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All Journal Journal on Education Jurnal Penelitian Rumpun Ilmu Teknik Journal of Innovative and Creativity Jurnal Teknik Informatika dan Teknologi Informasi
Ni Luh Putu Ika Candrawengi
Universitas Pendidikan Nasional
Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Segmentasi Siswa Berdasarkan Capaian Literasi dan Numerik Menggunakan Teknik Clustering Ni Luh Putu Ika Candrawengi
Journal on Education Vol 7 No 2 (2025): Journal on Education: Volume 7 Nomor 2 Tahun 2025 In Progress (Januari-Februari 2
Publisher : Departement of Mathematics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joe.v7i2.7891

Abstract

The Competency-Based National Assessment (CBNA) aims to measure students' basic competencies in literacy and numeracy as the foundation for learning at various levels of education. However, assessment results often show significant variations due to factors such as learning environment, teaching methods and socio-economic background, making it difficult for schools to design effective learning strategies. This study aims to map students based on literacy and numeracy achievement using clustering techniques with K-Means and Spectral Clustering algorithms. The data used is the 2023 National Assessment Public Report Card for SMA/SMK/MA/MAK levels. The analysis process includes pre-processing, exploratory analysis, application of clustering algorithm, and evaluation using Silhouette coefficient. The results showed that the K-Means algorithm with two clusters performed better (Silhouette coefficient 0.1901) than Spectral Clustering (-0.2218). The first cluster grouped students with low literacy and numeracy attainment, while the second cluster included students with higher attainment.
Segmentasi Konsumen Zero Waste Menggunakan Metode Gaussian Mixture Model dan Fuzzy C-Means Berdasarkan Preferensi dan Perilaku Pembelian Ni Luh Putu Ika Candrawengi; I Gusti Ngurah Putu Dharmayasa; I Gede Fery Surya Tapa; Anak Agung Sagung Istri Ratu
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.1319

Abstract

The increasing concern for the environmental problems has led to the growth of zero-waste stores as a form of sustainable consumption. This study aims to identify consumer segmentation in zero waste stores in North Kuta, Bali, based on preferences and purchasing behavior. Data was gathered through reliable and valid questionnaires involving 80 respondents who had shopped at three zero waste stores. The analysis was conducted using GMM (Gaussian Mixture Model) and FCM (Fuzzy C-Means) algorithms, with evaluation using silhouette coefficient and ICD Rate. The results showed that GMM was more optimal in forming homogeneous clusters (ICD Rate: 0.715). Three main clusters were identified: Eco-Engaged Advocates (respondents who are loyal and environmentally conscious), Value-Conscious Supporters (respondents who focus on product value), and Occasional Shoppers (pragmatic respondents who are responsive to promotions). The findings provide strategic implications for businesses in developing a more adaptive and data-driven marketing approach.
Deteksi Penyakit Daun Cabai Menggunakan Teknik Augmentasi Leafgan dan Deep Learning Model Ni Wayan Ariningsih; Ngakan Nyoman Kutha Krisnawijaya; Ni Luh Putu Ika Candrawengi; Adie Wahyudi Oktavia Gama
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 6 No. 2 (2026): Agustus : Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v6i2.7756

Abstract

Leaf diseases in chili plants, such as leaf spot and yellow virus, pose a significant threat to agricultural productivity in Indonesia, often leading to substantial economic losses for farmers. Traditional manual identification remains inefficient and highly dependent on individual expertise, which frequently results in inconsistent diagnosis. This research proposes an automated detection system utilizing the YOLOv8n deep learning architecture to provide a more reliable and real-time solution. A major hurdle in developing robust AI models for agriculture is the scarcity of balanced field dataset s and the presence of complex natural backgrounds. To address this, the study employs Leafgan , a generative augmentation technique capable of transforming healthy leaf images into realistic diseased samples while preserving the original field environment. By leveraging an attention mechanism, Leafgan  maintains high-frequency textural details, allowing the YOLOv8n model to generalize better across diverse environmental conditions. Data management is streamlined through the Roboflow platform to ensure consistent integration of primary and synthetic dataset s. The primary goal of this integration is to enhance model stability, aiming for a minimum mean Average Precision  (mAP) within actual plantation settings.
Klasifikasi Penyakit Daun Tanaman Cengkeh Menggunakan Arsitektur MobileNetV2 Berbasis Web Made Wahyu Sudharma; Ni Luh Putu Ika Candrawengi
Jurnal Penelitian Rumpun Ilmu Teknik Vol. 5 No. 3 (2026): Jurnal Penelitian Rumpun Ilmu Teknik
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juprit.v5i3.6652

Abstract

The clove plantation sector (Syzygium aromaticum) in Indonesia plays an important role in supporting national economic stability. However, its productivity faces threats from Plant Pest Organisms (PPOs), such as Clove Leaf Cacar Disease (CDC), Sooty Mold, and Dieback, which can cause losses of 10%-40% of total harvest yields. Conventional visual diagnosis is prone to subjectivity and human error, while the implementation of large-scale deep learning models is constrained by the computational capacity of end-user devices. This study aims to develop an objective and lightweight diagnostic system through the CloveVision web application based on the MobileNetV2 architecture. The study employed a quantitative experimental approach involving data collection, image preprocessing, model development using transfer learning and fine-tuning, system conversion, and evaluation. The dataset consisted of 1,000 clove leaf images collected from Tajun Village, Buleleng, Bali, with 250 images per class. The model was converted to TensorFlow.js to facilitate client-side execution directly in a web browser. The test results showed an accuracy of 78.75% on an independent test dataset, a model size of 9.24 MB, and an inference time of less than 100 milliseconds without an internet connection. Black-box testing achieved a 100% success rate, while usability testing using the System Usability Scale (SUS) with 15 farmers produced an average score of 86.67 (Best Imaginable). The study concludes that MobileNetV2 effectively balances accuracy and computational efficiency to support smart farming.
Co-Authors Adie Wahyudi Oktavia Gama Anak Agung Sagung Istri Ratu I Gede Fery Surya Tapa I Gusti Ngurah Putu Dharmayasa Made Wahyu Sudharma Ngakan Nyoman Kutha Krisnawijaya Ni Wayan Ariningsih