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All Journal International Journal of Advances in Intelligent Informatics Jurnal Ilmiah FIFO JURNAL MEDIA INFORMATIKA BUDIDARMA Bulletin of Informatics and Data Science International Journal of Informatics and Data Science
Soares, Teotino Gomes
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Implementation of a Combination of Rank Reciprocal and Additive Ratio Assessment Approaches for 3D Printer Selection Fatmayati, Fryda; Soares, Teotino Gomes; Tonggiroh, Mursalim; Alexander, Allan Desi
Bulletin of Informatics and Data Science Vol 2, No 2 (2023): November 2023
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v2i2.83

Abstract

With the wide selection of 3D printers available on the market, the challenge arises for consumers and businesses to choose the device that best suits their specific needs. To determine the choice, the decision-maker must know one by one the specifications of the 3D printer to be purchased, which results in making difficult decisions and requiring a long time. This research aims to implement a combination of the Rank Reciprocal and additive ratio assessment (ARAS) approaches to make it easier to determine decisions for selecting a 3D printer. The Reciprocal Rank approach provides weight values by utilizing the reciprocal or inverse principle. Meanwhile, the ARAS approach is used to obtain the best alternative by evaluating alternative rankings based on their utility function. From the case studies that have been carried out, the highest to lowest utility values are Anycubic 4Max Pro (A2) getting a score of 0.8289, Creality Ender-3 Pro (A1) getting a score of 0.6174, Anet 3D Printer ET4 Pro (A3) getting a score of 0.5510, and Mingda Magician X2 (A4) getting a score of 0.5116. The output produced by the system in the case study carried out produces the same value as the manual calculation, meaning that the ARAS method calculation in the system is declared valid. Based on usability testing, it got a score of 90%, which shows the system is suitable for use
Decision Support System Using a Combination of COPRAS and Rank Reciprocal Approaches to Select Accounting Software Erkamim, Moh.; Handayani, Nurdiana; Heriyani, Nofitri; Soares, Teotino Gomes
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7111

Abstract

Accounting software plays an important role in carrying out accounting processes that are fast, efficient, accurate and in accordance with applicable standards. With the emergence of various accounting software that offers a variety of features, users, both individuals and companies, often experience difficulty in determining the software that best suits their needs. The aim of this research is to develop a decision support system that makes it easier to choose accounting software through the application of the COPRAS approach and the Rank Reciprocal weighting technique. The Rank Reciprocal approach is used to rank or weight the criteria given by the decision-maker. The COPRAS (Complex Proportional Assessment) approach focuses on cognitive aspects so that it can accommodate the preferences and subjective assessments of decision-makers. Based on the case study that has been carried out, the highest to lowest utility value results are obtained, namely Zahir Online (A2), which obtained a score of 100. Since the decision support system's output yields a result that is identical to that of computations made by hand, it is deemed legitimate. Apart from that, the usability test obtained an average score of 91%, which proves that the system is in accordance with its usability and what is needed by its users.
Decision Support System for Selecting a Camera Stabilizer Using Rank Reciprocal and ARAS Approaches Erkamim, Moh.; Daniarti, Yeni; Shalahudin, Mohammad Imam; Mulyadi, Mulyadi; Soares, Teotino Gomes
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7560

Abstract

The selection of camera stabilizers is a crucial aspect in the photography and videography industry, especially with the increasing use of cameras. Typically, to select a camera stabilizer, decision-makers must be aware of all specifications of the available options. However, this necessarily results in a lengthy decision-making process, and various considerations lead to imprecise decisions. The aim of this research is to develop a Decision Support System (DSS) for choosing the appropriate and swift camera stabilizer through a combination of the Rank Reciprocal weighting approach and the Additive Ratio Assessment (ARAS) method. The Rank Reciprocal approach is utilized to obtain the criteria weight values, and the ARAS method is employed to evaluate and select the best alternative based on a number of criteria. This research produced a website-based DSS that can recommend the best alternative in the form of an alternative ranking. The results from the case study conducted obtained rankings from the highest to the lowest as follows: Zhiyun Tech Weebill S with a score of 0.9428, Beholder DS1 with a score of 0.8497, Gudsen Moza AirCross S with a score of 0.8205, and Feiyu Tech Scorp C (A3) with a score of 0.7197. The system built has been tested with a usability test scoring 88.75%, indicating that the system has met its users' needs.
Enhancing Support Vector Machine Performance for Heart Attack Prediction using RobustScaler-Based Outlier Handling Lasiyono, M Munawir; Nurhayati, Nurhayati; Soares, Teotino Gomes; Mulyadi, Mulyadi
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.94

Abstract

Cardiovascular disease remains the leading cause of death worldwide, with most cases attributed to heart attacks and strokes. Early detection is crucial, yet conventional diagnostic methods are often constrained by time, cost, and uneven distribution of clinical expertise. Consequently, machine learning-based approaches offer a promising alternative for efficiently supporting heart attack prediction. This study employs the Support Vector Machine (SVM) algorithm, focusing on enhancing its performance through RobustScaler as a preprocessing technique to address outliers common in medical datasets. The objective of this study is to evaluate the impact of RobustScaler on SVM performance in heart attack classification. The model was developed using a dataset of 303 patient records, consisting of eight numerical features and one binary target label. Experiments were conducted under two preprocessing scenarios: without scaling (baseline) and with RobustScaler. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. The results show that applying RobustScaler significantly improves model performance, with accuracy increasing from 64.77% to 85.23%, representing a 20.46% improvement, and ROC-AUC rising from 73.65% to 93.36%, indicating a 26.78% increase in discriminatory ability. Additionally, recall for the negative class improved dramatically from 26.47% to 99.02%, reflecting better sensitivity in identifying non-heart attack cases. These findings demonstrate that proper preprocessing, particularly using RobustScaler, plays a vital role in optimizing SVM performance, especially when handling clinical data with extreme values
Semantic-BERT and semantic-FastText model for education question classification Soares, Teotino Gomes; Azhari, Azhari; Rohkman, Nur
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1955

Abstract

Question classification (QC) is critical in an educational question-answering (QA) system. However, most existing models suffer from limited semantic accuracy, particularly when dealing with complex or ambiguous education queries. The problem lies in their reliance on surface-level features, such as keyword matching, which hampers their ability to capture deeper syntactic and semantic relationship in question. This results in misclassification and generic responses that fail to address the specific intent of prospective students. This study addresses, this gap by integrating semantic dependency parsing into Semantic-BERT (S-BERT) and Semantic-FastText (S-FastText) to enhance question classification performance. Semantic dependency parsing is applied to structure the semantics of interrogative sentences before classification processing by BERT and FastText. A dataset of 2,173 educational questions covering five question classes (5W1H) is used for training and validation. The model evaluation uses a confusion matrix and K-Fold cross-validation, ensuring robust performance assessment. Experimental results show that both models achieve 100% accuracy, precision, and recall in classifying question sentences, demonstrating their effectiveness in educational question classification. These findings contribute to the development of intelligent educational assistants, paving the way for more efficient and accurate automated question-answering systems in academic environments.
Decision Support System Combining Rank Sum and ARAS Methods for Job Promotion Selection Soares, Teotino Gomes; Tonggiroh, Mursalim
International Journal of Informatics and Data Science Vol. 1 No. 2 (2024): June 2024
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The promotion selection process within an organization is a crucial aspect that influences long-term performance and success. Proper selection can enhance employee motivation and productivity; however, traditional subjective methods often lead to bias and unfairness in the selection process. This study aims to develop a Decision Support System (DSS) for promotion selection using the Rank Sum and Additive Ratio Assessment (ARAS) methods to support more objective and structured decision-making. The Rank Sum method is used to objectively determine the weight of criteria based on their order of importance, while the ARAS method is employed to evaluate and rank alternatives based on various predetermined criteria. The combination of these two methods is expected to provide more accurate and transparent assessments of promotion candidates. The results of the study indicate that the developed DSS can provide the best alternative recommendations through ranking the relative performance values from highest to lowest. The case study shows that the highest utility value is Ahmad Nazir (A3) with a score of 0.9346, followed by Ricky Hamdani (A4) with a score of 0.9142, Andy Setiawan (A1) with a score of 0.8769, and Tati Maharani (A2) with a score of 0.8659. The consistency between the system output and manual calculations demonstrates the validity of the system results, while black box testing ensures that all main features function as expected.
Enhancing Liver Disease Classification Using Support Vector Machine with IQR-Based Outlier Handling Soares, Teotino Gomes; Tonggiroh, Mursalim; Erkamim, Moh.; Widarti, Erni
Jurnal Ilmiah FIFO Vol 17, No 1 (2025)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2025.v17i1.010

Abstract

Liver disease is a significant health issue that requires early and accurate diagnosis to prevent serious complications. In this study, we propose an outlier filtering approach using the Interquartile Range (IQR) to enhance the performance of the Support Vector Machine (SVM) algorithm in liver disease classification. A publicly available liver dataset consisting of 1,700 patient records with various clinical attributes was used, and the IQR method was applied to detect and remove extreme values before model training. The SVM model employed the Radial Basis Function (RBF) kernel to capture nonlinear relationships in the data. The classifier was evaluated under two conditions: without and with IQR-based outlier removal. Performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC were used to assess the model. The experimental results showed that the IQR-based preprocessing improved model performance, with the accuracy increasing from 84.41% to 84.74% and the ROC-AUC score rising from 92.08% to 93.28%. Notably, the recall for the negative class improved from 84.31% to 89.76%, indicating enhanced detection of healthy patients. These findings demonstrate that outlier handling using IQR can contribute to more stable and accurate classification outcomes, especially for models that are sensitive to data irregularities such as SVM.
Co-Authors Alexander, Allan Desi Azhari Azhari Daniarti, Yeni Erni Widarti Fryda Fatmayati Handayani, Nurdiana Heriyani, Nofitri Lasiyono, M Munawir Moh. Erkamim Mulyadi Mulyadi Nurhayati Nurhayati Rohkman, Nur Shalahudin, Mohammad Imam Tonggiroh, Mursalim