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I Gede Iwan Sudipa
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INDONESIA
Jurnal Krisnadana
Published by Yayasan Sinergi Widya Nusantara
ISSN : 28083520     EISSN : 28083563     DOI : 10.58982
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Jurnal Krisnadana merupakan jurnal yang dapat menjadi wadah bagi civitas akademika dan kalangan profesional dalam mempublikasikan karya ilmiah ataupun hasil penelitiannya dengan tetap mengutamakan orisinalitas karya, pengembangan kelimuan dan kontribusi dalam berbagai bidang. Jurnal Krisnadana berfokus pada bidang Ilmu Komputer, Sistem Kendali, dan Jaringan. Fokus dan Ruang lingkup pada Jurnal Krisnadana (Komputer, Sistem Kendali, & Jaringan) yang dapat menjadi topik makalah atau penelitian meliputi berbagai bidang minat, diantaranya: 1) Rekayasa perangkat lunak; 2)Sistem Informasi; 3) Sistem Pendukung Keputusan (SPK); 4) Sistem Pakar; 5) Kecerdasan Buatan; 6) Aplikasi Mobile; 7) Pengolahan Citra; 8) Robotika; 9) Smarts System; 10) Cloud Technology; 11) Image Processing; 12) Internet Of Things (IOT); 13) Jaringan Komputer; 14) Komputasi Paralel; 15) Sistem Terdistribusi; 16) Data Analytic; 17) Audit Teknologi Informasi; 18) Telekomunikasi dan Pemrosesan Sinyal; 19) Otomasi kontrol (Control Automation); 20) Topik studi relevan lainnya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 145 Documents
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Forecasting Diarrhea Incidence Using ARIMA, SARIMA, and Autotuned SARIMA Models Dedy Hidayat Kusuma; Moh Nur Shodiq; Herman Yuliandoko; Muh. Fuad Al Haris; Sri Andayani
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.972

Abstract

Diarrhea remains a major public health concern in developing countries, including Indonesia, contributing significantly to morbidity and mortality among children under five years old. Its incidence fluctuates due to environmental factors, making accurate forecasting essential for effective health resource planning. This study aims to predict monthly diarrhea cases at Community Health Center Sumberberas, Banyuwangi, from January 2023 to May 2025 using three time series models: ARIMA, SARIMA, and SARIMA with autotuning. A quantitative approach was applied, consisting of data preprocessing, model construction, evaluation, and comparison. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the SARIMA with autotuning model achieved the lowest MAE, RMSE, AIC, and BIC values, demonstrating superior accuracy and model fit compared to ARIMA and manually tuned SARIMA. These findings indicate that SARIMA with autotuning provides the most reliable forecasts for short-term diarrhea incidence, supporting data-driven decision-making and the development of early warning systems for disease prevention and control.
Evaluating User Interaction Efficiency Using the First Click Method: Case Study on a Honey Seller SME Website Niswatin Hasanah; Ery Setiyawan Jullev A; Budi Prasetyo; Nur Kholis
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.975

Abstract

This paper evaluates the usability of an e-commerce platform developed to empower micro, small, and medium-sized enterprises (MSMEs) in marketing and selling honey products online. A total of 50 participants—equally divided between novice and proficient users—were engaged in structured usability testing comprising task completion, performance measurement, and first-click analysis. The results indicate that the platform performs effectively in product search accuracy, information clarity, navigational intuitiveness, and perceived trustworthiness. Overall user satisfaction was high, with participants demonstrating a strong intention to reuse the platform, reflecting positive user acceptance. Nevertheless, usability challenges were observed in transaction-related operations, particularly in adding items to the cart and completing the checkout process. Quantitatively, 70% of beginners and 90% of proficient users reported challenges due to the number of required fields and perceived procedural complexity. Interface simplicity also received mixed evaluations, with beginners rating it at 70% compared to 100% among proficient users. This disparity highlights that although advanced users adapt quickly, novice users experience barriers that may discourage them from completing transactions. These findings are consistent with prior usability research emphasizing that users’ initial interactions with transaction-critical elements strongly influence trust, confidence, and willingness to continue using a platform.To address these limitations, this study proposes targeted design improvements, including enhanced cart visibility, a streamlined checkout flow, and the integration of contextual assistance features. The proposed enhancements aim to increase inclusivity, operational efficiency, and user satisfaction, thereby strengthening MSME digital adoption and competitiveness in online marketplaces.
Comparative Time Series Forecasting of Major Cryptocurrencies Using the GRU Deep Neural Network I Putu Bramasta Priadinata; I Gede Iwan Sudipa; Sani Inusa Milala
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.977

Abstract

Cryptocurrency investments are increasingly popular due to their potential as digital assets, but high price volatility remains a major challenge in making investment decisions. This study implements the Gated Recurrent Unit (GRU) model to forecast the closing prices of five popular cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Binance Coin (BNB), and Dogecoin (DOGE), using historical datasets from Yahoo Finance covering the period from November 30, 2019, to November 29, 2024. Performance evaluation was conducted using Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The results show that the GRU model achieved the best performance for BNB with a MAPE of 2.38% and an RMSE of 17.03, followed by ETH and XRP with MAPE values of 2.51% and 2.64%, respectively. BTC recorded the highest RMSE of 2280.73, reflecting its significant price volatility, while DOGE exhibited the lowest RMSE of 0.01 despite having the highest MAPE of 4.11%. Forecasts for the next six periods indicate that BTC and ETH are likely to experience gradual price increases, XRP and BNB show a flattening trend, and DOGE remains stable with low volatility. This study concludes that the GRU model is effective in forecasting cryptocurrency prices; however, it is recommended to complement the results with fundamental and technical analysis to improve accuracy and support more optimal investment decision-making.
Web-Based Information System at PT Panin Dai-Ichi Life Using Object-Oriented Analysis and Design Rio Febrian; Yanty Faradillah
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.980

Abstract

In today’s digital era, information systems play a strategic role in improving operational efficiency and competitive advantage, particularly in the financial services sector such as the insurance industry. This study proposes the development of a web-based information system due to its flexibility and ease of access. At PT Panin Dai-Ichi Life, the existing systems for agents, administrators, staff, and customers remain fragmented, leading to data duplication, lengthy processes, and inaccurate information. The objective of this research is to design a web-based information system for PT Panin Dai-Ichi Life using the Object-Oriented Analysis and Design (OOAD) approach as the system modeling and design methodology. The system will be implemented through a website (paninasuransi.my.id) accessible by administrators, staff, agents, and customers. This integration is expected to create an efficient, maintainable, and adaptive system that meets the company’s needs. The system design is developed using Unified Modeling Language (UML), including Use Case, Activity, Sequence, and Class Diagrams, and validated through Black Box Testing. The research demonstrates that the proposed information system can resolve major issues in managing user, customer, policy, and claim data, ensuring structured and well-documented data processing across the organization.
Inventory Forecasting System Using LSTM for Vape Store I Putu Agus Eka Darma Udayana; Ni Putu Suci Meinarni; I Gusti Ayu Agung Randhika Kerlania; I Putu Utama Arta; I Made Adi Sutrisna
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.981

Abstract

Inventory management remains a critical challenge for small and medium-sized retail businesses, including Gonvapestore, a vape retailer in Bali, where stock decisions are often made intuitively. This study aims to design and implement a stock forecasting system using the Long Short-Term Memory (LSTM) algorithm to enhance the accuracy of monthly inventory predictions. The research follows the Knowledge Discovery in Database (KDD) process, encompassing data selection, preprocessing, and time series transformation through a sliding window approach. The LSTM model was developed using TensorFlow and Keras, and its forecasting accuracy was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) metrics. Experimental results show that the LSTM model achieved superior performance compared to ARIMA, with RMSE and MAE values of 3.14 and 2.71, respectively, versus 6.05 and 5.21 for ARIMA. Product-level evaluation using MAPE indicates that Icy Lychee achieved a relatively low error rate of 37%, while Icy Mango (50%) and Icy Watermelon (52%) exhibited higher error rates, suggesting model performance may vary across product categories. These results demonstrate the LSTM model’s superior ability to capture nonlinear sales patterns compared to traditional statistical approaches. The model was integrated into a Django-based web system with a real-time visualization dashboard and sales logging features. The proposed system offers a practical and intelligent decision-support tool for retail inventory management, reducing stockout and overstock risks through data-driven forecasting.
Sentiment Analysis on E-commerce Reviews Using GRU and Naive Bayes I Putu Agus Eka Darma Udayana; Putu Yoka Angga Prawira; I Gede Bagus Arya Merta Tika; I Gede Iwan Sudipa
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.982

Abstract

The rapid growth of e-commerce platforms in Indonesia has heightened the need to understand customer sentiment for improving service quality and user experience. This study conducts sentiment analysis on customer reviews from two e-commerce platforms under the Baliyoni Group: Tokodaring.balimall.id and Balimall.id. A total of 45,257 reviews were collected through database dumps, consisting of 41,519 reviews from Tokodaring and 3,738 from Balimall. The raw textual data underwent a comprehensive preprocessing pipeline involving duplicate removal, tokenization, normalization, stopword elimination, stemming, and sentiment labeling. Two classification models were implemented and compared: Gated Recurrent Unit (GRU), a deep learning model that captures sequential dependencies, and Naïve Bayes, a probabilistic classifier utilizing TF-IDF features. Evaluation results indicate that the GRU model achieved higher precision and F1-score, whereas Naïve Bayes obtained perfect recall but lower precision. Specifically, for Balimall reviews, Naïve Bayes achieved an accuracy of 90.06%, precision of 84.03%, recall of 100%, and an F1-score of 91.32%. These results mirror the Tokodaring dataset performance, confirming the model’s ability to effectively identify positive sentiment but also its tendency to overestimate it, leading to false positives on negative reviews. Overall, the GRU model demonstrated a more balanced performance, making it more suitable for analyzing informal and diverse user-generated text on Indonesian e-commerce platforms. This research provides insights into developing robust sentiment analysis systems to support data-driven decision-making in digital commerce environments.
Web-Based National Mining Results Monitoring Information System Dwina Jasmine Fahira; Yanty Faradillah
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.986

Abstract

Information related to national mining results is still difficult for the public to access online. Price and production data are generally only known to certain parties, such as mining companies (miners) and trading companies. National pricing is adjusted to weekly market conditions, using the Indonesia Coal Index (ICI) as a reference for the domestic market and the Coal Reference Price (HBA) for exports. This situation limits transparency, necessitating a web-based system capable of presenting mining data in a structured and easily accessible manner. This study aims to design and develop a Web-Based National Mining Results Monitoring Information System to display price and production data in real-time. The method used is Research and Development (R&D) through literature review and interviews, with design using the Unified Modeling Language (UML). The system is equipped with content update features without code changes and chat support between visitors and administrators. Testing using the Black Box Testing method shows that all functions operate according to specifications. The developed system can be accessed via the website hasiltambangindonesia.com.
Visual Analytics of Spotify Music Data for Listener Behavior Insights Madek Jeani Purnama; Cokorda Pramartha; Christina Ayu Maha Dewi
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.987

Abstract

This study applies a visual analytics approach to explore Spotify music streaming data and uncover listener behavior patterns, genre preferences, and temporal listening trends. The dataset, obtained from the Maven Analytics Spotify Streaming History Data Playground, comprises 149,860 records with attributes such as track name, artist, duration, platform, and playback reason. Using Pentaho Data Integration (PDI), the data was cleaned, transformed, and enriched with a genre feature generated through ChatGPT-based classification of artist information. The processed dataset was then visualized in Tableau, producing interactive dashboards that revealed that The Beatles and The Killers were the most played artists, with Rock as the most dominant genre. Listening activity peaked during late-night hours, and Android was identified as the most-used platform. These results demonstrate how integrating ETL and visualization tools can convert raw streaming data into actionable insights for user engagement, personalized recommendations, and strategic music marketing.
Digital Preservation of Balinese Heritage through Educational Game: A Case Study of Perang Pandan Tradition Komang Redy Winatha; Ni Kadek Nita Noviani Pande; Ni Kadek Dwi Dina Oktaviani
Jurnal Krisnadana Vol 5 No 2 (2026): Jurnal Krisnadana- January 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i2.993

Abstract

Tradition is a series of long-established customs that have been continuously practiced, playing a significant role in the life of a community within a country, culture, time period, or religion. The Perang Pandan tradition is one of the unique cultural heritages of Tenganan Pegringsingan Village, Bali, embodying values of bravery and reverence for Lord Indra. This study aims to develop an Android-based Perang Pandan game by implementing the Finite State Machine (FSM) method to systematically manage character behavior and game mechanics. FSM was chosen for its advantages in controlling transitions between states—such as attacking, defending, and moving—creating an interactive and realistic gameplay experience. The game development process followed several stages: analysis, design, implementation, and testing. Black-box testing results indicate that the game runs smoothly on Android devices and successfully conveys traditional elements through engaging visuals and gameplay. Additionally, the User Experience Questionnaire (UEQ) testing revealed high scores in attractiveness, ease of use, and efficiency, highlighting the game's effectiveness in providing an enjoyable and educational experience.
Augmented Reality for the Architectural Layout of Pura Luhur Candi Narmada Tanah Kilap Made Suci Ariantini; I Dewa Gde Agung Krisna Dharma Prabhawa; Ketut Sepdyana Kartini; I Putu Gede Abdi Sudiatmika
Jurnal Krisnadana Vol 5 No 2 (2026): Jurnal Krisnadana- January 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i2.996

Abstract

By using Augmented Reality (AR) technology to introduce Pelinggih (shrines), this study seeks to aid Pura Luhur Candi Narmada Tanah Kilap's cultural digitization process. Through an interactive and visual approach, this technology offers the community and visitors a unique way to learn about the shrine's construction history. Through the use of markers inserted in a specially created AR Book, the AR implementation employs the Marker Based Tracking technique. This AR book allows users to examine 3D models of each shrine, complete with audio explanations, bilingual descriptions (in English and Indonesian), and interactive capabilities like object rotation and zooming. This invention provides a cutting-edge and instructive substitute for promoting cultural heritage. Software engineering is used in the development of the AR application. 20 respondents participated in a usability assessment using the System Usability Scale (SUS) approach. With an average score of 82.2%, which is classified as "good," the results demonstrated that the program is easy to use and widely recognized as a digital cultural teaching tool.

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