Article Per Year (5 Year)
p-Index From 2021 - 2026
4.763
P-Index
This Author published in this journals
All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal AKSI (Akuntansi dan Sistem Informasi) Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Manajemen Bisnis Innovation in Research of Informatics (INNOVATICS) Jurnal Nasional Manajemen Pemasaran dan SDM Adbis : Jurnal Administrasi dan Bisnis Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Business Analytics (IJBA) International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) Epicheirisi : Jurnal Manajemen, Administrasi, Pemasaran dan Kesekretariatan Dharma: Jurnal Pengabdian Masyarakat Jurnal Teknik Ilmu dan Aplikasi International Journal of Sharia Business Management Journal of Business Management and Economic Development International Journal of Applied Economics, Accounting and Management (IJAEAM) Jurnal Ilmiah Ekonomi dan Manajemen JEBD JURNAL RUMPUN MANAJEMEN DAN EKONOMI JURNAL AKADEMIK EKONOMI DAN MANAJEMEN Jurnal Pengabdian Kepada Masyarakat Jurnal Aplikasi Bisnis Jejak digital: Jurnal Ilmiah Multidisiplin
Afandi, Yosi
Politeknik Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 47 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search
Journal : International Journal on Advanced Technology, Engineering, and Information System (IJATEIS)

 1 
SEMANTIC AND NATURAL LANGUAGE PROCESSING DEVELOPMENT APPLICATIONS FOR CHATBOTS TO ENHANCE ONLINE STORE CUSTOMER SERVICE Afandi, Yosi; Maskur, Maskur; Fiernaningsih, Nilawati; Fauzi, Ahmad
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 2 No. 4 (2023): NOVEMBER
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v2i4.986

Abstract

Since customer support is time-limited, chatbot programs can assist potential online store visitors before they make a purchase. The general public cannot always answer inquiries or respond to customer requests. Virtual customer support allows potential customers to contact vendors regarding products they wish to purchase. This technology is very helpful in providing quick and accurate answers to various customer concerns and issues. The study focuses on the online retail environment where customer support is crucial for potential buyers before making a purchase. The Artificial Intelligence Markup Language (AIML) and Semantic Ontology were used by A.L.I.C.E. (Artificial Internet Linguistic Computer Agency) to develop an AI chatbot application. There are no online stores that use virtual customer service (chatbots) for customer support, so Batik Cloth, an application that offers batik textiles for sale in Malang, was chosen as the online store chatbot application for this study. Creating a chatbot with semantic capabilities involves using ontologies to process queries with more precise meaning. It achieves 92% accuracy for 15 types of relevant queries and responses, followed by 10 frequently asked questions as answers. Created by a potential buyer. Virtual customer support systems (chatbots) can respond to queries with similar terms or meanings by employing ontologies and semantics to deliver answers that fit the queries.
ONLINE STORE INTEGRATION WITH INTELLIGENT CHATBOT AND SEMANTIC-BASED PRODUCT SEARCH TO IMPROVE SERVICES Maskur, Maskur; Afandi, Yosi; Fauzi, Ahmad; Herijanto, Pudji
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 3 No. 4 (2024): NOVEMBER
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v3i4.1453

Abstract

The integration of online stores with chatbots is something that must be done so that chatbot answers are able to customize customer needs. Semantic-based product search is done to find products according to the context sought so that it will help customers find their products. The integration between online stores with intelligent chatbots and semantic-based search can significantly improve the user experience in online shopping. An intelligent chatbot assists users in finding products quickly, providing relevant product information, and addressing customer queries or concerns directly. Semantic-based search allows users to find products more accurately based on their context and preferences. The integration of online stores with intelligent chatbots and semantic-based search has great potential to improve services in the context of e-commerce. The cosine similarity method is used to give weight to each search result obtained, so that the search results obtained are more relevant to the keywords. Testing using the precission method to calculate the relevance value of the results obtained from the ontology, while testing with kappa statistics is used to calculate the value of the cosine similarity results by comparing the results obtained from the system and the results according to expert observations, it is expected that semantic-based product search is able to find products with a precision level of 98% so that customers will be satisfied.
DESIGNING AN OMNICHANNEL MARKETING BUSINESS MODEL TO IMPROVE CUSTOMER EXPERIENCE Afandi, Yosi; Maskur, Maskur; Fiernaningsih, Nilawati; Herijanto, Pudji
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 3 No. 4 (2024): NOVEMBER
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v3i4.1464

Abstract

System integration allows companies to bring together customer data from multiple channels, from websites to mobile apps and physical stores. Companies can have a more complete view of customer preferences and behavior, which in turn allows them to serve more relevant and personalized content to their customers. In an era where customers are inundated with information, content personalization is key to attracting customers' attention and maintaining their engagement. Furthermore, this business model emphasizes deep customer engagement across multiple channels. In an omnichannel environment, it is important for companies to stay connected with their customers no matter where they are. This can be achieved through responsive customer service, ongoing loyalty programs, and engaging content on social media and other online platforms. The results of this study discuss the analysis of application quality using five characteristics, namely Functional Suitability, Usability, Performance Efficiency, Portability and Compatibility. The results of the functional suitability characteristics of the omnichannel platform are said to be good. Usability gets 76.67% which means the omnichannel platform is called feasible. Performance efficiency of the website is good because the load process is less than 10 seconds. From these results it is concluded that the Omnichannel platform meets the predicate of satisfied. Compatibility of the Omnichannel platform did not find any location and performance problems on Edge, Chrome and Android browsers. The assessment results are expected to be recommendations and suggestions for developing an omnichannel platform to help the process of sending digital reminders that are better and more efficient.
Online Store Product Recommendation System Using Collaborative Filtering and Content-Based Filtering Algorithms to Increase Sales Afandi, Yosi; Maskur, Maskur; Widyananda, Wahyu; Fiernaningsih, Nilawati; Budiarti, Lina; Az Zuhri, Fahmi Muhammad
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.2007

Abstract

This study aims to evaluate and compare the performance of two recommendation system approaches, namely Collaborative Filtering (CF) and Content-Based Filtering (CBF), in providing relevant product recommendations to users in an e-commerce context. The dataset used consists of 120 data including 90 relevant and recommended products (True Positive), 20 recommended but irrelevant products (False Positive), and 10 relevant but not recommended products (False Negative). Based on the calculation results, both methods show a precision value of 0.818 and a recall of 0.900. This means that approximately 81.8% of products recommended by the system are truly relevant, while 90% of the total relevant products are successfully recommended to users. The F1-score value obtained of 0.857 illustrates a good balance between the accuracy and completeness of the recommendations generated by the system. Furthermore, to measure the level of rating prediction error, the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics are used. The evaluation results show that the CF method has an MSE value of 0.0784 and an RMSE of 0.28, while the CBF method shows an MSE of 0.0961 and an RMSE of 0.31. The lower RMSE value of CF indicates that this method has better accuracy in predicting user preferences than CBF. Overall, both methods show good performance with a low error rate. However, CF proved slightly superior in providing recommendations that match user preferences, so it can be used as a basis for developing smarter and more personalized recommendation systems on e-commerce platforms.
Development of Semantic-Based Voicebots and Natural Language Processing for E-Commerce Product Searches Maskur, Maskur; Afandi, Yosi; Widyananda, Wahyu; Fauzi, Ahmad; Armayrishtya, Zhulvardyan
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 3 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i3.2008

Abstract

Searching for products online is often an inefficient and confusing process, especially when users do not know the exact name of the product or use terms that differ from the search system. Keyword-based searches tend to produce irrelevant results because the system only matches text literally without understanding the meaning. As users increasingly talk to digital devices, voice-based search technology has become a more natural and intuitive alternative. This research aims to develop a semantic-based voicebot supported by Natural Language Processing (NLP) to improve the effectiveness of product searches on e-commerce platforms. The designed system not only recognizes user speech but also understands the context, intent, and semantic meaning of the given commands. The research stages include collecting user voice data, training the Automatic Speech Recognition (ASR) model for voice-to-text conversion, and applying the semantic NLP model for interpreting the context of product searches. The testing was conducted using Indonesian voice commands in a simulated e-commerce scenario. The results showed that the system achieved an average Word Error Rate (WER) of 1.29%, indicating a high level of accuracy in recognizing speech and understanding user intent. The integration between ASR and semantic NLP proved capable of creating a more natural, responsive search experience that resembles the way humans think and communicate when interacting with online search systems.
Co-Authors AA Sudharmawan, AA Abdul Waris Abdullah Helmy Achmad Zaini Agustina, Hiqma Nur Aini, Yulis Nurul Alfi Tranggono Agus Salim Alfreda Juni Purwa Aditya Almah Sagita Andriani Kusumawati Ane Fany Novitasari Arni Utamaningsih Arni Utamaningsih Ayu Sulasari Az zuhri, Fahmi Muhammad Bahari, Abigail Ellaura Fe Baroroh Lestari Baroroh Lestari Budi Artono Cahyani, Selin Anggi DWI SURYANTO Endang Astuti Endang Siti Astuti Farika Nikmah Fauzi Ahmad Muda Fiernaningsih, Nilawati Gardilla, Humaira Fathma Hanif Hanif Helmy, Abdullah Hendrawan, Muhammad Afif Herijanto, Pudji Heru Utomo, Heru Hidayatinnisa’, Nurul Ita Rifiani Permatasari Joko Samboro Joni Dwi Pribadi Joni Dwi Pribadi Joni Pribadi Lestariningsih, Tri Lestariningsih, Tri Lia Widia Tama, Kharisma Lilies Nur Ainie Lina Budiarti, Lina Mahmudatul Himma Mahmudatul Himmah Maskan, M Maskur Maskur Maskur Maskur Maskur Mega Rahayu Nur Kusuma Wardhani Mudofir, Imam Muhamad Muwidha Muhammad Ali Akbar Jasmine Muhammad Yusuf Hilmy Musthofa Hadi Musthofa Hadi, Musthofa Netty Lisdiantini Nilawati Fiernaningsih Noorsakti Wahyudi Norma Dilla Kharisma NOVITASARI Nugrahaningtyas Fatma Anyassari Nurtjahjani, Fullchis Nurudin Nurudin Pudji Herijanto Rafael Slamet Firdaus Rena Feri Wijayanti Rifki Abrory Rizky Kurniawan Murtiyanto Safitri, Yuyun Nur Samboro, Joko Septian Enggar Sukmana Sinaga, Yiswi Mariani Amelia Siti Nurbaya subiyantoro Tania Setya Ningsih Tri Afirianto Tri Afirianto Tri Istining Wardani Tri Yulistyawati Evelina Wahyu Widyananda Wida Yuliar Rezika Yakomina Mabel Yanik Lailinas Sakinah Yulis Nurul Aini Yusri Abdillah Zhulvardyan Armayrishtya Zubaidi Zubaidi