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All Journal Indonesian Journal of Mathematics and Natural Sciences Journal of Engineering and Technological Sciences Scientific Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) SANITAS: Jurnal Teknologi dan Seni Kesehatan Journal of Soft Computing Exploration INTERNATIONAL JOURNAL OF CULTURAL AND SOCIAL SCIENCE Journal of Advances in Information Systems and Technology
Hakim, M. Faris Al
Department Of Islamic Economic And Business, State Islamic University Of North Sumatera
Humanities Computer Science & IT Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences

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Journal : Journal of Soft Computing Exploration

 1 
Room occupancy classification using multilayer perceptron Wijaya, Dandi Indra; Aulia, Muhammad Kahfi; Jumanto, Jumanto; Hakim, M. Faris Al
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.53

Abstract

A room that should be comfortable for humans can create a sense of absence and appear diseases and other health problems. These rooms can be from boarding rooms, hotels, office rooms, even hospital rooms. Room occupancy prediction is expected to help humans in choosing the right room. Occupancy prediction has been evaluted with various statistical classification models such as Linier Discriminat Analysis LDA, Classification And Regresion Trees (CART), and Random Forest (RF). This study proposed learning approach to classification of room occupancy with multi layer perceptron (MLP). The result shows that a proper MLP tuning paramaters was able estimate the occupancy with 88.2% of accuracy
Rainfall prediction in Blora regency using mamdani's fuzzy inference system Damayanti, Dela Rista; Wicaksono, Suntoro; Hakim, M. Faris Al; Jumanto, Jumanto; Subhan, Subhan; Ifriza, Yahya Nur
Journal of Soft Computing Exploration Vol. 3 No. 1 (2022): March 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i1.69

Abstract

In the case study of weather prediction, there are several tests that have been carried out by several figures using the fuzzy method, such as the Tsukamoto fuzzy, Adaptive Neuro Fuzzy Inference System (ANFIS), Time Series, and Sugeno. And each method has its own advantages and disadvantages. For example, the Tsukamoto fuzzy has a weakness, this method does not follow the rules strictly, the composition of the rules where the output is always crisp even though the input is fuzzy, ANFIS has the disadvantage of requiring a large amount of data. which is used as a reference for calculating data patterns and the number of intervals when calculating data patterns and Sugeno has the disadvantage of having less stable accuracy results even though some tests have been able to get fairly accurate results. In research on the implementation of the Mamdani fuzzy inference system method using the climatological dataset of Blora Regency to predict rainfall, it can be concluded as follows: (1) The fuzzy logic of the Mamdani method can be used to predict the level of rainfall in the city of Blora by taking into account the factors that affect the weather, including temperature, wind speed, humidity, duration of irradiation and rainfall. (2) Fuzzy logic for prediction with uncertain input values is able to produce crisp output because fuzzy logic has tolerance for inaccurate data. (3) The results of the accuracy of calculations using the Mamdani fuzzy inference system method to predict rainfall in Blora Regency are 66%.
Optimization of breast cancer classification using feature selection on neural network Jumanto, Jumanto; Mardiansyah, M Fadil; Pratama, Rizka Nur; Hakim, M. Faris Al; Rawat, Bibek
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.78

Abstract

Cancer is currently one of the leading causes of death worldwide. One of the most common cancers, especially among women, is breast cancer. There is a major problem for cancer experts in accurately predicting the survival of cancer patients. The presence of machine learning to further study it has attracted a lot of attention in the hope of obtaining accurate results, but its modeling methods and predictive performance remain controversial. Some Methods of machine learning that are widely used to overcome this case of breast cancer prediction are Backpropagation. Backpropagation has an advantage over other Neural Networks, namely Backpropagation using supervised training. The weakness of Backpropagation is that it handles classification with high-dimensional datasets so that the accuracy is low. This study aims to build a classification system for detecting breasts using the Backpropagation method, by adding a method of forward selection for feature selection from the many features that exist in the breast cancer dataset, because not all features can be used in the classification process. The results of combining the Backpropagation method and the method of forward selection can increase the detection accuracy of breast cancer patients by 98.3%.
Co-Authors Adel S.M.A Alsaeed Ahniar, Nur Hasanah Annisa M Nasution Atika Hendryani Aulia, Muhammad Kahfi Budi Prasetiyo, Budi Damayanti, Dela Rista Faieza Abdul Aziz Gita Rindang Lestari Ilham, Ahmad Bagas Aditya Irwansyah P Marpaung Jumanto Jumanto, Jumanto Listiaji, Prasetyo Mardiansyah, M Fadil Mohd Khairol Anuar Mohd Ariffin Nurhotma Tambak Pradana, Fadli Dony Pratama, Rizka Nur Rawat, Bibek Reynaldi, Reynaldi Rinda Nur Hidayati Riski Ihsan S.Pd. M Kes I Ketut Sudiana . Shamsuddin Sulaiman Subhan Subhan Subhan Subhan Syafrizal, Wahyu Utama, Reiki Aziz Yoga Wicaksono, Suntoro Widya Pangestika Wijaya, Dandi Indra Yahya Nur Ifriza