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All Journal Kalbiscientia Jurnal Sains dan Teknologi KALBISIANA Jurnal Sains, Bisnis dan Teknologi
Lufty Abdillah
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Rancang Bangun Aplikasi E-Payment Menggunakan Famework Togaf Adm Studi Kasus PT XYZ Lufty Abdillah
KALBISCIENTIA Jurnal Sains dan Teknologi Vol. 9 No. 2 (2022): Sains dan Teknologi
Publisher : Research and Community Service INSTITUT TEKNOLOGI DAN BISNIS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/kalbiscientia.v9i2.376

Abstract

PT XYZ is a subsidiary established by IPC, specifically dealing with logistics services. In its business process, PT XYZ provides services to customers for more than one transaction regardless of whether the transaction has been completed or not. A condition that often occurs is when a customer makes a payment (via bank transfer), customer rarely attaches information about the payment that has been made. Customers also usually make payments for more than one note in one transfer. This poses a problem because PT XYZ has difficulty identifying payments made by customers. The purpose of this study is to analyze and design an E-Payment Application. This is done so that customers can receive bills and make payments that have been integrated with PT XYZ's financial application, thereby minimizing the risk of payment errors. The method of analysis and design of this E-Payment application will use THE OPEN GROUP ARCHITECTURE FRAMEWORK (TOGAF) by utilizing Cloud Computing technology. The final result of this research is the design of an e-payment application in the form of a TOGAF framework that can be used by PT XYZ to develop and implement an e-payment system in the company.
Implementasi Inventory Menggunakan Sistem ERP Ginee pada Toko Online Sembilan Nine Shop Pricilia Caroline; Lufty Abdillah
KALBISIANA Jurnal Sains, Bisnis dan Teknologi Vol. 9 No. 3 (2023): Kalbisiana
Publisher : UNIVERSITAS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/kalbisiana.v9i3.628

Abstract

Sembilan Nine Shop online shop is a baking material store that serves online purchases through several online merchants. Sembilan Nine Shop have many types and variants of products that for sale. In managing its inventory, the Sembilan Nine Shop online store does not yet use information technology. Currently for warehouse activities still use the method of managing and collecting inventory data in the warehouse manually. This causes the goods in the warehouse to have no categories in their storage and the stock is the difference between physical and online merchants. This is the reason for the need for an information system that can manage inventory in the Sembilan Nine Shop online store. In this study, the author uses the ERP life cycle system implementation method. It is hoped that with the implementation of the Ginee Inventory ERP system for the online store, Sembilan Nine Shop can better manage and record the goods in its warehouse.
Perbandingan Performa Algoritma Naïve Bayes, SVM dan Random Forest Studi Kasus Analisis Sentimen Pengguna Sosial Media X Putri Cahyani; Lufty Abdillah
KALBISCIENTIA Jurnal Sains dan Teknologi Vol. 11 No. 02 (2024): Jurnal Sains dan Teknologi
Publisher : Research and Community Service INSTITUT TEKNOLOGI DAN BISNIS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/kalbiscientia.v11i02.3624

Abstract

Sentiment analysis was explored to understand social media users' opinions towards the Indonesian Capital City (IKN) through the X platform with machine learning and lexicon-based algorithms. This research uses three algorithms: Naïve Bayes, Support Vector Machine (SVM), and Random Forest. The aim of this research is to test and compare the performance of the three algorithms to determine the best in classifying sentiment data from the X platform. The data consists of 10,000 tweets collected using the crawling method with the Python Harvest Library and Node.js, using keywords related to IKN. Based on the algorithm performance test, it was concluded that SVM had the highest performance compared to Naïve Bayes and Random Forest, producing an accuracy of 87%, precision 87%, recall 87%, and f-1 score 87%. This research uses the CRISP-DM Data Mining framework to ensure a structured and systematic approach to the analysis process.
Perbandingan Performa Algoritma Naïve Bayes, SVM dan Random Forest Studi Kasus Analisis Sentimen Pengguna Sosial Media X Putri Cahyani; Lufty Abdillah
KALBISCIENTIA Jurnal Sains dan Teknologi Vol. 11 No. 02 (2024): Jurnal Sains dan Teknologi
Publisher : Research and Community Service UNIVERSITAS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/kalbiscientia.v11i02.3624

Abstract

Sentiment analysis was explored to understand social media users' opinions towards the Indonesian Capital City (IKN) through the X platform with machine learning and lexicon-based algorithms. This research uses three algorithms: Naïve Bayes, Support Vector Machine (SVM), and Random Forest. The aim of this research is to test and compare the performance of the three algorithms to determine the best in classifying sentiment data from the X platform. The data consists of 10,000 tweets collected using the crawling method with the Python Harvest Library and Node.js, using keywords related to IKN. Based on the algorithm performance test, it was concluded that SVM had the highest performance compared to Naïve Bayes and Random Forest, producing an accuracy of 87%, precision 87%, recall 87%, and f-1 score 87%. This research uses the CRISP-DM Data Mining framework to ensure a structured and systematic approach to the analysis process.
Co-Authors Pricilia Caroline Putri Cahyani