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Nur Delifah
Department of Informatics Engineering, UIN Sultan Syarif Kasim Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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UI/UX design thinking adoption for integrated AI point-of-sale system (Case study: Plastic Poultry Wholesale Store) Okfalisa Okfalisa; Fahruddin Fahruddin; Haris Setiaji; M Farhan Aulia Pratama; Harry Finaldhi; Nur Delifah
Science, Technology, and Communication Journal Vol. 5 No. 3 (2025): SINTECHCOM Journal (June 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v5i3.311

Abstract

The development of artificial intelligence (AI) technology drives the need for a point of sales (POS) system that is not only efficient, but can also provide adaptive information according to the user's sudden wishes. This research implements the design thinking method in designing a user interface (UI/UX) for a smart POS system integrated with conversational AI features. A case study was conducted at a Plastic Poultry Wholesale Store to gain in-depth insights related to field needs and operational challenges faced. The design thinking method was chosen because of its user-centered approach, through the stages of empathize, define, ideate, prototype, and testing, it is hoped that the final results obtained can be aligned with the concrete needs of users, so that the output of the system that has been designed will not be abandoned, but will always be used. In this design, the implementation of conversational AI is used to enhance the user experience through a virtual assistant feature that is able to answer dynamic questions according to the wishes of the user, so that users can freely explore any information in detail related to their overall business performance. The implementation results show that this system not only increases operational efficiency, but also improves user experience through more intuitive interactions when they want to see their business performance. This research contributes to integrating AI technology with a user-centered design approach for smart, responsive, and adaptive POS system solutions.
A classification of Quran translations using K-nearest neighbors, support vector machine and random forest method Nur Delifah; Nazruddin Safaat Harahap; Surya Agustian; Muhammad Irsyad; Iwan Iskandar
Science, Technology, and Communication Journal Vol. 6 No. 1 (2025): SINTECHCOM Journal (October 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i1.337

Abstract

A Classification of Quranic verses based on topics is one of the efforts to facilitate understanding and searching for information in the holy book, especially for non-Arabic readers. This study aims to test and compare the performance of three text classification methods, namely K-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF), in grouping translated Quranic verses into 15 topic classes, such as Islamic arkanul, faith, the Quran, science and its branches, charity, da'wah, jihad, human and social relations, and others. The dataset used is the English translation of the Quran with full preprocessing and an 80:20 data split for training and testing. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results show that RF achieved the best performance with an average F1-score of 58.48% and testing accuracy of 90.81%. KNN followed with an F1-score of 54.07% and the highest testing accuracy of 92.05%, while SVM produced the lowest F1-score at 50.76% and accuracy of 88.20%. The RF demonstrates a more balanced ability in recognizing all classes, KNN excels in overall accuracy, and SVM performs less optimally in this classification task. This research is expected to serve as a foundation for developing a more intelligent and contextual topic-based verse classification system.
Interpretative comparative analysis of LSTM and random forest for multi-label classification of English Qur’an translation Nur Delifah; Nazruddin Safaat Harahap; Okfalisa Okfalisa; Elvia Budianita
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.373

Abstract

The rapid growth of digital Qur'anic resources has created a need for automated systems capable of accurately categorizing verses by thematic content. The thematic complexity of Qur'anic text, in which a single verse may simultaneously convey multiple moral, spiritual, and social messages, presents a significant challenge for automated classification systems. This study conducts a comparative and explainable evaluation of long short-term memory (LSTM) and random forest (RF) for multi-label classification of English Qur'an translations across six thematic categories: arkanul Islam, iman, amal, human and community relations, akhlak, and history and story. To address severe class imbalance, synthetic minority over-sampling technique (SMOTE) was applied per label, expanding the training set from 4,489 to 19,658 samples. LSTM captured sequential contextual relationships through integer token embeddings, while RF relied on TF-IDF vector representations. Evaluated on 1,248 unseen test verses, RF achieved a higher macro F1-score (0.2748) compared to LSTM (0.2432), while LSTM retained marginally higher accuracy (79.61% vs. 79.55%). Per-label analysis revealed that both models performed best on lexically explicit labels such as arkanul Islam and iman, but consistently failed on abstract categories such as akhlak, where LSTM recorded near-zero recall of 0.61% and RF only 6.10%. This study contributes empirical evidence that TF-IDF-based SMOTE interpolation is more effective for minority-class augmentation than token-sequence interpolation, and demonstrates that macro F1-score is a more appropriate evaluation metric than accuracy for imbalanced multi-label religious text classification.
Co-Authors Elvia Budianita Fahruddin Fahruddin Haris Setiaji Harry Finaldhi Iwan Iskandar M Farhan Aulia Pratama Muhammad Irsyad Nazruddin Safaat Harahap Okfalisa Okfalisa Surya Agustian