Article Per Year (5 Year)
p-Index From 2021 - 2026
4.269
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Dan Industri Pangan Jurnal Pustakawan Indonesia ComEngApp : Computer Engineering and Applications Journal Journal of Tropical Life Science : International Journal of Theoretical, Experimental, and Applied Life Sciences TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Ilmiah Kursor Biogenesis: Jurnal Ilmiah Biologi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Indonesian Journal of Biotechnology Seminar Nasional Informatika (SEMNASIF) Sosio Konsepsia Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Jamu Indonesia Journal of Electronics, Electromedical Engineering, and Medical Informatics VISI PUSTAKA: Buletin Jaringan Informasi Antar Perpustakaan JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Indonesian Journal of Electrical Engineering and Computer Science Nusantara Science and Technology Proceedings Bioinformatics and Biomedical Research Journal Jurnal Pustakawan Indonesia Jurnal Nasional Teknik Elektro dan Teknologi Informasi J-Icon : Jurnal Komputer dan Informatika Indonesian Journal of Jamu
Wisnu Ananta Kusuma
Department Of Computer Science, Faculty Of Mathematics And Natural Sciences, IPB University, Jl Meranti Kampus IPB Dramaga, Bogor 16680, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 84 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 84 Documents
Search

 4   5   6   7   8 
Accelerating Computation of DNA Multiple Sequence Alignment in Distributed Environment Ramdan Satra; Wisnu Ananta Kusuma; Heru Sukoco
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 12: December 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

Multiple sequence alignment (MSA) is a technique for finding similarity in many sequences. This technique is very important to support many Bioinformatics task such as identifying Single Nucleotide Polymorphism (SNP) and metagenome fragments binning. The simplest algorithm in MSA is Star Algorithm. The complexity of DNA multiple sequence alignment using dynamic programming technique is very high. This research aims to accelerate computation of Star Mutiple Sequence Alignment using Message Passing Interfaces (MPI). The performance of the proposed method was evaluated by calculating speedup. Experiment was conducted using 64 sequences of 800 bp Glycine-max-chromosome-9-BBI fragments yielded by randomly cut from reference sequence of Glycine-max-chromosome-9-BBI taken from NCBI (National Center for Biotechnology Information). The results showed that the proposed technique could obtain speedup three times using five computers when aligning 64 sequences of Glycine-max-chromosome-9-BBI fragments.  Moreover, the increasing of the number of computers would significantly increased speedup of the proposed. http://dx.doi.org/10.11591/telkomnika.v12i12.6572 
Prediksi interaksi protein-protein berbasis sekuens protein menggunakan fitur autocorrelation dan machine learning Syahid Abdullah; Wisnu Ananta Kusuma; Sony Hartono Wijaya
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.13984

Abstract

Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). The prediction models with the three autocorrelation methods can produce predictions with high average accuracy, which is 95.34% for Geary in KNN, 97.43% for Geary in RF, and 97.11% for Geary and Moran in SVM. In addition, the interacting protein pairs tend to have similar autocorrelation characteristics. Thus, the autocorrelation method can be used to predict PPI well.
Herbal Compound Screening with GPU Computation on ZINC Database through Similarity Comparison Approach Refianto Damai Darmawan; Wisnu Ananta Kusuma; Hendra Rahmawan
Khazanah Informatika Vol. 8 No. 2 October 2022
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v8i2.16349

Abstract

Covid-19 is a global pandemic that drives many researchers to strive to look for its solution, especially in the field of health, medicine, and total countermeasures. Early screening with in-silico processes is crucial to minimize the search space of the potential drugs to cure a disease. This research aims to find potential drugs of covid-19 disease in the ZINC database to be further investigated through the in-vitro method. About 997.402.117 chemical compounds are searched about their similarity to some of the confirmed drugs to combat coronavirus. Sequential computation would take months to accomplish this task. The general programming graphic processing unit approach is used to implement a similarity comparison algorithm in parallel, in order to speed up the process. The result of this study shows the parallel algorithm implementation can speed up the computation process up to 55 times faster, and also that some of the chemical compounds have high similarity scores and can be found in nature
Antioxidant Capacity, Phytochemical Profile, and Clustering of Pomegranate (Punica granatum L.) Peel Extracts Using Different Solvent Extraction Mohamad Rafi; Laela Wulansari; Dewi Anggraini Septaningsih; Tsania Firqin Purnomo; Reza Auliatifani; Khaydanur Khaydanur; Auliya Ilmiawati; Wina Yulianti; Nunuk Kurniati Nengsih; Irma Herawati Suparto; Wisnu Ananta Kusuma
Journal of Tropical Life Science Vol. 11 No. 3 (2021)
Publisher : Journal of Tropical Life Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/jtls.11.03.14

Abstract

Pomegranate has valuable nutritional content and contains various bioactive compounds, one found in the fruit's peel. The utilization of these bioactive compounds could be used as herbal medicines and supplements, such as antioxidants. This study aimed to determine the antioxidant capacity, phytochemical profile, and pomegranate peel extract grouping using different extracting solvents. The three extracting solvents used were water, 70% ethanol, and ethanol p.a. Antioxidant capacity of the three extracts was measured using the DPPH and CUPRAC methods. We also determined the total phenolic and flavonoid levels and the TLC fingerprint analysis and FTIR spectrum of the pomegranate peel extracts. The 70% ethanol extract owned the largest antioxidant capacity than the other two extracts with a value of 358.67 and 2981.59 µmol trolox/g dried sample using the DPPH and CUPRAC methods, respectively. The three pomegranate peel extracts' total phenolic and flavonoid levels ranged from 287.26–1068.81 mg GAE/g dried sample and 0.24-0.75 mg QE/g dried sample. TLC fingerprint analysis of pomegranate peel extract yielded 2, 6, and 6 bands for water extract, 70% ethanol, and p.a ethanol, respectively. The three extracts can be grouped based on FTIR spectrum data using principal component analysis using three principal components with a total variance of 93%. The results obtained show that using different extracting solvents provides different antioxidant capacities and phytochemical profiles.
Association of single nucleotide polymorphism and phenotype in type 2 of diabetes mellitus using Support Vector Regression and Genetic Algorithm Ratu Mutiara Siregar; Wisnu Ananta Kusuma; Annisa Annisa
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1283.194-202

Abstract

Precision Medicine is used to improve proper health care and patients' quality of life, one of which is diabetes. Diabetes Mellitus (DM) is a multifactorial and heterogeneous group of disorders characterized by deficiency or failure to maintain normal glucose homeostasis. About 90% of all DM patients are Type 2 Diabetes Mellitus (T2DM). Biological characteristics and genetic information of T2DM disease were obtained by looking for associations in Single Nucleotide Polymorphism (SNP) which allows for determining the relationship between phenotypic and genotypic information and identifying genes associated with T2DM disease. This research focuses on the Support Vector Regression method and Genetic Algorithm to obtain SNPs that have previously calculated the correlation value using Spearman's rank correlation. Then do association mapping on the SNP results from the SVR-GA selection and check pastasis interaction. The results produced 14 SNP importance. Evaluation of the model using the mean absolute error (MAE) obtained is 0.02807. If the value of MAE is close to zero, then a model can be accepted. The genes generated from the association can be used to assist other researchers in finding the right treatment for T2DM patients according to their genetic profile.
Identification of Java Tea Adulteration by Babadotan and Tekelan using Machine Learning Ary Prabowo; Wisnu Ananta Kusuma; Annisa; Mohamad Rafi
Jurnal Jamu Indonesia Vol. 7 No. 3 (2022): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jji.v7i3.273

Abstract

Java Tea (Orthosiphon aristatus) is a common herbal medicinal plant that functions as a health treatment and treats various diseases. The high demand for Java Tea causes high prices and a decrease in the amount of medicinal plant raw materials, causing various quality control problems such as the content of various bioactive components and adulteration from babadotan and tekelan. So far, the detection of adulteration has been carried out by various analyzes, including chemical analysis and statistical methods to process data. The data used is of high dimension with a very high-density level, thus causing difficulties in classification. The mixed data of Orthosiphon aristatus consists of 1201 features with a total sample of 216. This study uses a Random Forest (RF) method with a total of 100 trees, and the RF method is combined with the Recursive Feature Elimination (RFE) method. In the RF and RFE that can be produced, the optimum value for the number of features is 244. The experimental evaluation results revealed that the proposed method could achieve a high accuracy of 81.82% in identifying Orthosiphon aristatus.
Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit Refianto Damai Darmawan; Wisnu Ananta Kusuma; Hendra Rahmawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3111-3123

Abstract

The exponentially increasing bioinformatics data raised a new problem: the computation time length. The amount of data that needs to be processed is not matched by an increase in hardware performance, so it burdens researchers on computation time, especially on drug-target interaction prediction, where the computational complexity is exponential. One of the focuses of high-performance computing research is the utilization of the graphics processing unit (GPU) to perform multiple computations in parallel. This study aims to see how well the GPU performs when used for deep learning problems to predict drug-target interactions. This study used the gold-standard data in drug-target interaction (DTI) and the coronavirus disease (COVID-19) dataset. The stages of this research are data acquisition, data preprocessing, model building, hyperparameter tuning, performance evaluation and COVID-19 dataset testing. The results of this study indicate that the use of GPU in deep learning models can speed up the training process by 100 times. In addition, the hyperparameter tuning process is also greatly helped by the presence of the GPU because it can make the process up to 55 times faster. When tested using the COVID-19 dataset, the model showed good performance with 76% accuracy, 74% F-measure and a speed-up value of 179.
SELEKSI FITUR YANG BERPENGARUH MENGGUNAKAN NILAI MEAN PADA KLASIFIKASI FRAGMEN METAGENOME Arini Aha Pekuwali; Wisnu Ananta Kusuma; Agus Buono
J-Icon : Jurnal Komputer dan Informatika Vol 8 No 1 (2020): Maret 2020
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v8i1.2188

Abstract

Pekuwali (2018) has conducted research into the classification of metagenome fragments using spaced k-mers. Optimize the arrangement of features using Genetic Algorithms. Pekuwali (2018) concluded that the best arrangement of features or called chromosomes is 111111110001 with a fitness value of 85.42. Chromosome 111111110001 produces 336 features of extracting DNA fragments. This research aims to find out which features influence classi fi cation and the resulting accuracy. The method used is the Mean value. The mean value method was chosen because the data distribution is normal or close to normal. This study concludes that the influential features in the classification are features 22 to 27 with an accuracy of 78.83% and features 38 to 43 with an accuracy of 79.67%.
Hole Detection in Plastic Mulch Using Template Matching and Machine Learning Algorithms Abdul Aziz; Yandra Arkeman; Wisnu Ananta Kusuma; Farohaji Kurniawan
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.60628

Abstract

Mulch is a ground cover material to maintain soil moisture and temperature stability as a plant medium. Mulch also helps prevent weed growth for better plant growth. For planting with plastic mulch, farmers need to make holes in the mulch the day before planting. Precision agriculture is needed because it can obtain savings in input financing, labor, and better yields, so this research aims to identify holes in mulch based on Unmanned Aerial Vehicle images. The advantage of this research is that it can monitor each plant based on the mulch holes, and the number of holes identified can be used as a parameter to estimate the amount of crop production. This research combines Template Matching Algorithm and Machine Learning Algorithm to improve accuracy in predicting holes in mulch. Three machine learning algorithms are used, namely the Random Forest, Support Vector Machine, and XGBoost. The data used is an orthophoto mosaic from aerial photographs. Nine areas were taken from orthophotos to be used as research samples. The results of this study obtained the highest average recall, precision, and f-measure values using the Support Vector Machine algorithm with a recall value of 87.7%, precision of 97.5%, and f-score of 92.3%. This research focuses on reducing detected commission errors. Therefore, omission errors were still detected in the damaged or leaf-covered holes.
Network-Based Molecular Features Selection to Predict the Drug Synergy in Cancer Cells Syarifah Aini; Wisnu Ananta Kusuma; Medria Kusuma Dewi Hardhienata; Mushthofa
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.307

Abstract

Identifying synergistic drug combinations in cancer treatment is challenging due to the complex molecular circuitry of cancer and the exponentially increasing number of drugs. Therefore, computational approaches for predicting drug synergy are crucial in guiding experimental efforts toward finding rational combination therapies. This research selects the molecular features of cancer cells with a diffusion network-based approach. Additionally, a model is developed using non-linear regression algorithms, namely Random Forest, Extremely Randomized Tree, and XGBoost, to predict the synergy score of drug combinations against the selected cancer cell features. The data used are drug combination screening data and cancer cell molecules provided by AstraZeneca-Sanger DREAM Challenge. The feature selection results demonstrate the relevance of cancer cell molecular features selected by the diffusion network. The prediction results indicate that the Random Forest algorithm shows a good correlation value of 0.570 in the model with a small dataset. In contrast, for the model with an instance or row size larger than the number of features or columns, the XGBoost algorithm achieves a good correlation value of 0.932. INDEX TERMS cancer, drug combination, drug synergy, network diffusion kernel, non-linear regression.
Co-Authors Abdul Aziz Abdul Rahman Saleh Adrianus, Albert Afifa, Rizky Maulidya Agus Buono Ahmad, Tarmizi Aini Fazriani Aisah Rini Susanti Alami, Tegar Ali Djamhuri Annisa Annisa Annisa , Annisa Annisa Annisa Annisa Annisa Annisa Annisa Annisa Annisa Anton Suryatama Arini Aha Pekuwali Arini Pekuwali Arwan Subakti Ary Prabowo Ary Prabowo Auliatifani, Reza Auliya Ilmiawati Auriza Rahmad Akbar Azizah, Norma Nur Azzahra, Syarifah Fathimah Badollahi Mustafa Badrut Tamam Bahrul Ulum Budi Tjahjono Dahrul Syah Diah Handayani Dian Indah Savitri Dian Kartika Utami Essy Harnelly Fadli , Aulia Fahrury Romdendine, Muhammad Farhan Ramadhani , Hilmi Farit Mochamad Afendi Farohaji Kurniawan Fatriani, Rizka Fazriani, Aini Firman Ardiansyah Ginoga, Muh Fadhil Al-Haaq Halida Ernita Handayani, Vitri Aprilla Handayani, Vitri Aprilla Hanifah Nuryani Lioe Hardi, Wishnu Hasibuan, Lailan Sahrina Hendra Rahmawan Hendra Rahmawan Hera Dwi Novita Heru Sukoco Imas Sukaesih Sitanggang Indra Astuti Ira Maryati Irfan Wahyudin Irma Herawati Suparto Irman Hermadi Irmanida Batubara Irvan Lewenusa ISKANDAR ZULKARNAEN SIREGAR Isnan Mulia Janti G. Sudjana Jaya Sena Turana Joni Prasetyo Kana Saputra S Kangko, Danang Dwijo Karlisa Priandana Khaydanur Khaydanur Khaydanur, Khaydanur Laela Wulansari Larasati Larasati Lina Herlina Tresnawati Listina Setyarini Lusi Agus Setiani M. Rafi Maggy T. Suhartono Mala Nurilmala Medria Kusuma Dewi Hardhienata Mohamad Rafi Mohamad Rafi Mohamad Rafi Mohammad Romano Diansyah Mohammad Romano Diansyah Muchlishah Rosyadah Muhammad Asyhar Agmalaro Muhammad Subianto Mulyati Mulyati Mushthofa Muttaqin, Muhammad Rafi Nasution, Tegar Alami Nengsih, Nunuk Kurniati Norma Nur Azizah Nunuk Kurniati Nengsih Nur Choiriyati Nurdevi Noviana Ovi Sofia Pramita Andarwati Prihasuti Harsani Priyo Raharjo Pudji Muljono Purnajaya, Akhmad Rezki Purnomo, Tsania Firqin Ramadhanti, Nabila Sekar Ramdan Satra Ratu Mutiara Siregar Refianto Damai Darmawan Refianto Damai Darmawan Resnawati Reza Auliatifani Rif’ati, Lutfah Ronald Marseno Rosy Aldina Rosyadah, Muchlishah Rudi Heryanto SATRIYAS ILYAS Septaningsih, Dewi Anggraini Siti Syahidatul Helma Sony Hartono Wijaya Sri Nurdiati SUHARINI, YUSTINA SRI Sulistyo Basuki Sulistyo Basuki Supriyanto, Arif Syahid Abdullah Syarifah Aini Syukriyansyah Taufik Djatna Toni Afandi Tsania Firqin Purnomo Usman, Muhammad Syafiuddin Wa Ode Rahma Agus Udaya Manarfa Wahjuni, Sri Widya Sari Wijaya, Eko Praja Hamid Wina Yulianti Wishnu Hardi Wulansari, Laela Yandra Arkeman Yessy Yanitasari Yudhi Trisna Atmajaya Yulianah Yulianah Yunita Fauzia Achmad Zulkarnaen, Silvia Alviani