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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jiko (Jurnal Informatika dan komputer) eProceedings of Engineering Jurnal INFOTEL

Isman Kurniawan

Universitas Telkom

Author-ID : 2489758

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

Published : 22 Documents Claim Missing Document

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QSAR Study for Prediction of HIV-1 Protease Inhibitor Using the Gravitational Search Algorithm–Neural Network (GSA-NN) Methods Isman Kurniawan; Reina Wardhani; Maya Rosalinda; Nurul Ikhsan
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 2 (2021): Vol. 12, No. 02 August 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i02.p01

Human immunodeficiency virus (HIV) is a virus that infects an immune cell and makes the patient more susceptible to infections and other diseases. HIV is also a factor that leads to acquired immune deficiency syndrome (AIDS) disease. The active target that is usually used in the treatment of HIV is HIV-1 protease. Combining HIV-1 protease inhibitors and reverse-transcriptase inhibitors in highly active antiretroviral therapy (HAART) is typically used to treat this virus. However, this treatment can only reduce the viral load, restore some parts of the immune system, and failed to overcome the drug resistance. This study aimed to build a QSAR model for predicting HIV-1 protease inhibitor activity using the gravitational search algorithm-neural network (GSA-NN) method. The GSA method is used to select molecular descriptors, while NN was used to develop the prediction model. The improvement of model performance was found after performing the hyperparameter tuning procedure. The validation results show that model 3, containing seven descriptors, shows the best performance indicated by the coefficient of determination (r2) and cross-validation coefficient of determination (Q2) values. We found that the value of r2 for train and test data are 0.84 and 0.82, respectively, and the value of Q2 is 0.81.

Implementasi Newton Raphson Termodifikasi pada Prediksi Distribusi Tekanan Pipa Transmisi Gas Alam Annisa Aditsania; Isman Kurniawan
Indonesia Journal on Computing (Indo-JC) Vol. 1 No. 2 (2016): September, 2016
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2016.1.2.53

Prediksi profil distribusi tekanan disepanjang jaringan pipa transmisi merupakan salah satu prosedur penting untuk mengevaluasi performa desain jaringan pipa. Pada penelitian ini, distribusi tekanan untuk setiap segmen pipa dimodelkan menggunakan korelasi Panhandle A sebagai fungsi dari properti fluida, properti segmen pipa dan properti lingkungan jaringan pipa. Korelasi Panhandle A secara matematis dapat dipandang sebagai persamaan non-linear. Pada penelitian-penelitian terdahulu, metode Newton Raphson dipilih sebagai metode untuk mendapatkan solusi numerik, karena orde konvergensi tinggi. Sebagai upaya untuk mengoptimalkan waktu komputasi dari perhitungan distribusi jaringan, pada penelitian kali ini, metode Newton Raphson termodifikasi dipilih sebagai metode pencarian solusi numerik. Hasil simulasi menunjukan bahwa profile distribusi tekanan menggunakan metode newton Raphson termodifikasi akurat dengan error relative maksimum 0.28% untuk batas toleransi error bila dibandingkan dengan profile distribusi tekanan data lapangan

Pemodelan Dan Simulasi Produksi Biogas Dari Substrat Glukosa Menggunakan Anaerobic Digestion Model No. 1 (ADM1) Isman Kurniawan; Annisa Aditsania
Indonesia Journal on Computing (Indo-JC) Vol. 1 No. 1 (2016): March, 2016
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2016.1.1.54

This research focus in modeling of biogas production using Anaerobic Digestion Model No. 1 (ADM1). Initial simulation was performed using recommended parameter and its result will be used to determine the accuracy. Simulation result shows similar trend compare to experimental data even it is less accurate. The accuracy of calculation is improved by optimize the simulation parameter. The number of parameter is reduced by calculate the sensivity indices of each parameter. Optimization process using genetic algorithm result new optimized parameter value. The value of mean average percentage error (MAPE) of simulation using standard parameter and optimized parameter are 22,54% and 0,08%, respectively. It shows that simulation using optimized parameter give better accuracy. Simulation results shows the glucose concentration decrease significantly in the beginning of process and methane concentration increase simultaneously. The final concentration of methan after 500 mgCOD/L of glucose decomposed is 354,79 mgCOD/L.

Pemodelan Produksi Biogas pada Reaktor Tipe Batch Menggunakan Metode Hamming Predictor-Corrector Ali Assegaf; Rian Febrian Umbara; Isman Kurniawan
Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 1 (2019): Maret, 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2019.4.1.138

Penelitian ini memiliki tujuan untuk membuat sebuah model prediksi hasil produksi biogas pada reaktor tipe batch. Simulasi pencernaan anaerobik akan glukosa sebagai substrat utama dengan konsentrasi awal 500 mgCOD/l, dan simulasi akan dilakukan selama 120 jam. Dalam penelitian ini juga bertujuan untuk mengetahui konsentrasi mikroorganisme yang terlibat dalam proses pencernaan anaerobik, serta akan dilakukan beberapa analisis seperti perbandingan metana yang dihasilkan pada simulasi dan eksperimen, pengaruh jumlah iterasi terhadap waktu yang dibutuhkan untuk melakukan running program, perbandingan jumlah glukosa dan mikroorganisme yang digunakan dalam simulasi terhadap jumlah metana yang akan dihasilkan. Untuk memprediksi jumlah produksi biogas, terdapat sebuah model yang umum digunakan yaitu Anaerobic Digestion Model No 1 (ADM1). ADM1 dikembangkan oleh Asosiasi Water International (IWA) pada tahun 2002. Agar mendapatkan model yang memiliki akurasi yang tinggi akan digunakan sebuah metode numerik yaitu Hamming Predictor-Corrector. Setelah simulasi pencernaan anaerobik dilakukan, metana yang dihasilkan sebesar 417,48 MgCOD/l. Lalu mikroorganisme glukosa mengalami pertumbuhan yang maksimum jika dibandingkan dengan mikroorganisme lain yaitu sebesar 77 MgCOD/l. Konsentrasi awal substrat glukosa dan konsentrasi mikroorganisme yang digunakan pada proses simulasi sangat berpengaruh terhadap jumlah metana yang dihasilkan. Namun untuk konsentrasi awal mikroba yang lebih dari 30 MgCOD/l, cenderung menghasilkan metana yang konstan.

Implementation Information Gain Feature Selection for Hoax News Detection on Twitter using Convolutional Neural Network (CNN) Husnul Khotimah Farid; Erwin Budi Setiawan; Isman Kurniawan
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 3 (2020): December, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.3.506

The development of information and communication technology is currently increased, especially related to social media. Nowadays, many people get information through social media, especially Twitter, because of its easy access and it doesn't cost much. However, it has a negative impact in the form of spreading fake news or hoaxes that are difficult to detect. In this research, the authors developed a hoax news detection model using the Convolutional Neural Network and the TF-IDF weighting method. Feature selection is performed using Information Gain with various features, such as unigram, bigram, trigram and a combination of the three. Testing is done with 3 scenarios, classification, classification by weighting, classification by weighting and feature selection. The parameter used in the information gain feature selection is the threshold 0.8. The results showed that the classification by weighting and feature selection produced the highest accuracy that is equal to 95.56% on the unigram + bigram features with a comparison of training data and test data 50:50.

Classification Model of Consumer Question about Motorbike Problems by Using Naïve Bayes and Support Vector Machine Ekky Wicaksana; Danang Triantoro Murdiansyah; Isman Kurniawan
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.561

The motorbike plays an important role in supporting daily activity. The motorbike is known as one of the transportation modes that is frequently used in Indonesia. The number of motorbikes used in Indonesia is continuously increasing time by time. Hence, the occurrence of motorbike problems can affect community activity and disturb the economic condition in society. Since the problem of the motorbike can occur at any time, a prevention action is required by providing an online consultation platform. However, a classification model is required to handle a wide range of questions about the motorbike problem. By classifying those questions into a specific class of problems, the solution can be delivered to the consumer faster. In this study, we developed prediction models to classify consumer questions. The data set was collected from consumer questions regarding motorbike problems that are commonly occurring. The model was developed using two machine learning algorithms, i.e., Naïve Bayes and Support Vector Machine (SVM). Text vectorization was performed by using the n-gram and term frequency-inverse document frequency (TF-IDF) method. The results show that the SVM model with the uni-trigram model performs better with the value of accuracy and F-measure, which are 0.910 and 0.910, respectively.

Sistem Deteksi Hoax pada Twitter dengan Metode Klasifikasi Feed-Forward dan Back-Propagation Neural Networks Crisanadenta Wintang Kencana; Erwin Budi Setiawan; Isman Kurniawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Social media is one of the ways to connect every individual in the world. It also used by irresponsible people to spread a hoax. Hoax is false news that is made as if it is true. It may cause anxiety and panic in society. It can affect the social and political conditions. This era, the most popular social media is Twitter. It is a place for sharing information and users around the world can share and receive news in short messages or called tweet. Hoax detection gained significant interest in the last decade. Existing hoax detection methods are based on either news-content or social-context using user-based features. In this study, we present a hoax detection based on FF & BP neural networks. In the developing of it, we used two vectorization methods, TF-IDF and Word2Vec. Our model is designed to automatically learn features for hoax news classification through several hidden layers built into the neural network. The neural network is actually using the ability of the human brain that is able to provide stimulation, process, and output. It works by the neuron to process every information that enters, then is processed through a network connection, and will continue learning to produce abilities to do classification. Our proposed model would be helpful to provide a better solution for hoax detection. Data collection obtained through crawling used Twitter API and retrieve data according to the keywords and hashtags. The neural networks highest accuracy obtained using TF-IDF by 78.76%. We also found that data quality affects the performance.

Implementation of Convolutional Neural Network and Multilayer Perceptron in Predicting Air Temperature in Padang Isman Kurniawan; Lusi Sofiana Silaban; Devi Munandar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 6 (2020): Desember 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Weather prediction is usually performed for a reference in planning future activity. The prediction is performed by considering several parameters, such as temperature, air pressure, humidity, wind, rainfall, and others. In this study, the temperature, as one of weather parameters, is predicted by using time series from January 2015 to December 2017. The data was obtained from Lembaga Ilmu Pengetahuan Indonesia (LIPI) weather measurement station in Muaro Anai, Padang. The predictions were carried out by using Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), and the hybrid of CNN-MLP methods. The parameters used in the CNN method, such as the number of filters and kernel size, and used in the MLP method, such as the number of hidden layers and number of neurons, were selected by performing the hyperparameter tuning procedure. After obtaining the best parameters for both methods, the performance of both methods was evaluated by calculating the value of Root Mean Square Error (RMSE) and R2. Based on the results, we found that the prediction by CNN is more accurate than other method. This is indicated by the highest value of R2 of the prediction obtained by CNN method.

Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data Diya Namira Purba; Fhira Nhita; Isman Kurniawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Schizophrenia is a chronic mental illness that leads the patient to hallucinations and delusions with a prevalence of 0.4% worldwide. The importance early detection of Schizophrenia is tracking the pre-syndrome of Schizophrenia during the active phase, and could reduce psychosis symptomatic. However, the method sometimes cannot detect the symptoms accurately. As an alternative, machine learning can be implemented on microarray data for early detection. This study aimed to implement three ensemble methods, i.e., Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to identify Schizophrenia. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that the model 6, which is developed by the XGBoost method, performs better than other models with the value of accuracy and F1-score are 0.87 and 0.87, respectively.

QSAR Study on Aromatic Disulfide Compounds as SARS-CoV Mpro Inhibitor Using Genetic Algorithm-Support Vector Machine Rizki Amanullah Hakim; Annisa Aditsania; Isman Kurniawan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 2, May 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i2.1428

COVID-19 is a type of pneumonia caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus causes severe acute respiratory syndrome and 2 million active cases of COVID-19 have been found worldwide. A new strain of the SARS-CoV-2 virus emerged that proved to be more virulent than its predecessor. Regarding the design of a new inhibitor for this strain, SARS-CoV Main Protease (Mpro) was used as the target inhibitor. In the in silico development, the Quantitative Structure-Activity Relationship (QSAR) method is commonly used to predict the biological activity of unknown compounds to improve the process of drug design of a disease, including COVID-19. In this study, we aim to develop a QSAR model to predict the activity of aromatic disulfide compounds as SARS-CoV Mpro inhibitors using Genetic Algorithm (GA) – Support Vector Machine (SVM). GA was used for feature selection, while SVM was used for model prediction. The used dataset is set of features of aromatic disulfide compounds, along with information on the toxicity activity. We found that the best SVM model was obtained through the implementation of the polynomial kernel with the value of R2train and R2test scores are 0.952 and 0.676, respectively.

Co-Authors Ali Assegaf Annisa Aditsania Annisa Aditsania Annisa Aditsania Ardhyka Dewantara Arionmaro Asi Simaremare Bambang Hadi Prakoso Crisanadenta Wintang Kencana Danang Triantoro Murdiansyah Deni Saepudin Devi Munandar Didit Adytia Diya Namira Purba Ekky Wicaksana Erwin Budi Setiawan Fadhli Rahman Fhira Nhita Furqon Hidayat Husnul Khotimah Farid Hutama, Nanda Yonda I Kadek Andrean Pramana Putra Pramana Ikhsan, Nurul Indra A Aditya Kemas Muslim Lhaksmana, Kemas Muslim Komang Triolascarya Lusi Sofiana Silaban Maya Rosalinda Mela Mai Anggraini Mohammad Adietya Perdana Nabilla Kamil Nanda Yonda Hutama Reina Wardhani Reza Fauzi Iskandar Reza Rendian Septiawan Rian Febrian Umbara Rizki Amanullah Hakim Saut Sihol Ritonga

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