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All Journal JUTIK : Jurnal Teknologi Informasi dan Komputer Journal of Applied Data Sciences
Ni Putu Widya Yuniari
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Social Sciences

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PERSEPSI MAHASISWA BERDASARKAN GENDER TERHADAP SISTEM PEMBELAJARAN ONLINE DAN OFFLINE I Made Adi Bhaskara; I Made Surya Kumara; I Gede Wira Darma; Ni Putu Widya Yuniari; Gde Wikan Pradnya
Jurnal Teknologi Informasi dan Komputer Vol. 10 No. 2 (2024): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v10i2.2970

Abstract

Sistem pembelajaran secara online (daring) awalnya bertujuan sebagai alternatif darisistem pembelajaran di semua jenjang pendidikan. Dengan adanya pandemi Covid-19terjadi perubahan drastis yaitu sistem pembelajaran online secara penuh. Meredanyapandemi Covid-19 akhirnya sistem pembelajaran secara hibrid yaitu gabungan antaraoffline dan online. Penelitian ini bertujuan untuk mengetahui minat peserta didik tentangsistem pembelajaran dikaitkan dengan terkait faktor gender. Sebanyak 157 mahasiswaeksakta digunakan sebagai sampel yang terdiri dari 65 laki-laki dan 92 perempuan.Penelitian observasional menggunakan kuisener dengan pertanyaan tentang pemilihansistem pembelajaran (offline, online dan hibrid), pemilihan waktu (pagi, siang, sore danmalam) dan persepsi tentang pembelajaran offline (mudah memahami, menyenangkan,praktis). Hasil penelitian diperoleh bahwa pembelajaran online lebih banyak dipilih olehlaki-laki, sebaliknya perempuan lebih banyak memilih sistem offline. Berdasarkanpemilihan waktu pembelajaran online, peserta didik laki-laki lebih memilih waktu pagihari, sedangkan perempuan lebih banyak memilih waktu siang, sore dan malam. Secarakeseluruhan sistem pembelajaran offline lebih memudahkan pemahaman materipembelajaran dibandingkan online. Dapat disimpulkan bahwa ada persepsi yang berbedatentang pembelajaran online antara peserta didik laki-laki dengan yang perempuan. Perluteknis dan strategi yang berbeda antara pembelajaran online kepada peserta didik laki-laki dan perempuan
PENERAPAN TEKNIK MULTI-LEVEL THRESHOLDING FUZZY ENTROPY DAN DIFFERENTIAL EVOLUTION PADA KOMPRESI CITRA Ni Putu Widya Yuniari
Jurnal Teknologi Informasi dan Komputer Vol. 10 No. 2 (2024): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v10i2.2973

Abstract

Kompresi citra memainkan peran penting dalam berbagai aplikasi, seperti penyimpanan digital,transmisi citra, dan pemrosesan multimedia. Teknik kompresi citra yang efektif dapat secara signifikanmengurangi ukuran file citra digital tanpa mengorbankan kualitas visualnya. Penelitian ini mengusulkanevaluasi kinerja teknik kompresi citra dengan mengkombinasikan teknik Multi-Level Thresholdingdengan metode Fuzzy Entropy dan Differential Evolution. Metode ini diterapkan pada citra wajah dancitra medis. Kinerja metode ini dinilai berdasarkan Peak Signal-to-Noise Ratio (PSNR), StructuralSimilarity Index (SSIM), dan Feature Similarity Index Measure (FSIM). Analisis lebih lanjut diketahuibahwa nilai PSNR, SSIM, dan FSIM bertambah seiring dengan kenaikan level threshold, dengan nilaitertinggi diperoleh pada level threshold 40. Hal ini menunjukkan bahwa peningkatan level thresholdmenghasilkan kompresi citra yang selaras tanpa mengorbankan kualitas visual citra terkompresi.
Enhancing Aspect-based Sentiment Analysis in Visitor Review using Semantic Similarity Ni Made Satvika Iswari; Nunik Afriliana; Eddy Muntina Dharma; Ni Putu Widya Yuniari
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.249

Abstract

The global economy greatly depends on the tourism industry, which fosters job opportunities and stimulates economic development. With the growing reliance of tourists on online platforms for guidance, evaluations of tourist destinations have gained heightened significance. These assessments, frequently expressed through user-generated content, offer valuable perspectives on customer experiences, viewpoints, and levels of satisfaction. Nevertheless, analyzing and interpreting these reviews can pose difficulties because of the unstructured or semi-structured nature of user-generated content. Conventional sentiment analysis methods might not adequately grasp the intricacies and particular aspects of tourism encounters that users convey in their reviews. The efficacy of sentiment analysis can be augmented by integrating semantic similarity. This study explores methods to enhance aspect-based sentiment analysis within tourism reviews by utilizing semantic similarity approaches. Five aspects have been curated, representing keywords frequently reviewed by visitors to the tourist attraction. These aspects encompass scenery, dusk, surf, amenities, and sanitation. Based on the data analysis, F-Measure values with Semantic Similarity tend to increase for the scenery and dusk aspects. This is because in the sample data used, visitor reviews for the scenery and dusk categories may use other words that are semantically similar. The sample data used for these categories is also quite extensive, resulting in a better classification model for both categories. While it is valuable to analyze user-generated content data from visitor reviews, it's important to consider the limitations and potential biases associated with this data. The classification results per aspect need to be further reviewed in more depth. What aspects lead visitors to give positive reviews will certainly be maintained and even improved by stakeholders. Similarly, for negative review outcomes, it is necessary to investigate more deeply the factors contributing to visitor dissatisfaction so that they can be addressed by stakeholders.
Analyzing Student Sentiments and Insights on Generative AI for Independent Learning in Universities Ni Made Satvika Iswari; I Nyoman Yudi Anggara Wijaya; Ni Putu Widya Yuniari
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1083

Abstract

Transformations in higher education brought about by Generative AI have significantly changed how university students’ access, comprehend, and develop learning materials. This study explores Indonesian university students’ perceptions and experiences regarding the use of Generative AI for independent learning, employing qualitative surveys together with sentiment analysis powered by machine learning. Data were collected from open-ended questionnaires and analyzed using four key algorithms, such as Naive Bayes, Logistic Regression, Random Forest, and Linear SVM, to classify student sentiments towards generative AI technologies. These four classical machine learning models were employed as baseline algorithms commonly used in sentiment analysis to benchmark performance on small, imbalanced educational datasets before applying more complex transformer-based methods. In addition to quantitative analysis, this study also implements thematic analysis of open-ended responses to identify prominent issues, challenges, and student recommendations concerning the use of generative AI in learning. Evaluation results identified Linear SVM as the most consistent model, with the highest weighted F1-score (0.63), although all models showed limitations in detecting negative sentiment due to class imbalance (only three negative samples out of forty responses), which affected model generalization. Key findings indicate that students perceive Generative AI as a supportive tool that accelerates understanding, creativity, and reference searching; however, they remain wary of risks related to dependency, reduced originality, and academic integrity dilemmas. This article recommends the implementation of ethical policy, AI digital literacy training, and enhancement of campus infrastructure to ensure that AI technologies enrich the learning process without compromising student independence and integrity.
Co-Authors Dharma, Eddy Muntina I Gede Wira Darma I Made Adi Bhaskara I Made Surya Kumara I Nyoman Yudi Anggara Wijaya Ni Made Satvika Iswari Nunik Afriliana Wikan Pradnya Dana, Gde