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All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) AJAD : Jurnal Pengabdian kepada Masyarakat Mudabbir: Journal Research and Education Studies Ceddi Journal of Information System and Technology (JST) Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Asnimar
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences Other

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Journal : Ceddi Journal of Information System and Technology (JST)

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Decision Support System for Selecting the Best Bus Destination for Toraja using the Weighted Product Method Erwin Gatot Amiruddin; Kamaruddin; Asnimar; Lusi Dwi Putri; Hiknatius Rianto Madao
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 1 (2023): April
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i1.34

Abstract

This research aims to develop a Decision Support System (DSS) that can assist in selecting the best bus for tourism purposes in Toraja. The objective of this research is to facilitate the decision-making process for prospective tourists who want to use buses as their means of transportation during their visit to Toraja. The method employed in this research is the Weighted Product (WP) method. This method was chosen due to its ability to handle multiple criteria, enabling selection based on various relevant factors such as price, service quality, bus capacity, operational schedule, and level of comfort. Furthermore, the collected data will be input into the developed DSS using the WP method. The DSS will calculate relative scores for each bus based on the predetermined criteria weights. The results of this research are expected to provide objective and accurate recommendations for selecting the best bus. These recommendations can serve as a guide for prospective tourists in choosing a bus that suits their needs and preferences. Additionally, this research can also provide valuable insights for bus operators in improving service quality and customer satisfaction.
Application of the Adaptive Boosting Method to Increase the Accuracy of Classification of Type Two Diabetes Mellitus Patients Using the Decision Tree Algorithm Hao Chieh Chiua; Robbi Rahim; Mahmud Mustapa; Kamaruddin; Akbar Hendra; Asnimar; Abigail, Omita
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.47

Abstract

One of the data mining processes that is often used in machine learning is the data classification process. A decision tree is a classification algorithm that has the advantage of being easy to visualize because of its simple structure. However, the decision tree algorithm is quite susceptible to incorrect classification calculations due to the presence of noise in the data or imbalance in the data, which can reduce the overall level of accuracy. Therefore, the decision tree algorithm should be combined with other methods that can increase the accuracy of classification performance. Machine Learning is used through an artificial intelligence approach to solve problems or carry out optimization. Adaptive Boosting is used to optimize classification calculations. This study aims to examine the performance of Adaptive Boosting in the process of classifying second-degree diabetes mellitus patients using the Decision Tree algorithm. Diabetes mellitus is known as a chronic condition of the human body, the cause of which is an increase in the body's blood sugar levels because the body is unable to produce insulin or is unable to utilize insulin effectively, which is usually referred to as hyperglycemia.. By using a 60:40 data split, the Decision Tree algorithm produces an accuracy value of 95.71%, while the Adaptive Boosting-based Decision Tree results reach a value of 98.99%.
Co-Authors Abigail, Omita Akbar Hendra Akbar Iskandar Akbar Iskandar Andani Achmad Danil Aulia Rahman Erwin Gatot Amiruddin First Wanita Hao Chieh Chiua Harman Akbar Tullah Hasan, Hastrid Hidayat, Febri Hiknatius Rianto Madao Kamaruddin Lusi Dwi Putri Mahmud Mustapa Makorohim, M Fransazeli Mansyur Muh Akbar Nasir, Khaedir Rahman Oluwaseun Tobechukwu Pamula Trisna Suri Pato, Markani Rahman Juni Artha Robbi Rahim Rohayati Arifin, Rohayati Sonni, Alem Febri Wiky Ilham Saputra Wisda Yosi Arter Yuyun