Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Journal of Applied Engineering and Technological Science (JAETS) Journal of Innovative and Creativity
Diva Kurnianingtyas
Fakultas Ilmu Komputer Universitas Brawijaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Analisis Sentimen Pemindahan Ibu Kota Indonesia pada Media Sosial Twitter menggunakan Metode LSTM dan Word2Vec Yunico Ardian Pradana; Imam Cholissodin; Diva Kurnianingtyas
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 5 (2023): Mei 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemindahan ibu kota negara dari DKI Jakarta ke Pulau Kalimantan telah menimbulkan perdebatan dan meningkatkan minat publik terhadap isu tersebut. Twitter menjadi media sosial yang populer untuk menyampaikan pendapat dan aspirasi masyarakat. Oleh karena itu, penelitian ini bertujuan untuk menganalisis sentimen masyarakat terkait pemindahan ibu kota menggunakan metode analisis sentimen. Dalam penelitian ini, metode Deep Learning, khususnya Long Short Term Memory (LSTM) dan word2vec yang digunakan untuk menganalisis sentimen tweet masyarakat. Dengan menerapkan metode LSTM dengan Word2Vec, diharapkan dapat diklasifikasikan apakah tweet masyarakat bersifat positif atau negatif terkait pemindahan ibu kota. Model LSTM yang dikembangkan dalam penelitian ini menghasilkan akurasi sebesar 95%, precision sebesar 93%, recall sebesar 93%, dan F1-measure sebesar 95%. Hasil tersebut menunjukkan bahwa metode ini efektif dalam menganalisis sentimen masyarakat terkait pemindahan ibu kota dan dapat memberikan pemahaman yang lebih baik mengenai pandangan publik terhadap perubahan tersebut.
Naive Bayes Analysis for Nutritional Fulfillment Prediction in Children Satrio Agung Wicaksono; Satrio Hadi Wijoyo; Fatmawati Fatmawati; Tri Afirianto; Diva Kurnianingtyas; Mochammad Chandra Saputra
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i2.6105

Abstract

Stunting in children remains a significant global health challenge, particularly in low- and middle-income countries. Addressing this issue requires an effective approach to predicting and preventing inadequate nutritional fulfillment. This study uses the Naïve Bayes approach to forecast nutritional needs for children's growth and development, providing practical information for stunting prevention efforts. The data used were sourced from 174 infant and toddler examinations at the Puskesmas Lawang, involving eight key attributes: gender, age, weight, height, head circumference, pre-screening, vision tests, and nutritional status. Key performance metrics were evaluated to validate the model's predictive capabilities, including accuracy, precision, recall, and F1-score. Six test scenarios were conducted using different percentages of training data (90%, 80%, 70%, 60%, 50%, and 40%) to evaluate the reliability of the Naïve Bayes method. Results indicated that the highest accuracy of 78.84% was achieved in the sixth test scenario. The third test scenario produced the highest precision at 97.5%, while the highest recall (100%) was observed in the first three scenarios. The highest F-measure of 90.3% occurred in the fourth scenario. These results suggest the algorithm's potential for early detection to decrease the number of stunting children. The study’s implications are twofold: practically, the model can be integrated into health monitoring systems to assist healthcare professionals and policymakers in designing more effective nutrition programs; theoretically, it highlights the adaptability of Naive Bayes for handling complex, multi-dimensional health data.
The Poncokusumo Village Information System In The Context Of Moving Towards A Digital Village Arief Andy Soebroto; Agi Putra Kharisma; Diva Kurnianingtyas; Syakirah Dwi Anisa; Charles Eugene; Annisa Indah Fitriani
Journal of Innovative and Creativity Vol. 6 No. 1 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i1.4956

Abstract

Manual administrative processes in Poncokusumo Village have long been a source of inefficiency, resulting in common problems such as service delays, restricted public access to information, and difficulties with data archiving. To tackle these issues, this study set out to analyze the impact of the Sistem Informasi Manajemen Desa/Kelurahan (SIMDEK) on the effectiveness of village administration. The research employed the Community-Based Participatory Action Research (CBPAR) method, utilizing a combination of questionnaires and the Wilcoxon test to assess the understanding of the SIMDEK website among village officials and residents. The primary goal was to see if the system could genuinely improve administrative efficiency and transparency. The findings from the data analysis were compelling. The study's results demonstrated that the SIMDEK training had a significant positive effect on improving the understanding of village residents. This was statistically confirmed by an Asymp. Sig (2-tailed) value that was smaller than the significance level α, which is the standard measure for statistical significance. Based on these outcomes, the conclusion is clear: the implementation of SIMDEK can markedly improve the speed of services, the accuracy of data processing, and the transparency of information within Poncokusumo Village. This study holds significant implications, providing a strong case for village governments to expand the use of SIMDEK. Doing so is not just a technological upgrade; it represents a strategic and necessary step toward the broader digital transformation of public services, ensuring a more responsive and accountable local government for the community.
Vision Transformer for Active Compound Function Classification Based on 2D Molecular Structures Dian Eka Ratnawati; Diva Kurnianingtyas; Agus Wahyu Widodo; Rekyan Regasari Mardi Putri
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9418

Abstract

Accurate classification of active compounds based on molecular structure is crucial for accelerating drug discovery while reducing laboratory costs and time. However, existing structure-based classification methods, particularly convolutional neural networks and graph-based models, often struggle to capture long-range dependencies or require large-scale datasets and extensive feature engineering. This study investigates the use of the Vision Transformer (ViT) model to classify 2D molecular structure images of compounds into cancer and cardiovascular therapy categories. A dataset containing 500 images, consisting of 250 per class, was obtained from the PubChem database, processed for consistency, and divided into 72% training, 20% testing, and 8% validation. To address the limited dataset size, careful preprocessing, regularization through weight decay, and systematic hyperparameter tuning were applied to reduce overfitting risks. The ViT model was trained with the Adam optimizer and a linear learning rate scheduler. Hyperparameters were systematically tuned to identify the optimal configuration. Results show that the best settings, with batch size 60, weight decay 0.1, learning rate 3.0×10⁻⁶, and 15 epochs, achieve an accuracy, F1 score, and loss of 80.0%, 79.9%, and 0.597, sequentially. These findings highlight the potential of ViT for small-scale cheminformatics tasks, offering an alternative to conventional methods while maintaining competitive performance.
Co-Authors Agi Putra Kharisma Agus Wahyu Widodo Annisa Indah Fitriani Arief Andy Soebroto Charles Eugene Dian Eka Ratnawati Fatmawati Fatmawati Imam Cholissodin Mochammad Chandra Saputra Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Satrio Agung Wicaksono Satrio Hadi Wijoyo Syakirah Dwi Anisa Tri Afirianto, Tri Yunico Ardian Pradana