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All Journal International Journal of Advances in Intelligent Informatics Jurnal Ilmiah FIFO JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Bulletin of Informatics and Data Science
Soares, Teotino Gomes
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing 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.
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.
Decision Support System for Selection of Internet Services Providers using the ROC and WASPAS Approach Soares, Teotino Gomes; Sinlae, Alfry Aristo Jansen; Herdiansah, Arief; Arisantoso, Arisantoso
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4892

Abstract

Along with the growth of the internet service provider industry, selecting an Internet Service Provider (ISP) has become an important decision to ensure optimal internet access. However, with so many ISP options available, consumers often face difficulties in choosing the service that best suits their needs. The aim of this research is to produce a decision support system that can help users choose the ISP that best suits their needs and preferences using the ROC (Rank Order Centroid) approach as a weighting technique and the WASPAS (Weighted Aggregated Sum Product Assessment) approach to determine the best alternative. The ROC approach is used to obtain criteria weights based on the ranking order of the importance of the criteria. On the other hand, the WASPAS method is used to determine the best alternative through weighted addition and multiplication, producing a final value that reflects the extent to which each alternative meets the specified criteria. The outcomes of the case study reveal a ranking of alternatives from highest to lowest scores, as follows: First Media (A2) achieving 0.8629, Indihome (A3) at 0.8416, MyRepublic (A5) with 0.7954, Biznet (A1) scoring 0.7844, and Oxygen (A4) at 0.7469. The usability testing yields an average score of 89%, suggesting that the system is apt for utilization, as it aligns with the functionalities users are seeking.
MULTI-CRITERIA DECISION ANALYSIS USING COMPLEX PROPORTIONAL ASSESSMENTS AND RANK ORDER CENTROID METHODS IN THE SELECTION SYSTEM FOR TUTORING INSTITUTIONS Fatmayati, Fryda; Nuraini, Rini; Nugraheni, Murien; Soares, Teotino Gomes
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.1340

Abstract

Tutoring can help increase students' self-confidence, reduce anxiety about tests or assignments, and overcome learning barriers. The large number of tutoring institutions that offer various programs makes parents or students have to be observant when choosing them. To choose a tutoring institution, parents or students must know all the profiles and programs of the institution to be selected. This creates a difficult and long time to come up with a choice. The purpose of this study is to use the Multi Criteria Decision Analysis (MCDA) approach through the Complex Proportional Assessment (COPRAS) method and the Rank Order Centroid (ROC) method to create a Decision Support System (DSS) that will make it easier to choose a tutoring institution. The ROC approach serves to determine the weight based on the order of importance of the criteria. The COPRAS method is used because this method takes utility into account by assessing the usefulness of each alternative. This research produced a web-based tutoring institution selection DSS that can provide alternative recommendations based on criteria determined by decision-makers. The results of system calculations and manual calculations do not show a different value, which shows that the system produces a valid COPRAS approach value. Based on the results of usability testing, the built DSS scored 89.17%; in other words, the system is feasible to use.
Co-Authors Alexander, Allan D Alfry Aristo Jansen Sinlae Arisantoso Arisantoso Azhari Azhari Daniarti, Yeni Erni Widarti Fryda Fatmayati Handayani, Nurdiana Herdiansah, Arief Heriyani, Nofitri Lasiyono, M Munawir Moh. Erkamim Mulyadi Mulyadi Murien Nugraheni Nurhayati Nurhayati Rini Nuraini, Rini Rohkman, Nur Shalahudin, Mohammad Imam Tonggiroh, Mursalim