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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer 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) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sistem Informasi
Gusti Ayu Putri Saptawati
Institut Teknologi Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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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.
Simplifikasi Graf Pemanggilan Fungsi: Pendekatan Community Detection Untuk Mempermudah Pemahaman Struktur Kode Tioria Marlini Purba, Risa; Purba, Risa Tioria Marlini; Tonang, Ari Sandy Putra Ari; Karim, Abdulah; Soekidjo, Gusti Ayu Putri Saptawati; Muhamad, Koyimatu; Arifiansyah, Fitra
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.132

Abstract

Dalam pengembangan perangkat lunak skala besar, pemahaman terhadap struktur kode sangat penting untuk menganalisis interaksi antar-fungsi dalam kode sumber. Graf pemanggilan fungsi (function call graph) merupakan kakas yang efektif untuk memetakan hubungan antar-fungsi, yang membantu pengembang dalam menelusuri jalur eksekusi dan memahami pola struktur kode modular yang kompleks. Namun, pada kode sumber yang rumit, graf pemanggilan fungsi sering kali menjadi sangat besar dan sulit diinterpretasi karena banyaknya node dan edge yang terlibat. Untuk mengatasi masalah ini, teknik simplifikasi graf melalui community detection diterapkan sebagai solusi untuk mengelompokkan fungsi-fungsi yang saling terkait dalam cluster, sehingga menghasilkan visualisasi yang lebih terstruktur dan mudah dipahami. Penelitian ini bertujuan untuk mengembangkan kakas berbasis Python yang mampu menyederhanakan graf pemanggilan fungsi menggunakan algoritma Girvan-Newman. Kakas ini memanfaatkan pustaka networkx untuk membentuk graf dan menerapkan deteksi komunitas, ast untuk parsing kode, serta matplotlib dan streamlit untuk visualisasi dan interaksi pengguna. Hasil eksperimen pada 10 program dengan ukuran 10-85 baris kode menunjukkan bahwa metode community detection mampu mereduksi jumlah node dan edge dalam graf pemanggilan fungsi hingga 60%, dengan skor modularitas tertinggi 0.6605. Evaluasi dengan 25 pengembang perangkat lunak menunjukkan tingkat kepuasan 80% dalam hal kemudahan penggunaan dan peningkatan produktivitas analisis kode.   Abstract In large-scale software development, understanding the code structure is crucial for analyzing the interactions between functions in the source code. A function call graph is an effective tool for mapping the relationships between functions, assisting developers in tracing execution paths and understanding object-oriented complex code structures. However, in complex source code, the function call graph often becomes very large and complicated to interpret due to the many nodes and edges involved. To address this issue, graph simplification techniques, such as community detection, are applied as a solution to group related functions into clusters, thereby producing a more structured and easier-to-understand visualization. This study aims to develop a Python-based tool that simplifies function call graphs using the Girvan-Newman algorithm. The tool utilizes the networkx library to construct graphs and apply community detection, ast for code parsing, and matplotlib and streamlit for visualization and user interaction. The results of experiments on 10 programs, ranging in size from 10 to 85 LOC, showed that the community detection method was able to reduce the number of nodes and edges in the function invocation graph by up to 60%, achieving the highest modularity score of 0.6605. An evaluation of 25 software developers revealed an 80% satisfaction rate in terms of ease of use and increased productivity in code analysis.
Analyzing Software Evolution Dynamics using Execution Path-aware Graph Divergence Fitra Arifiansyah; Muhammad Zuhri Catur Candra; Gusti Ayu Putri Saptawati
Journal of ICT Research and Applications Vol. 20 No. 1 (2026)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2026.20.1.6

Abstract

Software comprehension is a fundamental activity in software maintenance, and its complexity grows as systems evolve across releases. Call graphs (CG) are widely used to support this process because they capture the calling relationships among functions. However, obtaining meaningful comparisons between different versions of a CG remains challenging. Network Portrait Divergence (NPD) provides a graph invariant and computationally efficient metric for assessing structural differences by analyzing global distributions of node distances and neighborhood patterns. Although it is effective, NPD does not include execution semantics, even though execution paths often convey the behavioral changes that matter to developers. This study introduces a refinement of NPD that replaces neighborhood-oriented features with features derived from execution paths, represented through distributions of path lengths. The modified metric is evaluated in a controlled scenario using synthetic data, designed to distinguish the effects of structural changes including new call and changes to control flow. The results show that the proposed NPD is more responsive to modifications that influence execution behavior. Scenarios that create new execution paths and restructure control flow result in substantially higher divergence. These findings suggest that including execution path information offers a more behavior-oriented view of software evolution and complements topology-based approaches.
Co-Authors Addin Gama Bertaqwa Akhyar, Amany Ari Apriansyah, Ari Arifiansyah, Fitra Bayu Samodra Benhard Sitohang Fazat Nur Azizah Irfan Walhidayah Karim, Abdulah Komiyatu, Muhamad Koyimatu, Muhamad Masayu Leylia Khodra Memen Akbar Muhammad Zuhri Catur Candra Mutaqin, Jatniko Nur Nasy`an Taufiq Al Ghifari Nasy`an Taufiq Al Ghifari Nazuli, Muhammad Furqan Prati Hutari Gani Purba, Risa Tioria Marlini Regina Yulia Yasmin Regina Yulia Yasmin Tioria Marlini Purba, Risa Tonang, Ari Sandy Putra Ari Tricya E. Widagdo Vebby Amelya Nora Wibowo, Sandi