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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Riset Informatika Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Riwayat: Educational Journal of History and Humanities
Devi Fitrianah
Bina Nusantara University
Humanities Computer Science & IT Education Engineering Social Sciences

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Text classification to predict skin concerns over skincare using bidirectional mechanism in long short-term memory Devi Fitrianah; Andre Hangga Wangsa
Computer Science and Information Technologies Vol 3, No 3: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i3.p137-147

Abstract

There are numerous types of skincare, each with its own set of benefits based on key ingredients. This may be difficult for beginners who are purchasing skincare for the first time due to a lack of knowledge about skincare and their own skin concerns. Hence, based on this problem, it is possible to find out the right skin concern that can be handled in each skincare product automatically by multi-class text classification. The purpose of this research is to build a deep learning model capable of predicting skin concerns that each skincare product can treat. By comparing the performance and results of predicting the correct skin condition for each skincare product description using both long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), The best results are given by Bi-LSTM, which has an accuracy score of 98.04% and a loss score of 19.19%. Meanwhile, LSTM results have an accuracy score of 94.12% and a loss score of 19.91%.
Ozone Gases Value Forecasting Using Encoder-Decoder LSTM Model Ni Ketut Intan Rahayu*; Devi Fitrianah; Elvin Elvin; Tannuru Marthamurtadh
Riwayat: Educational Journal of History and Humanities Vol 6, No 3 (2023): Social, Political, and Economic History
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v6i3.34435

Abstract

Climate change, as one of the impacts of global warming, has several consequences for the sustainability of living beings on Earth. It is necessary to monitor the trend of climate change. One way to monitor seasonal patterns of change is by analyzing the ozone content in the air. In addition to being an indicator of climate change, predicting the ozone gas content in the air is important because ozone gas has a direct impact on living organisms. By predicting the ozone gas content, it is hoped that preventive measures can be taken to prevent the adverse effects of ozone gas in the air. In the case of predicting ozone gases, there may be certain patterns that only become apparent over time, such as seasonal variations or long-term trends. A model that can capture these long-term dependencies will be better equipped to accurately predict ozone gas levels in the future. In this experiment, we proposed the use of Encoder-Decoder LSTM to predict ozone gas values.
A Study on Enhanced Spatial Clustering Using Ensemble Dbscan and Umap to Map Fire Zone in Greater Jakarta, Indonesia Silviya Hasana; Devi Fitrianah
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (724.615 KB) | DOI: 10.34288/jri.v5i3.234

Abstract

This research investigated ensemble clustering algorithms and dimensionality reduction for fire zone mapping, specifically DBSCAN + UMAP. We evaluated six clustering methods: DBSCAN, ensemble DBSCAN, DBSCAN + UMAP, ensemble DBSCAN + UMAP, HDBSCAN and Gaussian Mixture Model (GMM). We evaluated our results based on the Silhouette Score and the Davies-Bouldin (DB) index, emphasizing handling irregular cluster shapes, smaller clusters and resolving incompact clusters. Our findings suggested that ensemble DBSCAN + UMAP outperformed five other methods with zero noise clusters indicating clustering results are resistant to outliers, leading to a clearer identification of fire-prone areas, a high Silhouette Score of 0.971, indicating accurate cluster separation of distinct areas of potential fire hazards and an exceptionally low DB Index of 0.05 that indicates compact clusters to identify well-defined and geographically concentrated areas prone to fire hazards. Our findings contribute to the advanced techniques for minimizing the impacts of fires and improving fire hazard assessments in Indonesia.
Mitigating gender bias in STEM study field classification using GRU and LSTM with augmented dataset technique Devi Fitrianah; Sarah Safitri; Nadzla Andrita Intan Ghayatrie
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp447-455

Abstract

This study examines gender bias in artificial intelligence (AI), focusing on the classification of high school students into science, technology, engineering, and mathematics (STEM) and non-STEM fields. Using Indonesian student Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jakarta, 11480 data, conditional variational autoencoder (CVAE) and multilabel synthetic minority over-sampling technique (MLSMOTE) were employed for data augmentation to mitigate bias before training gated recurrent unit (GRU) and long short-term memory (LSTM) models for prediction. The combination of MLSMOTE and GRU demonstrated superior performance, achieving accuracies of 93% for female students and 94% for males. These results indicate that MLSMOTE and GRU effectively predict fields of study while addressing gender bias. The findings contribute to advancing fairness in AI systems for education and beyond, ensuring equitable opportunities across diverse applications.
Complexity aware cascade architecture for improving user satisfaction in conversational AI Constantinus Satrio; Devi Fitrianah
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp205-214

Abstract

Conventional task-oriented chatbots frequently suffer from task incompletions and low user satisfaction when handling complex queries. This research intro duces the complexity aware cascade, an adaptive architecture that improves user service quality by dynamically matching query complexity with the appropri ate computational response. The system uses confidence and relevance scores to intelligently route requests through a sequence of a natural language under standing (NLU) model, a retrieval-augmented generation (RAG) pipeline, or a large language model (LLM). The tiered architecture was evaluated via a ran domized controlled trial (RCT) with 150 participants, measuring task success and user satisfaction. The full cascade achieved a 90% journey completion rate, representing a 92.3% improvement over baseline system and substantial gains in SERVQUAL-based service quality scores. The experiment was conducted in a domain-specific knowledge base (essential oils) with a convenience sam ple that does not represent the global population, and no real-time deployment or long-term cost analysis was performed. Accordingly, the findings should be interpreted as evidence of effectiveness in a limited setting rather than as directly scalable to all domains. Even with these limitations, this study provides arigorously tested blueprint for developing more robust and user-centric conversational AI systems.
An integration clustering and multi-target classification approach to explore employability and career linearity Nadzla Andrita Intan Ghayatrie; Devi Fitrianah
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp189-197

Abstract

This study analyzes job placement waiting times and job linearity among female science, technology, engineering, and mathematics (STEM) graduates using clustering and multi-target classification (MTC) models. The K-means least trimmed square (LTS) algorithm, known for its robustness against outliers, was employed for clustering. With k = 2 and a trimming percentage of 30%, the model achieved a silhouette score of 77%, resulting in two distinct clusters: ideal and non-ideal. To enhance the dataset for classification, synthetic data was generated using the adaptive synthetic (ADASYN)-gaussian method. Principal component analysis (PCA) was used for visualization purposes, along with overlapping histograms, to illustrate that the synthetic data distribution closely resembled the original. For classification, a random forest (RF) model was used to predict both jobs waiting time and job linearity. Hyperparameter tuning produced an optimal model with a classification accuracy of 92%. Cross-validation (CV) confirmed the model’s robustness, with F1-micro and F1-macro scores of 94% and 93%, respectively. Results show that although women in STEM are underrepresented, 73% of the female alumni analyzed belonged to the short job waiting group. Furthermore, a strong negative correlation between GPA and job waiting time suggests that higher-GPA graduates tend to secure employment more quickly.
Co-Authors Andre Hangga Wangsa Constantinus Satrio Elvin Elvin Nadzla Andrita Intan Ghayatrie Ni Ketut Intan Rahayu* Sarah Safitri Silviya Hasana Tannuru Marthamurtadh