Journal of Dinda : Data Science, Information Technology, and Data Analytics
Journal of Dinda : Data Science, Information Technology, and Data Analytics as a publication media for research results in the fields of Data Science, Information Technology, and Data Analytics, but not implicitly limited. Published 2 times a year in February and August. The journal is managed by the Data Engineering Research Group, Faculty of Informatics, Telkom Purwokerto Institute of Technology. Journal of Dinda is a medium for scientific studies resulting from research, thinking, and critical-analytic studies regarding Data Science, Informatics, and Information Technology. This journal is expected to be a place to foster enthusiasm in education, research, and community service which continues to develop into supporting references for academics. FOCUS AND SCOPE Journal of Dinda : Data Science, Information Technology, and Data Analytics receive scientific articles with the scope of research on: Machine Learning, Deep Learning, Artificial Intelligence, Databases, Statistics, Optimization, Natural Language Processing, Big Data and Cloud Computing, Bioinformatics, Computer Vision, Speech Processing, Information Theory and Models, Data Mining, Mathematical, Probabilistic and Statical Theories, Machine Learning Theories, Models and Systems, Social Science, Information Technology
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87 Documents
<![CDATA[Comprehensive Lakehouse Data Architecture Model for College Accreditation ]]>
<![CDATA[Nenen Isnaeni]]>;
<![CDATA[Bambang Purnomosidi Dwi Putranto]]>;
<![CDATA[Widyastuti Andriyani]]>;
<![CDATA[Siti Khomsah]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1759
<![CDATA[Accreditation is an assessment activity that determines the feasibility of study programs at a university. College accreditation data comes from various sources and includes multiple data types: semi-structured, unstructured, or structured. Over time, the volume of data will continue to grow and develop, so there is a possibility of data redundancy and a long time to collect the data needed for accreditation activities. The solution is integrating data. This research aims to design a data architecture to facilitate the management of university accreditation data using the Lakehouse data architecture model. All data types can be stored on one platform in the Lakehouse data architecture. In this research, the identification, integration, and data transformation process for university accreditation data is carried out. The data used in this research is academic data in which there are with. The study's results provide an overview of the data flow process in the Lakehouse data architecture model to help better manage university accreditation data. This architecture also supports real-time data analysis so that the accreditation process can be carried out more effectively and efficiently. Keywords: accreditation, data analysis, data architecture, data lakehouse, data warehouse]]>
<![CDATA[Optimizing Search Efficiency in Ordered Data: A Hybrid Approach Using Jump Binary Search ]]>
<![CDATA[Gabriella Youzanna Rorong]]>;
<![CDATA[Syafrial Fachri Pane]]>;
<![CDATA[M Amran Hakim Siregar]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1764
<![CDATA[This research presents the development of a hybrid algorithm called Jump Binary Search (JBS), which integrates jump search and binary search techniques to improve search efficiency in sorted data distributions. JBS is designed to accelerate the search process using a jump technique to find the target block, after the block is identified, it is followed by a binary search to narrow down the search space. The results of this study show that the performance of JBS is superior compared to Jump Linear Search (JLS) when applied to non-uniform and ordered categorical data distributions. JBS only requires an execution time ranging from 0-15ms and 0-10ms, demonstrating efficiency and speed on elements consisting of 400 elements. The execution time of JBS demonstrates its efficiency compared to JLS. By minimizing unnecessary data access, JBS becomes the right solution for finding target elements in sorted data distribution.]]>
<![CDATA[Using the Random Forest Method in Predicting Stock Price Movements ]]>
<![CDATA[Muhammad Amsari Lubis]]>;
<![CDATA[Samsudin Samsudin]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1765
<![CDATA[In the era of globalization, rapid technological advancements have significantly impacted the financial sector, particularly stock price movements. This study aims to contribute to financial analysis and investment by providing a predictive tool to help investors make more informed investment decisions. The Random Forest method, a machine learning al-gorithm known for effectively handling complex and heterogeneous data, is used to pre-dict stock price movements. The study utilizes historical stock data from companies listed on the Indonesia Stock Exchange (IDX) as a case study. The resulting predictive model demonstrates high accuracy, achieving 98% accuracy, with an R-squared (R²) value of 0.94 and a Mean Absolute Percentage Error (MAPE) of 0.40%. This research identifies key factors, such as Previous, High, Low, Volume, and Change, that significantly influ-ence stock price movements. The strengths of this study lie in its use of an extensive da-taset, involving 104 stock codes as examples, and its integration of interactive visualiza-tion via Streamlit to enhance data interpretation. This tool is expected to be a reliable solu-tion that provides superior predictive capabilities compared to traditional methods and supports more accurate investment analysis in the stock market.]]>
<![CDATA[Combination of Fuzzy Weighted Product and Entropy in Determining the Eligibility of Poor Rice Recipients ]]>
<![CDATA[Intan Zahira]]>;
<![CDATA[Sriani Sriani]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1773
<![CDATA[The Indonesian currency crisis caused a significant decline in food production, which ultimately resulted in increased costs and reduced purchasing power for all basic needs. Therefore, the government is trying to launch a poverty alleviation program in the form of a social assistance program for the poor in the form of rice to ease the burden on poor families and increase their access to food that is essential for life. In order to avoid problems of inconsistency in the recipients of this assistance, a decision support system was created to determine the eligibility of recipients of poor rice assistance so that it is right on target. In determining the recipients of this social assistance, evaluation criteria are needed. The evaluation criteria required are income, age, dependents, occupation and status. The researchers developed a system that combines the weighting method (Entropy) and the decision method (Fuzzy Weighted Product) to determine recipients of social assistance. Where the data is converted into fuzzy numbers first and then processed with the decision method with the final weight of the entropy weighting method. The combination method of Fuzzy Weighted Product (F-WP) and Entropy allows for optimal selection of recipients of social assistance for the poor, and an automatic selection system that can be used by administrators to streamline the process in each district. The selection results data will be available to all registered users, sorted from highest to lowest according to target ranking, to fairly determine who will receive social assistance.]]>
<![CDATA[Implementation of ROP and EOQ in the Stock Management Information System at Panglong Siagian Bersaudara Based on Website ]]>
<![CDATA[Disnu Panggabean]]>;
<![CDATA[Samsudin Samsudin]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1774
<![CDATA[Advances in information and communication technology have encouraged the transformation from manual systems to digital-based systems, including in stock management. This research aims to develop a website-based stock management information system using the Reorder Point (ROP) and Economic Order Quantity (EOQ) methods for Panglong Siagian Bersaudara. ROP is used to determine reorder time, while EOQ calculates the optimal number of items that must be ordered to minimize total costs. The system was designed using the Rapid Application Development (RAD) method, resulting in a fast and structured design. The implementation results show that the system is able to increase stock management efficiency by reducing the risk of shortages or excess items, optimizing storage costs, and supporting real-time data-based decision making. With the integration of ROP and EOQ, this system provides a comprehensive solution for stock management at Panglong Siagian Bersaudara.]]>
<![CDATA[Sandal Product Inventory Prediction System Using Apriori Algorithm on Web-Based Home Industry Dlioshoes ]]>
<![CDATA[Septia Ona Sutra]]>;
<![CDATA[Triase Triase]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1783
<![CDATA[Sales of sandal products at Home Industry Dlioshoes are often faced with the problem of insufficient product inventory. This research aims to build a web-based sales information system by applying the Apriori algorithm to analyze purchasing patterns and predict product inventory needs. By utilizing sales transaction data, this system can identify product combinations that distributors often buy together. By implementing the Apriori Algorithm, it can help industry owners in making decisions regarding product inventory and can predict sales in the next period, thereby reducing the risk of product excess or shortage. The research results show that the types or models of sandals that are most popular with distributors are Heels, Flat Shoes, Mules, Ballet Shoes, High Heels, Ankle Strap and Pumps. With the highest Support value of 42% and Confidence value of 71.18%.]]>
<![CDATA[Implementation of TOPSIS in a Decision Support System for Selecting the Ideal Food Menu for GERD Patients ]]>
<![CDATA[Jesica Emarapenta Br Sinulingga]]>;
<![CDATA[Annisaa Utami]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1784
<![CDATA[Gastroesophageal Reflux Disease (GERD) is a condition characterized by the repeated reflux of stomach acid into the esophagus, causing symptoms such as heartburn and regurgitation. This condition can be triggered by irregular eating habits, including improper portion sizes, frequency, or types of food. Frequent consumption of foods that are excessively hot, spicy, or acidic can exacerbate stomach problems by increasing gastric acid production, leading to GERD recurrence. This study aims to develop a web-based decision support system to determine the ideal food menu for GERD patients. The system utilizes five main criteria protein, carbohydrates, fat, fiber, and calories to analyze ten food menu alternatives. The comparison between the system's results and manual calculations demonstrates consistent outcomes, as both produce identical preference index values. Based on the calculations, Wheat Pasta was determined as the most ideal food menu with the highest preference index value of 0.7415. Other menus, such as Kalasan Fried Chicken, Salmon Fish, and Stir-fried Tempe, also had high preference indexes, while menus such as Stir-fried Chayote, Stir-fried Spinach, and Oatmeal ranked lower.]]>
<![CDATA[Analysis of Bread Sales Patterns at Queen Bakery Stores Using Algorithms Fpgrowth ]]>
<![CDATA[Muhammad Ray Pratama Sembiring]]>;
<![CDATA[Raissa Amanda Putri]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1787
<![CDATA[The culinary industry, particularly the bakery business, is experiencing rapid growth with increasing competition. Changes in consumer trends, the rising number of market players, and fluctuating market dynamics pose significant challenges in maintaining stable sales. Queen Bakery, a bakery located in Medan, faces issues with fluctuating monthly sales, indicating that certain products are less in demand and that more effective marketing strategies are needed. To address this issue, the utilization of technology in data analysis is essential, particularly through the implementation of data mining techniques. Data mining enables the identification of consumer purchasing patterns more accurately and data-driven. One of the most effective algorithms for sales pattern analysis is FPGrowth, which can identify frequently occurring itemsets in transactions. Unlike the Apriori algorithm, which requires extensive computations, FPGrowth is more efficient in discovering product associations frequently purchased together. This study aims to analyze sales patterns at Queen Bakery using the FPGrowth algorithm to provide strategic insights into inventory management and product marketing. The results of this research are expected to assist Queen Bakery in improving operational efficiency, optimizing product offerings, and formulating more competitive business strategies. By implementing data mining, the bakery can gain a deeper understanding of consumer preferences, ultimately enhancing sales performance and competitiveness in the dynamic culinary market.]]>
<![CDATA[Web-Based Monitoring Information System for Official Travel Letters on Food Security and North Sumatera Horticulture ]]>
<![CDATA[Rafli Bima Sakti]]>;
<![CDATA[Rakhmat Kurniawan]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1789
<![CDATA[The Food Security and Horticulture Office of North Sumatra faces challenges in the management of Service Travel Letters (SPD), such as submitting letters, making letter notes, operational costs, which are still done manually using manual methods, so it is time consuming. This hampers the effectiveness of document management and data-based decision making. To overcome these problems, this research develops a web-based monitoring information system. This system is designed to manage official travel letters, to minimize the time and recording of letter reports. With the web-based Monitoring Information System, the management of official travel letters becomes more structured and can be accessed in real-time, thus increasing efficiency in carrying out official duties in various regions of North Sumatra. This research uses the Research and Development (R&D) method with stages including needs analysis, system design, prototype development, testing, and evaluation. The results of the system implementation show that the use of this Monitoring Information System is able to speed up the process of submitting and approving official travel letters, as well as reducing errors in data collection and recording of trips.]]>
<![CDATA[Stock Information System for All Smartphone Brands Using Barcode ]]>
<![CDATA[Wini Istya Sari Lubis]]>;
<![CDATA[Rakhmat Kurniawan]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1790
<![CDATA[In the era of globalization, rapid development in technology that brings convenience and speed in completing human tasks. A barcode is a collection of optical data that is read by a machine. In fact, this barcode collects data in width (lines) and parallel line spacing and can be referred to as a linear or 1D (1-dimensional) barcode or symbology. Stock reports on purchases and sales of a period are useful for helping employees to assess the amount of inequality in purchasing flows, monitor stock/inventory of goods, and provide reports, both purchase reports, sales reports, and stock condition reports. ALL BRAND SMARTPHONE is a company engaged in the sale of electronic equipment. The process of checking stock at ALL BRAND SMARTPHONE has not implemented a barcode application, but is only done by checking stock manually by conducting manual checks between incoming and outgoing goods, then the final stock of goods will be calculated. Manual calculations or checks allow errors to occur in the creation or input of data. The purpose of designing this inventory information system is to be able to build a web-based inventory system that is expected to minimize errors or manipulation of inventory data using barcodes. And assist companies and employees in managing incoming goods data and outgoing goods data. The research method used involves data collection through observation, interviews, and literature studies, system development with a waterfall methodology approach.]]>
