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Gede Iwan Sudipa
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Galaksi
Published by Sraddha Panca Widya Nusantara Foundation
ISSN : -     EISSN : 30482399     DOI : https://doi.org/10.70103/galaksi.v1i1.1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering
Jurnal Galaksi : Global Knowledge, Artificial Intelligence and Information System provides an international forum for researchers and professionals to share their knowledge and report new advances on all topics related to knowledge systems and advanced information systems. This archival journal publishes state-of-the-art research reports on emerging topics in Global Knowledge of Artificial Intelligence and Information System, reviews of important techniques in related areas, and application papers of interest to a general readership. The journal focuses on knowledge systems and advanced information systems, including their theoretical foundations, infrastructure and enabling technologies. We solicit submissions of original research, and experience and vision papers that address this theme. Suggested topics include, but are not limited to, the following areas: Knowledge and information processing: theory, techniques and systems Knowledge and data engineering Artificial intelligent Decision support Active and dynamic systems Data sharing and warehousing Temporal and spatial database processing Intelligent information retrieval Machine learning Learning and adaptation Knowledge discovery, data mining and big data analytic Computer vision and pattern recognition Modelling and object orientation Software re-engineering Co-operativeness, interoperability and software re-usability Human-computer interaction Hypertext, hypermedia and multimedia Data and knowledge visualization Immersive Technology Underlying computational techniques Soft computing (including neural nets, fuzzy logic, probabilistic reasoning, and rough set theory) Evolutionary computing Hybrid computing Uncertainty management Agent architectures and systems (including multi-agent scenarios) Internet of Things Platforms High performance computing systems Dstributed intelligent systems Mobile systems Control system Embedded System Application to specific problem domains Biomedical systems Geographical systems Software information systems Emerging applications (such as Internet technologies and digital libraries)
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 29 Documents
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Financial Data Warehousing at Village Credit Institution xyz Using a Star Schema Sandhiyasa, I Made Subrata; Nopianti, Ni Kadek Winda; Nugraha, Putu Gede Surya Cipta
Jurnal Galaksi Vol. 2 No. 1 (2025): Galaksi – May 2025
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v2i1.54

Abstract

The role of information technology is the main focus in financial management, especially in supporting the data analysis process for effective decision making.  Village Credit Institution xyz, as a financial institution owned by Pekraman Villages in Bali, faces obstacles in presenting financial data that is still in the form of tables. The presentation is not effective in providing a clear and adequate picture to support management decision-making. Makes it difficult to identify customers with bad or current credit. This research aims to build a financial data visualization system at  Village Credit Institution  xyz using Looker Studio, with a data warehouse design implemented using Kimball's Nine Steps method. The ETL process is carried out using Pentaho Data Integration (PDI) to compile data from various sources. The final result of this research is data visualization in the form of 9 main menus. This system allows the presentation of data in the form of interactive graphs, thus facilitating data analysis, accelerating the decision-making process, and increasing the efficiency of financial data management in  Village Credit Institution. System testing was conducted using the User Acceptance Testing (UAT) method with a result of 92.48% or strongly agree, indicating that the developed system has met the needs of users.
Design and Development of a Website-Based Reservation Information System at the Backyard Villa Ni Made Nadiya Aryadivani; I Wayan Ady Juliantara; I Made Agus Oka Gunawan
Jurnal Galaksi Vol. 2 No. 2 (2025): Galaksi - August 2025
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v2i2.68

Abstract

The Backyard Villa is a medium-scale lodging business that relies on manual booking methods like text messages and phone calls. This approach leads to various issues, including delayed confirmations, data duplication or loss, and limited access to information for prospective guests. These inefficiencies negatively impact the operational effectiveness and overall service quality. This study aims to design and implement a web-based reservation information system to enhance service performance and competitiveness in the digital era. The system was developed using the waterfall method, which includes requirement analysis, system design, implementation, and testing phases. The system defines two main types of users—guests and admins with different access rights and features. System functionality testing was conducted on 28 scenarios, covering the booking process, villa data management, and reporting. The test results demonstrated a 100% success rate. The system not only simplifies the reservation process for customers through self-service but also supports the management in handling booking data, customer information, and operational reports more efficiently. The implementation of this system is expected to significantly improve the operational efficiency and service quality of The Backyard Villa by automating manual tasks, reducing errors, and providing a better user experience for both guests and management.
Application of ELECTRE Method for Selection of Diet Instant Noodles Hugo, Veronika Novia; Sekarsari, Yulia Ayu; Kencana Putri, Ida Ayu Putu Calista; J.A, Qurrotul Ainia; Sudipa, I Gede Iwan
Jurnal Galaksi Vol. 2 No. 1 (2025): Galaksi – May 2025
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v2i1.51

Abstract

Healthy eating is becoming increasingly important, particularly among university students who often prefer convenient foods like instant noodles. However, not all instant noodles provide suitable nutritional content for dieting. This study aims to determine the best instant noodles for diet purposes using the ELECTRE (Elimination and Choice Expressing Reality) method, a multi-criteria decision- making approach. Data were collected from INSTIKI students via a questionnaire that covered criteria such as calorie content, fiber, fat, price, and taste. The ELECTRE method was then applied to analyze the data based on predetermined preferences and criteria weightings. Results identified the most suitable instant noodles for dietary needs, ranked highest in the ELECTRE analysis. This study aims to guide consumers in making healthier choices and provide insights for manufacturers to improve product quality.
A Structured Decision Intelligence Framework for Context-Aware Decision Making Sudipa, I Gede Iwan; Pandawana, I Dewa Gede Agung; Sandhiyasa, I Made Subrata
Jurnal Galaksi Vol. 2 No. 2 (2025): Galaksi - August 2025
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v2i2.96

Abstract

Decision Intelligence (DI) has emerged as an integrative paradigm that combines data, analytics, and artificial intelligence to enhance organizational decision-making. Despite this growing interest, many existing DI approaches place disproportionate emphasis on predictive intelligence while providing limited methodological guidance on how predictions are transformed into actionable and accountable decisions. Machine learning models are highly effective at forecasting and classification; however, they do not inherently incorporate organizational constraints, human preferences, or decision trade-offs. This study proposes a structured, end-to-end Decision Intelligence framework that explicitly integrates machine learning–based prediction with Decision Support System (DSS) modelling. The framework positions DSS as the core decision logic by employing the Analytic Hierarchy Process (AHP) to formalize contextual and human preferences and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to execute alternative ranking. Furthermore, contextual intelligence and outcome intelligence are embedded to ensure decision relevance, transparency, and continuous improvement. Using a Design Science Research approach, this study develops and demonstrates the proposed framework as a systematic solution for bridging the gap between predictive analytics and decision execution. The framework contributes to Decision Intelligence research by clarifying the role of DSS in AI-driven decision environments and by providing a replicable structure for integrating prediction, decision modelling, and outcome evaluation.
Profit-Based HUI-Miner for Discovering Consumer Shopping Patterns in a Retail Store I Putu Noven Hartawan; I Gede Sudiantara; Ni Kadek Bumi Krismentari; Gede Rudiharta Pratama Giri; I Made Dwi Putra Asana
Jurnal Galaksi Vol. 3 No. 1 (2026): Galaksi - May 2026
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Retail transaction logs often reveal products that are frequently purchased together, but frequency alone does not show their economic contribution. This study applies a profit-oriented High Utility Itemset approach using HUI-Miner to identify valuable shopping patterns at YSL Grocery Store. The analysis followed CRISP-DM on 266,394 sales records from 2022. After attribute selection and missing-value removal, 251,225 records containing transaction, item, quantity, and price were processed. Item utility was represented by quantity multiplied by selling price, while the mining stage used a minimum utility of IDR 5,000,000, minimum support of 2%, and minimum confidence of 10%. Positive associations were retained when lift exceeded 1. The procedure produced 248 frequent itemsets, 77 confidence-qualified rules, 64 positive-lift rules, and 23 rules that satisfied all criteria. The strongest association linked two Sedaap instant-noodle variants with a lift of 3.26. The findings also show that support is not proportional to total utility: some cross-category combinations generated substantially greater utility despite lower occurrence. Therefore, retail decisions should combine utility, support, confidence, and lift when prioritizing shelf placement, bundles, promotions, and stock.
Detection of Disease in Platycerium Ornamental Plant Leaves Using Yolo 12 I Made Subrata Sandhiyasa; Made Landiva; I Gede Sudiantara; I Putu Noven Hartawan
Jurnal Galaksi Vol. 3 No. 1 (2026): Galaksi - May 2026
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v3i1.120

Abstract

Platycerium is an epiphytic ornamental plant with high aesthetic and economic value, thus requiring proper care. Identifying Platycerium leaf diseases based on visual symptoms often requires precision and experience, thus necessitating an image-based automated approach. This study aims to develop a Platycerium leaf disease detection model using the deep learning-based YOLO method. The model was developed using Kaggle Notebook with P100 GPU support. The dataset used consisted of three disease classes, namely Bacterial Leaf Spot, Fern Scale, and Rizoctonia Blight. Model training was carried out with variations in the number of epochs of 50, 75, and 100 epochs, and evaluated using the Precision, Recall, and Mean Average Precision (mAP) metrics. The results showed that training with 50 epochs gave the best results with a mAP50 value of 0.953 and mAP50–95 of 0.577. Testing using test data and data outside the dataset showed that the model was able to detect Platycerium leaf disease in test images by displaying bounding boxes and class labels. Based on these results, the YOLO model developed can be used as an image-based approach for detecting Platycerium leaf disease and can be further developed.
Comparison of Grayscale and Binary Image Representations for Balinese Script Classification Using EfficientNetV2 I Made Dwi Putra Asana; I Komang Anom Widya Pratama; Putu Surya Wedra Lesmana; Made Leo Radhitya
Jurnal Galaksi Vol. 3 No. 2 (2026): Galaksi - August 2026
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v3i2.132

Abstract

Balinese script is a cultural heritage whose preservation increasingly depends on digital technology, yet automatic recognition of its characters remains difficult because many glyphs share similar strokes. This study compares two image representations, grayscale and binary, as input for classifying 28 classes of Balinese script consisting of eighteen basic characters (wreastra), six pengangge suara and four pengangge tengenan. A dataset of 1,337 images was resized to 224x224 pixels and augmented into 8,022 images, then classified using EfficientNetV2B0 with transfer learning, a batch size of 32, a learning rate of 0.001, a dropout rate of 0.3 and 30 epochs followed by fine tuning of the last thirty layers. Robustness was also examined through three hold-out splitting ratios of 80:10:10, 70:15:15 and 60:20:20, which produced accuracies of 90.55%, 89.55% and 88.81%. The grayscale representation reached a test accuracy of 91.92% with a macro F1-score of 92.11%, slightly higher than the binary representation with 91.67% and 91.78%. Deployment on new handwritten images revealed a domain gap that lowered recognition performance, indicating that data diversity is more decisive than the choice of representation.
Expert System Design for Laying Hen Disease Diagnosis Using Forward Chaining and Certainty Factor Ketut Jaya Atmaja; Kompiang Martina Dinata Putri; Ida Bagus Gede Rama Rasmana
Jurnal Galaksi Vol. 3 No. 2 (2026): Galaksi - August 2026
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v3i2.139

Abstract

Laying hens are a crucial part of the poultry industry, significantly contributing to the fulfillment of animal protein needs. However, the prevalence of various diseases remains a primary threat that causes a drastic decline in egg production and high mortality rates. The delay in diagnosis, exacerbated by the limited availability of veterinary specialists in rural areas, emphasizes the need for an accessible diagnostic tool. This research aims to develop a web-based expert system capable of diagnosing 11 types of laying hen diseases based on 27 observable symptoms. The system implements the Forward Chaining method as an inference engine to systematically trace disease rules starting from the user-inputted symptoms. Furthermore, the Certainty Factor (CF) method is integrated to calculate the confidence level of the diagnosis, utilizing the Measure of Belief (MB) and Measure of Disbelief (MD) values provided by an expert veterinarian. The system was tested using Blackbox testing for functional validation, which yielded 100% functionality. Furthermore, accuracy testing across 18 real-world test cases demonstrated that the system achieved an accuracy rate of 77.78%, with 14 cases perfectly matching the expert's diagnosis. This expert system proves to be highly effective in assisting farmers in performing early disease identification and taking timely preventive measures.
Retail Sales Forecasting Using ARIMA Based on Point-of-Sale Transaction Data I Gede Sudiantara; I Putu Noven Hartawan; I Kayan Herdiana; I Made Dwi Putra Asana
Jurnal Galaksi Vol. 3 No. 2 (2026): Galaksi - August 2026
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v3i2.140

Abstract

Retail businesses generate large volumes of Point-of-Sale (POS) transaction data that can be utilized for sales forecasting and inventory planning. However, transaction-level data need to be transformed into an appropriate time-series structure before forecasting can be performed. This study aims to apply the Autoregressive Integrated Moving Average (ARIMA) method to historical POS transaction data for retail sales forecasting. The study used 4,443,905 transaction records from a retail store covering the period from 2018 to 2021. The research process adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM), with the analysis focusing on data preparation, ARIMA modeling, and model evaluation. Sales data were aggregated into product-level time series, and the ARIMA model was identified using stationarity analysis, Autocorrelation Function (ACF), and Partial Autocorrelation Function (PACF). The resulting ARIMA(1,1,1) model was applied to five selected products represented by M01–M05. Model evaluation used the Ljung–Box test and Root Mean Square Error (RMSE), with forecasting conducted for January and February 2022. The results show that forecasting errors varied across products, indicating differences in forecasting performance according to their historical sales patterns. The study demonstrates the applicability of ARIMA for product-level retail sales forecasting based on historical POS transaction data.

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