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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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Sentinel-1A image classification for identification of garlic plants using decision tree and convolutional neural network Risa Intan Komaraasih; Imas Sukaesih Sitanggang; Annisa Annisa; Muhammad Asyhar Agmalaro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The Indonesian government launched a garlic self-sufficiency program by 2033 to reduce imports by monitoring garlic lands in several central garlic areas. Remote sensing using satellite imageries can assist the monitoring program by mapping the garlic lands. A previous study has classified Sentinel-1A satellite imageries to identify garlic lands in Sembalun Lombok Indonesia using the decision tree C5.0 algorithm with three scenarios data input and produced a model with an accuracy of 78.45% using scenarios with two attributes vertical-vertical (VV) and vertical-horizontal (VH) bands. Therefore, this study aims to improve the accuracy of the classification model from the previous study. This study applied two classification algorithms, decision tree C5.0 and convolutional neural network (CNN), with two new scenarios which used two new combinations of attributes). The results show that the use of new data scenarios as input for C5.0 can not increase the previous model's accuracy. While the use of the CNN algorithm shows that it can improve the previous study's accuracy by 7.91% because it produced a model with an accuracy of 86.36%. This study is expected to help garlic land identification in the Sembalun area to support government programs in monitoring garlic lands.
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.
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.
CO and PM10 Prediction Model based on Air Quality Index Considering Meteorological Factors in DKI Jakarta using LSTM Wattimena, Emanuella M C; Annisa, Annisa; Sitanggang, Imas Sukaesih
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.33791

Abstract

Purpose: This study aimed to make CO and PM10 prediction models in DKI Jakarta using Long Short-Term Memory (LSTM) with and without meteorological variables, consisting of wind speed, solar radiation, air humidity, and air temperature to see how far these variables affect the model.Methods: The method chosen in this study is LSTM recurrent neural network as one of the best algorithms that perform better in predicting time series. The LSTM models in this study were used to compare the performance between modeling using meteorological factors and without meteorological factors.Result: The results show that the use of meteorological predictors in the CO prediction model has no effect on the model used, but the use of meteorological predictors influences the PM10 prediction model. The prediction model with meteorological predictors produces a smaller RMSE and stronger correlation coefficient than modeling without using meteorological predictors.Novelty: In this paper, a comparison between the prediction model of CO and PM10 has been conducted with two scenarios, modeling with meteorological factors and modeling without meteorological factors. After the comparative analysis was done, it was found that the meteorological variables do not affect the CO index in 5 air quality monitoring stations in DKI Jakarta. It can be said that the level of CO pollutants tends to be influenced by factors other than meteorological factors.  
Scalability Testing of Land Forest Fire Patrol Information Systems Ahmad Khusaeri; Imas Sukaesih Sitanggang; Hendra Rahmawan
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.977

Abstract

The Patrol Information System for the Prevention of Forest Land Fires (SIPP Karhutla) in Indonesia is a tool for assisting patrol activities for controlling forest and land fires in Indonesia. The addition of Karhutla SIPP users causes the need for system scalability testing. This study aims to perform non-functional testing that focuses on scalability testing. The steps in scalability testing include creating schemas, conducting tests, and analyzing results. There are five schemes with a total sample of 700 samples. Testing was carried out using the JMeter automation testing tool assisted by Blazemeter in creating scripts. The scalability test parameter has three parameters: average CPU usage, memory usage, and network usage. The test results show that the CPU capacity used can handle up to 700 users, while with a memory capacity of 8GB it can handle up to 420 users. All users is the user menu that has the highest value for each test parameter The average value of CPU usage is 44.8%, the average memory usage is 69.48% and the average network usage is 2.8 Mb/s. In minimizing server performance, the tile cache map method can be applied to the system and can increase the memory capacity used.
Development ETL (Extract, Transform and Load) Module in Indonesian Agricultural Commodities OLAP System Aditia Yudhistira; Imas Sukaesih Sitanggang; Hari Agung Adrianto
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1758.335-343

Abstract

The SOLAP system for Indonesian Agricultural Commodities is a successful development based on previous studies. Agricultural commodity data are managed in a data warehouse with a galactic schema, which has 7 fact tables, namely cut flower horticulture, ornamental plant horticulture, horticulture, food crops, plantation, livestock population, and livestock production, as well as 3 dimensional tables, namely location, time, and commodity. The results of SOLAP operations on the system can be visualized in the form of crosstabs, graphs and maps. The system uses a web platform so that it can be accessed by the public. However, the SOLAP system cannot update data in real time. This study aims to develop a data warehouse for Indonesian Agricultural Commodities SOLAP in real time by creating a scraping system. This study has succeeded in developing a data warehouse in real time on the indonesian agricultural commodity SOLAP system by creating a real time scraping system that is applied to the SOLAP server and has succeeded in making the ETL process run in real time on the SOLAP server and optimizing polygon-based spatial data visualization using the Douglas-Peucker. This study has also carried out functional testing of OLAP features and functions on the Indonesian Agricultural Commodity SOLAP system using the black box testing method. The results of this study provide accurate and real-time data on the SOLAP of Indonesian Agricultural Commodities, with the results of SOLAP feature testing achieving 100 percent pass and the data conformity test results of OLAP function as expected. In addition, the results of this study make it possible to automatically update the data according to a predetermined schedule to provide real-time information.
Analisis Dampak Kabut Asap dari Kebakaran Hutan dan Lahan dengan Pendekatan Text Mining Zuliar Efendi; Imas Sukaesih Sitanggang; Lailan Syaufina
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20231057248

Abstract

Kebakaran hutan dan lahan (karhutla) berdampak buruk bagi lingkungan serta ekosistem. Kabut asap merupakan salah satu akibat yang ditimbulkan dari kebakaran hutan dan lahan. Keresahan dari munculnya kabut asap dan kebakaran hutan menjadi trending topic pada media sosial Twitter. Analisis Twitter perlu dilakukan untuk melihat kesesuaian hashtag yang digunakan dengan topik yang dibahas yaitu kabut asap. Data Twitter dapat dianalisis menggunakan text mining. Penelitian ini bertujuan untuk melihat hubungan antara percakapan di media sosial Twitter dengan kejadian kabut asap yang muncul dari kebakaran hutan dan lahan. Metode yang digunakan adalah teknik text mining yaitu menggunakan algoritme clustering. Data yang digunakan adalah data tweet terkait kabut asap di Provinsi Riau pada jarak 11 – 17 September 2019 dan juga data hotspot atau titik panas serta citra Sentinel2. Data tweet dikelompokkan dengan beberapa percobaan pada jarak antar cluster yaitu single linkage, complete linkage, average linkage, dan ward. Hasil clustering menunjukkan bahwa validitas cluster tertinggi memiliki silhouette index sebesar, 0,3360 dengan jarak antar cluster menggunakan ward. Hasil cluster menunjukkan bahwa terdapat tiga cluster yang dominan pembahasannya terkait kabut asap. Data Twitter pada ketiga cluster tersebut memiliki ciri istilah atau term yang berkaitan dengan kabut asap antara lain "kabut", "asap", dan "udara". terdapat di wilayah Pekanbaru serta wilayah Bengkalis, Provinsi Riau. Hasil dapat menjadi salah satu cara pengendalian karhutla yaitu deteksi dini dengan menggunakan media sosial Twitter.   Abstract  Forest and land fires have a harmful impact on the environment and ecosystem. Haze is one of the consequences that arise from forest fires and the environment. Anxiety about haze and forest fires is a trending topic on social media Twitter. Twitter analysis needs to be done to see the compatibility of the hashtags used with the haze topic. The Twitter data can be analyzed using text mining. This study aims to see the relation between conversations on social media Twitter and the occurrence of haze that arises from forest and land fires. The method used is a text mining technique that uses a clustering algorithm. The data used are tweet data related to haze in Riau Province in the range 11-17 September 2019 as well as hotspot data and Sentinel-2 imagery. Tweet data were clustered by several experiments on the distance between clusters, namely single linkage, complete linkage, average linkage, and ward. Clustering results show that the highest cluster validity has a silhouette index of 0.3360 with the distance between clusters using wards. The cluster results show that there are three clusters that are dominant in the discussion related to haze. The Twitter data for the three clusters has the characteristics of terms related to smog, including "kabut", "asap", and "udara". The impact felt by the people of Riau Province through social media Twitter related to the haze is the impact on health and air quality. Cluster tweets that discuss the topic of forest and land fires and haze are in the Pekanbaru and Bengkalis regions, Riau Province. The results can be one of the karhutla controls is early detection by using social media Twitter.
Metadata Modeling of LoRa Based Payload Information for Precision Agriculture Tea Plantation Eddy Prasetyo Nugroho; Taufik Djatna; Imas Sukaesih Sitanggang; Irman Hermadi; Agus Mulyana; Sri Wahjuni; Heru Sukoco
Scientific Journal of Informatics Vol 10, No 2 (2023): May 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i2.43432

Abstract

Purpose: The purpose of this study is to model the metadata of Payload Information on Agriculture Drones which consists of the results of images computational and the Onboard system of the Drone.Methods: The stages of the research were carried out with the process of forming Payload information metadata from the Agriculture Drone with sensors/actuators based on the architecture and computing with Image Processing or Computer Vision on the camera captures. This study describes the metadata modeling process formed from the Internet of Things system with Drone and GCS communication based on the Long Range or Long-Range Wide Area Network protocols with Payload information consisting of drone data and image computation results. Result: The result obtained is the formation of Payload information from LoRa-based Drones with a frame size of 142 bytes. Novelty: Payload information is formed into a metadata model indicator with the formation scheme being part of the tea plantation dataset. The metadata model will be test expected to obtain field data on Drones and GCS communication in the LoRaWAN Network in tea plantations which are rural environments. 
Scalability Testing of Land Forest Fire Patrol Information Systems Ahmad Khusaeri; Imas Sukaesih Sitanggang; Hendra Rahmawan
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.977

Abstract

The Patrol Information System for the Prevention of Forest Land Fires (SIPP Karhutla) in Indonesia is a tool for assisting patrol activities for controlling forest and land fires in Indonesia. The addition of Karhutla SIPP users causes the need for system scalability testing. This study aims to perform non-functional testing that focuses on scalability testing. The steps in scalability testing include creating schemas, conducting tests, and analyzing results. There are five schemes with a total sample of 700 samples. Testing was carried out using the JMeter automation testing tool assisted by Blazemeter in creating scripts. The scalability test parameter has three parameters: average CPU usage, memory usage, and network usage. The test results show that the CPU capacity used can handle up to 700 users, while with a memory capacity of 8GB it can handle up to 420 users. All users is the user menu that has the highest value for each test parameter The average value of CPU usage is 44.8%, the average memory usage is 69.48% and the average network usage is 2.8 Mb/s. In minimizing server performance, the tile cache map method can be applied to the system and can increase the memory capacity used.
Knowledge Management System For Forest and Land Fire Mitigation in Indonesia: A Web-Based Application Development Unik, Mitra; Rizki, Yoze; Sukaesih Sitanggang, Imas; Syaufina, Lailan
Jurnal Manajemen Hutan Tropika Vol. 30 No. 1 (2024)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.30.1.12

Abstract

Forest and land fires in Indonesia have serious impacts on many aspects, including the environment, health, economy, politics, and international relations. They cause haze pollution that extends to neighboring countries and peatland degradation. Despite extensive research and mitigation efforts, forest and land fires continue to occur and cost lives. Therefore, effective management and mitigation strategies are required. This research developed a web-based knowledge management system (KMS) using the Laravel framework as an effective forest and land fire mitigation platform. The KMS aims to support decision-making, facilitate knowledge exchange, improve coordination between stakeholders, and expand access to relevant information, while maintaining the sustainability of forest and land resources in Indonesia. The KMS evaluation results cover two important aspects: blackbox evaluation and performance evaluation. The blackbox evaluation showed that KMS provides knowledge retrieval features based on expert knowledge. The performance evaluation revealed that the KMS provides easy and quick access to information on forest and land fire prevention and management. Thus, this research has great potential to help overcome the problem of forest and land fires in Indonesia and protect the environment and society from their adverse effects.
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