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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Quantitative Research and Modeling Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Jurnal Sistem Informasi dan Teknologi Informasi Journal of Social Work and Science Education
Marwondo
Department Of Informatic, Faculty Of Technology And Informatic, Universitas Informatika Dan Bisnis Indonesia, Bandung, Indonesia.
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Physics Social Sciences Other

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PREDICTION OF INHIBITOR BINDING AFFINITY AND MOLECULAR INTERACTIONS IN MPRO DENGUE USING MACHINE LEARNING Venia Restreva Danestiara; Marwondo Marwondo; Nayla Nurul Azkiya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5994

Abstract

The dengue virus experiences rapid mutation and genetic variability, posing challenges in developing effective antiviral therapies. This study explores the prediction of binding affinities between potential antiviral drug inhibitors and the NS2B-NS3 protease of the dengue virus using machine learning models. Molecular docking simulations were conducted with AutoDock Vina to generate interaction data between viral proteins and ligands. The generated datasets were used to train several machine learning models, including Random Forest Regressor (RF Regressor), Support Vector Regression (SVR), and Extreme Gradient Boosting Regressor (XGBoost Regressor). The RF Regressor model demonstrated the highest accuracy in predicting binding affinities, measured through Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson Correlation Coefficient (R). However, the XGBoost Regressor and SVR models showed better generalization in practical scenarios. This study highlights the potential of machine learning to optimize the drug discovery process and provides significant insights into antiviral drug development for dengue fever.
Automatic Watering System Berbasis Internet of Thing (IoT) untuk Peningkatan Produksi Pertanian Marwondo; Nursyanti , Reni; Kurnia , Mohamad Erlangga
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v4i1.144

Abstract

Indonesia, as an agrarian country, faces challenges in efficient agricultural irrigation, primarily due to conventional watering methods that lack precision. The application of Internet of Things (IoT) technology offers a promising solution to improve water efficiency and agricultural productivity. This community service program aimed to enhance the understanding and skills of students and teachers at SMKN PP Lembang in designing and implementing an IoT-based automatic watering system. The program was conducted in three phases: preparation, training, and evaluation, using an educational and participatory approach. The results showed a significant increase in participants’ understanding of IoT concepts and applications—from 45% before training to 100% afterward. Participants also demonstrated strong readiness to adopt this technology in daily agricultural practices. This program proved effective in supporting the transformation of agriculture towards a more modern, precise, and sustainable system.
Automation Watering System Berbasis IoT Cerdas pada Bawang Merah Marwondo, Marwondo; Sardjono, Sardjono; Yonathan, Michael A.
INTERNAL (Information System Journal) Vol. 6 No. 2 (2023)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v6i2.851

Abstract

In the current era of rapid technological development, smart irrigation systems are increasingly needed in cultivating crops, including shallots. This system was built to increase plant productivity by using smart irrigation to anticipate climate changes that occur. Through the use of IoT technology and expert systems, this system can provide effective solutions in optimizing the growth and quality of shallots. This Automation Watering System uses a microcontroller as the brain of the system which is connected to various sensors and actuators. The microcontroller takes data from sensors and sends it to the server via a WiFi network or other wireless protocol. The server then analyzes the data using an expert system that has been programmed with expert knowledge in the field of shallot cultivation. This Automation Watering System is able to optimize water use. and avoid excess or lack of watering. The results showed that with the integration of the Internet of Things (IoT) and expert systems, this system can provide smart solutions in shallot cultivation, ensure optimal growth, and produce better harvest results.
Penerapan Fuzzy Multi Criteria Decision Making untuk Menentukan Bibit Ikan Lele Unggul di Kabupaten Bandung Marwondo, Marwondo; Nugroho, Abdul Hadi; Hidayah, Taufik; Nugraha, Dimas Adi
INTERNAL (Information System Journal) Vol. 7 No. 1 (2024)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v7i1.993

Abstract

The success of catfish cultivation is most influenced by the quality of the seeds. Proper selection of superior catfish seeds can increase crop yields and breeder profits. Seed selection needs to be assisted by the right decission making method. The aim of this research is to apply Fuzzy Multi Criteria Decision Making (FMCDM) in determining superior catfish seeds. Criteria for determining catfish seeds include growth rate, disease resistance, level of independence, seed size, and seed price. FCMC can support decision making for various criteria so that it can be used in determining superior catfish seeds. The research results show that FCMC can be used to determine superior catfish seeds. African catfish (clarias gariepinus) seeds were selected as superior seeds with the highest total score for each degree of optimism. At the optimization degree ? = 0, the total integral value reaches 0.349, at the optimization degree ? = 0.5, the total integral value reaches 0.417, and at the optimization degree ? = 1, the total integral value reaches 0.485, the highest compared to the other two seed variants. This research is useful in increasing catfish harvests, increasing profits for catfish farmers, and increasing the efficiency and effectiveness of catfish cultivation, especially in the Bandung Regency area.
Klasifikasi Kebutuhan Dokter untuk Kesejahteraan Masyarakat Menggunakan ANFIS Marwondo; Jepi Sutarlan Saputra; Habib Fauzan Mahardika; Fauzan Nur Aziz
NUANSA INFORMATIKA Vol. 18 No. 2 (2024): Nuansa Informatika 18.2 Juli 2024
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v18i2.204

Abstract

In social life, the need for medical workers is different in each region, because the population varies. If doctors cannot treat a large enough number of patients in an area, it can have various impacts on society, such as contracting dangerous diseases if not treated as soon as possible. The effect will be a decline in people's standard of living. By classifying the need for the number of health workers (doctors) relative to the population, the level of welfare in an area can be obtained. To assist in optimizing health workers (doctors) they can use fuzzy logic and ANFIS (Adaptive Neuro Fuzzy Inference System). By using ANFIS, it is hoped that we can find the optimal value for the classification of health workers that will be needed in each region. In the ANFIS test, the RMSE error value was 0.2698 and the first accuracy value was 73%. Then by adding a membership function, an RMSE error value of 0.17698 was obtained, this second accuracy value increased by 10% to 83%. By using the ANFIS method to classify health workers according to different population sizes in each region, you can measure the level of community welfare well.
Recognition and Prediction of Rice Variety–Climate Suitability Using YOLOv9 and Naïve Bayes in Agricultural Lands Marwondo, Marwondo; Danestiara, Venia Restreva; Badar, Arif Adnan; Ardiansyah, Fachrizal
Journal of Social Work and Science Education Vol. 7 No. 1 (2026): Journal of Social Work and Science Education
Publisher : Yayasan Sembilan Pemuda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52690/jswse.v7i1.1390

Abstract

The suitability of rice varieties to agroclimatic conditions is a key factor in determining rice productivity in Indonesia. Climate variability and land limitations require a decision support system capable of assisting farmers in selecting rice varieties suitable for local environmental conditions. This study aims to develop an integrated artificial intelligence-based system that combines YOLOv9 for image-based rice variety recognition and Naïve Bayes for climate suitability prediction based on temperature and humidity parameters. Image data of five rice varieties Ciherang, Inpari 32, Inpari Nutrizinc, Mekongga, and Baroma were collected directly from agricultural fields in Bandung Regency and processed through annotation, augmentation, and model training stages. The YOLOv9 model performed well in distinguishing rice varieties with relatively similar morphological characteristics, with an mAP@50 value of 0.8932. Meanwhile, the Naïve Bayes model achieved 78% accuracy in predicting climate suitability based on altitude, temperature, and humidity, and produced predictions consistent with agronomic recommendations. Both models were then integrated into a Gradio-based interactive interface to facilitate use by non-technical users. The results indicate that this integrated approach has the potential to be an effective decision support system for assisting in the selection of rice varieties that are adaptive to microclimate conditions, thereby supporting more efficient and sustainable rice cultivation practices.
Co-Authors Akbar, Imannudin Alif Gufron Pratama Ardiansyah, Fachrizal Badar, Arif Adnan Catherin Rumambo Mogot Pandin Eko Bambang Fachry Abda El Rahman Fauzan Nur Aziz Graha Prakarsa Habib Fauzan Mahardika Ikhsan Nur, Muhammad Iqbal Maulana Jepi Sutarlan Saputra Kurnia , Mohamad Erlangga Nasution, Vani Maharani Nayla Nurul Azkiya Nugraha Nugraha Nugraha, Dimas Adi Nugroho, Abdul Hadi Nursyanti , Reni R. Yadi Rakhman R. Yadi Rakhman R. Yadi Rakhman Alamsyah Reni Nursyanti Ruslan Efendi Santi Dwi Putri Sardjono Sardjono Sardjono Sardjono Sardjono Sarjono Sarjono Seny Nurhasanah Setiana, Elia Taufik Hidayah Taufik Hidayah Teofilus Hendrawan, Yesaya Titan Parama Yoga Ujang Riswanto Venia Restreva Danestiara Wahyu Kurnianto Wilner Saut Lamhot Wiyanudin Yonathan, Michael A.