Garuda - Garba Rujukan Digital

Penentuan Jurusan Siswa Sekolah Menengah Atas menggunakan Metode Fuzzy Tsukamoto Hikmah Quddustiani; Ummi Athiyah; Made Riza Kartika; Rayhan Hidayat; Luthfi Rakan Nabila
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 1 No 2 (2021): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

According to the Regulation of the Minister of National Education (Permendiknas) Number 22 of 2006, the determination of majors is carried out at the end of the second semester of class X and the implementation of Teaching and Learning Activities (KBM) according to the program majors starts in the first semester of class XI. In fact, there are still many high school students who are confused about choosing a major in a tertiary institution and this phenomenon can be seen in high school students. There are several majors in high school programs such as Natural Sciences (IPA), Social Sciences (IPS), and Language. This research is expected to be able to determine the majors according to the abilities of the new students for the process of selecting majors and provide recommendations that help students in determining the majors in SMA using the Fuzzy Tsukamoto method. The Tsukamoto method is an extension of monotonous reasoning. In the Tsukamoto method, each consequence of the IF-THEN rules must be represented by a fuzzy set with monotonous membership functions assisted by system design using the programming language PHP, HTML, Javascript, and MySQL database, resulting in a decision making system using the fuzzy method. tsukamoto which is a useful website for determining student majors so that it can lighten the work of the school in order to help speed up and make it easier for schools to make decisions in choosing majors.

Handling Imbalance Data using Hybrid Sampling SMOTE-ENN in Lung Cancer Classification Muhammad Abdul Latief; Luthfi Rakan Nabila; Wildan Miftakhurrahman; Saihun Ma'rufatullah; Henri Tantyoko
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 3 No 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3758

The classification problem is one instance of a problem that is typically handled or resolved using machine learning. When there is an imbalance in the classes within the data, machine learning models have a tendency to overclassify a greater number of classes. The model will have low accuracy in a few classes and high accuracy in many classes as a result of the issue. The majority of the data has the same number of classes, but if the difference is too great, it will differ. The issue of data imbalance is also evident in the data on lung cancer, where there are 283 positive classes and negative classes 38. Therefore, this research aims to use a hybrid sampling technique, combining Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) and Random Forest, to balance the data of lung cancer patients who experience class imbalance. This research method involves the SMOTE-ENN preprocessing method to balance the data and the Random Forest method is used as a classification method to predict lung cancer by dividing training data and testing 10-fold cross validation. The results of this study show that using SMOTE-ENN with Random Forest has the best performance compared to SMOTE and without oversampling on all metrics used. The conclusion is using the SMOTE-ENN hybrid sampling technique with the Random Forest model significantly improves the model's ability to identify and classify data.

Klasifikasi Produktivitas Pekerja Garmen Menggunakan Algoritma Random Forest: Classification of Garment Worker Productivity Using Random Forest Algorithm Luthfi Rakan Nabila; Fiqki Haidar Amrulloh; Ghilman Farhani Putra Aji; Rio Ghaniy Septiansyah; Vincentius Sagi Alban Anindyajati; Henri Tantyoko
Buffer Informatika Vol. 10 No. 1 (2024): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Penelitian ini membahas mengenai penerapan algoritma machine learning dalam melakukan klasifikasi produktivitas pekerja garmen. Penelitian ini menggunakan dataset produktivitas garmen yang didapatkan dari situs UC Irvine Machine Learning Repository dengan rentang waktu dari tanggal 1 januari 2015 sampai 11 maret 2015 dengan total data sebanyak 1197 baris. Algoritma yang diterapkan pada penelitian ini adalah random forest dengan hyperparameter tuning untuk melakukan klasifikasi produktivitas pekerja garmen. Metodologi penelitian ini melibatkan pengolahan data seperti pemilihan fitur yang relevan, transformasi data, dan normalisasi guna mendapatkan hasil evaluasi terbaik. pada penelitian ini juga dilakukan percobaan dengan decision tree dan algoritma svm sebagai pembandingnya. Algoritma random forest mengungguli algoritma lain dengan akurasi sebesar 94.36% di mana akurasi tersebut sudah cukup bagus dalam untuk mengklasifikasi produktivitas

Handling Imbalance Data using Hybrid Sampling SMOTE-ENN in Lung Cancer Classification Muhammad Abdul Latief; Luthfi Rakan Nabila; Wildan Miftakhurrahman; Saihun Ma'rufatullah; Henri Tantyoko
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3758

The classification problem is one instance of a problem that is typically handled or resolved using machine learning. When there is an imbalance in the classes within the data, machine learning models have a tendency to overclassify a greater number of classes. The model will have low accuracy in a few classes and high accuracy in many classes as a result of the issue. The majority of the data has the same number of classes, but if the difference is too great, it will differ. The issue of data imbalance is also evident in the data on lung cancer, where there are 283 positive classes and negative classes 38. Therefore, this research aims to use a hybrid sampling technique, combining Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) and Random Forest, to balance the data of lung cancer patients who experience class imbalance. This research method involves the SMOTE-ENN preprocessing method to balance the data and the Random Forest method is used as a classification method to predict lung cancer by dividing training data and testing 10-fold cross validation. The results of this study show that using SMOTE-ENN with Random Forest has the best performance compared to SMOTE and without oversampling on all metrics used. The conclusion is using the SMOTE-ENN hybrid sampling technique with the Random Forest model significantly improves the model's ability to identify and classify data.

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