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All Journal Jusikom : Jurnal Sistem Komputer Musirawas JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA Jurnal Digital Teknologi Informasi Informanika Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer JSK (Jurnal Sistem Informasi dan Komputerisasi Akuntansi) Jurnal Informatika dan Komputer (JIK) Jurnal Teknologi Informasi Mura Kohesi: Jurnal Sains dan Teknologi
Sunardi, Lukman
Program Studi Informatika – Universitas Bina Insan Lubuklinggau
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Other

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PERANCANGAN ALAT UKUR SISTEM MONITORING TERHADAP SUHU DAN KELEMBABAN TANAH BERBASIS IOT (INTERNET OF THINGS) DENGAN MENGGUNAKAN WEB MOBILE DI DESA MUARA KATI KABUPATEN MUSI RAWAS Sobri, Ahmad; Nurdiansyah, Deni; Sunardi, Lukman
Jusikom : Jurnal Sistem Komputer Musirawas Vol 8 No 2 (2023): Jusikom : Jurnal Sistem Komputer Musi Rawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v8i2.2145

Abstract

The purpose of this study was to determine the level of soil temperature and humidity in the village of Muara Kati, Musi Rawas Regency. The use of pH temperature and soil moisture measuring devices will have an impact on the crop yields of farmers in the Muara Kati sub-district and will also develop the community in cultivating other plantation crops if the soil impacts are good. The measurement tools used are arduino uno, usb cable, arduino IDE, Ethernet Shield, Twisted Pie cable, and a webmobile design system that uses UML, PHP and MySQL which helps this system. to make a temporary design for the system to be made, namely use cases, activity diagrams, sequence diagrams and also class diagrams. This system also displays a page that will detect the flow of the senor on the hardware and will display the level of soil acidity at soil pH and the temperature unstable in soil management and also good moisture content in the soil caused by unstable humidity.
IMPLEMENTASI DEEP LEARNING ALEXNET UNTUK DETEKSI DAN KLASIFIKASI TANDA TANGAN Nurdiansyah, Deni; Sobri, Ahmad; Sunardi, Lukman; Rusdiyanto, Rusdiyanto
Jurnal Teknologi Informasi Mura Vol 17 No 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2897

Abstract

The problem in this research is that the manual signature verification process is still widely used. However, this method is prone to human error and is highly subjective, so its accuracy in distinguishing genuine and fake signatures is not optimal. The pattern recognition extraction process in signatures uses the Alexnet algorithm. This study uses a digital signature image dataset consisting of two classes, with 90 images per class. Furthermore, the signature pattern recognition extraction process based on digital images can be performed using the Alexnet model. The purpose of this paper is to help classify signature types, which can facilitate the medical treatment process. The analysis uses deep learning with Python tools. Explicitly, the total sample size in Figure "Distribution of Classes in Training, Validation, and Testing Data" (image_98f1fc.png) shows that the number of samples for the 'full_forg' class is fewer than for the 'full_org' class. Although the model performs very well on the minority class, the presence of perfect recall for the 'full_org' class will be interesting to observe.
IMPLEMENTASI DEEP LEARNING ALEXNET UNTUK DETEKSI DAN KLASIFIKASI TANDA TANGAN Nurdiansyah, Deni; Sobri, Ahmad; Sunardi, Lukman; Rusdiyanto, Rusdiyanto
Jurnal Teknologi Informasi Mura Vol 17 No 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2897

Abstract

The problem in this research is that the manual signature verification process is still widely used. However, this method is prone to human error and is highly subjective, so its accuracy in distinguishing genuine and fake signatures is not optimal. The pattern recognition extraction process in signatures uses the Alexnet algorithm. This study uses a digital signature image dataset consisting of two classes, with 90 images per class. Furthermore, the signature pattern recognition extraction process based on digital images can be performed using the Alexnet model. The purpose of this paper is to help classify signature types, which can facilitate the medical treatment process. The analysis uses deep learning with Python tools. Explicitly, the total sample size in Figure "Distribution of Classes in Training, Validation, and Testing Data" (image_98f1fc.png) shows that the number of samples for the 'full_forg' class is fewer than for the 'full_org' class. Although the model performs very well on the minority class, the presence of perfect recall for the 'full_org' class will be interesting to observe.
Co-Authors Ali Amran Andri Anto Tri Susilo, Andri Anto Armanto, Armanto Bimastari, Tri Hasanah Bunga Intan dedi irawan Firlando, Rais Frima, Aren Hasbiana, Hasbiana Lukman Hakim Lukman Hakim LW, Harma Oktavia Muhammad Nur Alamsyah Nurdiansyah, Deni Priyadi Wisata3 Rohani, Dewi Rudi Kurniawan Rudi Kurniawan Rusdiyanto Rusdiyanto, Rusdiyanto Sinta Purnamasari, Sinta sobri, ahmad Syaifudin, Pebrian Wijaya, Harma Oktavia Lingga Wulandari, Cindi Yuliani Waruwu