IJISTECH
IJISTECH (International Journal of Information System & Technology) has changed the number of publications to six times a year from volume 5, number 1, 2021 (June, August, October, December, February, and April) and has made modifications to administrative data on the URL LIPI Page: http://u.lipi.go.id/1492681220 IJISTECH (International Journal Of Information System & Technology) is a peer-reviewed open-access journal published two times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and practice-oriented papers dealing with advances in intelligent informatics. All the papers are refereed by two international reviewers, accepted papers will be available online (free access), and no publication fee for authors. The articles of IJISTECH will be available online in the GOOGLE Scholar. IJISTECH (International Journal Of Information System & Technology) is published with both online and print versions. The journal covers the frontier issues in computer science and their applications in business, industry, and other subjects. Computer science is a branch of engineering science that studies computable processes and structures. It contains theories for understanding computing systems and methods; computational algorithms and tools; methodologies for testing of concepts. The subjects covered by the journal include artificial intelligence, bioinformatics, computational statistics, database, data mining, financial engineering, hardware systems, imaging engineering, internet computing, networking, scientific computing, software engineering, and their applications, etc. • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Autonomous Agents and Multi-Agent Systems • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Cognitive systems • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, fault analysis, and diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High-Performance Computing • Information storage, security, integrity, privacy, and trust • Image and Speech Signal Processing • Knowledge-Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Memetic Computing • Multimedia and Applications • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition, and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Stochastic systems • Support Vector Machines • Ubiquitous, grid and high-performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data
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Customer Loyalty Classification with Comparison of Naive Bayes, C4.5, and KNN Methods
Wati, Embun Fajar;
Perangin-Angin, Elvi Sunita;
Indriyani, Luthfi
IJISTECH (International Journal of Information System and Technology) Vol 8, No 3 (2024): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v8i3.361
Customer loyalty is indispensable for the survival of a company. Customer loyalty needs to be maintained in order to return to visit and transact with the Company. Customer data consisting of age, annual income, purchase amount, region, purchase frequency, and loyalty score features can produce new information, namely analyzing customers who have high loyalty. Data processing is carried out using three data mining algorithms, namely Naïve Bayes, C4.5 or Decision Tree, and KNN. The stages carried out in data processing consist of data selection, preprocessing, transformation, and modelling. The customer data used amounted to 238. Modelling is carried out using Rapid Miner Software. Customer loyalty classification can be easily done with the three algorithms, namely Naive Bayes, and C4.5 or Decision Tree, and KNN which is validated by the 10-fold cross-validation method so as to produce the highest percentage of accuracy and the similarity of the accuracy value of the Naive Bayes and C4.5 algorithms, which is 96.67%. In the AUC value, it can be seen that the Naive Bayes algorithm is superior to the C4.5 algorithm or Decision Tree and KNN. The result of the highest AUC value is 0.997, the highest precision percentage is 98.92% achieved by the Naive Bayes algorithm. The result of the highest recall percentage is C4.5 of 100%. The results of the AUC value and accuracy percentage on the three algorithms prove that the performance of the three algorithms is very good.
Implementation of Artificial Intelligence based on Natural Language Processing to Enhance MSME Sales
Alawiah, Enok Tuti;
Setyorini, Dini;
Apriyani, Helina
IJISTECH (International Journal of Information System and Technology) Vol 8, No 3 (2024): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v8i3.362
Micro, small and medium enterprises have contributed to gross domestic product and help support the people's economy. However, there are many challenges in the era of digital transformation so that MSMEs must innovate so that their products can be accepted by the market more widely. This study aims to help MSMEs increase their sales by carrying out digital transformation through a website-based sales information system equipped with a chatboot facility based on natural language processing. The goal is to provide a different experience for customers in shopping online and convenience for MSMEs in providing customer service satisfaction. The implementation of artificial intelligence based on natural language processing is expected to help micro, small and medium enterprises in expanding the market, increasing sales and providing a different experience in shopping.
Consumer Decision Support System in Online Shopping in Market Place
Sefrika, S
IJISTECH (International Journal of Information System and Technology) Vol 8, No 3 (2024): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v8i3.363
Online shopping transactions are common in today's society. Many people have changed their conventional shopping methods to online modes, so research needs to be conducted to find out what supports consumer decisions in making online shopping transactions through the market place platform. The research method uses TOPSIS, a multi-criteria method that offers an easier and more effective way to find out how or what underlies someone in determining certain decisions. TOPSIS is used to determine the preference value and distance of positive and negative ideal solutions as offered in the study. The results show that free shipping is the main reason for a consumer to make online transactions and fashion products are the most purchased items in online transactions.
Implementation Of The Multi-Attribute Utility Theory (Maut) Algorithm In The Web-Based Performance Evaluation System For The Administrators Of Nurul Hayah Islamic Boarding School
Fitria, Alfina;
Nada, Noora Qotrun;
Dewanto, Febrian Murti
IJISTECH (International Journal of Information System and Technology) Vol 8, No 3 (2024): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v8i3.360
Islamic Boarding School as an educational institution that focuses on character, moral, and religious formation that has an organizational structure involving many administrators with their respective roles and responsibilities. With this, an Islamic boarding school must improve the quality of management of the administrators by evaluating the performance of the administrators. In this study, we tried to select the best administrators at the Nurul Hayah Islamic Boarding School using the Multi-Attribute Utility Theory (MAUT) method. I used five main assessment criteria: discipline, loyalty, accountability, leadership, and educational background. To build the system, I applied the Extreme Programming (XP) methodology with the PHP programming language, MySQL database, and Visual Studio Code development environment. The sample was obtained from the data of the administrators of the education section of the Nurul Hayah Islamic Boarding School. With the weights that have been determined for each criterion, this study successfully identified Dimas as the administrator with the best performance, and obtained a score of 14.6667 as the best administrator at the Nurul Hayah Islamic Boarding School. With this, it is stated that the study using the MAUT method to determine the best administrator was successful.
The Application of Customers Segmentation Using RFM Analysis Method and K-Means Clustering to Improve Marketing Strategy
Robo, Salahudin;
Melani, Putri Indah;
Fernatyanan, Patrisia;
Widiantoro, Muh Riandi;
Bariyyah, Sitti Khairul
IJISTECH (International Journal of Information System and Technology) Vol 8, No 3 (2024): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v8i3.370
This research aims to overcome problems in improving marketing strategies in the Retail Business industry by using effective customer segmentation. The method used is RFM (Recency, Frequency, Monetary) analysis to measure the time proximity, frequency and monetary value of customer transactions, as well as K-Means Clustering to group customers based on their purchasing behavior. The results showed that the combination of these two methods successfully grouped customers into ten different segments, such as “Champions” and “Hibernating,” which provided deep insight into customer needs and behavior. The application of this segmentation provides practical benefits in increasing the efficiency of marketing strategies, customer retention and resource optimization. Overall, this research proves that applied customer segmentation techniques can significantly increase customer satisfaction and loyalty, making a valuable contribution to the field of retail marketing.
Accounting Information System For Tailoring Materials At Penjahit Ruslan Using The Accrual Basis Method
Sabri, Khairul;
Atmaja, Niko Surya;
Afrijal, A;
Yasdomi, Kiki;
Dona, D
IJISTECH (International Journal of Information System and Technology) Vol 8, No 3 (2024): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v8i3.371
Development of an Accounting Information System (AIS) is crucial for enhancing financial management, particularly for micro and small enterprises (MSEs). This study focuses on implementation of a web-based AIS utilizing the accrual basis method at Penjahit Ruslan, a tailoring business encountering challenges in financial transparency, transaction accuracy, and real-time monitoring. The system, constructed using HTML, PHP, and MySQL, facilitates structured data storage, automated financial reporting, and improved decision-making. Through adoption of UML-based system design and rigorous testing methods, including Blackbox Testing and Performance Testing, AIS ensures high reliability and accuracy in financial recording. Findings indicate that transitioning from manual bookkeeping to a digital financial system significantly enhances efficiency, reduces errors, and strengthens financial oversight. This research contributes to broader digital transformation in financial management for small businesses, offering a scalable solution for enterprises confronting similar challenges.