Winni Setiawati
Universitas Tarumanagara

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DENTAL CARIES SEVERITY DETECTION WITH A COMBINATION OF INTRAORAL IMAGES AND BITEWING RADIOGRAPHS Jennifer Jennifer; Winni Setiawati; Gabriella Adeline Halim; Tony Tony
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6042

Abstract

Dental caries is a multifactorial oral disease caused by plaque due to bacterial sugar fermentation. Quite a number of dentists have misdiagnosed caries due to the subjective nature of visual examination and radiograph in early-stage lesions. Thus, research on the implementation of deep learning technology is expected to improve the accuracy of diagnosis. However, caries detection with deep learning has accuracy problems. This problem makes researchers interested in developing a deep learning method that combines Faster R-CNN algorithm and texture feature extraction to more accurately detect carious teeth from bitewing radiography datasets and intraoral images. The overall performance of the model to detect the radiographic class was slightly better than the intraoral class. Overall, the classification accuracy of the model was 88.95% which is better than previous research that only used one or the other type of images. GLCM (Gray-Level Co-Occurrence Matrix) is effective in detecting contrast areas, but it still cannot specifically distinguish normal anatomical contrast from caries. The Faster R-CNN model learned well and was able to differentiate between each caries type and was successfully integrated with the GLCM matrix for radiographic image pre-processing to facilitate caries detection. This approach could have the potential of assisting dental professionals in reducing diagnostic errors and increasing patient care.
PERANCANGAN DAN IMPLEMENTASI APLIKASI MOBILE PENGELOLAAN PRODUKSI PADA PT SAHABAT MAKMUR ABADI Jennifer Jennifer; Jap Tji Beng; Tony Tony; Winni Setiawati
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 6 (2025): 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/jwhbh116

Abstract

PT Sahabat Makmur Abadi adalah usaha mikro, kecil, dan menengah (UMKM) yang memproduksi dan menjual sepatu melalui sistem konsinyasi di toko-toko ritel di wilayah Jakarta Raya. Saat ini, perusahaan mencatat proses produksi dan hasil produksinya secara manual, yang seringkali menyebabkan kesalahan seperti tanggal produksi yang salah, jumlah yang tidak akurat, dan pelaporan cacat produk. Studi ini bertujuan untuk mengembangkan aplikasi mobile guna mengotomatisasi manajemen produksi dan meminimalkan kesalahan-kesalahan tersebut. Aplikasi ini dirancang menggunakan model implementasi Waterfall berdasarkan metode System Development Life Cycle (SDLC). Pengembangan dilakukan menggunakan bahasa pemrograman Dart dan kerangka kerja Flutter, dengan penyimpanan data terintegrasi ke dalam basis data PostgreSQL. Hasil penelitian ini adalah sistem manajemen produksi berbasis mobile yang beroperasi di platform mobile. Sistem ini memungkinkan pencatatan produksi yang lebih akurat, pengambilan data yang lebih mudah, dan pengendalian proses produksi yang lebih baik bagi pemangku kepentingan PT Sahabat Makmur Abadi. Penelitian ini menyediakan solusi inovatif dengan menciptakan aplikasi seluler untuk proses manajemen produksi perusahaan manufaktur kecil, meningkatkan efisiensi, akurasi, dan aksesibilitas data. Kata Kunci: Produksi, UMKM, Efisiensi, Manajemen, Automasi.
Utilizing ROP in a Mobile Based Warehouse Management System for Small Retail Businesses Winni Setiawati; Jap Tji Beng; Tony Tony
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1326

Abstract

ABC Store, a wholesale sock retailer, operates through both physical and online platforms. With increasing online sales, the company has experienced frequent stockouts due to inefficient manual inventory processes. This study aims to address these challenges by developing a mobile-based Warehouse Management System (WMS) integrated with a Reorder Point (ROP) calculation. The system was developed using the V-Shaped model, which involved requirement gathering through interviews and warehouse observations. The application was built using the lightweight Flutter framework and an SQLite local database, with testing including white-box, black-box, and User Acceptance Testing (UAT). Additionally, QR code scanning was implemented to improve document tracking and inventory management. The results indicate that the system functions as expected, with the ROP calculation effectively supporting restocking decisions and minimizing stockouts. This study contributes to the field by demonstrating the practical application of integrating simplified ROP calculation into a mobile-based WMS, highlighting its potential to improve warehouse operations for small businesses with limited infrastructure. The approach offers a scalable solution for managing inventory efficiently and cost-effectively, especially in small-scale retail settings.