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All Journal Jurnal Krisnadana
Ayu Gde Chrisna Udayanie
Sistem Informasi, Universitas Bali Dwipa, Indonesia
Computer Science & IT Control & Systems Engineering

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Implementation of the Weighted Product Method to Determine Scholarship Recipients Putu Eka Parianthana; I Wayan Adi Sparta; Ayu Gde Chrisna Udayanie
Jurnal Krisnadana Vol 4 No 3 (2025): Jurnal Krisnadana May - July 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v4i3.891

Abstract

This study aims to help address the problems that occur at SMAN 2 KUTA SELATAN in determining the selection of scholarship recipients based on economics. In its Decision Support System (DSS) We use the Weighted Product (WP) method, by determining the criteria needed to be used in determining the best alternative by multiplying the normalized criteria values, where each criterion value is raised to the power of a predetermined weight. The results of this study show which students are more deserving or should receive scholarship assistance, However, due to the large amount of data that must be collected, the division of the criteria must be carefully twisted in such a way as to produce effectiveness in its determination. Despite the constraints, in fact this method is quite effective in determining system-based scholarship selection decisions, the school has no difficulty in making the selection because the WP method makes it easier for them and only takes a little time.
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.
Co-Authors I Wayan Adi Sparta I Wayan Adi Sparta Ni Putu Dea Sillviari Putu Eka Parianthana Putu Eka Parianthana