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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Data Sciences Indonesia (DSI) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII
Dewi, Aulia Kartika
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Design, Development, and Implementation of a Desktop-Based Laundry Management Application for Optimizing Operational Efficiency Rahmadani, Noni Fauzia; Syahputri, Rifdah; Nugroho, Agung; Nasution, Luftia Rahma; Siregar, Dzilhulaifa; Dewi, Aulia Kartika
TIN: Terapan Informatika Nusantara Vol 5 No 9 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i9.7045

Abstract

Manual management of laundry operations often faces various challenges, such as recording errors, limited monitoring of employee activities, and lack of transparency in financial reporting. To address these issues, this research aims to design and develop a desktop-based laundry management application that integrates order management, problem reporting, and financial management efficiently. The application is designed for three main user roles: staff, shop heads, and owners. Staff are responsible for inputting customer orders and reporting operational issues, shop heads monitor staff activities and handle problems, while owners can access financial reports and order activity recaps to support strategic decision-making. This research employs the Research and Development (R&D) method with the Waterfall software development model, encompassing requirements analysis, system design, implementation, and testing. Data collection was conducted through literature studies and direct observation of operational processes in multiple laundry businesses. The application was developed using the Java programming language and MySQL database and operates locally without requiring an internet connection. Testing results indicate that the system improves order processing efficiency by reducing recording time by approximately X% compared to manual methods, accelerates financial transaction recording, and enhances transparency in operational reporting. With this system, laundry management is expected to become more effective, accurate, and easily accessible to all users.
Deteksi Berita Hoax Pada Platform X Menggunakan Pendekatan Text Mining dan Algoritma Machine Learning Dewi, Aulia Kartika; Noni Fauzia Rahmadani; Syahputri, Rifdah; Luftia Rahma Nasution; Furqon, Mhd
Data Sciences Indonesia (DSI) Vol. 5 No. 1 (2025): Article Research Volume 5 Issue 1, June 2025
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v5i1.6011

Abstract

The spread of hoax news on digital platforms is increasingly becoming a serious concern as it can trigger mass disinformation and potentially cause widespread social disruption. Platform X, as one of the most widely used social media, is often used as a means of spreading unverified information. Therefore, an automatic detection system is needed that is able to identify hoax news effectively and efficiently. This research aims to build a text-based hoax news classification model by applying a text mining approach and the Support Vector Machine (SVM) algorithm. The dataset used comes from Platform X and has gone through a series of preprocessing stages, including case folding, tokenization, stopword removal, and stemming. The feature extraction process is performed using the Term Frequency-Inverse Document Frequency (TF-IDF) method to convert text into numerical representations that can be processed by the SVM algorithm. The built model is then evaluated using several performance metrics, such as accuracy, precision, recall, and F1-score. The evaluation results show that the SVM model is able to classify hoax news with 83.2% accuracy, 81.5% precision, 84.7% recall, and 83.0% F1-score. This finding shows that the SVM algorithm is quite reliable in detecting text-based hoax news and has the potential to be implemented as a solution to mitigate the spread of false information on digital platforms more broadly.
Deteksi Sentimen Publik terhadap Isu Lingkungan di Platform X (Twitter) Menggunakan Naïve Bayes dan Support Vector Machine untuk Mendukung SDGs 13: Climate Action Rahmadani, Noni Fauzia; Nasution, Luftia Rahma; Syahputri, Rifdah; Dewi, Aulia Kartika
Retii 2025: Prosiding Seminar Nasional ReTII ke-20 (Edisi Penelitian)
Publisher : Institut Teknologi Nasional Yogyakarta

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

Abstract

Isu lingkungan merupakan salah satu topik yang paling banyak dibahas di media sosial dan menjadi perhatian global seiring meningkatnya kesadaran terhadap perubahan iklim. Twitter sebagai salah satu platform dengan jumlah pengguna besar menjadi sumber data yang potensial untuk memahami persepsi publik terhadap isu lingkungan. Penelitian ini bertujuan untuk mendeteksi sentimen publik terhadap isu lingkungan di Twitter menggunakan algoritma Naïve Bayes dan Support Vector Machine (SVM). Data yang digunakan merupakan Climate Change Twitter Sentiment Dataset dari Kaggle, yang berisi ribuan cuitan tentang isu perubahan iklim dengan label sentimen positif, negatif, dan netral. Tahapan penelitian meliputi text preprocessing (pembersihan teks, tokenizing, dan stopword removal), ekstraksi fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), pelatihan model, serta evaluasi kinerja algoritma. Hasil pengujian menunjukkan bahwa SVM memiliki akurasi yang lebih tinggi dibandingkan Naïve Bayes, masing-masing sebesar 89,4% dan 84,7%. Temuan ini menunjukkan bahwa SVM lebih efektif dalam mendeteksi pola sentimen publik. Penelitian ini diharapkan dapat mendukung pencapaian Sustainable Development Goal (SDG) ke-13, yaitu Climate Action, melalui pemanfaatan teknologi kecerdasan buatan untuk memahami opini masyarakat terhadap isu lingkungan.
Co-Authors Furqon, Mhd Luftia Rahma Nasution Nasution, Luftia Rahma Noni Fauzia Rahmadani Nugroho, Agung Rahmadani, Noni Fauzia Siregar, Dzilhulaifa Syahputri, Rifdah