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All Journal Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Komang Dwiprayoga
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Humanities Computer Science & IT Engineering Other

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PENERAPAN SISTEM POINT OF SALE GUNA MEMBANTU PEMILIK TENANT MENGELOLA USAHA DI SOCIALSIP ULUWATU I Komang Dwiprayoga; Made Agung Raharja; I Wayan Santiyasa
Jurnal Pengabdian Informatika Vol. 4 No. 2 (2026): JUPITA Volume 4 Nomor 2, Februari 2026
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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Abstract

The operation of sales and warehouse management at SOCIALSIP Uluwatu presents challenges for tenants in managing transactions and business operations efficiently. In this context, a web-based Point of Sale (POS) system becomes the primary solution to integrate transactions, simplify data processing, and enhance efficiency and security. This study aims to develop a POS system that meets the tenants' needs through various features, such as sales recording, inventory management, payment method integration, and data reporting. By adopting a tenant-centered development approach, this initiative not only provides a technological solution but also empowers tenants through improved digital literacy.
Komparasi Ekstraksi Fitur BoW dan TF-IDF untuk Klasifikasi SMS Menggunakan Naive Bayes I Komang Dwiprayoga; Made Agung Raharja
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 2 (2025): JNATIA Vol. 3, No. 2, Februari 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i02.p03

Abstract

Short Message Service (SMS) has become one of the most popular communication media. However, the ease and speed of sending SMS is also utilized by irresponsible parties to send spam messages. These spam messages not only annoy users but can also cause financial losses and theft of personal data. The purpose of this research is to compare feature extraction methods that have the best performance such as TF-IDF and Bag of Word tested with Multinomial Naive Bayes machine learning algorithm. For the first research stage, load dataset, data balancing, data preprocessing, feature extraction, modeling with machine learning algorithms, and then testing and comparing confusion matrix models on each feature extraction. The results of this study show that the use of BoW feature extraction has better performance than the TF-IDF feature extraction model with an accuracy value of 94.44%. 
Co-Authors I WAYAN SANTIYASA Raharja, Made Agung