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Prodi Sistem Informasi Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang. Jln. Prof. K.H. Zainal Abidin Fikri KM. 3.5 Palembang 30126.
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JUSIFO : Jurnal Sistem Informasi
Published by Universitas Islam Negeri Raden Fatah Palembang
ISSN : 2460092X     EISSN : 26231662     DOI : -
Core Subject :
JUSIFO (Jurnal Sistem Informasi) is an Information System Journal that published by Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang. JUSIFO (Jurnal Sistem Informasi) publishes numerous research articles concerning the articles that integrate technological disciplines with Information System Audit, Software Engineering, Decision Support System, Artificial Intelligence, Application of Information Technology, Social Informatics. JUSIFO (Jurnal Sistem Informasi) is published twice a year; in June and December. In this journal, reviewers will review any submitted paper. Review process employs a double-blind review, which means that both the reviewer and author identities are concealed from the reviewers, and vice versa. We hope that the articles published by JUSIFO (Jurnal Sistem Informasi) can make a real contribution and have a widely impact.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 5 Documents
 1 
Search results for , issue "vol 9 no 2 (2023): december" : 5 Documents clear
Analisis Sentimen Masyarakat Indonesia Terhadap Aplikasi Threads di Twitter Menggunakan Naïve Baiyes Fredrick Lim; Afifah Trista Ayunda
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.19965

Abstract

Threads, a new application released by Meta in mid-2023, has sparked discussions across various social media platforms. This application has been widely compared to Twitter. This research aims to analyze the sentiment of Indonesian society regarding the emergence of Threads on social media. Various opinions were collected from Twitter posts and analyzed using the Naïve Bayes algorithm in RapidMiner to classify each public response. The results show that 28.8% of respondents view the introduction of Threads positively, while 71.2% express negative sentiment. Therefore, Threads should improve its application by adding additional features to gain broader acceptance among users. Although this research has not validated using other algorithms, it provides a general overview of the public's response to the newly emerging Threads application.
Analisis SWOT Pemasaran Digital Menggunakan WhatsApp Business Pada Asosiasi Pengusaha Pempek Palembang Kemas Ahmad Kurniawan; Tata Sutabri
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.20258

Abstract

This research focuses on analyzing the impact of using whatsapp business in digital marketing on the sales of pempek entrepreneurs' products in Palembang, as well as identifying the effects resulting from the use of whatsapp business. The role of ASPPEK is involved in the study to understand how the use of whatsapp business affects marketing and sales strategies for businesses, especially pempek entrepreneurs in Palembang. Interviews, observations, and literature review were conducted to provide an overview of each factor discussed in the SWOT analysis. This research indicates that the impact of using whatsapp business in digital marketing for the association of pempek entrepreneurs in Palembang has other advantages over marketing strategies, although there are some unavoidable weaknesses due to the use of the application. Increasing the visibility of UMKM products at the association of pempek entrepreneurs in Palembang through product diversification, coupon distribution, and social media promotions. Understanding whatsapp business features allows entrepreneurs to design intelligent digital marketing strategies. Despite the challenges, customer responsiveness and product visibility are strong reasons to adopt this platform.
Desain Prototype dan Implementasi Sistem Informasi Pencatatan Meter Air (SICAIR) Evi Maria; Sri Winarso Martyas Edi; Sekar Widhiyaningrum; Aji Andana
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.20649

Abstract

Penyediaan Air Minum dan Sanitasi Berbasis Masyarakat (PAMSIMAS) aims to improve access to and quality of drinking water and sanitation in rural areas. However, the manual management of water meter readings and billing by Kelompok Pengelola Sarana Prasarana Air Minum Sanitasi (KPSPAMS) Tirto Wening, Samirono Village, has led to issues with accuracy and timeliness of water bills. Therefore, this research aims to design a water meter recording system. The system design scope includes recording water meters, calculating bills, processing payments, and generating water usage reports. The development method employs a prototype approach with the Bootstrap framework. The resulting system, named SICAIR, provides accurate billing and water usage reports that meet user needs. SICAIR offers a solution to PAMSIMAS management issues related to the accuracy of meter readings and the timeliness of water bill generation in Samirono Village.
Pendekatan Machine Learning: Analisis Sentimen Masyarakat Terhadap Kendaraan Listrik Pada Sosial Media X Gathot Hanyokro Kusuma; Inggih Permana; Febi Nur Salisah; M. Afdal; Muhammad Jazman; Arif Marsal
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21354

Abstract

Environmental issues and the depletion of fossil fuels continue to escalate as the number of fossil fuel-based vehicle users increases in Indonesia. Electric vehicles emerge as one of the potential alternative solutions to address current environmental challenges, given their eco-friendly nature and lack of pollution emissions. Sentiment analysis is conducted to understand public responses, both supportive and opposing, towards electric vehicles. This research aims to analyze the sentiment of X-social media users regarding electric vehicles using machine learning techniques. The research stages include data collection, data selection, preprocessing, and classification using Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The test results show that on a balanced dataset using ROS, SVM performs the best with accuracy = 68.7%, precision = 77.9%, and recall = 68.4%. Meanwhile, NBC yields an accuracy of 60.3%, precision of 61.3%, and recall of 60.3%, while KNN has an accuracy of 53.9%, precision of 54%, and recall of 53.9%.
Perbandingan Algoritma KNN, NBC, dan SVM: Analisis Sentimen Masyarakat Terhadap Perparkiran di Kota Pekanbaru Sofia Fulvi Intan; Inggih Permana; Febi Nur Salisah; M. Afdal; Fitriani Muttakin
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21357

Abstract

The public response in Pekanbaru to parking policies and regulations has given rise to various sentiments, both positive and negative. This discussion extends not only within the local community but also across various social media platforms. This research aims to analyze public sentiment towards the new parking policies and regulations in the Pekanbaru area. The study involves the KNN, NBC, and SVM algorithms to classify public sentiment into positive, neutral, and negative categories. Balancing techniques used in this research include Random Over Sampling (ROS) and Random Under Sampling (RUS). The data utilized in this study were obtained from posts on the social media platform X. The testing of the dataset using ROS resulted in high accuracy, precision, and recall values. The findings of this research indicate that overall, the SVM algorithm outperforms KNN and NBC in terms of accuracy, precision, and recall. Additionally, the most dominant sentiment is negative, with 422 tweets expressing dissatisfaction with the current parking policies.

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