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Klasifikasi Liver Cirrhosis Menggunakan Teknik Ensemble: Studi Perbandingan Model Boosted Tree, Bagged Tree, dan Rusboosted Tree Mardewi, Mardewi; Wungo, Supriyadi La
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1302

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.

ARIMA Method Implementation for Electricity Demand Forecasting with MAPE Evaluation Wungo, Supriyadi La; Aziz, Firman; Jeffry, Jeffry; Mardewi, Mardewi; Syam, Rahmat Fuady; Nasruddin, Nasruddin
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1666

Electricity demand forecasting is critical for efficient energy management and planning. This study focuses on the development and implementation of the Autoregressive Integrated Moving Average (ARIMA) method for forecasting electricity demand in South Sulawesi's power system. The evaluation of forecasting accuracy was conducted using the Mean Absolute Percentage Error (MAPE), which measures the percentage error between predicted and actual values. Two experiments were conducted with different ARIMA models: ARIMA(5,1,0) and ARIMA(2,0,1). Results showed that the ARIMA(5,1,0) model achieved a MAPE of 2.15%, while the ARIMA(2,0,1) model performed slightly better with a MAPE of 1.91%, indicating highly accurate predictions. The findings highlight the effectiveness of the ARIMA method in forecasting electricity demand, providing a reliable tool for energy providers to optimize resource allocation and enhance operational efficiency. Future research may explore integrating ARIMA with other advanced methods to further improve forecasting performance.

Workshop Pelatihan Tingkat Lanjut Microsoft Office 2019 L.E.P, Benny; Aziz, Firman; Adriana, Andi Nur Ilmi; Wungo, Supriyadi La; Abasa, Sustrin; Ishak, Pertiwi
SENTRA DEDIKASI: Jurnal Pengabdian Masyarakat Vol. 1 No. 2 (2023): Mei 2023
Publisher : Arlisaka Madani Group

Seiring pesatnya perkembangan tehnologi, khusunya pada perkembangan software. Salah satunya adalah Microsoft Office, beberapa bagian dari Microsoft Office adalah Microsoft Word, Power Point dan Excel yang digunakan untuk pengelolaan kata dan angka. Aplikasi tersebut bisa membatu dalam menyelesaikan permasalahan dalam pengolahan kata yaitu surat-surat serta angka untuk pembuatan tugas kuliah maupun tugas akhir. Dalam dunia kerja maupun dunia pendidikan, aplikasi tersebut mempunyai peranan yang sangat penting dalam mendukung penyelesaian pekerjaan dan pendidikan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberikan pengetahuan dan wawasan, serta keterampilan kepada mahasiswa mengenai penggunaan microsoft office tingkat lanjut seperti Microsoft Word, Power Point dan Excel untuk proses belajar ataupun penyusunan tugas akhir. Melalui pelatihan ini mahasiswa juga dapat meningkatkan kompetensi profesional terutama dalam pemanfaatan teknologi pada proses belajar dan tugas akhir. Sasaran utama pelatihan adalah mahasiswa prodi Komputer Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Pancasakti Makassar. Metode pelaksanaan kegiatan meliputi ceramah, diskusi, dan praktek. Materi pengabdian terdiri dari tiga materi besar yaitu penggunaan Microsoft Word, Power Point dan Excel, dan praktek penyusunan tugas akhir. Hasil dari kegiatan workshop ini meningkatkan pengetahuan dan keterampilan mahasiswa dalam penggunaan MS Office tingkat lanjut dalam proses belajar dan penyusunan tugas akhir. Kendala yang dialami peserta pengabdian ialah keterbatasan waktu mahasiswa untuk mengikuti pelatihan serta keterbatasan fasilitas.

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

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

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.

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