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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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Classification with Single Constraint Progressive Mining of Sequential Patterns Regina Yulia Yasmin; Putri Saptawati; Benhard Sitohang
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 4: August 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (589.328 KB) | DOI: 10.11591/ijece.v7i4.pp2142-2151

Abstract

Classification based on sequential pattern data has become an important topic to explore. One of research has been carried was the Classify-By-Sequence, CBS. CBS classified data based on sequential patterns obtained from AprioriLike sequential pattern mining. Sequential patterns obtained were called CSP, Classifiable Sequential Patterns. CSP was used as classifier rules or features for the classification task. CBS used AprioriLike algorithm to search for sequential patterns. However, AprioriLike algorithm took a long time to search for them. Moreover, not all sequential patterns were important for the user. In order to get the right and meaningful features for classification, user uses a constraint in sequential pattern mining. Constraint is also expected to reduce the number of sequential patterns that are short and less meaningful to the user. Therefore, we developed CBS_CLASS* with Single Constraint Progressive Mining of Sequential Patterns or Single Constraint PISA or PISA*. CBS_Class* with PISA* was proven to classify data in faster time since it only processed lesser number of sequential patterns but still conform to user’s need. The experiment result showed that compared to CBS_CLASS, CBS_Class* reduced the classification execution time by 89.8%. Moreover, the accuracy of the classification process can still be maintained. 
Progressive Mining of Sequential Patterns Based on Single Constraint Regina Yulia Yasmin; Putri Saptawati; Benhard Sitohang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 2: June 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i2.5098

Abstract

Data that were appeared in the order of time and stored in a sequence database can be processed to obtain sequential patterns. Sequential pattern mining is the process to obtain sequential patterns from database. However, large amount of data with a variety of data type and rapid data growth raise the scalability issue in data mining process. On the other hand, user needs to analyze data based on specific organizational needs. Therefore, constraint is used to impose limitation in the mining process. Constraint in sequential pattern mining can reduce the short and trivial sequential patterns so that the sequential patterns satisfy user needs. Progressive mining of sequential patterns, PISA, based on single constraint utilizes Period of Interest (POI) as predefined time frame set by user in progressive sequential tree. Single constraint checking in PISA utilizes the concept of anti monotonic or monotonic constraint. Therefore, the number of sequential patterns will decrease, the total execution time of mining process will decrease and as a result, the system scalability will be achieved.
Improvement of CB & BC Algorithms (CB* Algorithm) for Learning Structure of Bayesian Networks as Classifier in Data Mining Benhard Sitohang; G. A. Putri Saptawati
Journal of ICT Research and Applications Vol. 1 No. 1 (2007)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2007.1.1.3

Abstract

There are two categories of well-known approach (as basic principle of classification process) for learning structure of Bayesian Network (BN) in data mining (DM): scoring-based and constraint-based algorithms. Inspired by those approaches, we present a new CB* algorithm that is developed by considering four related algorithms: K2, PC, CB, and BC. The improvement obtained by our algorithm is derived from the strength of its primitives in the process of learning structure of BN. Specifically, CB* algorithm is appropriate for incomplete databases (having missing value), and without any prior information about node ordering.
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.
Development a Generic Transaction Processing System Based on Business Process Metadata Addin Gama Bertaqwa; G.A. Putri Saptawati; Tricya E. Widagdo
Jurnal Sistem Informasi Vol. 16 No. 1 (2020): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1351.879 KB) | DOI: 10.21609/jsi.v16i1.861

Abstract

The business processes has a major role in the activities of an organization. Organizations have various types of information systems that support the business processes. One of the systems that support the operations of an organization’s business processes is a transaction processing system that serves to record the daily routine transactions necessary in running a business. In the implementation of business process on information systems, often occur repetitive coding activities for additions or changes to business processes. In general, business process consist of a set of activities and relate to each other between one activity to another. To overcome the problem of repetitive coding implementation, relations between activities must be modelled. In this thesis, business process mapping to data level using metadata approach is proposed as a solution to manage any execution services build on information systems, so that developers can focus to the business process flows. The constructed data model divide building blocks of business process into three type such as event, task and gateway. The data model that has been found capable of storing business process as well as the execution flow of the business process into database. The transaction processing system has been built based on the data model of the process that has been found. Executor of business process has been developed based on metadata that has been designed. From the test results concluded that metadata is able to define business process, and the executor can control the flow of executions of business process correctly. Control of the execution flow of the business process can be done manually by the user or automatically by the executor based on the defined rules in metadata.
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.
Pengembangan Engine Integrasi Tabel HTML pada Halaman Web Memen Akbar; Fazat Nur Azizah; G. A. Putri Saptawati
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 5 No 3: Agustus 2016
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (881.385 KB)

Abstract

Two problems are arisen while integrating number of tables from number of web pages, i.e. structural conflict and semantic conflict. To tackle those problems, the proposed study combines some existing methods that are already proven to solve problems in integrating process. The proposed integration process of HTML table consists of 4 phases: (1) locating the table in web pages, (2) separating attributes and data values, (3) integrating the table scheme, (4) migrating the data values into integrated scheme. Table location in web page is determined using heuristic approach. This approach also can separate the attributes and the data values of the table. Semantic conflict that is apparent while integrating the table scheme is handled using domain specific ontology. The resulted data value, then, is migrated to table scheme in line with duplication data checking using vector space model. Result of the integration is presented as single HTML table. This approach is implemented as an engine that is coded using Phyton language. Result of experiment shows that the proposed approach can be used to integrate number of HTML table from number of web pages into a single integrated table.
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.
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