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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Krisnadana
Ni Wayan Jeri Kusuma Dewi
Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
Computer Science & IT Control & Systems Engineering 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.
Sentiment Analysis of the National Capital Relocation to IKN Using TF-IDF and Logistic Regression Ni Wayan Jeri Kusuma Dewi; Indra Pratistha; Tasya Alifah; Ni Komang Sri Wahyuni
Jurnal Krisnadana Vol 5 No 3 (2026): Jurnal Krisnadana May - July 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/yhck9073

Abstract

The relocation of the national capital from Jakarta to East Kalimantan has sparked various reactions from the public, many of which have been expressed through social media platforms such as YouTube. This study aims to analyze public responses from the perspective of sentiment and emotion toward the policy. The methodology employed involves keyword weighting using TF-IDF and Logistic Regression techniques. The data was then processed through preprocessing stages and labeled using the EmoLex Dictionary. Out of a total of 5,835 comments analyzed, the results showed that 2,180 comments (37.4%) were neutral in sentiment, followed by 2,077 comments (35.6%) with positive sentiment and 1,578 comments (27.0%) containing negative sentiment. In emotion classification, anger was the most dominant emotion, with 2,525 comments (43.3%), followed by trust with 897 comments (15.4%), anticipation with 750 comments (12.9%), and other emotions, such as disgust, neutral, sadness, fear, joy, and surprise, with smaller proportions. The results of the model performance evaluation for sentiment classification showed an accuracy of 93,9%, a precision of 94,6%, a recall of 93,0%, and an F1-score of 93,5%.  
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.
Co-Authors I Gede Sudiantara I Kayan Herdiana Indra Pratistha Ni Kadek Bumi Krismentari Ni Komang Sri Wahyuni Ni Luh Wiwik Sri Rahayu Ginantra Suyud Setiawan Al Arif Tasya Alifah