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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Silvikultur Tropika (JST) Jurnal Pustakawan Indonesia BIOTROPIA - The Southeast Asian Journal of Tropical Biology Jurnal Ilmu Komputer dan Agri-Informatika Journal of Degraded and Mining Lands Management 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 Scientific Journal of Informatics Edulib JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control BAREKENG: Jurnal Ilmu Matematika dan Terapan ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Computer Science and Information Technologies Nusantara Science and Technology Proceedings International Journal of Electronics and Communications Systems Jurnal Pustakawan Indonesia Jurnal Informatika dan Teknologi Informasi Jurnal INFOTEL Policy Brief Pertanian, Kelautan, dan Biosains Tropika Scientific Journal of Informatics BACA: Jurnal Dokumentasi dan Informasi
Imas Sukaesih Sitanggang
Department Of Computer Science, Faculty Of Mathematics And Natural Sciences, IPB University, Jl. Meranti Wing 20 Level V, IPB Dramaga Campus Bogor, 16680, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Veterinary

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Journal : Jurnal Ilmu Komputer dan Agri-Informatika

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Pengembangan Modul Otomatisasi Pengunduhan Citra Sentinel-1A Berbasis Web Menggunakan Metode Prototyping Muhammad Asyhar Agmalaro; Imas Sukaesih Sitanggang; Taufik Hidayat
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 2 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.2.137-148

Abstract

Sentinel-1A imagery can be used for various purposes, such as surveys and agricultural land use mapping. For example, Sentinel-1A image can be used to carry out land processing and validate crop yields from horticultural crops such as garlic. However, the acquisition and download of Sentinel images are currently done manually with several stages, so it still needs to be more effective and efficient. Therefore, an alternative way to support the acquisition of sentinel data is necessary by optimizing the process of automating the download of Sentinel data. This study aims to build a front-end module to automate the downloading of web-based Sentinel image data using the Django Framework. The prototyping method is used to develop a front-end module for Sentinel image download automation. This method was chosen based on its advantages in getting feedback from each user from every iteration carried out so that improvements can be made quickly according to user needs. The result of this research is an automated system for downloading Sentinel-1A images that can download Sentinel image data via maps or by validating geoJson data entered by the user. The development of this system is carried out in two iterations. All functions in the developed module were successfully performed in black box testing without showing any errors.
Model Klasifikasi Kesesuaian Lahan Bawang Putih Menggunakan Interpolasi Spasial dan Algoritme Pohon Keputusan Dini Hayati; Imas Sukaesih Sitanggang; Annisa
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 11 No. 1 (2024)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.11.1.1-12

Abstract

Garlic stands as a vital horticultural product requiring consistent fulfillment each year. The production of garlic is not proportional to meet the consumption demand, prompting the government to import garlic to meet domestic needs. Among these efforts, expanding garlic cultivation lands holds significant importance. This study aims to determine a classification model for garlic suitability of land using the C5.0 algorithm based on land characteristics and temperature interpolation using the IDW method. The research resulted in 5 rules for land suitability classes with an accuracy of 97.81% on a dataset with temperature data for May 2022. The important variable in determining land suitability classes during this period is soil mineral depth. On the other hand, the accuracy value on a dataset with temperature data for July 2022 yielded 17 rules for land suitability with an accuracy of 95.91%. The important variable in determining land suitability classes during this period is base saturation.
Model Klasifikasi Fase Pertumbuhan Tebu dari Citra Sentinel 1 Multi-temporal Menggunakan Algoritma Random Forest Bramdito, Vandam Caesariadi; Wijaya, Sony Hartono; Sitanggang, Imas Sukaesih
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.10.2.212-223

Abstract

The Special Region of Yogyakarta, a designated sugarcane center, demands special attention for effective extensification efforts, necessitating spatial insights into sugarcane farming. Monitoring of sugarcane fields served to obtain information on the growth phases of sugarcane and its distribution for agricultural extensification strategies. For this reason, it is necessary to carry out image classification using the Random Forest reliable algorithm to classify sugarcane growth phases in multi-temporal Sentinel 1 images. The sugarcane planting calendar Map is conducted from the image classification outcomes and then tested for its accuracy for evaluation. The classification process involves analyzing each image captured monthly throughout 2020, with a dataset comprising 9690 sample pixels across six classification classes: buildings, vegetation, water bodies, rice fields, sugarcane phase class 1, and sugarcane phase class 2. The results show that the Sentinel 1 image consisting of 13 images has an average classification model accuracy of 65.38%. Notably, the image classification achieved its pinnacle performance in October, boasting the highest overall accuracy level at 73.33%, accompanied by an RMSE value of 2.05.
Klasifikasi Daerah Penangkapan Ikan Menggunakan Algoritma Random Forest dan Support Vector Machine Kurnianto, Andi; Imas Sukaesih Sitanggang; Medria Kusuma Dewi Hardhienata
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 11 No. 2 (2024)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.11.2.100-110

Abstract

The economic condition of traditional fishermen is still in a cycle of poverty, so solutions are needed to improve welfare. One solution is to use information technology regarding fishing ground so that fishermen can save fuel and increase the number of catches. Fishing ground information can be determined by processing satellite image data and using machine learning technology. This research aims to create a model that can classify fishing ground using Random Forest and Support Vector Machine algorithms using satellite image data of the Java Sea and its surroundings from 2019-2021 with the parameters chlorophyll, sea surface temperature, salinity, height of the sea, and water temperature. This research shows that the chlorophyll parameter has the greatest role (77.14%) in determining fishing ground. The precision value produced by the Support Vector Machine algorithm (99.83%) is higher than that produced by the Random Forest algorithm (99.80%). However, the classification model produced by the Random Forest algorithm has higher accuracy (99.90%), recall (100%) and F1 score (99.90%) compared to that produced by the Support Vector Machine algorithm, with an accuracy value of (99.89%), recall (99.96%) and F1 score (99.89%).
Identifikasi Kematangan Tomat dengan Principal Component Analysis dan K-Nearest Neighbour Berdasarkan Citra Warna Khairani; Sitanggang, Imas Sukaesih; Haryanto, Toto; Kustiyo, Aziz
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 11 No. 2 (2024)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.11.2.122-132

Abstract

Manually determining the level of ripeness of tomatoes has weaknesses because the standards are subjective and time consuming. This research aims to identify ripeness of tomatoes based on Hue Saturation Value (HSV) color representation using Principal Component Analysis (PCA) as feature extraction and K-Nearest Neighbor (KNN) for classification. This research uses 400 images with a spatial resolution of 400x400 which are grouped into 5 levels of maturity, namely green, turning, pink, light red and red. The data is divided into training data and test data with a ratio of 80:20. The scenario applied is a division of color space data, namely Hue (H), Saturation (S), Value (V), Hue-Saturation (HS), Hue-Value (HV), Saturation-Value (SV) and HSV. The values ​​of k as a neighbor in KNN used as a scenario are 1, 3, 5, 7, 9 and 11. The principal component values ​​applied are 5, 10, 15 and 65 with a variance ratio of 95%. The research results show that with K=7 and PC value =5 it produces the highest accuracy value with a percentage of 94% in HV testing. The results of this research show that by classifying test data of 80 image data, the results obtained were 75 accurate data and 5 inaccurate data.
Co-Authors -, Rachmawati Abdul Rahman Saleh Abdul Wakhid Aditia Yudhistira Agus Buono Agus Mulyana Agus Purwito Ahmad Khusaeri Albar, Israr Alusyanti Primawati Anak Agung Istri Sri Wiadnyani Andi Nurkholis Andita Wahyuningtyas Anna Qahhariana Annisa Annisa Annisa Annisa Annisa Awal, Elsa Elvira Aziz Kustiyo Baba Barus Badollahi Mustafa Boedi Tjahjono Bramdito, Vandam Caesariadi Despry Nur Annisa Ahmad, Despry Nur Annisa DEWI APRI ASTUTI Dhani Sulistiyo Wibowo Dini Hayati Eddy Prasetyo Nugroho Fakhri Sukma Afina Febriyanti Bifakhlina Firman Ardiansyah Hardhienata, Medria Kusuma Dewi Hari Agung Adrianto Hasibuan, Lailan Sahrina Hendra Rahmawan Hendra Rahmawan Herawan, Yoga Heru Sukoco HUSNUL KHOTIMAH I Nengah Surati Jaya Ikhsan kurniawan Irman Hermadi Ivan Maulana Putra Khairani Krisnanto, Ferdian Kurnianto, Andi Lailan Syaufina Lilis Syarifah Luki Abdullah Medria Kusuma Dewi Hardhienata Miftah Farid mufti, abdul Muhammad Abrar Istiadi Muhammad Asyhar Agmalaro Muhammad Murtadha Ramadhan Nalar Istiqomah Nia Kurniati Peggy Antonette Soplantila Pudji Muljono Purwanti , Endang Yuni Purwanti, Endang Yuni Putra, Fiqhri Mulianda Raden Fityan Hakim Raharja, Aditya Cipta Ramadhan, Jeri Rd. Zainal Frihadian Ridwan Raafi'udin Rina Trisminingsih Risa Intan Komaraasih Rizki, Yoze Safrudin, Muhammad Safrul Sakti, Harry Hardian Santoso, Angga Bayu Satyawan, Verda Emmelinda Shelvie Nidya Neyman Sobir Sobir Sonita Veronica Br Barus Sonita Veronica Br Barus Sony Hartono Wijaya Suci Indrawati Irwan Sulistyo Basuki Suradiradja, Kahfi Heryandi Suria Darma Tarigan Syarifah Aini Taihuttu, Helda Yunita Taufik Djatna Taufik Hidayat Tenda, Edwin Tiurma Lumban Gaol Toto Haryanto Trisminingsih, Rina Unik, Mitra Wa Ode Rahma Agus Udaya Manarfa Wattimena, Emanuella M C Wisnu Ananta Kusuma Wulandari WULANDARI Yenni Puspitasari Yoanda, Sely Zuliar Efendi