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I Gede Iwan Sudipa
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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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Customer Segmentation Using RFM Model and Fuzzy C-Means at PT SNS 21 Bali I Made Dwi Putra Asana; Made Leo Radhitya; Dewa Nyoman Yogantara; I Gede Sudiantara; I Putu Noven Hartawan
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.v5i1.929

Abstract

This study aims to develop an effective customer segmentation model for PT Sukses Nusantara Sakti 21 Bali, a large-scale multi-level marketing distributor with over 60,000 members. The proposed approach integrates the Recency, Frequency, Monetary (RFM) model with the Fuzzy C-Means (FCM) algorithm to analyze one year of sales transaction data. The CRISP-DM framework was adopted to ensure a structured process, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Customer transaction records were preprocessed to compute normalized RFM scores, which were then clustered using FCM to capture overlapping membership patterns and better reflect behavioral diversity. The segmentation results were validated using the Silhouette Coefficient and Davies–Bouldin Index, achieving scores of 0.6005 and 0.5093, respectively, indicating high-quality cluster compactness and separation. Three distinct customer segments were identified, each providing actionable insights for targeted marketing strategies, including retention, engagement, and reactivation programs. The findings confirm that integrating RFM and FCM offers a robust and flexible approach for customer segmentation in large-scale MLM contexts. Future work may involve real-time segmentation and integration with predictive analytics to further enhance marketing decision-making.
AI-Based Model for Predicting On-Time Graduation of INSTIKI Students Using K-NN and Particle Swarm Optimization Made Leo Radhitya; I Made Dwi Asana; Ni Luh De Sri Chandra Purahita; I Made Subrata Sandhiyasa; I Nyoman Tri Anindia Putra
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.v5i1.930

Abstract

This study aims to enhance the accuracy and generalization capability of student on-time graduation prediction by integrating the K-Nearest Neighbor (K-NN) algorithm with Particle Swarm Optimization (PSO) for parameter tuning. Historical academic records from INSTIKI were used as the primary dataset, and a 10-fold cross-validation technique was applied to ensure robust evaluation. The PSO algorithm was employed to determine the optimal k value for K-NN, with optimization parameters set to c1 = 0.5, c2 = 0.6, inertia weight w = 0.9, swarm size = 90 particles, and 100 maximum iterations. The optimized model achieved an optimal k = 23, resulting in a validation accuracy of 77.84%, outperforming the baseline K-NN’s 72.43%. In addition, improvements were observed in precision, recall, F1-score, and AUC, with the latter increasing from 0.56 to 0.68, indicating better discrimination capability. These results demonstrate that PSO effectively mitigates overfitting and enhances model stability compared to conventional K-NN. The proposed approach offers a reliable and scalable predictive model for academic early-warning systems, enabling institutions to identify at-risk students earlier and implement targeted interventions. Future work may involve incorporating non-academic features, addressing class imbalance, and exploring ensemble learning for further performance gains.
Optimalisasi Peringkat SERP Menggunakan Teknik White Hat SEO pada Industri Perhotelan I Gede Sudiantara; Ni Made Erni Marlina Yani; I Made Dwi Putra Asana; I Wayan Adi Putra Yasa; I Putu Noven Hartawan
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.v5i1.931

Abstract

In today’s digital era, online visibility has become a critical factor for business success, particularly in the highly competitive hospitality industry. Premierplacehotels.com, despite offering quality services, experienced low search engine visibility, limiting its ability to attract organic traffic and direct bookings. Interview data revealed that online reservations were minimal at the beginning of 2024, with only three bookings in January, rising to 13 in February and 25 in March, highlighting the urgent need for an effective digital marketing strategy to enhance competitiveness. This study examines the implementation of White Hat Search Engine Optimization (SEO) techniques to improve the website’s performance on Search Engine Result Pages (SERPs). White Hat SEO focuses on ethical, long-term strategies in line with search engine guidelines, with methods applied including on-page optimization (such as improving meta tags and creating high-quality content) and off-page optimization (such as acquiring authoritative backlinks). The results demonstrate a significant improvement in keyword rankings, with “Juanda Airport Hotel” rising to position 3, “Surabaya Airport Hotel” to position 4, “Airport Hotel Juanda” to position 3, “Surabaya Hotel Near Airport” to position 3, “Hotel Near Juanda Airport Surabaya” to position 7, and “Airport Hotel Sidoarjo” to position 5. Website performance also improved, with clicks increasing from 321 to 560, impressions from 5,208 to 5,993, and the SEO health score from 68/100 to 83/100. In conclusion, the implementation of White Hat SEO proved effective in improving search visibility and website performance, offering a practical digital marketing strategy for enhancing competitiveness in the hospitality sector.
Classification of Alumni Employment Fields of the Nursing Study Program at Politeknik Nusa Utara Using K-Means Clustering Method Noldy Sinsu; Arifin P Tindi; Oktavianus Lumasuge
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.v5i1.936

Abstract

Tracer study is a trace trace of graduates performed every year after graduation competence in the world of work. In addition, tracer studies are also a prerequisite for obtaining accreditation from the National Council for Accreditation of Education. Alumni field classification can be done using the K-means Clustering method to group data based on data feature similarities. This research aims to facilitate the analysis of the classification of alumni's field of work. Job classification data was obtained from the alumni tracer on the Nursing Studies Program. The study analyzed a healthcare tracer that contains a curriculum cover from 2014 to 2022 using a K-means Clustering algorithm using Microsoft excel. The attributes used are domicile, admission time, graduation time, and workplace authority. The formed cluster is two clusters. The results of this research can be used as a basis for decision-making to determine the promotion strategy as well as the preparation of curricula based on the cluster formed by the Nursing Studies Program available at the Health Department of Politeknik Nusa Utara.
Quality Function Deployment: Metodologi Integrasi ‘Suara Konsumen’ Dalam Perancangan Produk Otomotif Lucia Diawati; Bernadetta Kwintiana
Jurnal Krisnadana Vol 3 No 3 (2024): Jurnal Krisnadana – Mei 2024
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v3i3.945

Abstract

Efisiensi dan efektivitas sistem perancangan produk ditunjukkan oleh kemampuan sistem tersebut dalam menghasilkan produk dalam waktu relatif cepat dan tepat, serta sesuai dengan persyaratan dan preferensi konsumen. Untuk itu, dibutuhkan fungsi integrasi ‘suara konsumen’ (kebutuhan konsumen) dalam proses perencanaan dan pengembangan produk. Dalam penerapannya, untuk mengimplementasikan fungsi tersebut pada sistem perancangan produk otomotif dibutuhkan adanya strategi pendukung yang mencakup peningkatan kualitas sumber daya manusia, perbaikan fungsi pemasaran, pembentukan kerjasama terintegrasi, dan perbaikan aliran kerja dalam organisasi.
Application of the ADDIE Model in the Development of an Android-Based Physics Quiz Game Andryanto Aman; Tamra Tamra; Ramlah Ramlah; Andi Ridwan Makkulawu
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.v5i1.949

Abstract

Conventional physics instruction in junior secondary schools often struggles to maintain student interest, leading to disengagement and diminished comprehension. This research details the application of the ADDIE model to design and develop an Android-based physics quiz game intended as a supplementary learning tool to address this issue. The study utilised a Research and Development (R&D) methodology, systematically progressing through the model's five distinct phases: Analysis, Design, Development, Implementation, and Evaluation. The initial analysis involved literature reviews and field studies at SMP Negeri 17 Makassar to identify specific learning requirements. Following the development and implementation phases, the application was assessed for its functionality and user reception through a quantitative User Acceptance Test (UAT). The key finding of this research was an overall acceptance score of 86% from student participants. This significant result confirms that the developed game not only functions correctly but is also perceived by students as an engaging and effective educational resource, validating its potential to enhance the learning process in physics.
Web-Based Medical Record dnd Veterinary Clinic Service Information System Using The Waterfall Method at UPTD Veterinary Health Clinic, Medan City Sandra Kirana; Ahmad Zakir
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.v5i1.950

Abstract

The rapid advancement of information technology in veterinary healthcare has significantly impacted medical data management modernization. The Regional Technical Implementation Unit (UPTD) Veterinary Clinic of Medan City encounters challenges with manual recording systems that result in operational inefficiencies, documentation error risks, and loss of critical animal medical record data. This research develops an integrated Web-Based Veterinary Medical Records and Clinic Services Information System equipped with service-supporting features, enabling clinic staff to manage animal medical data digitally while providing organized and efficient services to pet owners. The development follows waterfall methodology through requirement analysis, system design utilizing Unified Modeling Language (UML), implementation employing PHP and MySQL, Black Box Testing procedures, and maintenance phases. Results demonstrate that the system significantly enhances medical record documentation efficiency, reduces data errors, and provides real-time accessibility to animal medical information. All core features including patient registration, medical record documentation, queue management, vaccination tracking, online consultation, and reporting have been tested and function effectively. The system successfully digitalizes medical documentation workflows and provides clinic staff with enhanced control in data management.
Hyperparameter Analysis of an LSTM Model for Product Sales Forecasting I Putu Noven Hartawan; I Gede Sudiantara; I Dewa Putu Gede Wiyata Putra; Ayu Gede Willdahlia; I Made Dwi Putra Asana
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.v5i1.952

Abstract

We study monthly retail demand forecasting with an LSTM using a large real-world transaction history. The model is trained on historical monthly aggregates and evaluated on a two-month held-out horizon (January–February 2022) across 450 items. To avoid conflating optimization with forecasting performance, we report test-set accuracy using RMSE and MAPE, while training/validation losses are used only for model selection via early stopping. We run a controlled sweep over learning rate, batch size, and hidden units and find that the learning rate primarily drives convergence and accuracy, whereas the other two hyperparameters are secondary under our configuration. We also document practical data-preparation choices (outlier handling and chronological splitting) common in retail deployments. The study is framed as a single-customer case to ensure a consistent assortment and complete history; we therefore discuss external validity and reproducibility guidance. These findings help practitioners prioritize hyperparameter-tuning effort and set realistic expectations for short-horizon monthly demand forecasting in operational settings
A Hybrid Model as a Decision Support System in Internet Service Providers (ISP) Selection for hotels I Wayan Surya Pramana; Gede Wirya Wardhana
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.v5i1.953

Abstract

Reliable internet connectivity has become a critical requirement in the hospitality industry because it directly impacts to service quality and guest satisfaction. Hotels must carefully select an Internet Service Provider (ISP); however, this decision is often complicated by the wide choice of providers, budget constraints, and vary in guest internet needs. A survey to hotel IT practitioners in Bali highlighted the challenges in ISP selection, where mistakes can decrease customer satisfaction and damage a hotel's reputation. To address this, this study proposes a hybrid decision support system model that integrates the Naïve Bayes algorithm with the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). Naïve Bayes is used for the classification process, while AHP and SAW are used for the ranking process. Based on the implementation and analysis results, it was found that this model achieved an accuracy of 65%. Furthermore, it was also found that the accuracy of this model was greatly influenced by the results of the classification process.
Implementation of the Fuzzy Time Series Method for Forecasting Silver Jewellery Sales Komang Kurniawan Widiartha; Gede Dana Pramitha; Kadek Ryan Saputra Galiharta
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.v5i1.971

Abstract

Art shop Onno Silver is one of the art shops located on Jalan Raya Celuk No. 27, Celuk, Sukawati District, Gianyar, selling various types of silver jewelry such as earrings, bracelets, necklaces, rings and other types. Art shop Onno Silver only sees sales data from previous months without any more accurate calculations in sales forecasting. Art shop Onno Silver faces challenges in planning sales strategies and predicting sales in the following month due to unpredictability and high sales fluctuations, due to each category having different sales patterns that are influenced by various factors. This sales forecasting research uses the Fuzzy Time Series method. The results of this research method show the accuracy level of the earrings category MSE results of 8032.48, MAE results of 72.24, and MAPE results of 34.78%. The accuracy results for the bracelet category show MSE results of 539.37, MAE results of 20.82, and MAPE results of 26.61%. And the ring category evaluation results show MSE results of 12567.78, MAE results of 87.60, and MAPE results of 36.19%. From the evaluation results of the earrings, bracelets, and rings categories where it is in the range of 20% ? x < 50% that the forecasting accuracy is quite good. While the evaluation results of the necklace category show the MSE result of 87.43, the MAE result of 6.51, and the MAPE result of 12.35% where it is in the range of 10% ? x < 20% that the forecasting accuracy is good.

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