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Journal : Journal of Applied Data Sciences

User Interface Design for DIVAYANA Evaluation Application Based on Positive-Negative Discrepancy Divayana, Dewa Gede Hendra; Suyasa, P. Wayan Arta; Ariawan, I Putu Wisna; Mariani, Ni Wayan Rena; Sugiharni, Gusti Ayu Dessy; Gama, Adie Wahyudi Oktavia
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.136

Abstract

This study aims to show the user interface design form of the DIVAYANA evaluation application based on Positive-Negative Discrepancy. The method in this research is a development method that uses the Borg and Gall model. The development refers to the design stage, initial design trials, and revisions to initial design trials. Tests on user interface design involved 104 respondents. The instrument was a questionnaire consisting of 15 questions. Analysis of the trial data used a quantitative descriptive technique. The results of the study show that the quality of the user interface design is quite good. The impact of the results of this research on educational evaluators is that there is new knowledge about the existence of a user interface design that is important to know to support the realization of physical quality evaluation applications.
Forms and Field Trials of a Digital Evaluation Tool: Integrating F-S Model, WP Method, and Balinese Local Wisdom for Effective E-Learning Ariawan, I Putu Wisna; Sugandini, Wayan; Ardana, I Made; Sugiharni, Gusti Ayu Dessy; Gama, Adie Wahyudi Oktavia; Divayana, Dewa Gede Hendra
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.v5i2.201

Abstract

This study purposed to show the tool display and the results of field trials on the digital evaluation tool. This tool is an evaluation tool in digital format which was from a combination of the concept of the educational evaluation model “F-S (Formative-Summative)”, the decision support system method “WP (Weighted Product)”, and Balinese local wisdom “TP (Tri Pramana)”. The importance of combining these concepts and methods is it makes it easier to obtain accurate calculation results following the needs of evaluation tools to determine the dominant aspects determining the effectiveness of e-learning. This research approach was development research. The development model was Borg and Gall, which focused on the field trial and field trial revision stages. The reason for focusing on those two stages was that we wanted to know how effective the evaluation tool was in getting the dominant aspects determining the effectiveness of e-learning. The research location was at several health colleges in Bali. Field trials data collection was using a measuring instrument in the form of a questionnaire. The respondents who were involved in conducting field trials were 54 people. Data analysis on the results of field trials was comparing the results of field trials with the standard effectiveness of five’s scale. The results of this study show that the appearance of the digital evaluation tool and the percentage of its effectiveness through field trials was 81.73%, so the tool was categorized as good. The impact of this research on informatics observers/informatics experts is that they will know an innovative evaluation tool used to determine the dominant aspect determining the effectiveness of e-learning based on decision support system methods and Balinese local wisdom.
Identifying Key Factors Causing Flooding Using Machine Learning Gama, Adie Wahyudi Oktavia; Dennatan, Monalisa; Dharmayasa, I Gusti Ngurah Putu; Maw, Me Me; Sugiana, I Putu; Suryanti, Irma
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.463

Abstract

The impact of flooding extends beyond physical and infrastructural damage, affecting social, economic, and environmental dimensions. This study aims to identify the key factors influencing flooding by developing a decision tree model. The research method applies the C4.5 algorithm to build a decision tree model using flood factors such as rainfall, soil type, elevation, land use, and distance from rivers. The model is then applied to 57 past flood data events to determine key contributors to flooding in Denpasar City, Bali, Indonesia. The analysis showed that land elevation is the most influential factor, with areas below 28 meters above sea level having a 71% likelihood of being flood vulnerability. Additionally, the model reveals unknown patterns contributing to flood vulnerability among the factors considered. These insights give a deeper understanding of how these factors combine to affect flood vulnerability. The model's effectiveness was evaluated using a confusion matrix, resulting in an accuracy rate of 90%, a precision rate of 100%, a sensitivity rate of 90%, a specificity rate of 100%, and a F1 Score rate of 94%, demonstrating its strong predictive power in identifying areas at risk of flood vulnerability. Although this study is limited by the availability of data, the focus on Denpasar City, and the potential omission of other relevant attributes, it advances flood risk assessment by applying machine learning to provide practical insights that could enhance flood management strategies, with potential applications to other urban areas facing similar risks.
HOG feature extraction in optimizing FK-NN and CNN for image identification of rice plant diseases Gama, Adie Wahyudi Oktavia; Gunawan, Putu Vina Junia Antarista; Darmaastawan, Kadek
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.722

Abstract

This study compares the performance of FK-NN and CNN models in identifying rice diseases from digital images, focusing on both effectiveness and efficiency. Additionally, this research utilizes HOG for feature extraction from the digital images. The stages include data collection, preprocessing, transformation, classification, and model evaluation. The results show that the FK-NN model achieves a higher accuracy of 86.26%, compared to the CNN model's accuracy of 71.37%. Furthermore, the precision value of the FK-NN model is also higher at 86.88%, compared to the CNN model’s precision of 72.74%. Similarly, the recall value for the FK-NN model is higher at 86.88%, compared to the CNN model’s 71.37%. The F1-score of the FK-NN model is likewise superior, with a value of 86.88%, compared to the CNN model’s F1-score of 71.37%. These findings suggest that the FK-NN model with HOG feature extraction is more effective. However, in terms of inference time, the CNN model is faster, taking 0.000282 seconds compared to FK-NN’s 0.002331 seconds. In conclusion, the FK-NN model with HOG feature extraction excels in identifying rice diseases, while the CNN model offers faster inference time in this study.
Co-Authors Adhiya Garini Putri, Dewa Ayu Agus Ariana, I Komang Ajeng Ayu Fitri Ariatmaja Ariesta, Ni Luh Wina Sinta Ariwangsa, I Gusti Ngurah Oka Arta, Kadek Ananda Dwi Pebri Arya Putra Sanjaya, I Ketut Gede Bunga, Melania Pritama Danang Utomo Dananjaya, Md. Wira Putra Darma, I Gede Wahyu Surya Darmaastawan, Kadek Davi, Nadine Kalina Dennatan, Monalisa Devi Anggreni, Ni Komang Ayu Devi, Ni Kadek Sintya Dewa Ayu Putu Adhiya Garini Putri Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewi Puspita Ningrat, Qorry Dharma, I Kadek Dwi Yudiarsana Diantari, Putu Yuliska Dwi Sanjani Mertaningsih, Ni Kadek Gede Hendra Divayana, Dewa Gede Humaswara Prathama Ginanita Utami, Cokorda Istri Ustana Grren, Agustini Degni Melsy Gunanti, A A Istri Indah Paristya Gunawan, Putu Vina Junia Antarista Gusi Putu Lestara Permana Gusti Ngurah Darma Paramartha, I Hari Putri, Tasya Prajna Pratisthita Hayu Mas Wrespatiningsih I Dewa Putu Arjun Suhartana Wisesa I Gede Artha Negara I Gusti Ayu Cintya Wardani I Gusti Ayu Intan Candra Dewi I Gusti Ngurah Darma Paramartha I Gusti Ngurah Putu Dharmayasa I Gusti Putu Riyan Nugraha I ketut Gede Darma Putra I Made Ardana I Made Riski Aditya Darma I Made Sudiksa I Made Wirya Darma I Nyoman Gde Artadana Mahaputra Wardhiana I Nyoman Hary Kurniawan I Putu Agung Bayupati I Putu Wisna Ariawan I Wayan Abimayu Angga Nugraha I Wayan Aditya Suranata I Wayan Dikse Pancane I Wayan Sukadana I Wayan Sukadana I Wayan Sutama I Wayan Sutama Irma Suryanti Ivan Surya Pramana Putra, Kadek Bagus John Junieargo Timotius John Timotius Junieargo Kadek Devi Kalfika Anggaria Wardani Kadek Devi Kalfika Anggria Wardani Kadek Prasilia Candra Dewi Komang Bagus Novan Bayu Pramana Putra Kurniawan, I Nyoman Hary Lin, Fanny Made Jana Narendra Made Widnyani, Ni Maharani, Faradita Putri Aura Maulidan, Bagus Maw, Me Me Negara, I Gede Artha Negara, Komang Ayu Aprillia Puspa Ngakan Nyoman Kutha Krisnawijaya Ngurah Komang Wiradnyana Ni Kadek Nadya Kartika Paramita Ni Nyoman Triana Margareta Ni Putu Jenifer Febriari Ni Putu Widayanti Ni Wayan Rena Mariani Nilton Da Conceicao Marques Nimadeni Yuniartika Nur Aprilya, Fira Nurullita Wardani, Venti Oktama Setyawan, I Kadek P. WAYAN ARTA SUYASA Permana, Putu Indra Pertama, Gusti Putu Lestara Praditya Maha Wiguna, I Made Putra, Komang Satria Wibawa Putri Prema Paramitha Putu Emy Samiadnyani Putu Purnama Dewi Putu Riska Indah Mentari putu suparna, putu Sastra Dewanti, Wayan Ari Sudestra, I Made Ardi Sugiana, I Putu Sugiharni, Gusti Ayu Dessy Suputra, Komang Yudi Swari, Luh Gede Widi T Krisna Narayana, Made Gede Bagus Wardhiana, I Nyoman Gde Artadana Mahaputra Wardhiana, Nyoman Dana Wayan Sugandini Widnyani, Ni Made Wisesa, I Dewa Putu Arjun Suhartana