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All Journal INTECOMS: Journal of Information Technology and Computer Science Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Teknologi Informasi Mura Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Deni Nurdiansyah
Universitas Bina Insan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Penerapan Metode OMAX (Objective Matrix) Untuk Monitoring Kinerja Karyawan Pada PT. Muratara Sejahtera Rio Rio; Deni Nurdiansyah
INTECOMS: Journal of Information Technology and Computer Science Vol 6 No 2 (2023): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v6i2.6600

Abstract

Masalah pada penelitian ini adalah kesulitanya dalam memantau atau memonitoring kinerja terhadap karyawan yang ada pada PT. Muratara Sejahtera, karna dari itu dibutuhkan sebuah sistem yang bisa membantu proses monitoring kinerja karyawan dengan menggunakan metode yang bisa membantu memonitoring dengan cepat dan tepat. Metode yang digunakan oleh peneliti dalam membuat aplikasi ini adalah metode OMAX (Objective Matrix), yaitu sistem pengukuran produktivitas parsial yang dikembangkan pada saat meninjau produktivitas setiap bagian dalam suatu perusahaan dengan menggunakan kriteria produktivitas berdasarkan keberadaan (objektif) bagian tersebut. Sistem ini dibangun dengan menggunakan bahasa pemrograman PHP dan database MySQL sebagai penyimpanan data.
PERANCANGAN APLIKASI DASHBOARD DATA MASYARAKAT MISKIN DI DESA MACANG SAKTI KABUPATEN MUSI BANYUASIN Deni Nurdiansyah; Joni Karman; Muhammad Nur Alamsyah
JURNAL ILMIAH BETRIK Vol. 13 No. 03 DESEMBER (2022): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v13i03 DESEMBER.32

Abstract

In building and designing a data dashboard application for the poor in Macang Sakti Village, Musi Banyuasin Regency which can process data for the poor so that a data processing process can be even more effective. Building a data dashboard application for the poor that can quickly and effectively produce data on poor people and the resulting information can be used by the Village Head of Macang Sakti as a basis for making decisions that can improve services to the poor in Macang Sakti Village. The method used is the development life cycle method which is carried out by designing a dashboard application for the data of the poor in Macang Sakti village, Musi Banyuasin Regency to conduct testing first with the results obtained that the application is feasible to use and the system being tested is appropriate and successful as desired, so this application, it is feasible, to use. In the application of the development life cycle method and a complete application, it can make it easier for the government to check the data of the poor in Macang Sakti Village, Musi Banyuasin Regency.
SMART ROBOT OBJECT DETECTION MENGGUNAKAN ESP-32 CAM Deni Nurdiansyah; Satrianansyah Satrianansyah; Ahmad Sobri
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i1.1296

Abstract

Object detection is a method to recognize the class and location of objects in an image. The main challenge is integrating complex algorithms into lightweight and portable hardware, especially with expensive sensor and camera technologies. This research aims to develop an object detection system using the ESP-32 Cam for robotics monitoring and security. The focus is on utilizing the Yolov5 model transformed into TensorFlow Lite for integration with ESP32 AI CAMERA, expected to detect objects in real-time at a low cost. The methodology includes collecting 1710 datasets from 27 images, dividing the data into 70% training, 20% validation, and 10% testing, and labeling the dataset in Roboflow. The object detection model uses Yolov5, transformed into TensorFlow Lite, and implemented in ESP32 AI CAMERA with ESP-32 Cam as the microcontroller. Model evaluation shows high performance with mAP 95%, precision 97%, and recall 100%, indicating high accuracy. The research successfully develops an efficient and affordable object detection system with ESP-32 Cam and TensorFlow Lite from Yolov5. This integration enables the development of wheeled robots capable of real-time object detection, providing an effective solution for portable robotics monitoring and security.
IMPLEMENTASI DEEP LEARNING ALEXNET UNTUK DETEKSI DAN KLASIFIKASI TANDA TANGAN Deni Nurdiansyah; Ahmad Sobri; Lukman Sunardi; Rusdiyanto Rusdiyanto; Budi Santoso
Jurnal Teknologi Informasi Mura (JTI) 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 Ahmad Sobri Budi Santoso joni karman Muhammad Nur Alamsyah Rio Rusdiyanto Rusdiyanto Satrianansyah Satrianansyah Sunardi, Lukman