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All Journal E-Jurnal Matematika The Journal of Experimental Life Sciences (JELS) Telematika : Jurnal Informatika dan Teknologi Informasi Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Natural B Jurnal Berkala Epidemiologi Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Prosiding Seminar Nasional Teknoka BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Irigasi Jurnal Sisfokom (Sistem Informasi dan Komputer) Indonesian Journal of Electrical Engineering and Computer Science COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Anam, Syaiful
Brawijaya University
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation

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Momentum Backpropagation Untuk Klasifikasi Fungsi Senyawa Aktif Berdasarkan Notasi SMILES (Simplified Molecular Input Line Entry System) Nyimas Ayu Widi Indriana; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Active compounds can be used to make certain drugs and very important in the medical sector. Classification of active compounds is the most important thing in making medicines. After classifying the active compound, it is continued with the process of making and testing drugs that require a variety of tools. The cost of making and testing these drugs requires a high cost and time. This is a major obstacle for medical experts to make certain medicines. By utilizing current technology, a system can be made to classification process of active compounds, so the performance of medical experts for making certain drugs can be faster. The classification process can be done by using a computer and utilizing the SMILES notation. SMILES notation allows a compound to be processed by a computer. The momentum Backpropagation method can be used to perform the classification process properly. Based on the program that has been made, there are 4 types of testing using 522 training data and 131 test data producing, the best accuracy of 70,99% with a learning rate of 0,00001, max epoch of 100, momentum of 0,25 and hidden layer neurons of 4.
Klasifikasi Fungsi Senyawa Aktif Data Berdasarkan Kode Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Modified K-Nearest Neighbor Yunita Dwi Alfiyanti; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Compounds are single chemical substances from two or more chemical elements that form bonds and can be described. The compound is divided into active compounds and inactive compounds. Active compounds are chemical compounds that have pharmacology or usability. Compounds have an arrangement that is difficult to process on a computer, for which code is created that is easy to process using a computer. The code is a SMILES (Simplified Molecular Input Line Entry System) which is a code of modern chemical bonds that will be converted into a line to facilitate the classification process in the system. The special character of SMILES is obtained by doing preprocessing with the results of 11 features consisting of B, Br, C, Cl, F, I, N, O, P, S and OH atoms. These features are then used for the classification process using the Modified K-Nearest Neighbor method, where this algorithm is the development of the KNN method which consists of two processing, training data validation and weighting. The classification of the function of active compounds aims to facilitate the grouping of active compounds based on their pharmacology through the help of information technology and computer science degeneration, which so far in the medical field requires a long time in its determination because it uses laboratory tests. Tests that have been conducted using 260 data are divided into 2 categories of classes, namely the Neural class and the Heart class which consists of 90% (234 data) training data and 10% (26 data) test data. The test gets results in the form of an accuracy value of 73% with a k value of 3, whereas in the k-fold cross validation test the value of accuracy is obtained an average of 62.69%.
Klasifikasi Senyawa Kimia dengan Notasi Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Extreme Learning Machine (ELM) Isti Marlisa Fitriani; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia has a huge natural's potential by the existence of various plants and animals discovery. This issue brings a good for Indonesian people through taking advantage of nature, especially in pharmacology. In pharmacology, active compounds can be used to prevent and cure disease. Therefore, a research is conducted in informatics's field by making an active compounds' classification system to determine its pharmacological benefits. SMILES is a chemical compound notation used in this research. SMILES's features which are used as many as 15, namely B, C, N, O, P, S, F, Cl, Br, I, OH, @, =, #, and (). ELM is an ANN method that can do a generalization better than conventional methods in a limited time. A number of hidden neurons test which were conducted using k-fold cross validation method in 2 classes produced the best accuracy, 85,03%, in Metabolism and Inflammation class scenario with a total of 5, 10, and 15 hidden neurons. A number of hidden neurons' test use k-fold cross validation method which were conducted in 3 classes produced the best accuracy, 55,06%, in Metabolism, Inflammation, and Cancer class scenario with a total of 300 hidden neurons. The best accuracy was obtained as many as 55,06% by testing 15 features with 300 hidden neurons, while in 11 features's test with 400 hidden neurons was found a number of 49,18% as the best accuracy.
Klasifikasi Fungsi Senyawa Aktif Berdasarkan Notasi Simplified Molecular Input Line Entry System (SMILES) Dengan Metode K-Means Naive Bayes (KMNB) Revi Anistia Masykuroh; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia is a tropical country that has the most biodiversity in the world. Almost all of the plants part like leaf, root, stem, fruit, flowers, seeds, and rhizome can be used for human health. In Indonesia the utilization of plants as medicine is so limited. Therefore, further research and continuous plant drugs or herbal remedies is really needed as well as the technologies are able to maximize the utilization. In 1980, David Weininger found a chemical notation for processing informations that related to a modern chemistry named Simplified Molecular Input Line System (SMILES) and that notation is specifically for computer used. On this research, K-Means Naive Bayes methods are used for the classification of the functions of the active compounds because this methods are able to grouping data according to their similarity and the classification process is much easier to understand. Based on the test results, the K-Means Naive Bayes are abled to give an accuracy system 85.45% with a 80% training data ratio and 20% testing data. The system also being tested using K-Fold Cross Validation with K-Fold as many as 10, the highest accuracy that can be given is 86.66% on 9th fold and the lowest is 70.37% on 1st fold. While the average of accuracy using the K-Fold Cross Validation is 82.6%.
Klasifikasi Fungsi Senyawa Aktif berdasarkan Data Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Support Vector Machine (SVM) Dwi Febry Indarwati; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Chemical compounds can be distinguished into active compounds or commonly called bioactive compounds and inactive compounds or commonly called passive compounds. At this time there are still many active compunds that the pharmacological role does not known yet, so the system being made for classify the functions of active compounds that expected to support chemists research in the laboratory. To simplify the process of making the system, the representation of molecular structure must be easily processed by a computer so that the SMILES notation will be used, the SMILES notation describes chemical formula in a row notation. This system is using the SVM (Support Vector Machine) method because the SVM method has high generalization capabilities without requiring additional datasets. In this research uses as many as 15 features and objects as many as 3 classes of active compound functions, including metabolism, infection, and anti-inflammation. The best test result is 83.33% when using the Gaussian kernel RBF, using a lambda value (λ) of 5, the complexity value is 0.1, the sigma value (σ) is 0.5, and with the number of iterations is 5.
Implementasi Metode Adaptive Moving Self- Organizing Maps Untuk Mengelompokkan Pelajar Berdasarkan Aktivitas Belajar Pada Media Pembelajaran Interaktif Onky Prasetyo; Ahmad Afif Supianto; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Artikel dipublikasikan di Jurnal Nasional Terakreditasi, JUITA: Jurnal Informatika
Klasifikasi Fungsi Senyawa Aktif berdasarkan Notasi Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Random Forest Faiz Anggiananta Winantoro; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 4 (2021): April 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A compound is a single substance composed of two or more elements that form chemical bonds. There are two types of compounds, namely active compounds and inactive compounds. Active compounds are compounds that have physiological effects on other organisms. In Indonesia, there are still many active compounds whose function is unknown. Therefore, a classification method is needed to help determine the function of the active compound. Classification is done with data written in SMILES notation. From the SMILES notation, features such as the number of atoms B, C, N, O, P, S, F, Cl, Br, I, OH, =, #, @, -, +, COC, C = C, are taken. O-], N +, C = O, and () go through the preprocessing process. Before being used for the classification process, all these features are divided by the length of the SMILES notation to get their value. This research was conducted to classify the function of active compounds by applying the Random Forest (RF) method with the SMILES data object with 4 classes of compound functions. RF was chosen because this method has almost no overfitting conditions, is able to handle data with many features, and this method is not affected by datasets that have missing values. The best accuracy resulted in testing with 4 class data is 69% and the best average in testing with the K-Fold Cross Validation method is 63%. Then, on the data with 3 classes of compound functions, the best accuracy is 76% and the best average in testing with the K-Fold Cross Validation method is 70%. Finally, testing data with 2 classes of compound functions produces the highest accuracy of 86% and the best average of 80%.
Counting Bacterial Colony and Reducing noise on Low-Quality Image Using Modified Perona-Malik Diffusion Filter with Sobel Mask Fractional Order Ibnu Mansyur Hamdani; Syaiful Anam; Nur Shofianah; Syamsumar Bustamin
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1661

Abstract

In the field of microbiology, the counting of bacterial colonies is fundamental and mandatory. This is done to estimate the number of bacterial cells in every 1 milliliter or gram of sample. The counting takes a long time and is tedious, so it requires an accurate and fast counting method. The image quality used is very low and contains noise. Therefore, a preprocessing method is needed to reduce the noise. The Perona-Malik filter method is known to be able to remove noise well. However, it is difficult to determine the appropriate gradient threshold parameter ( ) for each different image. To find the appropriate value of , the original Sobel Mask method and Sobel Mask Fractional-Order are used to estimate the value of . The experimental results show the results of noise reduction using PMD with a value of  from the original Sobel Mask and Sobel Mask Fractional-Order. The results of the accuracy of determining the value of k with the Sobel Mask Fractional-Order (α=1.0) show higher results based on the F-Measure values for samples 1, 2, and 3 respectively 97%, 98%, and 90%.
HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION Syaiful Anam; M. Rafael Andika Putra; Zuraidah Fitriah; Indah Yanti; Noor Hidayat; Dwi Mifta Mahanani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0797-0806

Abstract

The number of claims plays an important role the profit achievement of health insurance companies. Prediction of the number of claims could give the significant implications in the profit margins generated by the health insurance company. Therefore, the prediction of claim submission by insurance users in that year needs to be done by insurance companies. Machine learning methods promise the great solution for claim prediction of the health insurance users. There are several machine learning methods that can be used for claim prediction, such as the Naïve Bayes method, Decision Tree (DT), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The previous studies show that the SVM has some advantages over the other methods. However, the performance of the SVM is determined by some parameters. Parameter selection of SVM is normally done by trial and error so that the performance is less than optimal. Some optimization algorithms based heuristic optimization can be used to determine the best parameter values of SVM, for example Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). They are able to search the global optimum, easy to be implemented. The derivatives aren’t needed in its computation. Several researches show that PSO give the better solutions if it is compared with GA. All particles in the PSO are able to find the solution near global optimal. For these reasons, this article proposes the health claim insurance prediction using SVM with PSO. The experimental results show that the SVM with PSO gives the great performance in the health claim insurance prediction and it has been proven that the SVM with PSO give better performance than the SVM standard.
Particle Swarm Optimization – Extreme Learning Machine with Decreasing Inertia Weight for COVID-19 Prediction in Surabaya Mohamad Handri Tuloli; Syaiful Anam; Nur Shofianah
The Journal of Experimental Life Science Vol. 13 No. 3 (2023)
Publisher : Postgraduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jels.2023.013.03.01

Abstract

COVID-19 has spread all throughout the world, even to Indonesia. Surabaya becomes one of Indonesia's major cities where COVID-19 is fast spreading, culminating in a large number of positive cases and over 1000 deaths from the disease by November 2020. The number of positive COVID-19 cases predicted can be utilized to limit hospital facility availability and develop plans and policies for tackling the illness outbreak. One of the many prediction systems identified is the Extreme Learning Machine (ELM). ELM has a quick and precise training speed. However, the performance of ELM depends on the number of neurons. When the number of neurons is not precisely specified, prediction accuracy suffers. Particle Swarm Optimization (PSO) has the ability to optimize the number of node ELM neurons so the ELM can achieve better results. The number of neurons is determined using Particle Swarm Optimization (PSO) with decreased inertia weight. As a result, this research proposes predicting COVID-19 instances in Surabaya using a hybrid of PSO and ELM (PSO-ELM) with decreased inertia weight. The studies reveal that the offered techniques with different activation functions work comparably well in predicting COVID-19 instances in Surabaya. The best MAPE is achieved using the sigmoid activation function with the number of hidden layer nodes around . Keywords: Covid-19, Optimization, Prediction, PSO-ELM.
Co-Authors A.S. Adhiwibawa, Marcelinus Abdi Negara Guci Abu Bakar Sambah Agus Suryanto Agus Suryanto Agus Widodo Ahmad Afif Supianto Asyrofa Rahmi Candra Dewi Corina Karim, Ari Andari Dian Eka Ratnawati Dwi Agus Kuncoro Dwi Febry Indarwati Dwi Mifta Mahanani Faiz Anggiananta Winantoro Fery Widhiatmoko Fery Widhiatmoko Helen Yuliana Angmalisang Hidayat, Noor Hilman Ferdinandus Pardede Ibnu Mansyur Hamdani Indah Yanti Indah Yanti Isti Marlisa Fitriani Ivan C. Onggara Kestrilia Rega Prilianti Komang Agus Arta Wijaya M. Chandra Cahyo Utomo M. Rafael Andika Putra Marcelinus A.S. Adhiwibawa Mila Kurniawaty Mochamad Hakim Akbar Assidiq Maulana Mochamad Hakim Akbar Assidiq Maulana Mochammad Iskandar Ardiyansyah Rochman Mohamad Handri Tuloli Mohamad Handri Tuloli Noor Hidayat Nur Khilmiyatul Ilmiyah Nur Shofianah Nur Shofianah Nyimas Ayu Widi Indriana Onggara, Ivan C. Onky Prasetyo Onky Prasetyo R. Budiarianto Suryo Kusumo Raden Rizky Widdie Tigusti Raqqasyi Rahmatullah Musafir Revi Anistia Masykuroh Ririn Nirmalasari Sherly Witanto Sobri Abusini Suhhy Ramzini Suryanto, Agus Syamsumar Bustamin Tatas H.P. Brotosudarmo Trisilowati, Trisilowati Vicky Zilvan Wayan Firdaus Mahmudy Wuryansari Muharini K. Yunita Dwi Alfiyanti Zuraidah Fitriah ZURAIDAH FITRIAH