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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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Smart Veterinary Facilities: Integrating IoT and Data-Driven Facilities Management for Enhanced Animal Health and Welfare Fadimatu Dauda Muhammad; Sani Inusa Milala
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.v5i2.1015

Abstract

The veterinary sector in developing nations like Nigeria faces significant efficiency and welfare challenges due to a continued reliance on traditional management systems. Consequently, this study investigates the impact of integrating the Internet of Things (IoT) and data-driven facility management on animal health and welfare. Adopting a quantitative research design, the study analyzed data from a stratified random sample of 120 veterinarians, facility managers, and animal health officers using structured questionnaires Data were collected using structured questionnaires and analyzed using descriptive and inferential statistics, including Pearson correlation and multiple regression. Results revealed a strong positive correlation between IoT adoption and animal health and welfare (r = 0.762, p < 0.01), and between data-driven facility management and operational efficiency (r = 0.703, p < 0.01). The regression analysis indicated that these two predictors explained 63.4% (R² = 0.634) of the variance in animal welfare outcomes (F (2,117) = 45.326, p < 0.001). Respondents also reported improved disease prevention, reduced animal stress, and enhanced decision-making due to real-time monitoring and data analytics. The study concludes that IoT integration and data-driven management significantly improve animal welfare and operational performance in veterinary facilities. However, challenges such as high implementation costs, poor internet infrastructure, and limited technical expertise must be addressed to achieve wider adoption. The findings provide valuable insight for policymakers, veterinarians, and facility managers aiming to develop smart, sustainable, and welfare-oriented veterinary systems in Nigeria and beyond.
Smart Banjar Concept Development: A Qualitative Research I Dewa Made Adi Baskara Joni; Bazilah A. Talip; Shamsul Anuar Mokhtar; I Putu Hendika Permana
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.v5i2.1041

Abstract

The village and Banjar administrations manage cultural, religious, and socioeconomic activities in Bali. As a result of its customs and heritage, Bali has grown to be a world-famous tourist destination with substantial economic consequences. The influence of Villages and Banjar on tourism growth must benefit the community. The Banjar function must be strengthened to maintain the community's economic resilience by involving financial institutions such as Micro Finance. The synergy between Banjar and Micro Finance can support the development of Micro Enterprises, thereby impacting people's welfare. Based on this, the concept of the Smart Banjar was developed. To conduct qualitative research, interviews were conducted with the Leader of the Village, the Leader of the Banjar, the Leader of Micro Finance, and the Micro Enterprise. The analysis process produces four network diagrams: Micro Enterprise, Micro Finance, Information and Communication Technology Adoption, and Smart Concept. Based on the research results, Banjar, as a community entity, is part of the village-centered program. One existing program is the digitization of Micro Enterprises. It can be done by developing a smart concept that involves community-based Micro Finance. With Micro Finance's involvement as an optimal community-based economy, it can achieve community welfare.
Emotional Detection of Students During The Learning Process Using Yolo V8 Ni Luh Wiwik Sri Rahayu Ginantra; Suyud Setiawan Al Arif; Ni Wayan Jeri Kusuma Dewi
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.v5i2.1061

Abstract

This research develops an emotional detection system for students during learning using YOLOv8 model N and Computer Vision technology. The main objective measures accuracy of facial expression recognition across happy, sad (murung), neutral (normal), and confused classes while evaluating lighting and camera position impacts. Dataset of 1500 Kaggle images was labeled on Roboflow with augmentations including flip, crop, blur, and noise, then trained on Google Colab for 100 epochs using pre-trained YOLOv8n weights. Model validation employed confusion matrices, precision-recall curves, and real-time webcam testing on 10 students at 5-15 cm distances. Key findings show accuracies of 75% (happy), 62% (sad), 69% (neutral), and 33% (confused), averaging 59.75% under >39 lux frontal lighting. Optimal performance occurred at 0°-10° angles, but backlit conditions reduced efficacy by 25-35% due to shadow occlusion. The system enables real-time classroom monitoring with 4.2 ms inference latency, supporting cognitive engagement assessment despite bingung class limitations from dataset imbalance. Future improvements recommend expanded ambiguous samples and lighting augmentations.
A Binary Particle Swarm Optimization–Based Association Rule Mining Model for Retail Transactions Ni Kadek Bumi Krismentari; I Made Dwi Putra Asana; I Kayan Herdiana; Ida Bagus Gede Anandita; Nabila Fitri Syarifah
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.v5i2.1064

Abstract

This study aims to optimize the association rule mining process on retail transaction data by applying the Binary Particle Swarm Optimization (BPSO) algorithm. Classical methods such as Apriori and FP-Growth often face efficiency limitations, particularly when dealing with large-scale datasets, due to repeated candidate generation processes and high memory requirements. BPSO is employed as a metaheuristic approach capable of adaptively exploring the search space through binary itemset representation and a fitness function based on support, confidence, and lift values. This research follows the CRISP-DM framework, encompassing the stages of data understanding, data preparation, modeling, and evaluation. Based on retail transaction data from KLM, the BPSO process produced the best particle containing 16 potential itemsets. The calculation of support and confidence resulted in seven item combinations that met the minimum threshold and generated eighteen association rules. Evaluation using the lift ratio showed that all rules have lift values greater than one, indicating strong and meaningful relationships among products. These findings demonstrate that BPSO is effective in discovering relevant association patterns and can support retail decision-making, such as product arrangement, cross-selling strategies, and the development of recommendation systems.
Digital Mathematics Learning and Its Challenges: A Systematic Literature Review Ni Wayan Suardiati Putri; Kadek Suryati; Evi Dwi Krisna
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.v5i2.1067

Abstract

The development of digital technology has driven changes in mathematics learning in higher education, while also presenting new opportunities and challenges. This study aims to examine the implementation of digital mathematics learning and its accompanying challenges through the Systematic Literature Review (SLR) method with reference to the PRISMA guidelines. A total of 20 articles that met the inclusion criteria were analyzed qualitatively using thematic synthesis. The results of the study indicate that digital mathematics learning in higher education utilizes various technologies, such as Learning Management Systems (LMS), interactive media, learning videos, digital simulations, and blended and hybrid learning models. Digital mathematics learning has a positive impact on students' conceptual understanding, motivation, independence, and digital literacy, but its effectiveness is greatly influenced by the learning design and pedagogical strategies of lecturers. The main challenges identified include limited interaction, digital literacy gaps, technological readiness, and infrastructure and pedagogical constraints. Therefore, the integration of technology in mathematics learning needs to be supported by strengthening lecturer competencies and appropriate learning planning for effective and sustainable implementation.
Real-time Data Visualization of Inventory Lending Services using Metabase: A Case Study Ida Bagus Gede Anandita; Ni Putu Anggi Karolina; I Made Dwi Putra Asana
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.v5i2.1079

Abstract

Effective inventory management is critical in higher education but is often constrained by rigid reporting mechanisms. At the Institut Bisnis dan Teknologi Indonesia (INSTIKI), substantial asset volumes are currently presented in static tabular formats, resulting in suboptimal information availability. This impedes the Bureau of General Affairs from extracting rapid strategic insights regarding asset conditions and maintenance. This study addresses these inefficiencies by developing a real-time data visualization system utilizing Metabase as an open-source Business Intelligence (BI) tool. The system integrates directly with operational databases to transform raw data into interactive visual insights without complex ETL processes. The implementation yielded 14 comprehensive dashboard menus, covering metrics such as asset distribution, depreciation analysis, lending trends, and infrastructure quality control. User Acceptance Testing (UAT) indicated a 96% approval rate within the "Strongly Agree" category. These results demonstrate that the system successfully simplifies inventory data complexity, accelerates managerial decision-making, and provides an accurate foundation for budget planning and facility maintenance.
A Data-Driven LSTM Framework for Stock Price Forecasting to Support Investment Decision-Making Yeffriansjah Salim; Muhammad Amin Paris; Darmansyah Tjitradi; Eliatun Eliatun; Erna Herliani
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.v5i2.1080

Abstract

Stock investment decisions in emerging markets are often hindered by high volatility and non-linear price patterns. Conventional statistical methods frequently fail to capture the long-term dependencies inherent in financial time series data. This study proposes a data-driven forecasting framework using Long Short-Term Memory (LSTM) optimized with Hyperparameter Grid Search to predict the closing prices of Indonesia's top capitalization banking stocks: BBCA, BBNI, and BMRI. The methodology involves comprehensive data preprocessing, including MinMax normalization and an 80:20 train-test split, followed by a rigorous hyperparameter tuning process to determine the optimal epochs and batch sizes. The model's performance was evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results demonstrate that the proposed LSTM framework achieves high forecasting accuracy. Specifically, the model yielded a MAPE of 0.99% for BBCA, 1.35% for BBNI, and 1.38% for BMRI, indicating a very low error rate. These findings confirm that the optimized LSTM model can effectively capture dynamic market trends, providing investors and portfolio managers with a reliable decision support tool for strategic asset allocation.
K-Means Clustering of Students’ Anxiety Levels Based on DASS-42 Socres Ni Putu Dea Sillviari; Ayu Gde Chrisna Udayanie
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.v5i2.1097

Abstract

Anxiety is a psychological state that may adversely influence students’ focus, thinking ability, and academic achievement. This study seeks to group students’ anxiety levels using the K-Means clustering method based on DASS-42 questionnaire scores. The dataset consisted of responses from 835 tenth-grade students enrolled at a private vocational high school in Gianyar, Bali. In the preprocessing phase, 14 anxiety-related items from the DASS-42 scale were selected, and an overall anxiety score was computed for each participant. The K-Means algorithm was applied with five clusters (K = 5) corresponding to anxiety categories: normal, mild, moderate, severe, and extremely severe. The clustering process generated centroid values of 4.20, 9.15, 13.69, 19.30, and 27.50, respectively. The results showed that most students were grouped into the moderate anxiety cluster, representing 32% of the total sample. Meanwhile, 24% were classified as normal, 11% as mild, 19% as severe, and 14% as extremely severe. When compared with the standard DASS-42 classification, the K-Means approach demonstrated greater flexibility than interval-based methods. The findings are expected to help schools better understand students’ psychological conditions through computational analysis and support informed educational decision-making.
Robust Forecasting Model of Hotel Room Occupancy Rates in Bali: A SARIMA Algorithm Approach Ayu Gde Chrisna Udayanie; I Wayan Adi Sparta; Putu Eka Parianthana
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.v5i2.1102

Abstract

The dynamics of post-pandemic tourism recovery create significant fluctuations in Bali's hospitality industry, demanding a precise capacity management method. This study aims to build a robust forecasting model to project the hotel Room Occupancy Rate (TPK) using the Seasonal Autoregressive Integrated Moving Average (SARIMA) algorithm approach. This study utilizes monthly time series datasets sourced from the Central Bureau of Statistics (BPS) during the recovery phase, namely the period January 2022 to October 2025. The research methodology applies an analytical framework that includes stationarity test, differencing process, and identification of the optimal model through Auto-ARIMA mechanism to capture complex seasonal patterns. Model performance is validated using the statistical metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results show that the SARIMA(0,1,0)(1,0,0)[12] model architecture is the best model with the lowest Akaike Information Criterion (AIC) value. The performance evaluation resulted in a MAPE value of 8.68%, which indicates a "very good" level of accuracy in minimizing prediction errors. Based on the model, projections for the period November 2025 to October 2026 show a positive stability trend with an estimated average occupancy of 63.32% (range 58.01%-66.48%). This research contributes to providing tourism stakeholders with a reliable quantitative instrument for the formulation of pricing strategies and more efficient resource allocation.
Enhancing Sales Prediction Accuracy: A Hybrid Model of Single Exponential Smoothing and Golden Section Search Ketut Jaya Atmaja; Emmy Febriani Thalib; I Komang Surya Nata Darma Wiguna
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.v5i2.1104

Abstract

Accurate sales forecasting is a component in inventory management, particularly for determining appropriate stock levels for upcoming periods. Forecasting inaccuracies may result in overstocking or understocking, leading to increased operational costs and decreased service quality to customers. In practice, many retail businesses still encounter difficulties in producing reliable sales forecasts due to fluctuating demand patterns and the use of forecasting methods with suboptimal parameter selection. Single Exponential Smoothing (SES) is widely used because of its simplicity and ease of implementation; however, its forecasting performance is highly dependent on the choice of the smoothing parameter (alpha), which is often determined using a trial-and-error approach. This study proposes a hybrid forecasting approach that combines Single Exponential Smoothing with Golden Section Search to enhance sales prediction accuracy. Golden Section Search is employed as a numerical optimization technique to systematically determine the optimal alpha value by minimizing forecasting errors measured using Mean Absolute Percentage Error (MAPE). The proposed approach is applied to sales data from XYZ as a case study using varying lengths of historical data, namely 3 months, 6 months, 12 months, 24 months, and 36 months. The results demonstrate that the proposed hybrid method is capable of producing forecasts with a good level of accuracy, particularly for short-term forecasting. The lowest MAPE value of 7.02% is achieved when using 3 months of historical data, indicating high responsiveness to recent demand changes. As the length of historical data increases, the model tends to become more stable but less responsive to trend fluctuations, resulting in higher error values. Overall, the proposed approach is effective in supporting inventory management decision-making by providing accurate and reliable sales forecasts.

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