Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

Journal Of Information System And Artificial Intelligence

jisai
Website

Published by Universitas Mercu Buana Yogyakarta

ISSN : - EISSN : 27976777 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Journal of Information System and Artificial Intelligence (JISAI) diterbitkan oleh Program Studi Sistem Informasi, Fakultas Teknologi Informasi Universitas Mercu Buana Yogyakarta. JISAI memuat naskah hasil-hasil penelitian dibidang Sistem Informasi, Teknologi Informasi dan Sistem Komputer. JISAI berkomitmen untuk memuat artikel berbahasa Indonesia yang berkualitas dan dapat menjadi rujukan utama para akademisi, peneliti dan praktisi dalam bidang Sistem Informasi, Teknologi Informasi dan Ilmu Komputer. Jurnal ini diterbitkan 2 kali dalam 1 tahun yakni pada bulan November dan Mei dengan periode penerimaan artikel sepanjang tahun. 10 artikel pertama yang lolos seleksi akan diterbitkan pada periode penerbitan yang paling dekat. Sedangkan, artikel ke-11 dan seterusnya akan diterima untuk diterbitkan pada periode yang akan datang. Artikel yang masuk ke jurnal ini akan di-review oleh mitra bestari sebelum diterbitkan. Proses review artikel dilakukan secara double blind review yang mana mitra bestari tidak mengetahui siapa penulis artikel tersebut dan juga sebaliknya penulis tidak mengetahui mitra bestari yang menilai artikel tersebut. Jurnal JISAI merupakan jurnal akses terbuka (open access) sehingga seluruh artikel yang diterbitkan oleh jurnal ini dapat diakses kapan saja dan di mana saja oleh siapa saja tanpa dipungut biaya. Selain itu, untuk Submit dan Review Manuskrip adalah Bebas Biaya.

Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)

Articles 105 Documents

7 8 9 10 11

Analysis Of The BPBD Website Of South Sumatra Province Using The Pieces Method eka puji agustini
Journal Of Information System And Artificial Intelligence Vol. 6 No. 2 (2026): Vol.6 No. 2 (2026): Journal of Information System and Artificial Intelligence V
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v7i2.295

The rapid development of information technology has made it easier for people to access various information through the internet. Websites are one of the most effective media in disseminating information to the public. The Regional Disaster Management Agency (BPBD) of South Sumatra Province already has a website that provides various information related to disasters, organizational structure, public services, and publication of activities. However, since the beginning of its creation, the website has not been visited many times and has never been analyzed on its performance. This study aims to analyze the performance of the BPBD website of South Sumatra Province using the PIECES (Performance, Information, Economics, Control, Efficiency, and Service) method. The results of the analysis show that from the Performance aspect, the website obtained a grade C score on Google PageSpeed Insight and a grade D on Pingdom; the Information aspect shows a scale value of 3 (good); the Economics aspect is considered efficient due to low maintenance costs; the Control aspect obtained a grade C on the Sucuri Sitecheck and a grade D on the Observatory; the Efficiency aspect shows quite good results; while the Service aspect is considered quite good but still needs improvement in the presentation of information to the public. Overall, the South Sumatra Province BPBD website functions quite well but still needs optimization in its performance and service quality so that it can become a more effective disaster information medium.

Book Recommendation System Using Similarity-Based Collaborative Filtering Approach Uswatun Hasanah
Journal Of Information System And Artificial Intelligence Vol. 6 No. 2 (2026): Vol.6 No. 2 (2026): Journal of Information System and Artificial Intelligence V
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v7i2.297

This study implements and compares the performance of three primary approaches to book recommendation systems: rank-based methods, similarity-based collaborative filtering (both user-user and item-item), and matrix factorization-based collaborative filtering. The dataset comprises 433,671 user ratings from 78,805 users on 185,973 books, enriched with book metadata such as title, author, publication year, and publisher. The recommendation systems were developed using the Surprise library, with hyperparameter optimization performed via grid search cross-validation. Model performance was evaluated using precision@k, recall@k, and F1-score metrics, as well as RMSE for prediction accuracy. Results indicate that the user-user similarity-based collaborative filtering model achieved the best performance in terms of relevance, attaining an F1-score of 0.86. This model effectively identifies users with similar preferences and recommends books based on collective behavior patterns. Meanwhile, the matrix factorization approach yielded the lowest RMSE value of 1.50, highlighting its strength in capturing latent factors that influence user preferences. The item-item similarity model also showed reasonable performance but did not surpass the other approaches, possibly due to homogeneity in item rating patterns across users. Overall, the study confirms that user-user similarity is highly effective for datasets exhibiting consistent user behavior, while matrix factorization excels in minimizing prediction error by leveraging latent feature structures. These findings offer valuable insights for developing adaptive recommendation systems in book-centric literacy platforms and content-driven e-commerce applications.

Optimization of the K-Means Algorithm Using PCA Dimensionality Reduction for E-Commerce Customer Segmentation Mahara Bengi; Syarifah Atika; Chici Rizka Gunawan; Chica Rizka Gunawan
Journal Of Information System And Artificial Intelligence Vol. 6 No. 2 (2026): Vol.6 No. 2 (2026): Journal of Information System and Artificial Intelligence V
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v7i2.306

The rapid growth of the e-commerce industry in recent years has generated increasingly large and complex volumes of customer data. This data holds strategic potential to be analyzed in order to understand customer behavior patterns and to support data-driven decision-making. This study aims to identify customer segmentation through an unsupervised learning approach using Principal Component Analysis (PCA) and the K-Means algorithm. The dataset used in this research demonstrates good quality with no missing values, making it suitable for further analysis. Initial exploratory findings indicate that Total Spending, Number of Items Purchased, and Average Rating are the most significant variables in representing customer characteristics. The application of PCA successfully reduced data dimensionality while retaining 79.41% of the total variance, thus producing a more concise representation without compromising essential information. The clustering process using K-Means grouped customers into three clearly distinguishable clusters. The first cluster represents customers with high activity levels, the second cluster reflects customers with moderate activity, and the third cluster corresponds to customers with lower engagement intensity. Validation using the Elbow Method and Silhouette Score confirmed that k = 3 is the most optimal number of clusters. Cluster visualizations show strong separation between groups and consistent relationships among variables. This study demonstrates that the combination of PCA and K-Means is effective in producing informative and interpretable customer segmentation. These findings provide a foundation for subsequent analyses and support data-driven decision-making in e-commerce customer management.

Evaluation of 32 mm Automatic Hair Curlers on Shopee Using AHP Customer Reviews Alya Esa Mentari; Ari Muzakir
Journal Of Information System And Artificial Intelligence Vol. 6 No. 2 (2026): Vol.6 No. 2 (2026): Journal of Information System and Artificial Intelligence V
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v7i2.315

Abstract This study aimed to evaluate 32 mm automatic hair curler products available on the Shopee e-commerce platform by applying the Analytic Hierarchy Process (AHP) combined with customer review analysis. The data were obtained through direct observation of three products with similar characteristics. The collected data included product ratings, number of reviews, star-based review distribution, sales volume, and price. Sentiment analysis was conducted by categorizing customer reviews into positive, neutral, and negative groups based on their star distribution. The AHP method was used to determine the priority weights of each evaluation criterion. The results showed that sales volume was the most influential criterion in the decision-making process, followed by product ratings and the number of reviews. The final AHP score revealed that the MAIMEITE product achieved the highest value, making it the most recommended option. This study demonstrated that integrating AHP with customer review analysis provided an objective, systematic, and data-driven approach to product evaluation.

Mobile Financial Management Application at Yanto Pulsa Using Flutter Naufal latiful Hakim; Ike Yunia Pasa
Journal Of Information System And Artificial Intelligence Vol. 6 No. 2 (2026): Vol.6 No. 2 (2026): Journal of Information System and Artificial Intelligence V
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v7i2.330

The utilization of digital technology in financial management has become an important need for micro, small, and medium enterprises (MSMEs) to improve recording accuracy and facilitate financial monitoring. However, many MSMEs still rely on manual financial recording, which may lead to recording errors and difficulties in data management. This study aimed to develop a mobile-based financial recording application as a solution for financial management at Yanto Pulsa. The system was developed using the Waterfall method, which consisted of requirement analysis, system design, application development, and testing stages. The application was built using the Flutter framework with Firebase as the backend and Cloud Firestore as the database. The results showed that the application was able to record income and expense data in real-time, present financial summaries, and support effective data management and backup. Black-box Testing results indicated that all application functions operated according to user requirements.

Page 11 of 11 | Total Record : 105

7 8 9 10 11

Filter by Year

2020 2026

Filter By Issues

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Albert Yakobus Chandra

Contact Email
albert.ch@mercubuana-yogya.ac.id

Phone
+6285239280085

Journal Mail Official
jisai@mercubuana-yogya.ac.id

Editorial Address
Jl.Jembatan Merah, No.84C, Gejayan, Yogyakarta

Location
Kab. bantul,

Daerah istimewa yogyakarta

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Albert Yakobus Chandra

Contact Email albert.ch@mercubuana-yogya.ac.id

Phone +6285239280085

Journal Mail Official jisai@mercubuana-yogya.ac.id

Editorial Address Jl.Jembatan Merah, No.84C, Gejayan, Yogyakarta

Location Kab. bantul, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Albert Yakobus Chandra

Contact Email
albert.ch@mercubuana-yogya.ac.id

Phone
+6285239280085

Journal Mail Official
jisai@mercubuana-yogya.ac.id

Editorial Address
Jl.Jembatan Merah, No.84C, Gejayan, Yogyakarta

Location
Kab. bantul,

Daerah istimewa yogyakarta

INDONESIA