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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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Profil Kimia dan Toksisitas Jamu Berpotensi Antidiabetes yang Diformulasi dengan Metode Statistika dan Machine Learning Norma Nur Azizah; Rudi Heryanto; Wisnu Ananta Kusuma
Jurnal Jamu Indonesia Vol. 3 No. 1 (2018): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1327.466 KB) | DOI: 10.29244/jji.v3i1.47

Abstract

Bahan alam yang dapat menghambat spermatogenesis merupakan suatu alternatif untuk kontrasepsi pria. Penelitian ini bertujuan mengevaluasi formulasi optimum berbasis biji jarak pagar dan buah pare yang dapat menginhibisi spermatogenesis. Bahan sampel dimaserasi alkohol 70% untuk memperoleh ekstrak kasar. Ekstrak diujikan pada tikus Wistar jantan dewasa sebanyak 30 ekor dan dibagi menjadi enam kelompok secara acak (n=5 ekor). Kelompok I sebagai kontrol memperoleh pelarut akuades; kelompok II dan III secara berurutan memperoleh ekstrak biji jarak dan pare dengan dosis 50 mg/kgBB. Tiga kelompok lainnya, yaitu IV, V, dan VI memperoleh formulasi gabungan dengan rasio ekstrak biji jarak pagar dan pare 1:3, 3:1 serta 1:1, secara berurutan. Perlakuan diberikan per oral satu hari sekali selama 48 hari sesuai dengan siklus spermatogenesis. Pada akhir perlakuan, pemeriksaan sperma untuk konsentrasi dan kualitasnya serta bobot testis. Hasil uji rendemen ekstrak biji jarak pagar dan buah pare secara berurutan sebesar 6.11 % dan 3.32 %. Senyawa fitokimia yang terdapat pada ekstrak pare dan biji jarak pagar antara lain, alkaloid, fenol, flavonoid, tanin, saponin, triterpenoid, dan steroid. Efek ekstrak terhadap bobot testis dan konsentrasi sperma tidak ada perbedaan yang nyata antar kelompok (ANOVA P >0.05) sedangkan untuk kualitas motilitas sperma ada kecenderungan menurun pada formulasi ekstrak biji jarak dan buah pare (1:3).
Pengembangan Sistem Manajemen Pengetahuan Tumbuhan Obat Indonesia Berbasis Ontologi Syukriyansyah; Wisnu Ananta Kusuma; Annisa
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 10 No. 2 (2023)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.10.2.147-163

Abstract

Pengetahuan tumbuhan obat oleh masyarakat atau etnis lokal untuk penyakit atau gejala tertentu telah berperan penting dalam penemuan beberapa obat berharga yang telah digunakan secara turun-temurun selama bertahun-tahun. Selain itu, banyak sumber pengetahuan tumbuhan obat Indonesia yang heterogen dan terpisah-pisah sehingga sangat penting untuk mengintegrasikannya. Oleh karena itu, sangat penting untuk mengembangkan sistem manajemen pengetahuan (KMS) yang dapat menyimpan, mengelola, berbagi, dan merepresentasikan pengetahuan tumbuhan obat Indonesia sehingga dapat dibagikan, digunakan kembali, dan dimanfaatkan dalam kesehatan Indonesia. Penelitian ini menggunakan ontologi sebagai pola dalam membangun grafik pengetahuan dengan menggunakan basis data graf Neo4j dan kueri Chyper untuk melakukan penalaran pengetahuan berbasis graf. Penalaran pengetahuan berbasis graf digunakan untuk memperoleh pengetahuan terkait. Ontologi dibangun berdasarkan konsep kunci dalam pengobatan tradisional kemudian dipadukan dengan ontologi penyakit (DO) untuk mengatasi kesenjangan antara istilah pemanfaatan tumbuhan tradisional dan istilah medis serta memperkaya pengetahuan kedokteran Indonesia. Sumber data yang digunakan untuk membangun ontologi antara lain adalah Laporan Nasional Eksplorasi Pengetahuan Lokal Etnomedisin dan Tumbuhan Obat di Indonesia Berbasis Komunitas, integreted Digitized Biocollections (iDigBio), Global Biodiversity Information Facility (GBIF), Disease Ontology (DO), Basis Data Tanaman Obat Indonesia (HerbalDB), Dr. Duke’s Phytochemical and Ethnobotanical Databases (Dr. Duke’s), Indian Medicinal Plants, Phytochemystry and Teurapeutics (IMPPAT), Collection of Open Natural Products (COCONUT), KNApSAcK, BioGRID, DisGeNET, dan Side Effect Resource (SIDER). Sistem dikembangkan dengan arsitektur REST API yang terdiri dari front-end (klien) dan back-end (server). Klien memiliki dua sistem utama, yaitu pencarian pengetahuan dan manajemen pengetahuan.
Implementasi Pendekatan Algoritma Deep Learning CNN untuk Identifikasi Citra Pasien Keratitis Agmalaro, Muhammad Asyhar; Kusuma, Wisnu Ananta; Rif’ati, Lutfah; Pramita Andarwati; Anton Suryatama; Rosy Aldina; Hera Dwi Novita; Ovi Sofia
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 10 No. 2 (2023)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.10.2.164-175

Abstract

The incidence of keratitis globally ranges from 0.4 to 5.2 per 10,000 people annually. Keratitis can only be identified by an ophthalmologist using a slitlamp as a fundamental instrument for specific eye examination in secondary care facilities. In primary care facilities, eye specialists and slitlamps are not available. This causes delay in the diagnosis and treatment of keratitis patients in public health centers or areas with limited facilities and access to doctors/ophthalmologists. This research aims to develop a keratitis identification model using the convolutional neural network (CNN) method and training data consisting of images produced by smartphones and combined with slitlamp images. The training accuracy of the developed model is 92% with a dropout layer set at 0.3, and the average validation accuracy is 83%, indicating that the model training did not experience overfitting. The testing results with new data achieved an accuracy of 90%. Next, the parameters of the best model will be integrated into an application running on the Android operating system. However, the application’s functionality and UX/UI performance need to be improved to facilitate seamless use of the model.
Kecerdasan Buatan untuk Monitoring Hama dan Penyakit pada Tanaman Eucalyptus: Systematic Literature Review Nasution, Tegar Alami; Yeni Herdiyeni; Wisnu Ananta Kusuma; Budi Tjahjono; Iskandar Zulkarnaen Siregar
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 10 No. 2 (2023)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.10.2.224-237

Abstract

Eucalyptus plants, renowned for their economic and environmental significance, are cultivated globally. Despite their value, these plants are vulnerable to pest and disease attacks, impacting productivity and quality. Accurate and timely monitoring is required to control pests and diseases in eucalyptus plants. The conventional method of human-based direct observation for monitoring pests and diseases in eucalyptus plants is fraught with weaknesses. Therefore, efforts are needed to enhance the effectiveness and efficiency of monitoring pests and diseases in eucalyptus plants through artificial intelligence or AI technology. AI is used to automatically detect and classify pests and diseases in eucalyptus plants using machine learning or deep learning algorithms and image processing. This study aims to provide a comprehensive review of the use of AI for detecting pests and diseases in eucalyptus plants using the Systematic Literature Review (SLR) method. Through this approach, this study identifies, evaluates, and analyzes relevant literature on the research topic from various digital sources. This study also provides an overview of the latest developments, methods used, and results achieved, as well as challenges and opportunities in the field of AI research for detecting pests and diseases in eucalyptus plants.
Identification of Significant Proteins in Hypertension Using The Clustering Molecular Complex Detection (MCODE) Method Setiani, Lusi Agus; Kusuma, Wisnu Ananta; Zulkarnaen, Silvia Alviani
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v20i2.7905

Abstract

Hypertension is a condition where the systolic blood pressure value is more than 140 mmHg and the diastolic blood pressure value is more than 90 mmHg. A significant protein is a protein that has the greatest effect or is the center of protein regulation in all biochemical processes. The purpose of this study was to determine the significant protein that has the greatest ef- fect on hypertension by using the clustering Molecular Complex Detection (MCODE) method which will identify areas in the network with the highest density value locally and to determine the mechanism of action of the significant proteins obtained in the setting blood pressure using Gene Ontology and Kyoto Encyclopedia and Genome Analysis (KEGG) by looking at protein signaling pathways for hypertension. The results showed that the STAT3, MAPK3, AKT1, and EDN1 proteins were significant proteins involved in the mechanism of the response to leptin, the ERK1 and ERK2 cascades, the process of nitric oxide biosynthesis, and the cellular response to ROS.
Machine learning for potential anti-cancer discovery from black sea cucumbers Fahrury Romdendine, Muhammad; Fatriani, Rizka; Ananta Kusuma, Wisnu; Annisa, Annisa; Nurilmala, Mala
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3157-3163

Abstract

Despite being an abundant marine organism in Indonesia, black sea cucumbers (Holothuria atra) is still underutilised due to its slightly bitter taste. This study aims to identify potential anti-cancer compounds from black sea cucumbers using machine learning (ML) to perform drug discovery. ML models were used to predict interactions between compounds from the organism with cancer-related proteins. Following prediction, all compounds were computationally validated through molecular docking. The validated compounds were then screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) Lab 2.0 to assess their druglike properties. The results showed that ML predicted seven out of 86 compounds were interacted with cancer-related proteins. Computational validation from the results showed that four out of seven compounds demonstrated stable interaction with proteins where only one compound meet the criteria of drug-like compound. The framework of ML and computational validation highlighted in this study shows a great promise in the future of drug discovery specifically for marine organisms. Since computational method only works in prediction realms, wet lab validation and clinical trials are imperative before the drug candidate can be produced as actual anti-cancer drug.
Klasifikasi Kanker Tumor Payudara Menggunakan Arsitektur Inception-V3 Dan Algoritma Machine Learning Supriyanto, Arif; Kusuma, Wisnu Ananta; Rahmawan, Hendra
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 7, No 3 (2022): September 2022
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v7i3.1284

Abstract

Breast cancer is a disease that arises due to breast tissue cells that grow abnormally and continuously. This disease is a disease with a large increase in number of around 13 million per year, with a mortality rate of 9.6% from a total of 65,858 cases. Early detection of breast cancer for prevention needs to be done, with the hope that breast cancer is easier to treat and cure and can even be prevented before it enters an advanced stage. In this research, build a model with transfer learning technique for breast cancer classification. There are 4 methods tested, namely Inception-V3 feature extraction with the Radial Basic Function Neural Network classification method, FeedForward Neural Network, Logistic Regression and feature extraction by making changes to the hyperparameter layer. This study compares the four models to get the best one to solve the problem of breast cancer classification. The data used in this study are breast cancer image data with a zoom scale of 40X, 100X, 200X and 400X. The dataset was sourced from The Laboratory University of Parana with P&D Laboratory Pathological Anatomy and Cytopathology, Parana, Brazil. The results of this study indicate that the Inception-V3 feature extraction method with the Logistic Regression classification method on the 40X zoom scale data provides the best accuracy (93.00%), precision (94.00%), and recall (91.00%) F1-score (92.00%).
SAE-DNN-GA: Sebuah Pendekatan Klasifikasi Multilabel dalam Prediksi Senyawa Herbal Potensial Untuk Penyakit COVID-19 Wijaya, Eko Praja Hamid; Kusuma, Wisnu Ananta; Wijaya, Sony Hartono
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 11 No. 2 (2024)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.11.2.111-121

Abstract

COVID-19 adalah penyakit dengan laju penyebaran yang tinggi. Percepatan proses penemuan obat untuk penyakit tersebut sangat dibutuhkan. Penggunaan kembali obat (drug repurposing) merupakan salah satu alternatif dalam pengembangan dan penemuan obat dengan biaya murah serta waktu yang singkat. Tanaman herbal dapat digunakan sebagai obat dengan khasiat yang lebih baik, efek samping yang lebih sedikit, dan lebih murah. Prediksi interaksi obat-target dan penggunaan kembali obat dapat digunakan untuk mengeksplorasi senyawa herbal potensial. Penelitian ini mengatasi kelemahan klasifikasi biner dengan model DSSL-DTI (Deep Semi Supervised Learning-Drug Target Interaction) yang dioptimasi menggunakan Algoritma Genetika. Tujuan penelitian ini adalah mendeteksi kemungkinan adanya hubungan antar label menggunakan pendekatan klasifikasi multilabel dengan model yang dioptimasi. Data yang digunakan penelitian ini antara lain: data protein, data interaksi senyawa-protein, dan data senyawa herbal. Data protein diperoleh dari situs GeneCards yang berisi kumpulan protein yang berasosiasi dengan COVID-19 dan ditemukan pada manusia. Data interaksi senyawa-protein diperoleh dari situs DrugBank dan SuperTarget. Adapun data senyawa herbal diperoleh dari HerbalDB. Hasil penelitian menunjukkan bahwa dengan menggunakan model SAE-DNN-GA yang diusulkan, prediksi senyawa herbal menghasilkan sepuluh senyawa yang berinteraksi dengan dua protein bernilai relevansi tertinggi, yaitu protein INS (7.094) dan ALB (3.178). Hasil ini diharapkan mampu meningkatkan hasil prediksi kandidat senyawa herbal sebagai obat penyakit COVID-19 menjadi lebih akurat.
Identification of Significant Proteins Associated with Diabetes Mellitus Using Network Analysis of Protein-Protein Interactions Usman, Muhammad Syafiuddin; Kusuma, Wisnu Ananta; Afendi, Farit Mochamad; Heryanto, Rudi
Computer Engineering and Applications Journal Vol 8 No 1 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (492.82 KB) | DOI: 10.18495/comengapp.v8i1.283

Abstract

Computation approach to identify significance of proteins related with disease was proposed as one of the solutions from the problem of experimental method application which is generally high cost and time consuming. The case of study was conducted on Diabetes Melitus (DM) type 2 diseases. Identification of significant proteins was conducted by constructing protein-protein interactions network and then analyzing the network topology. Dataset was obtained from Online Mendelian Inheritance in Man (OMIM) and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) which provided protein data related with a disease and Protein-Protein Interaction (PPI), respectively. The results of topology analysis towards Protein-Protein Interaction (PPI) showed that there were 21 significant protein associated with DM where INS as a network center protein and AKTI, TCF7L2, KCNJ11, PPARG, GCG, INSR, IAPP, SOCS3 were proteins that had close relation directly with INS.
Biological constraint in digital data encoding: A DNA based approach for image representation Muttaqin, Muhammad Rafi; Herdiyeni, Yeni; Buono, Agus; Priandana, Karlisa; Siregar, Iskandar Zulkarnaen; Kusuma, Wisnu Ananta
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1747

Abstract

Digital data encoding is crucial for communication and data storage, but conventional techniques, such as ASCII and binary coding, have drawbacks in terms of processing speed and storage capacity. A potential substitute with parallel processing and high-capacity storage is DNA-based data encoding. The goal of this research is to develop a digital data encoding technique based on DNA, while considering biological constraints such as homopolymer and GC-content. The process involves converting image pixel values into binary format, followed by encoding into DNA sequences, ensuring they meet biological constraints. The validity of the resulting DNA sequences is assessed through transcription and translation processes. Additionally, Multiple Sequence Alignment analysis is conducted to compare the similarities between the encoded DNA sequences. The results indicate that the DNA sequences from MNIST images share similar characteristics, reflected in the phylogenetic tree's close clustering. Multiple Sequence Alignment analysis shows that biological constraints successfully preserved the core visual features, allowing accurate clustering. However, this method also faces drawbacks, particularly in the reduction of visual information and sensitivity to changes in image intensity. Despite these challenges, DNA-based encoding shows potential for digital image representation. Further development, particularly the integration of deep learning, could lead to more efficient, secure, and sustainable data storage systems, especially for image data.
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