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Andi Nur Rachman
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jaisi@unsil.ac.id
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Department of Information Systems, Faculty of Engineering, Siliwangi University Tasikmalaya email: jaisi@unsil.ac.id Jalan Siliwangi No. 24 Kelurahan Kahuripan Kecamatan Tawang Kota Tasikmalaya 46115
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International Journal of Applied Information Systems and Informatics
Published by Universitas Siliwangi
ISSN : -     EISSN : 3031254X     DOI : https://doi.org/10.37058/jaisi
Core Subject : Science,
Computer Science & IT
International Journal of Applied Information Systems and Informatics (JAISI) is an international journal in Information Systems and Informatics as a forum for research publications that are systematically arranged based on clear background, appropriate methods, well presented research results, as well as concise conclusions and reference sources appropriate. Journal of Applied information Systems and Informatics (JAISI) published every 2 (two) times in a year on Mei and November.. JAISI has open access for public so that articles are published online and can be accessed for free without having to subscribe. Articles submitted must be written in English for initial review by the editor and further review by the reviewer. JAISI is managed by the Information Systems Department, Faculty of Engineering, Siliwangi University.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 6 Documents
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Search results for , issue "Vol 3, No 2 (2025): November 2025" : 6 Documents clear
Analysis of Website Service Quality of Sistem Perizinan Online Kota Tasikmalaya (SIPENTAS) Using the Modified Webqual 4.0 and Importance Performance Analysis (IPA) Methods Rosita, Dea; Shofa, Rahmi Nur; Sulastri, Heni
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.17218

Abstract

Sistem Perizinan Online Kota Tasikmalaya (SIPENTAS) is a web-based service used to simplify the public licensing process. The existence of SIPENTAS is expected to increase the efficiency and effectiveness of services and support the implementation of online-based government in Tasikmalaya City. However, until now there has been no evaluation of the quality of the website based on user perceptions so it is not known to what extent this system meets user expectations and needs. This study aims to analyze the quality of the SIPENTAS website using Modified Webqual 4.0 which consists of four dimensions: Usability Quality, Information Quality, Interaction Quality, and Interface Quality and uses the Importance Performance Analysis (IPA) method to compare the level of performance and user interests and determine improvement priorities through quadrant mapping. The results of the study indicate that the quality of SIPENTAS is in the good category, with an average value of the level of conformity between performance and user expectations obtaining a percentage of 87.35%, an average value of the gap level of -0.58, and the results of the quadrant analysis there are six indicators that are priority for improvement related to the dimensions of Usability Quality and Interface Quality. Therefore, even though the quality of SIPENTAS services is in the good category, improvements to the six indicators need to be made so that website performance is more optimal and meets user expectations.
Travel Package Recommendation System Using Collaborative Filtering Method at Loka Travel Iswanto, Muhammad Edi; Rachman, Andi Nur; Noorsyabani, Fauzi
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.16954

Abstract

The rapid development of information technology drives the need for a system that can help tourists in determining the choice of tourist destinations that suit their preferences. The Loka Travel application was developed as a web-based platform that provides various tour packages and is equipped with a recommendation system to suggest relevant destinations for users. This study aims to design and implement a tour package recommendation system using the Collaborative Filtering method with a memory-based approach. This method works by calculating the similarity between users based on their rating or booking history for tour packages, allowing the system to suggest packages that are preferred by other users who have similar preferences. The cosine similarity algorithm is used in the process of calculating the similarity between users, with interaction data obtained from booking and payment activities in the application. The implementation of this system is carried out using the Laravel framework and MySQL database. The results of the system test show that the system is able to provide recommendations with an accuracy level of 80.63%, based on the calculation of Mean Absolute Error (MAE). Thus, this system can help users find suitable tourist destinations and improve their experience in using the Loka Travel application.
Analysis of the Usability Level of the JKN Mobile Application Using the User Experience Questionnaire (UEQ) and Importance–Performance Analysis (IPA) Methods Haris, Gendhi; Tri Julianto, Indri; Ridwan Ibrahim, Maulana
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.17008

Abstract

The JKN Mobile application developed by BPJS Kesehatan is a digital service for National Health Insurance (JKN) participants to access features for checking membership, online queues, and complaint services. JKN Mobile contributes significantly to the digitalization of healthcare services, but various negative reviews still emerge, especially regarding the interface aspect. This study analyzes the usability level of the application using the User Experience Questionnaire (UEQ) and Importance Performance Analysis (IPA) methods. The UEQ assesses six aspects of user experience, while IPA maps attributes based on importance and performance. The analysis results show a gap between expectations and experience. Clarity (1.1056, 0.4021), efficiency (1.0181, 0.0026), and appeal (1.0946, -0.019) fall into the "maintain performance" quadrant. Novelty (0.0965, 0.4113) is in the "top priority" quadrant, stimulation (0.8763, -0.1192) is a low priority, while accuracy (0.9514, -0.1224) is excessive. These findings provide a comprehensive overview of aspects that need to be maintained or improved. User feedback-driven strategies and agile approaches are recommended to make application development more innovative, optimal, and responsive to the needs of digital healthcare services.
Text Mining-Based Sentiment Analysis of ChatGPT Users on X Platform Using Naïve Bayes Algorithm Alkamal, Chaerulsyah; Kurniadi, Dede
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.17062

Abstract

ChatGPT (Generative Pre-trained Transformer) is a natural language processing model based on Artificial Intelligence (AI) that is currently trending. ChatGPT is widely used by the public because it is considered very helpful in completing tasks or solving problems faced by society. However, as the use of ChatGPT grows, questions have arisen about how people perceive and respond to interactions with ChatGPT present. The use of ChatGPT not only creates opportunities but also new challenges in understanding user perceptions and sentiments toward this technology. For example, various controversies have emerged regarding the presence of ChatGPT. Therefore, this research aims to determine the sentiments of society, particularly among users of social media X, toward ChatGPT, and whether most of society views it positively, negatively, or neutrally. By conducting sentiment analysis and implementing Text Mining, the tendency of a particular sentiment or opinion, whether it leans toward positive, negative, or neutral, can be obtained relatively easily. The method used in this research is SEMMA (Sample, Explore, Modify, Model, Assess) with Naïve Bayes as the algorithm to be implemented. To evaluate the model, a Confusion Matrix is used. The sentiment analysis results show that out of a total of 1,314 data points, 39.4% were positive, 37.7% were neutral, and 22.9% were negative. The classification model achieved an accuracy of 72.78%, which is considered quite good.
Sentiment Analysis Of The Shopee Marketplace On Twitter Using The Naive Bayes Classifier Method Natalia, Nila; Astikarani, Ester Krisdianti; Adi Khairul, Muhammad; Nafis Sjamsuddin, Irfan
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.17077

Abstract

Shopee is the number one most downloaded marketplace application on the App Store and Play Store. In its promotion, Shopee provides discounts on shipping costs, price discounts, and cashback for each transaction; however, not all of its users are satisfied with the service. There are criticisms and suggestions, one of which is conveyed via social media, Twitter. Sentiment analysis was conducted to extract information related to Shopee user reviews on Twitter. The stages of the research carried out followed the Cross Industry Standard Process for Data mining (CRISP-DM) method, namely Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data collection was carried out by scraping, and all classification processes were carried out using the RapidMiner tool. The data obtained tends to contain negative sentiment rather than positive; most reviews are made by buyers and discuss promos. Sentiment classification is carried out by applying the Naive Bayes Classifier and TF-IDF as feature extraction. Testing using 10-fold cross-validation and a Confusion Matrix resulted in an accuracy value of 84.20%, a precision value of 87.21%, and a recall value of 84.20%.
Implementation of Neural Collaborative Filtering for Social Aid Recipient Recommendation Febriyanto, Erick; Tarempa, Genta Nazwar; Dewi, Euis Nur Fitriani; Al-Husaini, Muhammad; Faishal, Rifda Tri
Journal of Applied Information System and Informatic (JAISI) Vol 3, No 2 (2025): November 2025
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v3i2.16944

Abstract

Social assistance needs system accurate recommendations for ensure distribution appropriate target. Research This aims to implement Neural Collaborative Filtering (NCF) to recommend recipient help social based on integration of dynamic parameters of poverty data. The NCF method was chosen Because his ability combines Generalized Matrix Factorization (GMF) and Multi-Layer Perceptron (MLP) to catch non-linear relationship between data. The dataset is taken from 845 recipients assistance in Cijulang Village, District Ciamis, with criteria covering employment, income, health, and family history assistance. The preprocessing stage includes data cleaning, label encoding, one-hot encoding, and data splitting (training-validation 80:20). The NCF architecture is built with embedding layer (dimension 32), hidden layer MLP (128-64-32 neurons), and output layer that combines GMF and MLP. Evaluation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results show that the model achieves RMSE 0.63 and MAE 0.47 on the training data, but overfitting occurred with a validation RMSE of 1.40 and MAE of 1.24. Analysis indicates the need for hyperparameter optimization (e.g., regulation, dropout rate) for an increase in generalization. Findings This prove NCF potential in increase accuracy recommendation help social, at the same time highlight importance data handling no balance and sparsity in context poverty. Implications study covers improvement transparency distribution assistance and reduction jealousy social through recommendation data -based. This study gives contribution methodological in NCF adaptation for sector public.

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