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All Journal INFORMAL: Informatics Journal SPIRIT Abdiformatika: Jurnal Pengabdian Masyarakat Informatika
Muhamad Arief Hidayat
Universitas Jember
Computer Science & IT Electrical & Electronics Engineering

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KLASIFIKASI BERBASIS GRAVITASI DATA DAN PROBABILITAS POSTERIOR Muhamad Arief Hidayat; Arif Djunaidy
SPIRIT Vol 7, No 1 (2015): SPIRIT
Publisher : STMIK YADIKA BANGIL

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (755.016 KB) | DOI: 10.53567/spirit.v7i1.23

Abstract

The classification method based on data gravitation (DGC) is one of the new classification techniques that uses data  gravitation as the criteria of the classification. In the case of DGC, an object is classified on the basis of the class that creates the largest gravitation in that object. However, the DGC method may cause inaccurate result when the training data being used suffer from the class imbalanced problem. This may be caused by the existence of the training data containing a class having excessively big mass that will in turn tend to classify an uknown object as a member of that class due to the high degree of the data gravitation produced, and vice versa. In this research, a modification to the DGC method is performed by constructing a classificaion method that is based on both the data gravitation and posterior probability (DGCPP). In DGCPP, the mass concept defined in the DGC method as the prior probability is replaced by the posterior probability. By using this modification, data gravitation calculation process is expected to produce more accurate results in compared to those produced by the DGC method. In addtion, by improving the data gravitation calculation, it is expected that the DGCPP method willproduce more accurate classification results in compared to those produced by the DGC method for both normal dataset as well as dataset having class imbalanced problems. A thorough tests for evaluating the classification accuracy are performed using a ten-fold cross-validation method on several datasets containing both normal andimbalanced-class datasets. The results showed that DGCPP method produced positive average of accuracy differences in compared to those produced by the DGC method. For the tests using the entire normal datasets showed that the average of accuracy differences are statistically significant with a 95% confidence level. In addition, results of the tests using the four imbalanced-class datasets also showed that the average accuracy differences are statistically significant with a 95% confidence level. Finally, results of the tests for evaluating the computing times required by the classification program showed that the additional computing time needed by DGCPP method to perform the classification process is insignificant and less than the human response time, in compared to that needed by DGC method for running all datasets being used.  Keywords—data gravitation-based classification, class imbalanced problem,posterior probability 
Klasifikasi Resiko Kehamilan Menggunakan Ensemble Learning berbasis Classification Tree Muhamad Arief Hidayat
INFORMAL: Informatics Journal Vol 6 No 3 (2021): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v6i3.28396

Abstract

In health science there is a technique to determine the level of risk of pregnancy, namely the Poedji Rochyati score technique. In this evaluation technique, the level of pregnancy risk is calculated from the values ​​of 22 parameters obtained from pregnant women. Under certain conditions, some parameter values ​​are unknown. This causes the level of risk of pregnancy can not be calculated. For that we need a way to predict pregnancy risk status in cases of incomplete attribute values. There are several studies that try to overcome this problem. The research "classification of pregnancy risk using cost sensitive learning" [3] applies cost sensitive learning to the process of classifying the level of pregnancy risk. In this study, the best classification accuracy achieved was 73% and the best value was 77.9%. To increase the accuracy and recall of predicting pregnancy risk status, in this study several improvements were proposed. 1) Using ensemble learning based on classification tree 2) using the SVMattributeEvaluator evaluator to optimize the feature subset selection stage. In the trials conducted using the classification tree-based ensemble learning method and the SVMattributeEvaluator at the feature subset selection stage, the best value for accuracy was up to 76% and the best value for recall was up to 89.5%
Pelatihan Pemrograman Visual Kodular Bagi Siswa SMPS Mitra Patrang Jember Muhammad Ariful Furqon; Muhamad Arief Hidayat; Priza Pandunata; Mohammad Zarkasi; Yanuar Nurdiansyah; Katarina Leba
Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Vol. 4 No. 1 (2024): Mei 2024 - Abdiformatika: Jurnal Pengabdian Masyarakat Informatika
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/abdiformatika.v4i1.211

Abstract

Pelatihan pemrograman visual menggunakan platform Kodular di SMPS Mitra Patrang Jember bertujuan untuk meningkatkan pemahaman siswa terhadap konsep pemrograman komputer. Metode yang digunakan dalam kegiatan pengabdian ini mencakup: (1) spesifikasi tujuan dan identifikasi masalah; (2) desain pelatihan; (3) implementasi pelatihan; serta (4) evaluasi dan umpan balik. Desain pelatihan yang terstruktur melibatkan partisipasi siswa dalam serangkaian sesi yang mencakup konsep dasar pemrograman visual dan penggunaan platform Kodular. Hasil menunjukkan peningkatan signifikan dalam pemahaman siswa setelah pelatihan, dengan mayoritas menyatakan kepuasan dan minat yang tinggi. Pelatihan ini efektif dalam meningkatkan pemahaman pemrograman visual dan merangsang minat siswa dalam teknologi. Studi ini memberikan kontribusi penting dalam memperluas pemahaman tentang pendekatan pembelajaran inovatif dalam konteks pendidikan sekolah menengah.
Co-Authors Arif Djunaidy Furqon, Muhammad Ariful Katarina Leba Mohammad Zarkasi Pandunata, Priza Yanuar Nurdiansyah, Yanuar