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All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Hukum Pendas : Jurnah Ilmiah Pendidikan Dasar Enggang: Jurnal Pendidikan, Bahasa, Sastra, Seni, dan Budaya Sosio-Didaktika: Social Science Education Journal JURNAL ILMIAH TEKNIK INDUSTRI DAN INOVASI (JISI) Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) APPA : Jurnal Pengabdian Kepada Masyarakat Causa: Jurnal Hukum dan Kewarganegaraan Unizar Recht Journal Journal of Religion and Social Community
Muhammad Irfan Maulana
Unknown Affiliation
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Social Sciences Other

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Journal : Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA)

 1 
ROBOTIKA DALAM MANUFAKTUR: KECERDASAN BUATAN MENGANTARKAN OTOMATISASI DAN KOLABORASI MANUSIA-ROBOT KE TINGKAT YANG NYATA Muhammad Subhan Abdullah; Rizko Ramdhan Priatna; Muhammad Irfan Maulana; Maulana Yusuf; Perani Rosyani
Buletin Ilmiah Ilmu Komputer dan Multimedia Vol 1 No 1 (2023): Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) INPRESS
Publisher : Shofanah Media Berkah

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Abstract

The use of collaborative robots (cobots) in the manufacturing industry aims to enhance productivity, address the shortage of skilled labor, and improve job satisfaction. The "Collaborative APAC-Cobot Expo 2021" event, organized by Universal Robots, serves as a response to the challenges faced by businesses regarding an aging workforce and labor shortages. The cobot market is projected to continue growing worldwide, including in the Asia Pacific region and Indonesia. The adoption of robotic technology provides significant benefits to the Indonesian industry, particularly in realizing the vision of Industry 4.0. A case study on the implementation of cobots in Yokota Corporation is also presented as an example of successful cobot integration, leading to increased production without requiring additional human resources. Collaborative automation, as a future business strategy post-Covid-19 pandemic, combines the strengths of humans and robots, leading to improved job satisfaction and career advancement. Universal Robots addresses supply chain challenges and labor shortages in the industry through the implementation of collaborative automation. Successful implementation of collaborative automation has been observed in the Asia Pacific region, and this article highlights the opportunities offered by this automation trend. The development of collaborative robots (cobots) as an alternative automation solution within the context of Industry 4.0 in Indonesia is crucial. The Indonesian government's "Making Indonesia 4.0" roadmap recognizes automation as a significant step towards Industry 4.0. Cobots are considered a necessary technology for the development of Industry 4.0 in Indonesia. The advancement of robotic automation in the manufacturing industry in Indonesia is essential for maintaining competitiveness and efficiency in challenging economic conditions. This article highlights Universal Robots, a leading collaborative robot technology company, offering cobots to enhance productivity in the manufacturing sector. The potential benefits of utilizing cobots in improving efficiency and productivity in the manufacturing industry are also discussed.
Systematic Literature Review Penerapan Gradient Boosting Untuk Klasifikasi Penyakit Diabetes Tipe 2 Bintang; Agni Tri Pratiwi; Ahmad Barizi; Muhammad Irfan Maulana; Perani Rosyani
Buletin Ilmiah Ilmu Komputer dan Multimedia Vol 2 No 3 (2024): Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA)
Publisher : Shofanah Media Berkah

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Abstract

Penerapan algoritma Gradient Boosting dalam klasifikasi penyakit diabetes tipe 2 telah menunjukkan hasil yang signifikan dalam meningkatkan akurasi diagnosis. Penelitian inimengkajiberbagai studi yang menggunakan Gradient Boosting untuk memprediksi diabetes tipe 2 dengan memanfaatkan dataset medis. Hasil menunjukkan bahwa algoritma ini mampu mencapai akurasi hingga 82%, lebih baik dibandingkan dengan metode lain seperti Random Forest dan Naïve Bayes. Dengan mengidentifikasi fitur-fitur penting yang berkontribusi terhadap perkembangan diabetes, Gradient Boosting tidak hanya meningkatkan akurasi tetapi juga memberikan wawasan berharga untuk pencegahan dan pengelolaan penyakit ini. Hasil penelitian ini mendemonstrasikan potensi machine learning dalam diagnosis dini diabetes, yang penting untuk pengelolaan kesehatan masyarakat.
Co-Authors Adinda Rachma Pradiah Afifah Nur Ismawanti Agni Tri Pratiwi Ahmad Barizi Aisha Shinta Az-Zahra Andriyanto Adhi Nughroho Bintang Devina Yadita Dimas Bagus Darmawan Dwi Cinta Wiliananda Putri Elza Izzul Aryas Fahadil Amin Al Hasan Fathia Mahira Ramadhanissa Ferida Yuamita Firdaus Firdaus Gladys Trias Puspadewi Gritia Putri Pakpahan Haerani Hisyam Zaini Ibnu Rafli Azahwa Ikhsan Kamil, Muhammad Indira Yekti Widya Pramesti Khaila Aurellia Maulana Yusuf Muhammad Fadhillah Nursyabana Muhammad Parhan Muhammad Subhan Abdullah Mulyadi Mulyadi Novianti Perani Rosyani Ramadhani, Dwi Aryanti Rangga Irgi Saputra8 Rendy Wijaya Saputra Ridwan Ridwan Rifansyah Nugraha Rima Dwi Anggraini Rinna Rachmatika Rizki Ramadhan Rizko Ramdhan Priatna Robby Septiadi Siti Nurhalizah Siti Su'adah Soultan Raffly Akbar Syaiful Malik Ulfia Hasanah Vaganti Safa Sukma Rubianti Yashinta Nurul Imani Yolanda Putri Debi