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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding SENDI_U JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Telematika KOMPUTA : Jurnal Ilmiah Komputer dan Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Infotekmesin Jurnal Mnemonic Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Jurnal Teknik Informatika (JUTIF) Bulletin of Computer Science Research JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Karya Abdi Masyarakat INTERNAL (Information System Journal) Artinara SISFOTENIKA
Juanita, Safitri
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Implementasi Electronic Customer Relationship Management pada Aplikasi Layanan Pelanggan Hotel Wildan, Muhammad; Juanita, Safitri
Infotekmesin Vol 13 No 1 (2022): Infotekmesin: Januari, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i1.1045

Abstract

The interest of hotel visitors has decreased since the Covid-19 pandemic. So it is recommended that hotels implement Customer Relationship Management (CRM) which has three stages: getting new customers (Acquire), improving customer relationships (Enhance), and retaining customers (Retain). However, hotels currently do not have practical service applications that help to maintain the relationship between hotel management and consumers, so it requires research that implements E-CRM by designing a prototype of web-based Hotel customer service applications. This study aims to standardize hotel customer service applications that implement E-CRM. It uses qualitative research methods by applying the prototype system development method. The research contribution is to make an E-CRM prototype in a hotel customer service application with several features such as complaints, testimonials, promotions, and point redemption. Based on experiments and testing from a user using UAT and interview, the implementation of E-CRM using a prototype of web-based Hotel customer service applications helps the staff to find customer complaints. The promotion feature helps staff add promotional information that appears on the website's front page. Hotel customers can use the feedback feature in complaints, testimonials and redeem points exchange with prizes. In future research, sentiment analysis on hotel customer reviews using a classification algorithm can be carried out.
COMPARISON OF K-NEAREST NEIGHBORS AND NAÏVE BAYES CLASSIFIER ALGORITHMS IN SENTIMENT ANALYSIS OF USER REVIEWS FOR INTERMITTENT FASTING APPLICATIONS Kusuma, Muhammad Varhan; Juanita, Safitri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2345

Abstract

Applications that focus on health, especially obesity prevention, are scattered in the Google Play Store, one of which is the "Intermittent Fasting" application, which, according to the developer, aims to help users maintain a healthy lifestyle and regulate eating habits. With the increasing number of similar health applications, this research focuses on sentiment analysis of user reviews of "Intermittent Fasting" to find out how users respond. The purpose of this research is to find the best algorithm to analyze sentiment on user reviews on the Google Play Store against the "Intermittent Fasting" application, as well as provide recommendations for new or old users and for application developers based on the results of processing review data. The data mining methodology used in this research is CRISP-DM, using a dataset collected on user reviews on the Google Play Store for five years (2019-2024), which is annotated with three sentiment labels (positive, negative, and neutral) based on user ratings, then modeling using two algorithms K-Nearest Neighbors (KNN) and Naïve Bayes Classifier (NBC). The contribution of this research is to test, evaluate, and compare the two algorithms (KNN and NBC) using two testing models (Split and K-Fold Cross Validation) and then provide recommendations for the best algorithm. The research concludes that the NBC algorithm is superior to KNN with an accuracy value of 80%, while the KNN algorithm has an accuracy value of only 71.43%. In addition, the K-Fold Cross Validation testing model is more optimal in improving the accuracy of the algorithm's performance than the Split model.
Model Deteksi Berita Hoaks Bahasa Indonesia Menggunakan Multinomial Naïve Bayes dan AdaBoost Classifier Hafiizh, Haniifaa; Juanita, Safitri
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.927

Abstract

The rapid growth of the internet has led to the massive and uncontrolled dissemination of information across various digital platforms, allowing hoax news to reach a wide audience and influence public opinion in a short period of time. This condition highlights the need for a reliable automated detection system. However, existing methods still face limitations in terms of accuracy, result stability, and reliance on manual verification processes. Therefore, this study aims to compare and analyze the performance of two classification algorithms in detecting Indonesian-language hoax news accurately and effectively. This study follows the CRISP-DM framework, beginning with the collection of hoax and non-hoax news articles from turnbackhoax.id and detik.com, resulting in 2,281 samples. The data understanding stage involves analyzing dataset characteristics and evaluating data quality. During data preparation, text elements that explicitly indicate hoax labels are removed, followed by feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF). The dataset is then trained and tested using data split ratios of 70:30, 80:20, and 90:10 by applying Multinomial Naïve Bayes and AdaBoost Classifier algorithms. Model performance is evaluated using a confusion matrix. The results show that AdaBoost achieves superior performance, with an accuracy of 0.9879 (98.79%), outperforming Multinomial Naïve Bayes, which attains an accuracy of 0.9712 (97.12%). The performance of AdaBoost is also consistent across different evaluation scenarios, indicating that it is more suitable as an automated hoax news detection model for the dataset used in this study.
Co-Authors Abdillah, Abid Famasya Amru, Syahnaz Sofinar Anif, M Anif, M. Diana Purwitasari Dolly Virgian Shaka Yudha Sakti Efa Pratiwi, Aisah Tri Eka Noviyanti, Agnes Fadhillah, Ahmad Fadilah, Nabilah Yadzhan Fajarita, Lusi Fawwaz Prabowo, Demas Febriani, Suci Indah Ferdiansyah Ferdiansyah Hafiizh, Haniifaa Hardjianto, Mardi I Ketut Eddy Purnama Ilham Ilham Isnaeni, Desy Kusuma, Muhammad Varhan Larasati, Pamela Mauridhi Hery Purnomo Muflih, Fathi Muhammad Anif Muhammad Wildan Nur’aini, Fadhillah Pratiwi Ba’arsyah, Nanda Putri Cahyani Sugiarto, Aprilia Raihan, Muhammad Rasyid, Faturrahman Ar Reza Wibisono Rozi, Ilham Fahrul Samrotul Fu’adah, Dewi Umaeydah, Umaeydah Wahyu Pramusinto Waluyo, Sejati Wardana, Farill Andika Windarto, Windarto Yuliazmi, Yuliazmi Yurmina Tanjung, Neno