Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal bit-Tech Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence
Alfin, Anggi
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimalisasi Rencana Produksi untuk Mengurangi Overstock dan Stockout di Divisi PPIC Menggunakan Random Forest Ratna Sari, Nanda; Alfin, Anggi
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1040

Abstract

PT Multi Tehnik Solution kerap mengalami kesulitan dalam menjaga ketepatan perencanaan produksi, yang berujung pada kelebihan atau kekurangan stok. Penelitian ini bertujuan untuk meningkatkan akurasi perencanaan dengan mengimplementasikan metode peramalan penjualan berbasis algoritma Random Forest. Model dibangun menggunakan data historis penjualan selama satu tahun, dengan mempertimbangkan berbagai faktor yang memengaruhi permintaan produk. Random Forest dipilih karena kemampuannya dalam memetakan hubungan data yang kompleks, nonlinier, dan multivariat, serta menunjukkan performa prediksi yang lebih unggul dibandingkan metode konvensional. Hasil evaluasi menunjukkan bahwa model ini mampu meningkatkan akurasi prediksi sebesar 20% dan menurunkan risiko overstock dan stockout hingga 15%. Temuan ini menegaskan kontribusi signifikan model dalam meningkatkan efisiensi operasional dan ketepatan alokasi produksi. Selain itu, model ini juga berpotensi untuk diintegrasikan ke dalam sistem informasi perusahaan guna mendukung pengambilan keputusan yang cepat dan berbasis data.
Implementation of an Employee Attendance System with Web and Mobile-Based Face Recognition Technology (OpenCV) Pradana, Novant Nanda; Sardi, Sophian Andhika; Alfin, Anggi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3737

Abstract

The attendance system implemented at PT Sinergi Karya Mandiri previously depended on a paid third-party platform that still presented several weaknesses in validation accuracy. The platform only allowed manual photo uploads, creating opportunities for attendance manipulation and making employee location verification difficult to ensure. This study was conducted to design and implement an attendance system based on facial recognition technology using OpenCV in order to improve the reliability, validity, and transparency of attendance records. The proposed solution was developed as an integrated system consisting of a mobile application for employees to perform attendance through face verification and GPS-based location capture, as well as a web-based application that enables the HRD division to monitor attendance activities and generate reports in real time. System development adopted the Rapid Application Development (RAD) approach, which covers the stages of Requirements Planning, User Design, Construction, and Cutover. The implementation results indicate that the system was able to apply facial recognition using the Local Binary Pattern Histogram (LBPH) method, with the recognition process executed locally on the mobile device. This mechanism supports offline functionality while also providing faster response times. Based on black box testing, all major features operated properly and produced valid results, including HRD login, employee OTP verification, facial-recognition-based attendance, employee data management, attendance location mapping, and offline synchronization reporting. Overall, the developed system reduced dependence on external paid services, minimized fraud potential, and improved operational efficiency in attendance management.
Co-Authors Pradana, Novant Nanda Ratna Sari, Nanda Sardi, Sophian Andhika