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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 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

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Klasifikasi Metagenom dengan Metode Naïve Bayes Classifier Utami, Dian Kartika; Kusuma, Wisnu Ananta; Buono, Agus
Jurnal Ilmu Komputer dan Agri-Informatika Vol 3, No 1 (2014)
Publisher : Departemen Ilmu Komputer IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1294.449 KB)

Abstract

Studi metagenom merupakan langkah penting pada pengelompokan taksonomi. Pengelompokan pada metagenom dapat dilakukan dengan menggunakan metode binning. Binning diperlukan untuk mengelompokkan contigs yang dimiliki oleh masing-masing kelompok spesies filogenetik. Pada penelitian ini, binning dilakukan dengan menggunakan pendekatan komposisi berdasarkan supervised learning (pembelajaran dengan contoh). Metode supervised learning yang digunakan yaitu Naïve Bayes Classifier. Adapun metode yang digunakan untuk ekstraksi ciri adalah dengan melakukan perhitungan frekuensi k-mer. Klasifikasi pada metagenom dilakukan berdasarkan tingkat takson genus. Dari proses klasifikasi yang dilakukan, akurasi yang diperoleh dengan menggunakan fragmen pendek (400 bp) adalah 49.34 % untuk ekstraksi ciri 3-mer dan 53.95 % untuk ekstrasi ciri 4-mer. Sementara itu, untuk fragmen panjang (10 kbp), akurasi mengalami peningkatan yaitu 82.23 % untuk ekstraksi ciri 3-mer dan 85.89 % untuk esktraski ciri 4-mer. Dari hasil tersebut dapat disimpulkan bahwa akurasi semakin tinggi seiring dengan semakin panjangnya ukuran fragmen. Selain itu, penelitian ini juga menyimpulkan bahwa metode ekstrasi ciri yang memberikan hasil paling maksimal adalah dengan menggunakan ekstraksi ciri 4-mer.Kata Kunci: metagenom, k-mer, Naïve Bayes Classifier, binning, klasifikasi
SISTEM MANAJEMEN PENGETAHUAN PERLINDUNGAN ANAK (STUDI KASUS: SAKTI PEKSOS DI KEMENTERIAN SOSIAL) Ahmad, Tarmizi; Hermadi, Irman; Kusuma, Wisnu Ananta
Sosio Konsepsia Vol 8, No 3 (2019): Sosio Konsepsia
Publisher : Puslitbangkesos Kementerian Sosial RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33007/ska.v8i3.1599

Abstract

In 2014, The Ministry of Social Affairs ofThe Republic of Indonesia (MOSA) send 670 social workers, called ?Sakti Peksos? to all provinces in Indonesia. Sakti Peksos, which stands for ?Satuan Bakti Pekerja Sosial? is the social workers who assist the Child Welfare Program in Indonesia or known as ?PKSA?. To assist the children?s case, the social workers need many practice knowledge of how to deal with the children?s issues. Therefore, it is beneficial to manage the existing information by developing knowledge management system. Through the application based web program, this knowledge management system will develop all the Sakti Peksos?s knowledge. This web aims at organizing all the informations that they have so that it can be shared to other Sakti Peksos. The purpose of this study was to develop a Child Protection Knowledge Management System for Sakti Peksos in the Ministry of Social Affairs. This research uses Knowledge Management System Life Cycle methodology which is adopted from award and ghaziri methodology. This research involves four steps; gathering the knowledge, designing the brueprint of knowledge management system, helding verification as well as validation process, and implementing knowledge management system. The result of this research is a web based child protection knowledge management system, equipped with CakePHP framework and MySQL as Relational Database Management System (RDBMS).
Identification of Significant Proteins in Coronavirus Disease 2019 Protein-Protein Interaction Using Principal Component Analysis and ClusterONE Ananta Kusuma, Wisnu; Farhan Ramadhani , Hilmi; Annisa
Bioinformatics and Biomedical Research Journal Vol. 3 No. 2 (2020): Volume 3 issue 2
Publisher : Future Science

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Abstract

Coronavirus Disease 2019 (COVID-19) will cause disease complications and organ damage due to excessive inflammatory reactions if left untreated. Computational analysis of protein-protein interactions can be carried out in various ways, including topological analysis and clustering of protein-protein interaction networks. Topological analysis can identify significant proteins by measuring the most important nodes with centrality measurements. By using Principal Component Analysis (PCA), the types of centrality measures were extracted into the overall centrality value. The study aimed to found significant proteins in COVID-19 protein-protein interactions using PCA and ClusterONE. This study used 57 proteins associated with COVID-19 to obtain protein networks. All of these proteins are homo sapiens organism. The number of proteins and the number of interactions from 57 proteins were 357 proteins and 1686 interactions. The results of this study consisted of two clusters; the best cluster was the first cluster with a lower p-value but had an average overall centrality value that closed to the second cluster. There are twenty important proteins in that cluster, and all of these proteins are related to COVID-19. These proteins are expected to be used in the process of discovering medicinal compounds in COVID-19.
Antioxidant Capacity, Phytochemical Profile, and Clustering of Pomegranate (Punica granatum L.) Peel Extracts Using Different Solvent Extraction Rafi, Mohamad; Wulansari, Laela; Septaningsih, Dewi Anggraini; Purnomo, Tsania Firqin; Auliatifani, Reza; Khaydanur, Khaydanur; Ilmiawati, Auliya; Yulianti, Wina; Nengsih, Nunuk Kurniati; Suparto, Irma Herawati; Kusuma, Wisnu Ananta
Journal of Tropical Life Science Vol 11, No 3 (2021)
Publisher : Journal of Tropical Life Science

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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.
Prediction of Drug-Target Interaction Using Random Forest in Coronavirus Disease 2019 Case Fadli , Aulia; Annisa , Annisa; Kusuma, Wisnu Ananta
Bioinformatics and Biomedical Research Journal Vol. 4 No. 1 (2021): Vol 4 No 1 (2021)
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/bbrj.04.01.01

Abstract

Coronavirus disease 2019 is an infectious disease that causes severe respiratory, digestive, and systemic infections that caused a pandemic in 2019. One of the focuses of the drug development process to fight the coronavirus disease 2019 is by carrying out drug repurposing. This study uses random forest with a feature-based chemogenomics approach on the drug-target interaction data of coronavirus disease 2019. The feature extraction process is carried out on compounds and protein using PubChem fingerprint and amino acid composition respectively. Feature selection using XGBoost is done to reduce the data dimension. The random undersampling process was also carried out to solve the problem of imbalanced data in the dataset. Using the cross-validation process, the random forest model produced an average accuracy value of 0.98, recall value of 0.92, precision value of 0.95, AUROC value of 0.95, and F1 score of 0.93. The random forest model also produced an accuracy value of 0.99, recall value of 0.93, the precision value of 0.94, AUROC value of 0.99, and F-measure of 0.94 when used to predict the original dataset (dataset without random undersampling process).
Hadoop Performance Analysis on Raspberry Pi for DNA Sequence Alignment Jaya Sena Turana; Heru Sukoco; Wisnu Ananta Kusuma
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.1886

Abstract

The rapid development of electronic data has brought two major challenges, namely, how to store big data and how to process it. Two main problems in processing big data are the high cost and the computational power. Hadoop, one of the open source frameworks for processing big data, uses distributed computational model designed to be able to run on commodity hardware. The aim of this research is to analyze Hadoop cluster on Raspberry Pi as a commodity hardware for DNA sequence alignment. Six B Model Raspberry Pi and a Biodoop library were used in this research for DNA sequence alignment. The length of the DNA used in this research is between 5,639 bp and 13,271 bp. The results showed that the Hadoop cluster was running on the Raspberry Pi with average usage of processor 73.08%, 334.69 MB of memory and 19.89 minutes of job time completion. The distribution of Hadoop data file blocks was found to reduce processor usage as much as 24.14% and memory usage as much as 8.49%. However this increased job processing time as much as 31.53%.
Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve Bayes Aisah Rini Susanti; Taufik Djatna; Wisnu Ananta Kusuma
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.4284

Abstract

Telecommunication users are rapidly growing each year. As people keep demanding a better service level of Short Message Service (SMS), telephone or data use, service providers compete to attract their customer, while customer feedbacks in some platforms, for example Twitter, are their souce of information. Multinomial Naïve Bayes Tree, adapted from the method of Multinomial Naïve Bayes and Decision Tree, is one technique in data mining used to classify the raw data or feedback from customers.Multinomial Naïve Bayes method used specifically addressing frequency in the text of the sentence or document. Documents used in this study are comments of Twitter users on the GSM telecommunications provider in Indonesia.This research employed Multinomial Naïve Bayes Tree classification technique to categorize customers sentiment opinion towards telecommunication providers in Indonesia. Sentiment analysis only included the class of positive, negative and neutral. This research generated a Decision Tree roots in the feature "aktif" in which the probability of the feature "aktif" was from positive class in Multinomial Naive Bayes method. The evaluation showed that the highest accuracy of classification using Multinomial Naïve Bayes Tree (MNBTree) method was 16.26% using 145 features. Moreover, the Multinomial Naïve Bayes (MNB) yielded the highest accuracy of 73,15% by using all dataset of 1665 features. The expected benefits in this research are that the Indonesian telecommunications provider can evaluate the performance and services to reach customer satisfaction of various needs.
Cluster Analysis for SME Risk Analysis Documents Based on Pillar K-Means Irfan Wahyudin; Taufik Djatna; Wisnu Ananta Kusuma
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.2385

Abstract

In Small Medium Enterprise’s (SME) financing risk analysis, the implementation of qualitative model by giving opinion regarding business risk is to overcome the subjectivity in quantitative model. However, there is another problem that the decision makers have difficulity to quantify the risk’s weight that delivered through those opinions. Thus, we focused on three objectives to overcome the problems that oftenly occur in qualitative model implementation. First, we modelled risk clusters using K-Means clustering, optimized by Pillar Algorithm to get the optimum number of clusters. Secondly, we performed risk measurement by calculating term-importance scores using TF-IDF combined with term-sentiment scores based on SentiWordNet 3.0 for Bahasa Indonesia. Eventually, we summarized the result by correlating the featured terms in each cluster with the 5Cs Credit Criteria. The result shows that the model is effective to group and measure the level of the risk and can be used as a basis for the decision makers in approving the loan proposal. 
Improving DNA Barcode-based Fish Identification System on Imbalanced Data using SMOTE Wisnu Ananta Kusuma; Nurdevi Noviana; Lailan Sahrina Hasibuan; Mala Nurilmala
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.5011

Abstract

Problem in imbalanced data is very common in classification or identification. The problem is raised when the number of instances of one class far exceeds the other. In the previous research, our DNA barcode-based Identification System of Tuna and Mackerel was developed in imbalanced dataset. The number of samples of Tuna and Mackerel were much more than those of other fish samples. Therefore, the accuracy of the classification model was probably still in bias. This research aimed at employing Synthetic Minority Oversampling Technique (SMOTE) to yield balanced dataset. We used k-mers frequencies from DNA barcode sequences as features and Support Vector Machine (SVM) as classification method. In this research we used trinucleotide (3-mers) and tetranucleotide (4-mers). The training dataset was taken from Barcode of Life Database (BOLD). For evaluating the model, we compared the accuracy of model using SMOTE and without SMOTE in order to classify DNA barcode sequences which is taken from Department of Aquatic Product Technology, Bogor Agricultural University. The results showed that the accuracy of the model in the species level using SMOTE was 7% and 13% higher than those of non-SMOTE for trinucleotide (3-mers) and tetranucleotide (4-mers), respectively. It is expected that the use of SMOTE, as one of data balancing technique, could increase the accuracy of DNA barcode based fish classification system, particularly in the species level which is difficult to be identified.
Comparison of Data Partitioning Schema of Parallel Pairwise Alignment on Shared Memory System Auriza Rahmad Akbar; Heru Sukoco; Wisnu Ananta Kusuma
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 2: June 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i2.1415

Abstract

The pairwise alignment (PA) algorithm is widely used in bioinformatics to analyze biological sequence. With the advance of sequencer technology, a massive amount of DNA fragments are sequenced much quicker and cheaper. The alignment algorithm needs to be parallelized to be able to align them in a shorter time. Many previous researches have parallelize PA algorithm using various data partitioning schema, but it is unclear which one is the best. The data partitioning schema is important for parallel PA performance, because this algorithm use dynamic programming technique that needs intense inter-thread communication. In this paper, we compared four partitioning schemas to find the best performing one on shared memory system. Those schemas are: blocked columnwise, rowwise, antidiagonal, and blocked columnwise with manual scheduling and loop unrolling. The last schema gave the best performance of 89% efficiency on 4 threads. This result provided fine-grain parallelism that can be used further to develop parallel multiple sequence alignment (MSA).
Co-Authors Abdul Aziz Abdul Rahman Saleh Agus Buono Ahmad, Tarmizi Aini Fazriani Aisah Rini Susanti Alami, Tegar Albert Adrianus 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 Auliatifani, Reza Auliya Ilmiawati Auriza Rahmad Akbar Badollahi Mustafa Badrut Tamam Bahrul Ulum BUDI TJAHJONO 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 Halida Ernita 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 Maggy T. Suhartono Mala Nurilmala Medria Kusuma Dewi Hardhienata Mohamad Rafi Mohamad Rafi Mohamad Rafi Mohammad Romano Diansyah Mohammad Romano Diansyah Muchlishah Rosyadah Muh Fadhil Al-Haaq Ginoga Muhammad Asyhar Agmalaro Muhammad Subianto Mulyati Mulyati Mushthofa Mushthofa Mushthofa Muttaqin, Muhammad Rafi Nabila Sekar Ramadhanti 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 Ramdan Satra Ratu Mutiara Siregar Refianto Damai Darmawan Refianto Damai Darmawan Resnawati Reza Auliatifani Rif’ati, Lutfah Rizky Maulidya Afifa Ronald Marseno Rosy Aldina Rudi Heryanto SATRIYAS ILYAS Septaningsih, Dewi Anggraini Siti Syahidatul Helma Sony Hartono Wijaya Sri Nurdiati Sulistyo Basuki Sulistyo Basuki Supriyanto, Arif Syahid Abdullah Syarifah Aini Syarifah Fathimah Azzahra 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