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All Journal Jurnal Kridatama Sains dan Teknologi Journal of Computer Networks, Architecture and High Performance Computing Journal of Language and Health
Dananjaya, Md. Wira Putra
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Health Professions Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences

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Penerapan Algoritma Naïve Bayes untuk Prediksi Penyakit Depresi pada Mahasiswa gama, Adie Wahyudi Oktavia; Grren, Agustini Degni Melsy; Paramartha, I Gusti Ngurah Darma; Prathama, Gede Humaswara; Widnyani, Ni Made; Dananjaya, Md. Wira Putra
Journal of Language and Health Vol 6 No 2 (2025): Journal of Language and Health
Publisher : CV. Global Health Science Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37287/jlh.v6i2.6926

Abstract

Depression is one of the most serious mental health problems among college students, but it often goes unnoticed due to social stigma and limited access to psychological services. This study aims to apply the Naïve Bayes algorithm to predict depression in college students based on various factors, such as academic pressure, sleep duration, eating habits, financial stress, and family history of mental disorders. The model was built using 502 data obtained from the Kaggle platform, through the stages of data preprocessing, transformation, classification using Gaussian Naïve Bayes, and evaluation using a confusion matrix. The implementation process was carried out in Google Colab using the scikit-learn library. The evaluation results showed very good model performance with an accuracy of 97%, precision of 96%, recall of 98%, and F1-score of 97%. These findings indicate that the Naïve Bayes algorithm can be used effectively as an anonymous and efficient early screening tool for depression and has the potential to support increased awareness and mental health interventions in the college student environment.
User-Centered Design Approach in Developing User Interface and User Experience of Sculptify Mobile Application Dananjaya, Md. Wira Putra; Prathama, Gede Humaswara; Darmaastawan, Kadek
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4206

Abstract

In the increasingly digital era, user interface (UI) and user experience (UX) design have become crucial factors in application development. The success of an application is not only determined by its functionality, but also by how well users can interact with the application. User Centered Design (UCD) is an approach that places users as the main focus in every stage of design, from initial research to final evaluation, to ensure that the resulting product truly meets user needs and expectations. This study applies the UCD approach to the UI and UX design of the Sculptify application, which is designed to facilitate the buying and selling of sculptures and other three-dimensional works of art. Given the complexity and uniqueness of art product transactions, effective UI and UX design is very important. This study involves the active participation of potential users through methods such as interviews, surveys, and usability testing to create an intuitive interface and provide a satisfying experience for users. The research stage begins with research to understand user needs and preferences, followed by initial design and a series of tests and iterations based on user feedback. The final evaluation is carried out to measure the extent to which the final design meets user needs and expectations. The results of the UCD implementation are expected to provide valuable insights into the importance of placing users at the center of the design process and how this can improve the quality of interactions and overall user satisfaction.
Analisis Determinan Karakter Siswa Menggunakan Explainable Machine Learning (SHAP) dan Klasterisasi Profil Sekolah Studi Kasus Rapor Pendidikan Provinsi Bali Dananjaya, Md. Wira Putra; Krisnawijaya, Ngakan Nyoman Kutha; Prathama, Gede Humaswara; Paramartha, I Gusti Ngurah Darma; Gama, Adie Wahyudi Oktavia
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1988

Abstract

Strengthening student character is a key performance indicator in the Merdeka Belajar curriculum, but the identification of the school environment's most influential determinants of character achievement is often assumed. This study aims to quantitatively deconstruct the relationship between school climate and student character quality in Bali Province. Using the Indonesian Education Report dataset released by the Ministry of Primary and Secondary Education (Kemendikdasmen) for the 2023-2025 period with a total of 727 data entries, this study applies the Educational Data Mining methodology with the Random Forest algorithm enhanced by the Synthetic Minority Over-sampling Technique (SMOTE) to address data inequality. The novelty of this study lies in the use of SHapley Additive exPlanations (SHAP) for model transparency and K-Means Clustering for zoning mapping. Experimental results show the model is able to predict character achievement with 77.03% accuracy. The SHAP analysis revealed the interesting finding that Climate for Diversity (influence score of 0.45) and Climate for Gender Equality (0.22) were the strongest predictors, far exceeding the influence of Climate for Security (0.13). This finding challenges the common assumption that physical security is the single most important factor. Furthermore, the clustering analysis identified three school typologies in Bali, including one "Vulnerable" cluster that scored critically on gender equality and diversity despite having adequate security scores. This study recommends shifting the focus of education policy in Bali from a physical security approach to strengthening tolerance and gender equality programs, which have been shown to have a more statistically significant impact
Pendekatan Transformer Deep Learning dalam Meramalkan Harga Minyak Sumatran Light Crude Candrawengi, Ni Luh Putu Ika; Amritha, Yadhurani Dewi; Dananjaya, Md. Wira Putra
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1993

Abstract

Time series forecasting plays an important role in understanding the dynamics of volatile data that depends on long-term historical patterns, such as crude oil prices. Parametric statistical approaches often face limitations due to strict assumptions, making nonparametric deep learning methods a more flexible alternative. This study proposes the application of a Transformer-based deep learning model to predict the price of Sumatran Light Crude Oil (SLC), utilizing a self-attention mechanism to capture long-term dependencies in time series data. Experiments were conducted by evaluating various configurations of multi-head attention and number of layers, while keeping the model dimensions and input-output windows consistent. The results show that the Transformer configuration with 16 heads and 4 layers provides the best performance with a Root Mean Square Error (RMSE) value of 8.19818. These findings indicate that Transformer is capable of effectively modeling long-term trends in SLC prices, although its sensitivity to short-term fluctuations is still limited. The main contribution of this research lies in the use of Transformer as an alternative approach to forecasting crude oil prices in Indonesia, which was previously dominated by statistical methods and recurrent models. In practical terms, the results of this study provide a basis for the development of a more adaptive oil price forecasting system to support energy analysis and data-driven decision making
Co-Authors Adie Wahyudi Oktavia Gama Amritha, Yadhurani Dewi Candrawengi, Ni Luh Putu Ika Darmaastawan, Kadek Gede Humaswara Prathama Grren, Agustini Degni Melsy I Gusti Ngurah Darma Paramartha Ngakan Nyoman Kutha Krisnawijaya Widnyani, Ni Made