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All Journal Techno.Com: Jurnal Teknologi Informasi Galaksi
I Gede Sudiantara
Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Penerapan Metode WASPAS untuk Penentuan Prioritas Destinasi Wisata di Bali Berbasis Kuantifikasi Fasilitas dan Ulasan Digital I Kayan Herdiana; I Gede Sudiantara; Ni Kadek Bumi Krismentari; Ni Wayan Jeri Kusuma Dewi
Techno.Com Vol. 25 No. 1 (2026): February 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i1.15681

Abstract

Banyaknya pilihan destinasi wisata di Provinsi Bali seringkali menimbulkan kebingungan bagi wisatawan dalam menentukan lokasi yang paling optimal, terutama ketika dihadapkan pada pertimbangan antara biaya, jarak, dan kelengkapan fasilitas. Penelitian ini bertujuan membangun Sistem Pendukung Keputusan (SPK) untuk merekomendasikan prioritas destinasi wisata menggunakan metode Weighted Aggregated Sum Product Assessment (WASPAS). Studi ini membandingkan 10 destinasi wisata populer di Bali berdasarkan empat kriteria utama, yaitu Biaya Masuk, Jarak dari Bandara, Rating Ulasan Digital, dan Kelengkapan Fasilitas. Kebaruan penelitian terletak pada proses pra-pemrosesan data fasilitas menggunakan teknik kuantifikasi matriks biner terhadap lima indikator fisik untuk meningkatkan objektivitas penilaian. Pengolahan data dilakukan secara komputasi menggunakan algoritma Python. Hasil penelitian menunjukkan bahwa Pantai Melasti menempati peringkat pertama dengan nilai preferensi tertinggi (Qi = 0,7872). Uji sensitivitas parameter λ pada rentang 0,1–0,9 menghasilkan nilai korelasi Spearman sebesar 0,9636–1,0000 terhadap baseline (λ = 0,5), yang menunjukkan tingkat stabilitas perankingan yang sangat tinggi. Temuan ini membuktikan bahwa metode WASPAS memberikan rekomendasi yang konsisten dan robust terhadap variasi parameter. Penelitian ini diharapkan dapat menjadi rujukan bagi pengambil kebijakan dalam evaluasi dan pengembangan fasilitas destinasi wisata secara objektif.   Kata Kunci: SPK, Pariwisata Bali, WASPAS, Kuantifikasi Fasilitas, Python.
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.
Co-Authors Gede Rudiharta Pratama Giri I Kayan Herdiana I Made Dwi Putra Asana I Made Subrata Sandhiyasa I Putu Noven Hartawan Made Landiva Ni Kadek Bumi Krismentari Ni Wayan Jeri Kusuma Dewi