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All Journal Journal of Information Technology and Computer Engineering IJID (International Journal on Informatics for Development) Journal Sensi: Strategic of Education in Information System Edu Komputika Journal
Prasetyo, Septian Eko
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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A Machine Learning Framework for Improving Classification Performance on Credit Approval Prastyo, Pulung Hendro; Prasetyo, Septian Eko; Arti, Shindy
IJID (International Journal on Informatics for Development) Vol. 10 No. 1 (2021): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2021.2384

Abstract

Credit scoring is a model commonly used in the decision-making process to refuse or accept loan requests. The credit score model depends on the type of loan or credit and is complemented by various credit factors. At present, there is no accurate model for determining which creditors are eligible for loans. Therefore, an accurate and automatic model is needed to make it easier for banks to determine appropriate creditors. To address the problem, we propose a new approach using the combination of a machine learning algorithm (Naïve Bayes), Information Gain (IG), and discretization in classifying creditors. This research work employed an experimental method using the Weka application. Australian Credit Approval data was used as a dataset, which contains 690 instances of data. In this study, Information Gain is employed as a feature selection to select relevant features so that the Naïve Bayes algorithm can work optimally. The confusion matrix is used as an evaluator and 10-fold cross-validation as a validator. Based on experimental results, our proposed method could improve the classification performance, which reached the highest performance in average accuracy, precision, recall, and f-measure with the value of 86.29%, 86.33%, 86.29%, 86.30%, and 91.52%, respectively. Besides, the proposed method also obtains 91.52% of the ROC area. It indicates that our proposed method can be classified as an excellent classification.
A Cardiotocographic Classification using Feature Selection: A comparative Study Prasetyo, Septian Eko; Prastyo, Pulung Hendro; Arti, Shindy
JITCE (Journal of Information Technology and Computer Engineering) Vol. 5 No. 01 (2021)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.01.25-32.2021

Abstract

Cardiotocography is a series of inspections to determine the health of the fetus in pregnancy. The inspection process is carried out by recording the baby's heart rate information whether in a healthy condition or contrarily. In addition, uterine contractions are also used to determine the health condition of the fetus. Fetal health is classified into 3 conditions namely normal, suspect, and pathological. This paper was performed to compare a classification algorithm for diagnosing the result of the cardiotocographic inspection. An experimental scheme is performed using feature selection and not using it. CFS Subset Evaluation, Info Gain, and Chi-Square are used to select the best feature which correlated to each other. The data set was obtained from the UCI Machine Learning repository available freely. To find out the performance of the classification algorithm, this study uses an evaluation matrix of precision, Recall, F-Measure, MCC, ROC, PRC, and Accuracy. The results showed that all algorithms can provide fairly good classification. However, the combination of the Random Forest algorithm and the Info Gain Feature Selection gives the best results with an accuracy of 93.74%.
Benchmarking YOLOv3 and SSD: A Performance Comparison for Multi-Object Detection Prasetyo, Septian Eko; Atmaja, Chandra; Ardian, Muhammad; Ardhiansyah, Alfian; Sudarni, Ajeng Rahma; Khaira, Mulil
Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i2.28005

Abstract

Multiple object detection remains a significant challenge in the field of computer vision. One of the key factors affecting detection performance is the feature extraction process, especially when objects are relatively small or positioned closely together. This study aims to compare the effectiveness of two popular object detection models, YOLO (You Only Look Once) and Single Shot MultiBox Detector (SSD), in detecting multiple objects within images. These models were selected due to their reported high accuracy and real-time processing capabilities, outperforming traditional methods such as the Hough Transform, Deformable Part-based Models (DPM), and conventional CNN architectures. The models were evaluated using a subset of the PASCAL VOC dataset, which includes object categories such as aircraft, faces, cars, and others, with a total of 1,447 annotated images used in training and testing. The evaluation metric used was mean Average Precision (mAP) to assess detection accuracy. Experimental results indicate that YOLO achieves a mAP of 82.01%, while SSD achieves 70.47%. These findings demonstrate that YOLO provides better performance in detecting multiple objects under the same conditions. Overall, this study confirms the advantages of YOLO in scenarios requiring fast and accurate multi-object detection, highlighting its potential for deployment in real-time applications such as autonomous vehicles, surveillance systems, and robotics. The main contribution of this study lies in providing a comparative performance benchmark between YOLO and SSD on a standard multi-object dataset to guide practical model selection in real-time computer vision tasks.
Ontology Engineering for Modeling National Student Achievements in Higher Education Sudarni, Ajeng Rahma; Prasetyo, Septian Eko; Ardhiansyah, Alfian; Khaira, Mulil
Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i2.28254

Abstract

The need for structured and semantically rich data in higher education underscores the role of ontology-based knowledge modeling. This study develops an ontology to represent national-level student achievements, covering key aspects such as institution, achievement field, category, year, level, and student status. Using a formal ontology engineering approach, the ontology was developed in Protégé and encoded in OWL. Evaluation involved technical validation and reasoning tests including class subsumption, consistency checking, instance classification, and rule-based inference to assess logical soundness and semantic correctness. Description Logic (DL) queries were also executed based on competency questions to evaluate the ontology’s ability to support semantic querying. The results demonstrate that the ontology effectively supports knowledge inference and structured data retrieval, offering strong potential for integration within semantic web environments. This provides a foundation for data interoperability and knowledge sharing across educational systems at the national level. Future work includes expanding the ontology to incorporate dynamic achievement updates and linking with external educational data sources.
Decision Support System for Employee Bonus Recommendation Using Fuzzy Logic Ardhiansyah, Alfian; Sudarni, Ajeng Rahma; Khaira, Mulil; Prasetyo, Septian Eko
Journal Sensi: Strategic of Education in Information System Vol 11 No 2 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i2.4069

Abstract

A decision support system is indeed something that should be used to make it easier for organizations to determine a policy. With the existence of information technology, all data analysis and calculation is carried out automatically through computers. Similarly, in making recommendations to give bonuses to an employee in a company or institution. To speed up the decision-making process, a system is needed that can provide recommendations like calculations made by human intelligence. The system was developed using the fuzzy logic method that expresses classical logic into linguistic forms. The advantage offered by this logic is that it produces a more just and humane decision such as a decision that results from human feelings and thoughts. This system uses four variables used to determine the receipt of bonus wages, namely the age of the employee, the length of service, the amount of salary and productivity in one month. Each of these variables has a linguistic variable that is used to represent a certain state or condition that utilizes natural language. This research produces a system that can provide recommendations for organizations or companies to use in determining the receipt of bonus wages in accordance with the rules applied.
Co-Authors Ardhiansyah, Alfian Arti, Shindy Atmaja, Chandra Khaira, Mulil Muhammad Ardian, Muhammad Prastyo, Pulung Hendro Sudarni, Ajeng Rahma