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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 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

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Model deep learning untuk klasifikasi fragmen metagenom dengan spaced k-mers sebagai ekstraksi fitur Nur Choiriyati; Yandra Arkeman; Wisnu Ananta Kusuma
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2020.13407

Abstract

An open challenge in bioinformatics is the analysis of the sequenced metagenomes from the various environments. Several studies demonstrated bacteria classification at the genus level using k-mers as feature extraction where the highest value of k gives better accuracy but it is costly in terms of computational resources and computational time. Spaced k-mers method was used to extract the feature of the sequence using 111 1111 10001 where 1 was a match and 0 was the condition that could be a match or did not match. Currently, deep learning provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. In this research, two different deep learning architectures, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), trained to approach the taxonomic classification of metagenome data and spaced k-mers method for feature extraction. The result showed the DNN classifier reached 90.89 % and the CNN classifier reached 88.89 % accuracy at the genus level taxonomy.
REKAYASA PERANGKAT LUNAK MOBILE LIBRARY DI PERPUSTAKAAN TRISAKTI SCHOOL OF MANAGEMENT Yulianah Yulianah; Wisnu Ananta Kusuma; Sri Wahjuni
VISI PUSTAKA: Buletin Jaringan Informasi Antar Perpustakaan Vol 21, No 2: Agustus 2019
Publisher : Perpustakaan Nasional RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37014/visipustaka.v21i2.531

Abstract

Introduction. This study aims to develop prototype of mobile library application as recommendation model to be applied in college libraries to provide easiness and velocity of information access for users by smartphone. This study took case in Trisakti School of Management Library that uses SLiMS software as its library application.Data Collection Method. This research uses software engineering approach with protyping adopted from Laudon (2016), consist of indentify basic requirements, develop working prototype, use the prototype, revise and enchance the prototype.Analysis Data. Software engineering uses Android platform.Results and Discussions. The study results show that there are six main basic requirements of mobile library applications that can be implemented in TSM Library, including online catalog, library account, renew, borrow, reserve, and journal. By using prototype, users can perform 8 tasks, include login, view personal library account, renew, searching of books, borrow book, reserve book, cancel of reserve, and searching of journals. The final testing results show that all functions of prototype run well without error.Conclusions. This research has succeeded in developing prototype of mobile library application which can be used as a model that is suitable to be applied in college libraries, especially TSM Library.
PENGELOMPOKAN TOPIK DOKUMEN BERBASIS TEXT MINING DENGAN ALGORITME K-MEANS: STUDI KASUS PADA DOKUMEN KEDUTAAN BESAR AUSTRALIA JAKARTA Wishnu Hardi; Wisnu Ananta Kusuma; Sulistyo Basuki
VISI PUSTAKA: Buletin Jaringan Informasi Antar Perpustakaan Vol 21, No 1: April 2019
Publisher : Perpustakaan Nasional RI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37014/visipustaka.v21i1.77

Abstract

The Australian Embassy in Jakarta is storing a wide array of media release document. Analyzing particular and vital patterns of the documents collection is imperative as it will result in new insights and knowledge of significant topic groups of the documents. K-Means algorithm was used as a non- hierarchical clustering method which partitioning data objects into clusters. The method works through minimizing data variation within cluster and maximizing data variation between clusters. Of the documents issued between 2006 and 2016, 839 documents were examined in order to determine term frequencies and to generate clusters. Evaluation was conducted by nominating an expert to validate the cluster result. The result showed that there were 57 meaningful terms grouped into 3 clusters. “People to people links”, “economic cooperation”, and “human development” were chosen to represent topics of the Australian Embassy Jakarta media releases from 2006 to 2016. This research concluded that text mining can be used to cluster topic groups of documents. It provides a more systematic clustering process as the text analysis is conducted through a number of stages with specifically set parameters.
Penguraian Mekanisme Kerja Jamu Berdasarkan Jejaring Bahan Aktif-Protein Target-Gene Ontology Vitri Aprilla Handayani; Farit Mochamad Afendi; Wisnu Ananta Kusuma
Jurnal Jamu Indonesia Vol. 1 No. 3 (2016): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1168.201 KB) | DOI: 10.29244/jji.v1i3.21

Abstract

Jamu merupakan obat tradisional Indonesia. Pada dasarnya obat herbal yang dibuat dari bahan-bahan alami yang diambil dari beberapa bagian dari tanaman obat yang mengandung beberapa zat dan senyawa yang penting dan bermanfaat bagi tubuh. Sejauh ini, khasiat untuk beberapa jenis jamu secara empiris telah terbukti. Dalam peneitian ini, kami bermaksud untuk menguraikan mekanisme kerja jamu menggunakan pendekatan komputasi. Penelitian ini berfokus pada ramuan jamu type 2 diabetesyang terdiri dari empat tanaman, yaitu: jahe, bratawali, sembung, dan pare. Kerangka analisis awal dengan membentuk 3 komponen jejaring yang terdiri dari: (1) bahan aktif tanaman (diperoleh dari Knapsack: 58 senyawa aktif), (2) protein target (diperoeh dari database pubchem: 416 protein target), dan (3) gene ontoogy(diperoeh dari database DAVID: 3104 GO). Selanjutnya, kami menerapkan analisis klaster-klasterdengan menggunakan konsep graf tri-partite. Graf tri-partite digunakan untuk mengelompokkan komponen-komponen penyusun jejaring dari empat tanaman yang disebutkandiatas, sehingga diperoleh system bagian-bagian penyusun ramuan jamu. Hal ini dilakukan untuk mengungkapkan mekanisme kerja jamu. Menggunakan metode fuzzy clustering pada data jejaring, kami memperoleh 15 senyawa aktif yang diduga potensial sebagai antidiabetes berada dalam kelompok berbeda. Pada 15 senyawa aktif memiliki nilai peluang cukup tinggi terbagi dalam kelompok yang berbeda, setiap kelompok terdiri dari pasangan bahan aktif yang memiliki efek sinergis tinggi. Berdasarkan koneksi antara klaster-klasterprotein dan GO-BP, penelitianini memperoleh informasi protein-protein yang menyebabkan T2D dan mekanisme proses biologis yang terkait. T2D bukan hanya disebabkan oleh protein kelainan sekresi insulin (insulin-merendahkan enzim isoform 1) saja, tetapi juga disebabkan oleh protein lain yang terlibat dalam penghambatan insulin di pankreas.
Penguraian Mekanisme Kerja Jamu dengan Menggunakan Analisis Graf Tripartit pada Jejaring Senyawa-Protein-Penyakit Muchlishah Rosyadah; Farit Mochamad Afendi; Wisnu Ananta Kusuma
Jurnal Jamu Indonesia Vol. 2 No. 1 (2017): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1408.148 KB) | DOI: 10.29244/jji.v2i1.25

Abstract

Jamu adalah obat tradisional di Indonesia. Berbeda dengan konsep one drug-one target pada obat kimia, jamu memiliki konsep multi components-network target. Hal ini disebabkan oleh keterlibatan senyawa aktif di jamu yang menargetkan beberapa protein dalam tubuh manusia.Jaringan yang menghubungkan senyawa aktif dan protein target, serta penyakit yang berhubungan dengan protein target, memberikan dasar yang kuat guna menjelaskan menjelaskan mekanisme kerja jamu secara komputasi.Data yang digunakan berasal dari jamu yang terdiri dari 4 tanaman, yaitu: pare (Momordica charantia), sembung (Blumea balsamifera), bratawali (Tinospora crispa), dan jahe (Zingiber officinale). Setiap tanaman memiliki senyawa aktif dan protein target dari tiap-tiap senyawa. Terdapat 47 senyawa aktif yang diperoleh dari jahe, 4 senyawa aktif dari sembung, 4 senyawa aktif dari pare, dan 3 senyawa aktif dari bratawali. Total ada 58 senyawa aktif yang diperoleh dari empat tanaman. Database PubChem mengidentifikasi bahwa terdapat 3.059 koneksiantara senyawa aktif dan protein tergetnya, dari 3059 koneksi tereduksi menjadi 396 protein yang unik. Selanjutnya, dengan menggunakan database disgenet, PharmGKB, dan Theurapetic Target Database didapatkan 118 sasaran penyakit yang memiliki koneksi terhadap 396 protein yang unik. Jejaring senyawa, protein target, dan penyakit yang telah dianalisis menggunakan analisis graf tripartit menunjukkan bahwa 396 protein unik dari jamu terkait dengan beberapa penyakit, sebagian besar berkaitan dengan penyakit metabolik, penyakit kardiovaskular (jantung), penyakit mata, neoplasma, stomatognatik, penyakit sistem saraf, dan penyakit Saluran pernapasan.
Sistem Berbasis Pengetahuan Tumbuhan Obat Pusat Studi Biofarmaka Aini Fazriani; Wisnu Ananta Kusuma; Irmanida Batubara
Jurnal Jamu Indonesia Vol. 4 No. 1 (2019): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1724.057 KB) | DOI: 10.29244/jji.v4i1.88

Abstract

Jamu terdiri atas berbagai macam tumbuhan obat yang diolah. Tumbuhan obat memiliki kandungan senyawa metabolit sekunder yang berperan sebagai khasiat. Pengetahuan tumbuhan obat beserta senyawanya perlu dikembangkan lagi menjadi pengetahuan eksplisit yang lebih spesifik dan mudah dimengerti sehingga berguna untuk masyarakat. Maka dari itu perlu mengembangkan sistem untuk digunakan oleh masyarakat sehingga pengobatan dengan tanaman obat lebih populer di kalangan masyarakat luas. Penelitian yang dilakukan mengenai manajemen pengetahuan berbasis ontologi terkait dengan tumbuhan obat dan senyawa. Penelitian ini menggunakan pendekatan proses manajemen pengetahuan model SECI, selain itu pengembangan ontologi menggunakan metode Ontology building life cycle, sedangkan bahasa representasi yang digunakan adalah Resource Description Framework (RDF) dan Web Ontology Language (OWL) dengan toolsProtégé5.0. Pengembangan model ontologi dengan bahasa representasi RDF/OWL dapat menghasilkan pengetahuan dengan melakukan query menggunakan SPARQL. Hasil query tersebut dapat digunakan untuk diimplementasikan pada mobile.
Asosiasi Single Nucleotide Polymorphism pada Diabetes Mellitus Tipe 2 Menggunakan Random Forest Regression Lina Herlina Tresnawati; Wisnu Ananta Kusuma; Sony Hartono Wijaya; Lailan Sahrina Hasibuan
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 4: November 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1046.657 KB)

Abstract

Precision medicine can be developed by determining association between genomic data, represented by Single Nucleotide Polymorphism (SNP), and phenotype of diabetes mellitus type 2 (T2D). The number of SNP is actually very abundance. Thus, sorting and filtering the SNP is required before conducting association. The purpose of this paper was to associate SNP with T2D phenotypes. SNP ranking was conducted to choose significant SNPs by calculating importance score. Selected SNPs were associated with T2D phenotype using random forest regression. Moreover, the epistasis was also examined to show the interactions among SNPs affecting phenotype. This paper obtained 301 importance SNPs. Top ten SNPs have association with five T2D protein candidates. The evaluation results of the proposed models showed the Mean Absolute Error (MAE) of 0.062. This results indicate the success of random forest regression in conducting SNP and phenotype association and epistatic examination between two SNPs.
Correlation of Diabetes Mellitus and Cellular Components using Fuzzy K-Partite Wa Ode Rahma Agus Udaya Manarfa; Wisnu Ananta Kusuma; Imas Sukaesih Sitanggang
Nusantara Science and Technology Proceedings 2nd Basic and Applied Science Conference (BASC) 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2022.2509

Abstract

PPI clustering is one of the computational methods to identify proteins that affect type 2 diabetes mellitus. One of the graph-based fuzzy clustering algorithms, namely fuzzy k-partite clustering was built to solve the problem of biological network data that has more than one function and is present in more than one cluster. which may have overlapping cluster members. A previous study analyzed the mechanism of herbal medicine using the fuzzy k-partite graph clustering method, it was found that there are three groups of proteins that have the same role in overcoming type 2 DM. in the form of backbone tissue (GO-protein). The stages in this research are type 2 DM protein data, search for significant MCL clustering proteins, mining of cellular components in the Uniprot web database, adjacency matrix construction, bipartite network formation with Fuzzy k-Partite Clustering and cluster analysis. This shows that the output of using the algorithm is a network that can provide information on biological processes in type 2 DM. If the weight of the relationship between clusters is high, it can be ascertained that the value of the degree of membership in the cluster is low and there are few cluster members. Conversely, if the weight of the relationship between clusters is low, the degree of cluster membership is high and there are many cluster members. In other words, the value of the degree of membership resulting from the application of this algorithm is inversely proportional to the value of the connectivity between clusters.
Development of Fertilizer Selection using Knowledge Management System Yessy Yanitasari; Irman Hermadi; Wisnu Ananta Kusuma
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2015
Publisher : Institute of Advanced Engineering and Science

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

Abstract

Fertilizer is a chemical substance or organism that has the role in supplying the nutrient substance to the plants directly or indirectly added to the soil in order for the plant to ordinary grow.  Much information on fertilizer is available on the internet, however the existence is still widely spread. Therefore, a forum to especially accommodate fertilizer selection using knowledge management system (KMS) is needed. In this research on fertilizer selection using knowledge management system is developed using the KMS Life Cycle (KMSLC) method, and implemented using Web based application. The knowledge in this system is obtained from expert explicit data entry and uploaded file from the users that has to be validated by an expert. The expert validation process is carried out with approval system, communication through messages and conversation. The outcome of the research is a web based application that was tried out by the users with good average tested value. DOI:  http://dx.doi.org/10.11591/telkomnika.v13i3.7224 
Clustering Fragments Metagenome Using Self-Organizing Map Yunita Fauzia Achmad; Wisnu Ananta Kusuma; Heru Sukoco
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2015
Publisher : Institute of Advanced Engineering and Science

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

Abstract

Metagenome is a combination of several microorganisms collected from the environment. In metagenome analysis, it is required binning for grouping metagenome fragment yielded by sequencer. This research used the composition approach for conducting metagenome fragment binning. In this approach, binning could be implemented using unsupervised or supervised learning. We used Self-Organizing Map (SOM) for conducting binning used on unsupervised learning. We compared two techniques of training in SOM, namely sequential training and batch training for finding the best techniques. The results showed that the batch training could obtain 3.8% error valued on the map of [10 15]. This error value is smaller than that of sequential training. DOI:  http://dx.doi.org/10.11591/telkomnika.v13i3.7115 
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