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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
M. Syukri Mustafa
Universitas AMIKOM Yogyakarta
Computer Science & IT

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Sistem Pendukung Keputusan Kelayakan Mendirikan Bangunan Menggunakan MOORA Rafli Junaidi Kasim; Samsul Bahri; Syukirman Amir; Rudi Prietno; Rahim Jamal; Andi Sunyoto; M. Syukri Mustafa
Prosiding SISFOTEK Vol 5 No 1 (2021): SISFOTEK V 2021
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The selection of the feasibility of building a building permit is one of the tasks of the Ternate City Investment and One Stop Integrated Service. In building feasibility selection is still done manually by going down a location survey assessing the criteria needed one by one for each proposal without a method that can provide an assessment priority with various criteria including type of building, foundation, building level, building area and walls, there are many criteria for proposals that are submitted to DPMPTSP. Decision Support System (SPK) is needed to facilitate the Investment and One Stop Integrated Service. The system that was built was web-based, in this research it was carried out through literature reviews and direct interviews with DMPTSP employees. The model used is the method of Multy Objective Optimization On The Basis Of Ratio Analysis. The results of the Multy Objective Optimization Method On The Basis Of Ratio Analysis are considered to have a good level of selectivity in determining an alternative, easy to understand and flexible in separating objects to the evaluation process of the decision weight criteria.
Kombinasi Algoritma Sampling dengan Algoritma Klasifikasi untuk Meningkatkan Performa Klasifikasi Dataset Imbalance Gagah Gumelar; Norlaila2; Quratul Ain; Riza Marsuciati; Silvi Agustanti Bambang; Andi Sunyoto; M. Syukri Mustafa
Prosiding SISFOTEK Vol 5 No 1 (2021): SISFOTEK V 2021
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (368.284 KB)

Abstract

A class to be imbalanced when there is a class that has more data than other classes. A comparison between minority classes and the majority class is called Imbalance Ratio (IR). The greater the difference between the minority class and the majority class the value of the Imbalance Ratio (IR) is getting larger. Dataset imbalance in data mining is a serious problem. The application of the classification algorithm regardless of class balance resulted in a good prediction for the majority class and a neglected minority class. Therefore, in this research, the SMOTE algorithm was applied to balance the dataset. The study used 4 datasets with different Imbalance Ratio and used classification algorithms, C45, Naïve Bayes, K-NN, and SVM. Then compared before and after using SMOTE. The research results that have been done accuracy value and value G-mean Naïve Bayes algorithm is consistent with its performance at each level of imbalance ratio, before the implementation has no good performance, whereas after the implemented SMOTE algorithm Naïve Bayes has a consistent increase in accuracy. So it can be concluded that the combination SMOTE + Naïve Bayes most effectively used in the imbalance dataset with different levels in the scheme of 10 fold cross validation and 80% data testing tested as much as 50 times.
Co-Authors Andi Sunyoto Gagah Gumelar Norlaila2 Quratul Ain Rafli Junaidi Kasim Rahim Jamal Riza Marsuciati Rudi Prietno Samsul Bahri Silvi Agustanti Bambang Syukirman Amir