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All Journal Jurnal Ilmu Komputer dan Informasi JURNAL INSTEK (Informatika Sains dan Teknologi) Indonesian Journal of Electrical Engineering and Computer Science
Muhammad Okky Ibrohim
Faculty Of Computer Science - Universitas Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering 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.
Indonesian sentiment analysis in natural environment topics Octovianto, Christofer; Ibrohim, Muhammad Okky; Budi, Indra
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1353-1366

Abstract

Indonesia is one of the countries that is rich in biodiversity and has a high population growth. This condition can cause Indonesia to have problems related to the natural environment that are more complex than other countries. Hence, this has created a lot of discussions regarding natural environmental issues in Indonesia on social media platforms. In this case, stakeholders like the government in general can utilize sentiment analysis (SA) to comprehend the public’s views to allow them to better fit the public’s expectations when formulating a particular policy that related to the environmental sustainability (ES) issues. This paper built the first open dataset of Indonesian SA dataset in ES topics collected from Instagram. As the benchmark of our dataset, we used IndoBERT model variant for constructing the model and the experiment result shows that model based on IndoBERT-large-p2 obtained the best performance with 72.44% of F1-score.
DOES PERSONALIZATION MATTER IN PROMPTING? A CASE STUDY OF CLASSIFYING PAPER METADATA USING ZERO-SHOT PROMPTING Lesmana, Chandra; Muhammad Okky Ibrohim; Indra Budi
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 1 (2025): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i1.57445

Abstract

Systematic Literature Review (SLR) is one way for researchers to obtain information on research developments on a topic in a structured manner. This makes SLR a preferred method by researchers because the process involves systematic, objective analysis and focuses on answering research questions. In general, there are three stages to conducting SLR, namely planning, implementation, and reporting. However, compiling an SLR takes a long time because it goes through all the stages one by one. To overcome this problem, an automation process is needed so that it can speed up the SLR compilation process. Previous studies have carried out an automation process in the form of SLR document classification by utilizing several machine learning models that require a lot of training data like Naïve Bayes, Support Vector Machine, and Logistic Model Tree. In this study, the authors conducted an automation process by utilizing open-source Large Language Model (LLM) namely Mistral-7B-Instruct-v0.2 and LLaMA-3.1–8B to classify title and abstract of SLR documents. We compared the effect of using personalization on zero-shot prompting. By using LLM with zero-shot prompting, the classification process no longer requires training data, so that it does not need data annotation cost. Experiment results showed that personalization improved classification performance, getting the best results with Macro F1 0.5538 using the Llama 3.1 model.
Co-Authors Budi, Indra Chandra Lesmana Indra Budi Octovianto, Christofer Saptawijaya, Ari