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LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics
Published by Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan
ISSN : 30634504     EISSN : 30629098     DOI : https://doi.org/10.28918/logiclink.v1i1
Core Subject : Science,
Computer Science & IT
LogicLink : Journal of Artificial Intelligence and Multimedia in Informatics is free of fee, open access, and peer-reviewed journal, published by Informatics Department - UIN K.H. Abdurrahman Wahid Pekalongan Indonesia, which is a dissemination medium for research results from scientists and engineers in the Artificial Intelligence and Multimedia. LogicLink is a biannual journal issued in June and December with the objectives to explore, develop, and elucidate the knowledge of computational intelligence or Multimedia to keep practitioners and researchers informed on current issues and best practices, as well as serving as a platform for the exchange of ideas, knowledge, and expertise among technology researchers and practitioners. LogicLink : Journal of Artificial Intelligence and Multimedia in Informatics focuses on issues of Computational Intelligence, such as : 1. Artificial Intelligence 2. Information Security 3. Image Processing 4. Data Mining 5. Decision Support System 6. Mobile Computing 7. Expert System 8. Multimedia, 9. and other topic related to computer technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 8 Documents
 1 
Search results for , issue "Vol. 2 No. 1, Juni 2025" : 8 Documents clear
Artificial Intelligence and the Transformation of Digital Services in Islamic Banking: A Case Study of Bank Syariah Indonesia Luqman Syakirunni’am; Zohaib Hassan Sain; Syauqie Muhammad Marier; Syukron Jamil
LogicLink Vol. 2 No. 1, Juni 2025
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v2i1.10707

Abstract

Digital transformation in the Islamic banking sector is necessary in the era of technological disruption. However, implementing artificial intelligence (AI) still faces challenges, especially in non-metropolitan areas. This research aims to explore the role of AI in transforming digital services at Bank Syariah Indonesia (BSI) Semarang Branch, while emphasizing its compliance with maqashid Sharia principles. This research uses a phenomenological qualitative approach through in-depth interviews, participatory observation, and analysis of internal documentation of employees and customers. The main findings show that AI significantly improves service efficiency through digital onboarding, chatbot “Hasanah Assistant”, and a pattern-based automated transaction system. This innovation is considered to accelerate services while maintaining the principles of property protection (hifzh al-mal) and life (hifzh al-nafs). However, challenges arise regarding AI’s limited adaptation to local cultural contexts, senior employees’ resistance to digitalization, and customers’ limited digital literacy. This highlights the importance of a hybrid approach between technology and humanization of Islamic banking services. The uniqueness of this research lies in its focus on regional branches that have not been widely explored in the academic literature, as well as its contribution to expanding the understanding of AI as a strategic medium, not just an automation tool. The implications of this research emphasize the need for an efficient, ethical, and inclusive AI system design, oriented towards Islamic values. The research suggests a participatory digital transformation strategy that is adaptive to local values and consistent with maqashid Sharia to ensure the sustainability of technological innovation in Islamic banking.
The Ethical Aspects of Applying Artificial Intelligence in the Healthcare System Yuliana
LogicLink Vol. 2 No. 1, Juni 2025
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v2i1.10710

Abstract

Artificial intelligence is abundant nowadays, including healthcare practice. However, there are ethical aspects of applying artificial intelligence (AI) in healthcare practice. The paper aims to describe the ethical aspects of applying artificial intelligence (AI) in the healthcare system. This paper is a narrative literature review. Literature was taken from Science Direct, PubMed, and Google Scholar. Selected journals were published within 5 years. AI-based algorithms commonly work based on the provided data. Nevertheless, the variations among patients’ conditions, insurance records, and diagnostic results are wide. Some data such as social determinants might not be seen by the AI system. This kind of data determines the biases in data sets and health disparities. Therefore, the algorithms should be well designed to minimize bias and align with the ethical dimensions. Predictive algorithms might be used to predict care decisions and outcomes. Incorrect diagnosis might be developed while using AI tools directly without expert opinion. In conclusion, the ethical dimensions of applying AI in the healthcare practice are minimizing bias, determining the variations of social conditions among patients, and using expert opinion other than relying on AI only.
Identifikasi Citra Jenis Rempah-Rempah Menggunakan Arsitektur RestNet50 Sari, Christy Atika; Pradana, Luthfiyana Hamidah Sherly; Rachmawanto, Eko Hari
LogicLink Vol. 2 No. 1, Juni 2025
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v2i1.10713

Abstract

Indonesia has various types of spices used in culinary and traditional medicine. However, changes in lifestyle and modernization have made it increasingly difficult for the younger generation to recognize spices directly. Conventional identification still relies on manual observation which is prone to errors. Therefore, an artificial intelligence-based solution is needed to improve the accuracy of spice classification. This study applies the Convolutional Neural Network (CNN) method with the ResNet50 architecture, which is part of Deep Learning, to classify digital images of spices. This model utilizes Computer Vision to recognize visual patterns, Transfer learning to improve training efficiency, and Data Augmentation Techniques such as rotation, flipping, and scaling to improve model robustness. Evaluation using Confusion Matrix was carried out with various dataset division scenarios, including ratios of 90:10, 80:20, 70:30, 60:40, and 50:50. The experimental results showed that the model with a ratio of 90:10 provided the best accuracy, reaching 98.04%, with high precision, recall, and F1-score. In conclusion, the CNN method with ResNet50 has proven effective in identifying spices based on digital images. Further development can be done by adding variations of datasets and exploring other Deep Learning architectures to improve model performance.
Analisis Cluster Hierarki Pada Tingkat Kemiskinan di Provinsi Sumatera Utara tahun 2023 Bagus Candra Setiawan; Arum Nilawati; Rahmad Ferdian; Raihan Aditya Saputra; Yemima Putri Santoso; Muhammad Riefky
LogicLink Vol. 2 No. 1, Juni 2025
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v2i1.10762

Abstract

Poverty is one of the problems that is still a priority in social and economic development in Indonesia. In 2023, North Sumatra Province had a poverty rate of 8.15% with a total of 1,239.71 thousand poor people. The condition of poverty by district/city in North Sumatra Province is very diverse. The purpose of this study is to group districts/cities in North Sumatra Province in 2023 based on poverty rate indicators using hierarchical clusters. The data used in this study is secondary data with four poverty variables from 33 districts/cities in the North Sumatra Province in 2023. The results of the analysis contained 3 clusters with the number of members in clusters 1, 2, and 3. Suggestions for the North Sumatra Provincial Government need to formulate adaptive and specific policies based on the characteristics of each regional cluster prioritizing high poverty problem areas through strengthening access to education, health, and productive economic development. In addition, intensive cooperation between regions within the same cluster should be developed to support information exchange, joint empowerment programs, and local economic development synergies to improve the effectiveness of interventions and the welfare of the community in a sustainable manner.
Analisis Komparatif Kemampuan Kecerdasan Buatan dalam Menganalisis Konten Media Sosial: Studi Kasus Grok (XAI), ChatGPT, dan Gemini Herdian Nugroho, Bernardus
LogicLink Vol. 2 No. 1, Juni 2025
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v2i1.10819

Abstract

This study aims to compare the capabilities of three artificial intelligence (AI) platforms—Grok (XAI), ChatGPT (OpenAI), and Gemini (Google)—in analyzing social media content, specifically on Twitter (X). A descriptive qualitative method with a case study approach was employed. Three types of content—text, images, and videos—were used as test materials. Each AI platform was tasked with analyzing sentiment, context, and meaning embedded within the content. The results indicate that Grok excels in direct integration with Twitter, enabling real-time contextual reading of posts and interactions. In contrast, ChatGPT and Gemini demonstrated superior performance in in-depth analysis and multimodal interpretation when provided with explicit input. This study provides early insights into the comparative strengths and limitations of AI platforms for social media content analysis, offering practical recommendations for researchers and practitioners in selecting appropriate AI tools for digital public opinion analysis.
Analisis Komparatif Efisiensi Memori dan Waktu Komputasi pada 8 Algoritma Sorting menggunakan C++ Irfan Ali, Muhammad; Rangga Dzikri Fardiarsyah; Lukman Shodik; Fadilah Zahra Dwi Kinanti; Imam Prayogo Pujiono
LogicLink Vol. 2 No. 1, Juni 2025
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v2i1.10868

Abstract

This study aims to analyze the efficiency of computation time and memory allocation of eight sorting algorithms (Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, Merge Sort, Heap Sort, Counting Sort, and Radix Sort) implemented in C++ programming language. The test dataset consists of three size categories: 100, 1,000, and 10,000 elements, randomly generated with values between 1 and 99. This range was chosen so that the tests are conducted under conditions of limited value and contain a lot of duplication, in order to support consistent efficiency evaluation. The research method involved generating datasets using the random array function, measuring execution time in nanoseconds, and monitoring memory usage through the WorkingSetSize metric. Each algorithm was tested three times on each category of data to ensure consistency of results. The results showed that Heap Sort achieved the fastest execution time on small data (101,266 nanoseconds for 100 elements), Counting Sort and Radix Sort excelled on medium data, while Counting Sort delivered the best performance on large data (1,483,166 nanoseconds for 10,000 elements). Counting Sort also demonstrated stable memory efficiency compared to the other algorithms, whereas Bubble Sort consistently exhibited the poorest performance across all scales. The research conclusion recommends Heap Sort for small-scale data, Counting Sort and Radix Sort for medium-scale data, and Counting Sort for large-scale data. The findings provide practical guidance for developers in selecting algorithms according to data scale and resource availability.
Expert System untuk Rekomendasi Pemilihan Bahasa Pemrograman bagi Pemula Menggunakan Algoritma Decision Tree Manza, Yuke; Wayahdi, M. Rhifky
LogicLink Vol. 2 No. 1, Juni 2025
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v2i1.10924

Abstract

This study develops an expert system based on the Decision Tree algorithm to recommend suitable programming languages for beginners, addressing the challenge of selecting the right language amid the abundance of options and diverse learning goals. This topic is significant because choosing the appropriate language can accelerate the learning process and improve the effectiveness of programming education. The research methodology includes the creation of a synthetic dataset comprising 1,500 entries, with the addition of 5% noise. This noise is introduced to simulate real-world data imperfections and to test the model's robustness against unclean or imperfect data. The next stages involve data preprocessing through encoding and normalization, followed by modeling using the Decision Tree algorithm with hyperparameter optimization to enhance model performance. Evaluation results show an accuracy of 95%, with learning goals (38% contribution) and platform preference (35%) emerging as the most influential factors in decision-making. A 10-fold cross-validation produced an average error of 0.046, indicating model stability across various data subsets. Feature importance analysis revealed that the model logically prioritizes technical relevance, for example, by ranking learning goals and platform preference above demographic features, as these are more directly related to the context and practical use of programming languages. The implemented system successfully provided relevant recommendations, such as Python for Data Science and JavaScript for Web Development. This study concludes that the Decision Tree algorithm is effective for recommendation systems based on user profiles, although data enhancement is needed for minority classes such as Java. These findings contribute to the development of more personalized and adaptive programming learning support tools.
Consumer Decision-Making in Car Purchases: Insights from a Logistic Regression Analysis Simamora, Sandy J. H.; Ulkhaq, Mujiya; Handayani, Naniek Utami
LogicLink Vol. 2 No. 1, Juni 2025
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v2i1.10934

Abstract

Indonesia, with its large population, presents significant consumer demand, particularly in the automotive sector. Consequently, numerous car brands actively invest and market their products within the country. Among the 36 car brands officially operating in Indonesia, Toyota has consistently dominated the market. This study specifically examines consumer perceptions and decision-making processes regarding Toyota car purchases using logistic regression analysis. It investigates the impact of perceived risk, price sensitivity, convenience, and customer satisfaction on consumers' purchasing choices. Using a quantitative approach, data were collected from Indonesian Toyota consumers through purposive sampling and analyzed using the logistic regression. The findings reveal that perceived risk and price significantly influence consumers' decisions to purchase Toyota cars, highlighting the importance for Toyota and other automotive brands to strategically manage risk perceptions and pricing policies to enhance consumer appeal and market share.

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