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All Journal Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) Jurnal Teknologi Dan Industri Pangan Jurnal Teknologi Industri Pertanian Jurnal Komunikasi Pembangunan Jurnal Pustakawan Indonesia Jurnal Sain Veteriner Buletin Peternakan Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Keteknikan Pertanian TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Pengkajian dan Pengembangan Teknologi Pertanian Jurnal Studi Komunikasi dan Media Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Jurnal PIKOM (Penelitian Komunikasi dan Pembangunan) JOIV : International Journal on Informatics Visualization Nyimak: Journal of Communication Matra Pembaruan: Jurnal Inovasi Kebijakan International Journal of Supply Chain Management Warta Penelitian Perhubungan JURNAL PANGAN Journal of Information System, Applied, Management, Accounting and Research Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pustakawan Indonesia
Kudang Boro Seminar
Department Of Mechanical And Biosystem Engineering, Faculty Of Agricultural Technology, IPB University, Bogor, 16680
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Journal : JOIV : International Journal on Informatics Visualization

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A Novel Approach for Bali Cattle Classification: Integrating the Fuzzy Inference System with Certainty Factor and Morphometric Parameters Arnaldy, Defiana; Seminar, Kudang Boro; Neyman, Shelvie Nidya; Sukoco, Heru; Muladno, -
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3218

Abstract

Enhancing the productivity and quality of Balinese cattle is a crucial goal for improving livestock management practices in Indonesia. Traditional evaluation methods used by farmers are often subjective and inconsistent, leading to inaccuracies in cattle classification and limiting the effectiveness of breeding and selection processes. To address these challenges, this study proposes a Fuzzy Inference System with Certainty Factor (FIS-CF) to improve cattle classification by providing more objective and reliable grading criteria. The model utilizes key physical parameters, including shoulder height, body length, and chest circumference, as input features to categorize cattle into three quality classes. A diverse dataset was collected from the People's Animal Husbandry School (SPR) and various farms across Indonesia to evaluate the model's performance. The FIS-CF model achieved a classification accuracy of 95.93% and a balanced accuracy of 96.20%, outperforming traditional methods that rely on subjective assessment. These results demonstrate that the proposed model provides a consistent, scalable, and data-driven solution for livestock classification, helping farmers make more informed decisions in cattle selection and breeding. Additionally, the model addresses key limitations of current practices by reducing reliance on manual evaluations, which often vary between assessors. The findings highlight the potential for wider adoption of the FIS-CF model across the livestock sector to improve productivity and streamline herd management processes. Future research will aim to refine the model further by incorporating additional parameters, such as age and weight, and expanding its validation to larger datasets covering different cattle breeds and farming environments to ensure broader applicability in sustainable livestock management.
Machine Learning Model to Predict Manganese Micronutrient Content in Oil Palm Plantation Soil Using Sentinel 1A and Sentinel 2A Image Integration Suhendi, -; Boro Seminar, Kudang; Sudradjat, -; Liyantono, -; Munir, Sirojul; Az Zahra, Fatimah
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3306

Abstract

This study aims to predict manganese micronutrients in oil palm plantation soil using machine learning. Materials and technological tools use remote sensing with the integration of Sentinel 1A and Sentinel 2A satellites for monitoring micronutrients in peat soil in oil palm plantations. Integrating Sentinel 1A with Sentinel 2A will complement the shortcomings of Sentinel 2A, which is not free from cloud cover. Sentinel 1A has the advantage of being free from cloud cover. Meanwhile, Sentinel 2A has a high spectral resolution with 12 to 13 bands, which Sentinel 1A does not have, and only has dual polarization (VV-VH) and local incident angle (LIA). This study uses a machine learning method to obtain a model with a random forest regression algorithm and 103 soil samples in Central Kalimantan and Riau locations. The results of the model performance evaluation using integration showed MAPE and correctness of 25% and 75%, respectively. Suppose using Sentinel 1A, MAPE, and accuracy are 59.63% and 40.23%. Using Sentinel 2A, the MAPE and accuracy obtained are 48.40% and 51.59%. These results suggest that the integration of Sentinel 1A and Sentinel 2A plays a significant role, given their good predictive power. The implications of this study are the status of nutrient distribution maps, which can help determine the status of manganese micronutrients in soil in oil palm plantations for fertilizer application plans according to the needs of each oil palm plant.
Co-Authors - Sudradjat, - A. Haris Rangkuti Abdi Kurniawan Abung Supama Wijaya Aditya, Edit Lesa Agik Suprayogi Agus Buono Agus Ghautsun Niam Agus Maulana ahmad yani Akhiruddin Maddu Ali Djamhuri Ali Usman, Ali Alimuddin Alimuddin Alvin Fatikhunnada Amiruddin Saleh Amrozi Annisa Utami Seminar Arief Ramadhan Arif Imam Suroso Arif Kurnia Wijayanto Az Zahra, Fatimah Azka Bazil Danish Rahmat B Mustafa Badollahi Mustafa Bagus Sartono Bayu Ardy Kresna Bayu Indrayana Bonang Waspadadi Ligar Budhi Hascaryo Iskandar Dedy Wirawan Soedibyo Defiana Arnaldy Diki Gita Purnama, Diki Gita Djuara P. Lubis Dodi Nandika Dodik Briawan Drajat Martianto Dwi Susanto Edi Sukmadirana Eneng Tita Tosida Eni Sumarni Eva Maulina Aritonang Evy Damayanthi Faiz Ridhan Faroka Firman Ardiansyah Firmansyah, Raden Arief Gananda Hayardisi Gibtha Fitri Laxmi Hardinsyah Haris Budiman Harry Imantho Hartoyo Herry Suhardiyanto Heru Sukoco Herwindo Dharmawan I Dewa Made Subrata I Wayan Astika Imam Teguh Saptono Imantho, Harry Irman Hermadi Janti G. Sudjana Joko Hermanianto Joko Ratono Joko Ratono Karlisa Priandana koekoeh santoso komariah komariah Kurniawan, Abdi Liyantono . Liyantono, - Lucia Cyrilla Luh Putu Ratna Sundari Maria Margrith Tirtasari Marimin , Melva Linda Aritonang Mohamad Agus Setiawan Mohamad Solahudin Mohammad Aftaf Muhajir Mohammad Solahudin Mokhamad Fakhrul Ulum, Mokhamad Muhammad Firdaus Muhammad Riza Muladno - Muliati, Vika Febri Muyassar Allam Suyuthi Nakao Nomura Nanda, Muhammad Achirul Nasution, Syahrial Nelwan, Leopold Oscar Nila Susila Yulianti Nugraha Edhi Suyatma Oktaviana Purnamasari Onno Widodo Purbo Pradeka Brilyan Purwandoko Pudji Muljono Purwandoko, Pradeka Brilyan Rachma Fitriati Rani Audona RATNA SARI Ridi Arif Rivangga Yuda Hendika Rizky Mulya Sampurno RR. Ella Evrita Hestiandari Rudi Afnan Samudra, Ami Anggraini Sarwititi Sarwoprasodjo Satyanto Krido Saptomo Shelvie Nidya Neyman Sirojul Munir Siska Mulyawaty Sitti Eha Faihah Sofyan Sjaf Sugiyanta Suhendi, - Sumiati Sumiati Suryo Wiyono Sutrisno Mardjan Sutrisno Sutrisno Sutrisno Sutrisno Sutrisno, Sutrisno Syahrial Nasution Taufik Makbullah Triani Rahmawati Usman Ahmad Veranus Sidharta Wawan Hermawan Wawan Wiraatmaja Wichitra Yasya Widodo Widodo Widodo Widodo Yandra Arkeman Yulia Dwi Indriani Yusra Fernando