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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Saptawijaya, Ari
Faculty Of Computer Science - Universitas Indonesia
Computer Science & IT Engineering Library & Information Science

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TABLING WITH INTERNED TERMS ON CONTEXTUAL ABDUCTION Muhammad Okky Ibrohim; Ari Saptawijaya
Jurnal Ilmu Komputer dan Informasi Vol 12, No 1 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (529.492 KB) | DOI: 10.21609/jiki.v12i1.569

Abstract

Abduction (also called abductive reasoning) is a form of logical inference which starts with an observation and is followed by finding the best explanations. In this paper, we improve the tabling in contextual abduction technique with an advanced tabling feature of XSB Prolog, namely tabling with interned terms. This feature enables us to store the abductive solutions as interned ground terms in a global area only once so that the use of table space to store abductive solutions becomes more efficient. We implemented this improvement to a prototype, called as TABDUAL+INT. Although the experiment result shows that tabling with interned terms is relatively slower than tabling without interned terms when used to return first solutions from a subgoal, tabling with interned terms is relatively faster than tabling without interned terms when used to returns all solutions from a subgoal. Furthermore, tabling with interned terms is more efficient in table space used when performing abduction both in artificial and real world case, compared to tabling without interned terms.
Surrogate Model-based Multi-Objective Optimization in Early Stages of Ship Design Nanda Yustina; Ari Saptawijaya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4248

Abstract

The abstract is the early stages of ship design, the decision of the ship's main dimensions significantly impacts the ship's performance and the total cost of ownership. This paper focuses on an optimization approach based on surrogate models at the early stages of ship design. The objectives are to minimize power requirements and building costs while still satisfying the constraints. We compare three approaches of surrogate models: Kriging, BPNN-PSO (Backpropagation Neural Network-Particle Swarm Optimizer), and MLP (Multi-Layer Perceptron) in two multi-objective optimization algorithms: MOEA/D (Multi-Objective Evolutionary Algorithm Decomposition) and NSGA-II (Non-Dominated Sorting Genetic Algorithm II). The experimental results show that MLP surrogate models get the best performance with MAE 6.03, and BPNN-PSO gets the second position with MAE 7.2. BPNN-PSO and MLP with MOEA/D and NSGA-II improve the design with around 58% smaller adequate power and 6% less steel weight than the original design. However, BPNN-PSO and MLP have lower hypervolume than Kriging for both optimization algorithms MOEA/D and NSGA-II. On the other hand, Kriging has the most inadequate model accuracy performance, with an MAE of 22.2, but produces the highest hypervolume, lowest computational time, and far lower objective values than BPNN-PSO and MLP for both optimization algorithms, MOEA/D and NSGA-II. Nevertheless, the three surrogate model approaches can significantly improve ship design solutions and reduce work time in the early stages of design.
Analysis and Mitigation of Religion Bias in Indonesian Natural Language Processing Datasets Muhammad Arief Fauzan; Ari Saptawijaya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5035

Abstract

Previous studies have shown the existence of misrepresentation regarding various religious identities in Indonesian media. Misrepresentations of other marginalized identities in natural language processing (NLP) datasets have been recorded to inflict harm against such marginalized identities in cases such as automated content moderation, and as such must be mitigated. In this paper, we analyze, for the first time, several Indonesian NLP datasets to see whether they contain unwanted bias and the effects of debiasing on them. We find that two of the three data sets analyzed in this study contain unwanted bias, whose effects trickle down to downstream performance in the form of allocation and representation harm. The results of debiasing at the dataset level, as a response to the biases previously discovered, are consistently positive for the respective dataset. However, depending on the data set and embedding used to train the model, they vary greatly at the downstream performance level. In particular, the same debiasing technique can decrease bias on a combination of datasets and embedding, yet increase bias on another, particularly in the case of representation harm.
Large Language Model-Based Extraction of Logic Rules from Technical Standards for Automatic Compliance Checking Nugroho, Rizky; Krisnadhi, Adila; Saptawijaya, Ari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6285

Abstract

In this research, we design logic rules as a representation of technical standards documents related to ship design, which will be used in automatic compliance checking. We present a novel design of logic rules based on a general pattern of technical standards’ clauses that can be produced automatically from text using a large language model (LLM). We also present a method to extract said logic rules from text. First, we design data structures to represent the technical standards and logic rules used to process the data. Second, the representation of technical standards is produced manually and tested to ensure that it can give the same conclusion as human judgment regarding compliance. Third, a variation of prompting methods, namely pipeline method and few-shot prompting, is given to LLM to instruct it to extract logic rules from text following the design. Evaluation against the logic rules produced shows that the pipeline method gives an accuracy score of 0.57, a precision of 0.49, and a recall of 0.62. On the other hand, logic rules extracted using few-shot prompting have an accuracy score of 0.33, precision of 0.43, and recall of 0.5. These results show that LLM is able to extract a logic rule representation of technical standards. Furthermore, the representation resulting from the prompting technique that utilizes the pipeline method has a better performance compared to the representation resulting from few-shot prompting.
Co-Authors Krisnadhi, Adila Muhammad Arief Fauzan Muhammad Okky Ibrohim Nanda Yustina NUGROHO, RIZKY