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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

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

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

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

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.

Smart Waste-to-Energy Conversion Systems Using AI-Driven Process Optimization for Urban Sustainability Joni Karman; Ahmad Sobri; Deni Nurdiansyah
Green Engineering: International Journal of Engineering and Applied Science Vol. 1 No. 2 (2024): April: Green Engineering: International Journal of Engineering and Applied Scie
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v1i2.243

This study explores the integration of AI-driven process optimization in Waste-to-Energy (WtE) systems to enhance urban sustainability. The research focuses on designing a gasification-based WtE system, incorporating AI predictive control to optimize energy conversion processes. The AI system adjusts operational parameters in real-time, improving energy conversion efficiency by 25% and reducing carbon emissions by 40%. Additionally, the system's waste-to-energy conversion rate is projected to increase by 20%, and operational costs are expected to decrease by 30%. Data collection and analysis are carried out using advanced sensors to monitor key parameters such as temperature, gas composition, and energy output, which are then processed by machine learning algorithms for predictive analysis. The results show that the AI optimization significantly enhances system performance, offering a sustainable solution for urban waste management. The study highlights the technical and operational challenges of integrating AI into existing WtE systems, including the need for infrastructure upgrades and scalability considerations. It also discusses the socio-economic impacts, including job creation, reduced energy costs, and improved public health. The findings demonstrate the potential of AI-based WtE systems in reducing waste, generating clean energy, and mitigating climate change, positioning them as a viable solution for sustainable urban development.

PENERAPAN TEXT MINING UNTUK MENENTUKAN KEKUATAN BRAND MOBIL LISTRIK CHINA (BYD, WULING DAN CHERY) PADA DATA YOUTUBE DAN BERITA DARING Muhammad Dzaki Mualantsa Mualantsa; Muhammad Akbar; Deni Nurdiansyah
JUTIM (Jurnal Teknik Informatika Musirawas) Vol. 11 No. 2 (2026): JUTIM (Jurnal Teknik Informatika Musirawas) Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Perkembangan kendaraan listrik di Indonesia mengalami peningkatan yang signifikan, ditandai dengan masuknya berbagai merek mobil listrik asal China seperti BYD, Wuling, dan Chery. Persepsi publik terhadap merek-merek tersebut menjadi faktor penting dalam menentukan kekuatan brand di pasar. Penelitian ini bertujuan untuk menganalisis sentimen publik serta mengukur kekuatan brand mobil listrik China berdasarkan data media digital menggunakan pendekatan hybrid text mining. Data penelitian diperoleh dari komentar YouTube dan berita daring otomotif yang membahas mobil listrik BYD, Wuling, dan Chery, dengan total sebanyak 9.070 data teks. Metode yang digunakan adalah pendekatan hybrid, yaitu kombinasi antara lexicon-based sentiment analysis dan algoritma Support Vector Machine (SVM). Proses penelitian meliputi pengumpulan data, preprocessing teks, normalisasi kata, pembobotan fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), klasifikasi sentimen, serta perhitungan skor kekuatan brand. Sentimen diklasifikasikan ke dalam tiga kategori, yaitu positif, netral, dan negatif. Hasil penelitian menunjukkan bahwa sentimen netral mendominasi pembahasan mobil listrik China dengan persentase lebih dari 80%, diikuti sentimen positif sebesar 15,11% dan sentimen negatif sebesar 3,63%. Evaluasi model SVM menghasilkan akurasi sebesar 96% dengan confusion matrix tanpa kesalahan klasifikasi. Berdasarkan perhitungan skor kekuatan brand, Chery memiliki skor tertinggi sebesar 0,142857, diikuti oleh Wuling sebesar 0,131268 dan BYD sebesar 0,061358. Hasil ini menunjukkan bahwa Chery memiliki kekuatan brand tertinggi di mata publik digital. Penelitian ini menyimpulkan bahwa pendekatan hybrid lexicon-based dan SVM efektif dalam menganalisis sentimen dan mengukur kekuatan brand mobil listrik berbasis data media digital. Hasil penelitian diharapkan dapat menjadi referensi bagi produsen otomotif dan peneliti selanjutnya dalam memanfaatkan text mining untuk analisis brand.

IMPLEMENTASI LARAVEL UNTUK PREDIKSI PELUANG KERJA LULUSAN SMK MENGGUNAKAN DECISION TREE Yesi Tri Maita; Fido Rizki; Deni Nurdiansyah
JUTIM (Jurnal Teknik Informatika Musirawas) Vol. 11 No. 2 (2026): JUTIM (Jurnal Teknik Informatika Musirawas) Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v11i2.3395

SMK Negeri Tugumulyo possesses comprehensive data on students' productive subject scores and industrial internship portfolios; however, these data have not been optimally utilized to predict graduates' employment opportunities. The evaluation of graduates' job readiness is still conducted manually, resulting in a lack of objective information for the school. This study aims to develop a web-based system for predicting graduates' employment opportunities using the Decision Tree method. The research employed the Research and Development (R&D) approach with the CRISP-DM framework, which consists of the business understanding, data understanding, data preparation, modeling, evaluation, and deployment phases. The dataset comprised 1,018 student records collected from three graduating cohorts. The system was developed using the Laravel framework and generates three prediction categories: Accepted, Considered, and Rejected. The evaluation results showed that the proposed model achieved an accuracy of 67.65% based on the confusion matrix, while Black Box Testing confirmed that all system functions operated as expected. Therefore, the developed system can assist schools in evaluating graduates' employment opportunities more objectively and based on data

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

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.

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