, Mu'alfi Fahrul Fanani Samsudin Anis Setianingsih, Lita Dwi Setiawan Sariyoga, Setiawan Sri Haryono Sri Haryono Sri Sukamta Sri Suning Kusumawardani Sunarno Sunarno Suryanto, Agus Syamsiar, Syamsiar Ulfah Mediaty Arief Uswatun Hasanah Wiujianna, Atri Yanuar Arif Pramana