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All Journal Bulletin of Electrical Engineering and Informatics Journal of ICT Research and Applications JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IT JOURNAL RESEARCH AND DEVELOPMENT JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknik Informatika (JUTIF)
Gusti Ayu Putri Saptawati
School Of Electrical Engineering And Informatics, Institut Teknologi Bandung, Jalan Ganesa 10 Bandung 40132, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Pengembangan Model Fast Incremental Gaussian Mixture Network (IGMN) pada Interpolasi Spasial Prati Hutari Gani; Gusti Ayu Putri Saptawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3490

Abstract

Gathering geospatial information in an organization is one of the most critical processes to support decision-making and business sustainability. However, many obstacles can hinder this process, like uncertain natural conditions and a large geographical area. This problem causes the organization only to obtain a few sample points of observation, resulting in incomplete information. The data incompleteness problem can be solved by applying spatial interpolation to estimate or determine the value of unavailable data. Spatial interpolation generally uses geostatistical methods. These geostatistical methods require a variogram as a model built based on the knowledge and input of geostatistic experts. The existence of this variogram becomes a necessity to implement these methods. However, it becomes less suitable to be applied to organizations that do not have geostatistics experts. This research will develop a Fast IGMN model in solving spatial interpolation. In this study, results of the modified Fast IGMN model in spatial interpolation increase the interpolation accuracy. Fast IGMN without modification produces MSE = 1.234429691, while using Modified Fast IGMN produces MSE = 0.687391. The MSE value of the Fast IGMN-Modification model is smaller, which means that the smaller the MSE value, the higher the accuracy of the interpolation results. This modified Fast IGMN model can solve problems in gathering information for an organization that does not have geostatistics experts in the spatial data modeling process. However, it needs to be developed again with more varied input data.
Mengatasi Cold Start New User dalam Sistem Rekomendasi berbasis Pendekatan Hybrid: Review dan Analisis Bibliometrik Nasy`an Taufiq Al Ghifari; Benhard Sitohang; Gusti Ayu Putri Saptawati
IT Journal Research and Development Vol. 6 No. 1 (2021)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2021.vol6(1).6118

Abstract

Increasing number of internet users today, the use of e-commerce becomes a very vital need. One of the keys that holds the success of the e-commerce system is the recommendation system. Collaborative filtering is the popular method of recommendation system. However, collaborative filtering still has issues including data sparsity, cold start, gray sheep, and dynamic taste. Some studies try to solve the issue with hybrid methods that use a combination of several techniques. One of the studies tried to solve the problem by building 7 blocks of hybrid techniques with various approaches. However, the study still has some problems left. In the case of cold start new users, actually, the method in the study has handled it with matrix factorizer block and item weight. But it will produce the same results for all users so that the resulting personalization is still lacking. This study aims to map an overview of the themes of recommendation system research that utilizes bibliometric analysis to assess the performance of scientific articles while exposing solution opportunities to cold start problems in the recommendation system. The results of the analysis showed that cold start problems can be solved by utilizing social network data and graph approaches.
The Evaluation of DyHATR Performance for Dynamic Heterogeneous Graphs Nasy`an Taufiq Al Ghifari; Gusti Ayu Putri Saptawati; Masayu Leylia Khodra; Benhard Sitohang
Journal of ICT Research and Applications Vol. 17 No. 2 (2023)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.17.2.7

Abstract

Dynamic heterogeneous graphs can represent real-world networks. Predicting links in these graphs is more complicated than in static graphs. Until now, research interest of link prediction has focused on static heterogeneous graphs or dynamically homogeneous graphs. A link prediction technique combining temporal RNN and hierarchical attention has recently emerged, called DyHATR. This method is claimed to be able to work on dynamic heterogeneous graphs by testing them on four publicly available data sets (Twitter, Math-Overflow, Ecomm, and Alibaba). However, after further analysis, it turned out that the four data sets did not meet the criteria of dynamic heterogeneous graphs. In the present work, we evaluated the performance of DyHATR on dynamic heterogeneous graphs. We conducted experiments with DyHATR based on the Yelp data set represented as a dynamic heterogeneous graph consisting of homogeneous subgraphs. The results show that DyHATR can be applied to identify link prediction on dynamic heterogeneous graphs by simultaneously capturing heterogeneous information and evolutionary patterns, and then considering them to carry out link predicition. Compared to the baseline method, the accuracy achieved by DyHATR is competitive, although the results can still be improved.
SYSTEMATIC LITERATURE REVIEW OF DOCUMENTS SIMILARITY DETECTION IN THE LEGAL FIELD: TREND, IMPLEMENTATION, OPPORTUNITIES AND CHALLENGE USING THE KITCHENHAM METHOD Nazuli, Muhammad Furqan; Walhidayah, Irfan; Akhyar, Amany; Saptawati Soekidjo, Gusti Ayu Putri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2444

Abstract

This research conducted a Systematic Literature Review (SLR) to observe the application of graph mining techniques in detecting document law similarities. Graph mining, where nodes and edges represent entities and relations respectively, has proven effective in identifying patterns within legal documents. This review encompasses 93 relevant studies published over the past five years. Despite its potential, graph mining in the legal domain faces challenges, such as the complexity of implementation and the necessity for high-quality data. There is a need to better understand how these techniques can be optimized and applied effectively to address these challenges. This SLR utilized a comprehensive approach to identify and analyze trends, implementations, and popular domains related to graph mining in legal documents. The study reviewed trends in the number of studies, categorized the implementations, and evaluated the prevalent techniques employed. The review reveals a growing trend in the use of graph mining techniques, with a noticeable increase in the number of studies year by year. The implementation of these techniques is the most popular category, with applications predominantly in legal domains such as laws, legal documents, and case law. The most frequently used graph mining techniques involve Natural Language Processing (NLP), Information Retrieval, and Deep Learning. Although challenges persist, including complex implementation and the need for quality data, graph mining remains a promising approach for developing future information systems in law.
DEVELOPMENT OF GRAPH GENERATION TOOLS FOR PYTHON FUNCTION CODE ANALYSIS Bayu Samodra; Vebby Amelya Nora; Fitra Arifiansyah; Gusti Ayu Putri Saptawati Soekidjo; Muhamad Koyimatu
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6177

Abstract

The increasing complexity of programs in software development requires understanding and analysis of code structure, especially in Python, which dominates machine learning and data science applications. Manual static analysis is often time-consuming and prone to errors. Meanwhile, static analysis tools for Python, like PyCG and Code2graph, are still limited to generating call graphs without including dependency and control flow analysis. This research addresses these shortcomings by proposing the development of a web-based tool that integrates the generation of function call graphs, function dependency graphs, and control flow graphs using Abstract Syntax Tree (AST), Graphviz, and Streamlit. With an iterative SDLC methodology, this tool was developed gradually to visualize Python function code as a heterogeneous graph. Evaluation of 11 Python function codes showed a success rate of 95.45% in analyzing and visualizing Python function codes with various levels of complexity. The limitations of Graphviz present an opportunity for future research to focus on improving scalability and Python code analysis.
A systematic literature review to address overlapping laws in Indonesia Akhyar, Amany; Saptawati, Gusti Ayu Putri
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8407

Abstract

The vast number of laws often result in legal uncertainty due to overlapping, conflicting, and inconsistent regulations. Identifying and resolving these overlaps is essential for ensuring legal clarity and coherence. This systematic literature review (SLR) explores technologies that have the potential to address the issue of overlapping laws in Indonesia. This study reviews numerous works on knowledge graphs (KGs) and graph mining, focusing on their potential to automate the detection of overlapping laws, thereby streamlining the process of legal harmonization. The review identifies several key research opportunities, such as refining KG construction, exploring semantic similarity measures, enhancing the interlinking of legal information, and ensuring explainability and interpretability. These opportunities promise to enhance the efficiency and effectiveness of detecting overlapping laws and contribute to a more consistent legal system in Indonesia.
Development of Lung Cancer Risk Screening Tool with Causal Discovery Model Evaluation Approach Wibowo, Sandi; Mutaqin, Jatniko Nur; Apriansyah, Ari; Komiyatu, Muhamad; Soekidjo, Gusti Ayu Putri Saptawati
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2188

Abstract

Causal graph discovery approaches in healthcare for detecting high-risk diseases have been more widely applied in the last decade. The main challenge in causal graph discovery in healthcare data is the complexity of big data, which requires appropriate algorithms to reveal causal relationships between variables. This study focuses on evaluating the performance of seven causal discovery models—Peter-Clark (PC), Greedy Equivalent Search (GES), Direct LiNGAM, Directed Acyclic Graph-Graph Neural Network (DAG-GNN), Greedy Sparsest Permutation (GraSP), and Recursive Causal Discovery (RCD)—on opensource healthcare datasets. The model performance was evaluated using the Structural Intervention Distance (SID), Structural Hamming Distance (SHD), Matthews Correlation Coefficient (MCC), and Fobernius Norm (FN) metrics. The evaluation results conclusively show that the GES model performs best on low-complexity datasets. Meanwhile, the DAG-GNN model offers consistent performance on high-complexity data with MCC values ranging from 0.77 to 0.88. The application of the GES model for lung cancer risk screening, based on user question responses, demonstrated effectiveness by measuring MCC, SID, and SHD scores between the reference adjacency metrics and the resulting screening metrics.
Co-Authors Akhyar, Amany Ari Apriansyah, Ari Bayu Samodra Benhard Sitohang Benhard Sitohang Fitra Arifiansyah Komiyatu, Muhamad Masayu Leylia Khodra Muhamad Koyimatu Mutaqin, Jatniko Nur Nasy`an Taufiq Al Ghifari Nasy`an Taufiq Al Ghifari Nazuli, Muhammad Furqan Prati Hutari Gani Vebby Amelya Nora Walhidayah, Irfan Wibowo, Sandi