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Implementasi backbone CSPDarknet53 pada algoritma YOLOv4 sebagai sistem pendeteksi wajah manusia Rauf, Muhammad; Kristiana, Lisa
Nautical : Jurnal Ilmiah Multidisiplin Indonesia Vol. 2 No. 11 (2024): Nautical: Jurnal Ilmiah Multidisiplin Indonesia
Publisher : ARKA INSTITUTE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55904/nautical.v2i11.609

Abstract

Sistem pendeteksi wajah manusia merupakan salah satu teknologi yang banyak digunakan pada bidang komputer vision. Algoritma YOLOv4 merupakan algoritma yang dapat digunakan sebagai object detector. Algoritma YOLOv4 mampu mendeteksi secara realtime sebuah object benda termasuk pada wajah manusia. Algoritma YOLOv4 mempunyai beberapa struktur salah satunya adalah backbone yang akan digunakan pada penelitian ini. Backbone yang digunakan pada penelitian ini adalah CSPDarknet53. CSPDarknet53 merupakan struktur yang optimal sebagai ekstrasi fitur detector. Pada penelitian ini sistem pendeteksi wajah manusia dirancang menggunakan algoritma YOLOv4 dengan struktur backbone CSPDarknet53 yang dimana sistem ini diuji untuk mendapatkan nilai akurasi dan kecepatan respon deteksi dari jarak yang ditentukan. Hasil pengujian deteksi dan kecepatan respon mendapatkan nilai akurasi terbesar pada pengujian pendeteksian jarak 1meter dengan nilai akurasi sebesar 89.6% dan kecepatan respon sebesar 0.433 detik.
The Relationship between Hba1c Levels and Body Mass Index with Severity of Diabetic Neuropathy Rauf, Muhammad; Sari, Masyita
Sriwijaya Journal of Neurology Vol. 1 No. 1 (2023): Sriwijaya Journal of Neurology
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjn.v1i1.220

Abstract

Introduction: Diabetic neuropathy is the most common microvascular complication in diabetes mellitus patients. Diabetic neuropathy is known to be associated with conditions of hyperglycemia and obesity that occur in diabetic patients. Hyperglycemia in diabetic patients can be monitored through HbA1c levels. This study aimed to assess the relationship between HbA1C values and body mass index with the severity of diabetic neuropathy based on nerve conduction velocity examination. Methods: A study with a cross-sectional design with a total of 25 subjects with diabetes mellitus. The severity of diabetic neuropathy was determined based on Baba's diabetic neuropathy classification (BDC), degrees 0 to 4. In all study subjects, plasma HbA1c levels were examined, and body mass index was assessed. The relationship between categorical variables was tested with the chi-square test, and the relationship between numerical and categorical variables with a one-way ANOVA test, the value was considered statistically significant if the p-value <0.05. Results: The 25 subjects with diabetes found a mean age of 54.88 (±SD 8.918) years, with a gender distribution of 46.4% for women and 42.9% for men. The average HbA1c level was 8.9560 (± 2.21850), and the highest body mass index was obese (50%). There was a significant relationship between HbA1c levels and the severity of diabetic neuropathy based on electrophysiological examination (p<0.05), but there was no significant relationship between the value of body mass index and the severity of diabetic neuropathy. Conclusion: Increased HbA1c levels are associated with increased severity of peripheral neuropathy in patients with diabetes mellitus.
EFEKTIVITAS APLIKASI GAMMA AI DALAM MENGEMBANGKAN SOFTSKILLS MAHASISWA DI UIN KIAI HAJI ACHMAD SIDDIQ JEMBER rauf, Muhammad; Sahlan, Moh; Puspitarini, Dwi
EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi Vol. 5 No. 2 (2025)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia (P4I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/edutech.v5i2.6164

Abstract

Mastery of soft skills has become an urgent necessity in the era of the Industrial Revolution 4.0 and Society 5.0. Skills such as critical thinking, creativity, and collaboration are crucial in an increasingly digitalized workforce. This study aims to explore the impact of using Gamma AI, an artificial intelligence-based learning platform, on the development of soft skills among students at the State Islamic University of Kiai Haji Achmad Siddiq Jember. This research employed a qualitative approach through observation and interviews involving 15 students from the Islamic Education Study Program. Data were analyzed through data reduction, data presentation, and conclusion drawing. The results show that Gamma AI fosters active student engagement and provides benefits in enhancing communication, creativity, and collaboration. However, the study also found risks of dependency and decreased motivation for independent learning. This research fills a gap as previous studies have not specifically addressed the influence of AI technology on soft skills within the context of Islamic higher education grounded in Islamic values. These findings imply the importance of supervision and the integration of digital ethics in the use of educational technology. ABSTRAK Penguasaan soft skills menjadi kebutuhan mendesak di era Revolusi Industri 4.0 dan Society 5.0. Keterampilan seperti berpikir kritis, kreativitas, dan kolaborasi menjadi krusial dalam dunia kerja yang semakin terdigitalisasi. Penelitian ini bertujuan untuk mengeksplorasi dampak penggunaan Gamma AI, platform pembelajaran berbasis kecerdasan buatan, dalam pengembangan soft skills mahasiswa Universitas Islam Negeri Kiai Haji Achmad Siddiq Jember. Penelitian ini menggunakan pendekatan kualitatif dengan metode observasi dan wawancara terhadap 15 mahasiswa Program Studi Pendidikan Agama Islam. Analisis data dilakukan melalui reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa Gamma AI mendorong keterlibatan aktif mahasiswa dan memberikan manfaat dalam pengembangan komunikasi, kreativitas, dan kolaborasi. Namun, juga ditemukan risiko ketergantungan dan penurunan motivasi belajar mandiri. Penelitian ini mengisi celah karena studi sebelumnya belum menyoroti secara khusus pengaruh teknologi AI terhadap soft skills dalam konteks pendidikan tinggi Islam yang berbasis nilai-nilai keislaman. Temuan ini memberikan implikasi pada pentingnya pengawasan dan integrasi etika digital dalam penggunaan teknologi pembelajaran.
WHY DOES THE REPORT OF SPECIAL ALLOCATION FUNDS IN LOCAL GOVERNMENT OVERDUE? Arif, Alma; Rauf, Muhammad; Khaq, Akhsanul; Akbar, Bahrullah; Pramono, Agus Joko
Moestopo International Review on Social, Humanities, and Sciences Vol. 5 No. 2 (2025)
Publisher : Universitas prof. Dr. Moestopo (Beragama)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32509/mirshus.v5i2.129

Abstract

This study aims to identify the factors that hinder the reporting process and describe the ideal aspects of reporting the Nonphysical Special Allocation Fund for Women and Children Protection Services in Dumai City. This research uses data from the report on the realization of absorption and realization of the use of Non-physical Special Allocation Funds for Women and Children Protection Services in Dumai City in 2020-2022 and primary data from interviews. The research method uses a constructivist paradigm with an inductive qualitative approach and data analysis using thematic analysis methods. The method of determining the validity of research data uses data source triangulation techniques. Factors caused the report of special allocation funds overdue are poor internal coordination, lack of understanding of the reporting system caused by lack of socialization and ineffective use of communication media, and unsynchronized reporting between agencies and technical implementation units caused by the complexity of the reporting format and the absence of the person in charge of reporting are inhibiting factors in the reporting process. A good coordination process and socialization related to the reporting system are ideal aspects that should be done so that the reporting process becomes better.
Implementasi backbone CSPDarknet53 pada algoritma YOLOv4 sebagai sistem pendeteksi wajah manusia Rauf, Muhammad; Kristiana, Lisa
Nautical : Jurnal Ilmiah Multidisiplin Indonesia Vol. 2 No. 11 (2024): Nautical: Jurnal Ilmiah Multidisiplin Indonesia
Publisher : ARKA INSTITUTE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55904/nautical.v2i11.609

Abstract

Sistem pendeteksi wajah manusia merupakan salah satu teknologi yang banyak digunakan pada bidang komputer vision. Algoritma YOLOv4 merupakan algoritma yang dapat digunakan sebagai object detector. Algoritma YOLOv4 mampu mendeteksi secara realtime sebuah object benda termasuk pada wajah manusia. Algoritma YOLOv4 mempunyai beberapa struktur salah satunya adalah backbone yang akan digunakan pada penelitian ini. Backbone yang digunakan pada penelitian ini adalah CSPDarknet53. CSPDarknet53 merupakan struktur yang optimal sebagai ekstrasi fitur detector. Pada penelitian ini sistem pendeteksi wajah manusia dirancang menggunakan algoritma YOLOv4 dengan struktur backbone CSPDarknet53 yang dimana sistem ini diuji untuk mendapatkan nilai akurasi dan kecepatan respon deteksi dari jarak yang ditentukan. Hasil pengujian deteksi dan kecepatan respon mendapatkan nilai akurasi terbesar pada pengujian pendeteksian jarak 1meter dengan nilai akurasi sebesar 89.6% dan kecepatan respon sebesar 0.433 detik.