Knowbase : International Journal of Knowledge in Database
Knowbase: International Journal of Knowledge in Database Journal Title : Knowbase: International Journal of Knowledge in Database ISSN : 2797-7501 (Online) / 2798-0758 (Print) DOI Prefix : Prefix 10.30983/knowbase by Editor in Chief : Dr. Riri Okra, M.Kom Managing Editor : Yulifda Elin Yuspita, M.Kom Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi Frequency : 2 issues per-year (June and December) Citation Analysis : Scopus | SINTA | Google Scholar Knowbase: International Journal of Knowledge in Database (e-ISSN: 2797-7501 | p-ISSN: 2798-0758) is a peer-reviewed, and open-access journal published by Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi, West Sumatera, Indonesia. The journal is issued biannually in June and December and serves as a multidisciplinary platform for disseminating the latest research and developments in the areas of database systems, data management, and knowledge discovery. Knowbase seeks to advance scientific understanding of how data, information, and knowledge can be effectively modeled, integrated, and utilized to support intelligent decision-making, system optimization, and innovation in the era of Big Data and Artificial Intelligence (AI). The journal’s vision is to become a reputable international publication that bridges the gap between academic research and industrial applications in Database Systems, Data Science, and Knowledge Engineering. It welcomes original research articles, case studies, and reviews that contribute to both theoretical foundations and practical advancements in data management and knowledge processing. All submitted manuscripts undergo a double-blind peer-review process to ensure quality, originality, and academic integrity. Knowbase adheres to the ethical standards set by the Committee on Publication Ethics (COPE). As an academic journal, Knowbase: International Journal of Knowledge in Database has been accredited by the Decree of the Director General of Research, Technology, and Development Reinforcement, Ministry of Research, Technology, and Higher Education of the Republic of Indonesia (Number: 72/E/KPT/2024), and has achieved SINTA Rank 4 accreditation. Since 2024, the journal has been a member of CrossRef, which means that every article published by Knowbase is assigned a unique Digital Object Identifier (DOI). All manuscripts published in this journal are written in standard English since its first issue. Journal Secretariat : Rumah Jurnal, Data Center Building (2nd Floor), Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi, Gurun Aua St., Kubang Putih, Banuhampu, Agam, West Sumatra, Indonesia.
Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi
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The Effectiveness of ASIAP Digital Innovation in the Management of SPPT PBB in Pekanbaru City: DAIGUSI Analysis of Innovation and User Satisfaction
Digital innovation in public services is increasingly becoming a primary need for efficient and responsive governance. One such innovation is ASIAP (Aplikasi Antar SPPT PBB), a digital system developed by the Pekanbaru City Regional Revenue Agency (Bapenda) to distribute Tax Payable Notification Letters (SPPT) online. However, as a silent innovation, ASIAP is not widely known, despite its significant contribution to regional tax management. This study aims to assess user satisfaction with the ASIAP application and analyze the accompanying innovation diffusion process. The study used a mixed-method approach with quantitative and qualitative methods. Data collection was conducted by distributing questionnaires to ten ASIAP user employees, based on five dimensions in the End-User Computing Satisfaction (EUCS) model: content, accuracy, format, ease of use, and timeliness. In-depth interviews were also conducted with several Bapenda employees to explore the innovation adoption process based on Rogers' innovation diffusion theory. The results showed that users found the ASIAP application quite satisfactory, particularly in terms of information accuracy and ease of use. However, aspects of information up-to-dateness and visual presentation still require improvement. The ASIAP diffusion process is considered to have progressed gradually through five stages of innovation adoption, supported by the role of internal change agents and informal communication between employees. In conclusion, ASIAP is a potential digital innovation for strengthening information technology-based public services at the regional level, but it still requires further development to achieve broader and more equitable benefits.
Copyright (c) 2025 Zahra Aqilah Dytihana, Ikra Novar Rizqi http://creativecommons.org/licenses/by-sa/4.0
Lung X-Ray Image Classification Using DenseNet-169 and Bayesian Optimization
The increasing prevalence of lung diseases caused by infections such as Pneumonia and COVID-19 highlights the urgent need for accurate and efficient early detection methods. This study aims to improve the classification performance of chest X-ray images using the DenseNet-169 deep learning architecture, with a focus on hyperparameter optimization through Bayesian Optimization. The dataset used consists of 3,000 chest X-ray images—1,000 each for Normal, Pneumonia, and COVID-19 classes—sourced from Mendeley Data and split with an 80:20 ratio for training and testing. The baseline DenseNet-169 model initially achieved an accuracy of 96.837%, although slight overfitting was observed. By applying Bayesian Optimization, several key hyperparameters—such as learning rate, number of epochs, batch size, and kernel size—were systematically optimized. The optimized model demonstrated an improved accuracy of 97.33%, with the most notable increase in the recall score of the Normal class, which rose by 3.19% to 97%, effectively reducing the false negative rate for healthy cases. In addition, the final model recorded a precision of 99% and a specificity of 99.50% for the COVID-19 class, indicating a strong discriminative capability in identifying critical conditions. Analysis of the training and validation curves showed good convergence, confirming the effectiveness of the optimization in reducing overfitting and enhancing the model's generalization ability. Overall, the results of this study demonstrate that the application of Bayesian Optimization significantly enhances the performance of DenseNet-169 in chest X-ray image classification. The resulting model is more balanced, robust, and reliable, showing great potential for integration into AI-based automated diagnostic systems in the field of respiratory healthcare.
Copyright (c) 2025 Fayza Shahira, Benny Sukma Negara, Febi Yanto, Suwanto Sanjaya http://creativecommons.org/licenses/by-sa/4.0
AS Spatiotemporal Analysis of LSCI Variations in ASEAN Using ANOVA and Cluster Techniques (2017–2022)
This study investigates maritime connectivity performance in ASEAN countries using the Liner Shipping Connectivity Index (LSCI) from 2019 to 2023. It aims to identify significant trends, segment national performance, and provide policy-relevant insights on regional maritime development. The research is grounded in transport connectivity and regional integration theory, emphasizing the role of liner shipping as a critical enabler of trade efficiency and economic cooperation in Southeast Asia. The study employs a quantitative approach using longitudinal LSCI data across ten ASEAN member states. It applies descriptive statistics, linear regression modeling for each country, and clustering through k-means (fastclus) to categorize national maritime connectivity performance. Indonesia records the highest average LSCI (49.28), indicating a consistent lead in regional maritime connectivity. Cambodia demonstrates the strongest upward trend with a significant positive slope (β = 0.98; p < 0.01), followed by Myanmar (β = 0.61; p < 0.05) and Laos (β = 0.58; p < 0.01). Cluster results suggest three distinct groups of countries based on average connectivity levels, highlighting disparities and the need for policy harmonization. The regression models explain up to 94% of the variance in several countries' LSCI growth. The findings support regional policy formulation to strengthen weaker maritime economies and align ASEAN maritime strategies with trade facilitation goals. This study presents a novel integration of trend modeling and cluster segmentation of LSCI data within the ASEAN context. It contributes both theoretically to the study of maritime connectivity metrics and practically to policy and infrastructure development.
Copyright (c) 2025 Ariyono Setiawan, Antoni Arif Priadi, Abdul Razak Bin Abdul Hadi, Erwin Faller http://creativecommons.org/licenses/by-sa/4.0
Artificial Neural Network Prediction Model for Agricultural Commodity Production Using Backpropagation Algorithm
The development of Artificial Intelligence (AI) technology has been widely used by the Government and Society to support daily activities, including supporting the decision-making process. In Indonesia's agricultural sector, innovations are currently being implemented using Machine Learning methods, especially Artificial Neural Networks, to estimate the yield of an agricultural commodity. This technology is very relevant to be applied in the agricultural sector, especially since the majority of Indonesians are farmers. With prediction of production and prices, the Government can estimate the amount of production and immediately set a strategy to keep prices stable. The use of predictive data on agricultural production results is very important in maintaining food availability and preventing price fluctuations that affect society. This study uses data on chili commodities, employing a qualitative method with the Backpropagation Algorithm of Artificial Neural Networks. The objective is to generate projections of the Artificial Neural Network (ANN) model using the Altair AI Studio with minimal error so that better prediction values and performances are produced. Based on the results obtained, the best network architecture is the 12-25-1 model for large chili production, and 12-15-1 for bird’s eye chili pepper. This model is proven to be able to help production planning, supply distribution arrangements, and maintain price and supply stability by related agencies.
Copyright (c) 2025 Rina Wahyuni, Sakti Wira Adi Utomo, TB. Muhammad Endra Zhafir Al Ghifari http://creativecommons.org/licenses/by-sa/4.0
Implementation of a K-Means-Based Intelligent Patient Complaint Clustering System to Identify Handling Priorities
Patient complaints are the body’s response to health disturbances, triggered by internal factors such as genetics or external ones like the living environment. Understanding these causes allows community health centers (puskesmas) to take more effective preventive measures and design more targeted services. This study utilizes patient complaint data sourced from medical records, which include biodata and medical history, as well as complaint details that form the research subject. The main goal of this study is to develop an intelligent system that can generate clusters of patient complaints using the K-Means Clustering algorithm. The system is developed using the Research and Development (RnD) method. The clustering process applies a data mining approach, producing clusters based on patient complaints. A total of 600 complaint records, categorized into 72 distinct types, were used. The output consists of three clusters: C1 (high intensity) with 24 categories, C2 (moderate intensity) with 14 categories, and C3 (low intensity) with 34 categories. A practicality test yielded a score of 0.81, indicating the system is highly practical, while an effectiveness test by medical staff scored 0.88, showing the system is highly effective. This system enables health centers to identify trending complaints in the community and develop more focused prevention and treatment strategies. The clustering results also serve as a valuable foundation for strategic decision-making in disease control.
Copyright (c) 2025 M. Agung vafky Ideal, Nurfiah, Idir Fitriyanto http://creativecommons.org/licenses/by-sa/4.0
End-to-End Text-to-Speech for Minangkabau Pariaman Dialect Using Variational Autoencoder with Adversarial Learning (VITS)
Language serves as a medium of human communication to convey ideas, emotions, and information, both orally and in writing. Each language possesses vocabulary and grammar adapted to the local culture. One of the regional languages that enriches Indonesian as the national language is Minangkabau. This language has four main dialects, namely Tanah Datar, Lima Puluh Kota, Agam, and Pesisir. Within the Pesisir dialect, there are several variations, including the Padang Kota, Padang Luar Kota, Painan, Tapan, and Pariaman dialects. This study discusses the application of Text-to-Speech (TTS) technology to the Minangkabau language, specifically the Pariaman dialect, using the Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech (VITS) method. This dialect needs to be preserved to prevent extinction and supported through technological development that broadens its use. The VITS method was chosen because it is capable of producing natural and high-quality speech. The research stages include voice data collection and recording, VITS model training, and speech quality evaluation using the Mean Opinion Score (MOS). The final results show a score of 4.72 out of 5, indicating that the generated speech closely resembles the natural utterances of native speakers. This TTS technology is expected to support the preservation and development of the Minangkabau language in the Pariaman dialect, as well as enhance information accessibility for its speakers.
Copyright (c) 2025 Muhammad Dzaki Fakhrezi, Yusra, Muhammad Fikry, Pizaini, Suwanto Sanjaya https://creativecommons.org/licenses/by-sa/4.0
Design of a Decision Support System to Determine Scholarship Recipients at SMKN 2 Padang Panjang
This research is based on field findings through observations and interviews at SMKN 2 Padang Panjang, which revealed that the process of managing school fee relief scholarship acceptance data is still done manually. This condition causes slow data input processes, slows down administration, and increases the potential for errors in processing student data. Thus, the main focus of this research is to create a valid, practical and effective SPK design in determining scholarship acceptance. This research is a type of Research and Development (R&D) research, using the Analytical Hierarchy Process (AHP) method and the Agile development model. Based on the results of the validity test with 3 lecturers, the system obtained a score of 0.86, indicating a very high level of validity. The practicality test with 3 teachers obtained a score of 0.97, indicating that the system is easy to use. Meanwhile, the results of the effectiveness test with 22 students obtained a score of 0.87, indicating that this system is effective in supporting the scholarship recipient selection process. Unlike previous studies that generally only apply one decision-making method or use conventional development models, this study integrates AHP with an Agile approach to produce a system that is more accurate, practical, and adaptive to school needs. With these achievements, the developed decision support system is worthy of being used as a reliable and efficient tool in determining scholarship recipients at SMKN 2 Padang Panjang.
Copyright (c) 2025 Ariati Efandari, Hari Antoni Musril, Sarwo Derta https://creativecommons.org/licenses/by-sa/4.0
Named Entity Recognition for Uncovering Clinical and Emotional Entities from Breast Cancer Patient Interviews
This study aims to develop a Named Entity Recognition (NER) system capable of identifying clinical and emotional entities within interview transcripts of breast cancer patients. The corpus was manually annotated using the BIO scheme across seven main entity categories: Social Support (Dukungan Sosial), Medical Actions (Tindakan Medis), Diagnosis, Negative Emotions (Emosi Negatif), Positive Emotions (Emosi Positif), Symptoms (Gejala), and Spiritual. The annotation process was followed by the implementation of a rule-based method supported by entity dictionaries and word normalization, and the model was evaluated using precision, recall, and F1-score metrics. The analysis results revealed that Dukungan Sosial was the most dominant entity with 347 occurrences, followed by Tindakan Medis and Diagnosis. The rule-based NER model achieved an F1-score of 0.50 for the Diagnosis entity, although its performance on emotional and social entities remained low due to data imbalance. These findings highlight the importance of integrating clinical and emotional aspects in natural language processing to gain a more comprehensive understanding of patient narratives. The proposed approach has potential applications in healthcare text mining for detecting emotional experiences and medical contexts, and it can be further enhanced through the integration of transformer-based models such as IndoBERT to improve entity recognition accuracy.
Copyright (c) 2025 Norma Alias, Agus Sundari http://creativecommons.org/licenses/by-sa/4.0