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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ILKOM Jurnal Ilmiah Journal of System and Computer Engineering Sentra Dedikasi: Jurnal Pengabdian Masyarakat
Wungo, Supriyadi La
STMIK Kreatindo Manokwari, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Public Health Social Sciences

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Performance Exploration of Tree-Based Ensemble Classifiers for Liver Cirrhosis: Integrating Boosting, Bagging, and RUS Techniques Aziz, Firman; Jeffry, Jeffry; Wungo, Supriyadi La; Rijal, Muhammad; Usman, Syahrul
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2031

Abstract

Liver cirrhosis, as a significant chronic liver disease, exhibits a rising global prevalence, demanding more effective preventive approaches. In an effort to enhance early detection and patient management, this research proposes the development of a liver cirrhosis risk prediction model using machine learning technology, specifically comparing the performance of three ensemble tree models: Ensemble Boosted Tree, Ensemble Bagged Tree, and Ensemble RUSBoosted Tree. Utilizing clinical and laboratory data from adults with a history or risk of cirrhosis, the study reveals that Ensemble Bagged Tree achieved the highest accuracy at 71%, followed by Ensemble Boosted Tree (67.2%) and Ensemble RUSBoosted Tree (66%). Analysis of clinical and laboratory variables provides further insights into the most significant contributors to risk prediction. The findings lay the groundwork for the advancement of a more sophisticated liver cirrhosis risk prediction tool, supporting a vision of more personalized and effective preventive strategies in liver disease management
Decision Support System for Selecting Used Cars Using the Analytical Hierarchy Process (AHP) Method Based on a Website at CV Auto Mobil Manokwari Marhaba, Melvi; Mardewi, Mardewi; Sangka, Yuliana; Hasbi, Hasbi; Wungo, Supriyadi La
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2106

Abstract

Buying a used car is often considered by the public as an alternative because it is more affordable than a new one. However, the process of choosing a used car is not easy because there are various factors that must be considered, such as engine condition, completeness of documents, physical condition, price, engine capacity, and year of manufacture. At CV Auto Mobil Manokwari, prospective buyers often have difficulty determining the choice of a used car that best suits their needs and budget. This research aims to design a website-based decision support system using the Analytical Hierarchy Process (AHP) method to assist buyers in choosing used cars objectively and systematically. The AHP method is used to compare each criterion in pairs and determine the priority weight of each criterion. The system was developed using the PHP programming language and MySQL database with a waterfall approach. With this system, the process of selecting used cars becomes more directed, accurate, and efficient, as well as helping users make decisions practically and quickly, and objectively.
Classification of Multiclass Ensemble SVM for Human Activities based on Sensor Accelerometer and Gyroscope Wungo, Supriyadi La; Mardewi, Mardewi; Aziz, Firman; Ishak, Pertiwi; SHILI, Hechmi
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1270.107-117

Abstract

Human Activity Recognition is technology introduced to recognize human activities. Several technologies that have been applied are Accelerometer sensors, Gyroscope sensors, Cameras, and GPS. The selection of the Support Vector Machine algorithm is due to its capabilities to minimize errors in training data sets and the Curse of dimensionality which can estimate parameters as well as its ability to find the best hyperplane that separates two classes. The SVM algorithm was originally developed for the classification of two classes. Problem raised if there are more than two classes. In addition, the performance will not optimal for the large-scale data. Therefore, modification the current design is needed. An ensemble technique can be used to combine the Support Vector Machine algorithm with the bagging algorithm. This study proposes the application of an ensemble SVM algorithm to classify human activities based on accelerometers and gyroscope sensors on smartphones.  The total data is 13725 records with 4575 representatives of each class. From the results of the overall data partition carried out in the calcification process using the ensemble SVM algorithm, the best performance was generated when comparing datasets with 80% training data and 20% test data from a total of 13725 records because it succeeded in increasing accuracy, precision, and sensitivity.
Co-Authors Abasa, Sustrin Adriana, Andi Nur Ilmi Firman Aziz Hasbi Ishak, Pertiwi Ispanti Zaman Jeffry Jeffry L.E.P, Benny Mardewi, Mardewi Marhaba, Melvi Nasruddin Nasruddin Nipsa Asdiana Supak Rijal, Muhammad Rosdiana Rosdiana Shili, Hechmi Sukarno Abby Syahrul Usman Syam, Rahmat Fuady Wibowo. K, Trisno Yadda Undeng Yeti Kule Yuliana Sangka