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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 Simetris Bulletin of Electrical Engineering and Informatics 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 Bina Hukum Lingkungan 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 Bina Hukum Lingkungan 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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Internet of things-drone trajectory planning model with edge computing based on long range payload in rural areas Prasetyo Nugroho, Eddy; Djatna, Taufik; Sukaesih Sitanggang, Imas; Hermadi, Irman
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8776

Abstract

The integration of internet of things (IoT) with unmanned aerial vehicle (UAV) or drone, for precision agriculture (PA) in rural tea plantations is required to ensure optimal outcomes. However, rural settings presents exceptional challenges for data transmission, particularly in maintaining effective communication between drone and ground control stations (GCS). Therefore, this research aimed to develop a payload metadata identification model using long range (LoRa) technology, known for robust IoT capabilities of the model. LoRa was used to transmit drone data packets to GCS, including image data computations and onboard sensor information. Additionally, the research proposed IoT-drone trajectory planning model, specifically designed for PA in rural tea plantations. This model incorporated LoRa technology for data transmission, leveraging the effectiveness of the model in remote areas. Edge computing was also integrated into model to classify the suitability of tea plantation picking areas based on image captured with drone. An important component of the research was trajectory planning system, which optimized drone flight paths by considering location data, throughput data, battery energy consumption, and the computation of suitable picking locations. Finally, experimental results showed the effectiveness of the proposed model in identifying payload metadata, monitoring drone trajectory, and optimizing picking location paths in rural tea plantations.
Effects of Semi-Automated Preprocessing in The Beef Freshness Prediction based on Near Infrared Spectroscopy Raafi'udin, Ridwan; Purwanto, Yohanes Aris; Sitanggang, Imas Sukaesih; Astuti, Dewi Apri
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.15142

Abstract

This study investigates the application of near-infrared spectroscopy (NIR) within the wavelength range of 1350–2550 nm to predict key quality parameters of beef, specifically focusing on tenderloin cuts. The quality indicators assessed include drip loss, color, pH, moisture content, storage duration, and total plate count (TPC) as a measure of microbial load. Predictive modeling was conducted using three machine learning algorithms: Partial Least Squares (PLS), Support Vector Regression (SVR), and Random Forest Regressor (RFR). To enhance model accuracy, a semi-automated preprocessing pipeline was employed utilizing the Nippy library. This library integrates several spectral preprocessing techniques including Savitzky-Golay filtering, Standard Normal Variate (SNV), Robust Normal Variate (RNV), Local Standard Normal Variate (LSNV), as well as clipping, resampling, baseline correction, and smoothing.  Among the models developed using raw spectral data, the RFR model exhibited the highest performance, achieving coefficient of determination (R²) values of 0.82 for drip loss, 0.65 for color, 0.67 for pH, 0.61 for moisture content, 0.81 for storage duration, and 0.76 for TPC. Post preprocessing, the predictive accuracy improved significantly with R² values increasing to 0.89, 0.82, 0.87, 0.85, 0.91, and 0.90 respectively for the same parameters. These findings underscore the potential of combining advanced machine learning techniques with robust preprocessing methods to enhance the non-destructive, rapid assessment of beef quality parameters. This approach offers a promising tool for quality control in the meat processing industry, facilitating more efficient and accurate monitoring of product standards.
Model Klasifikasi Lahan Hijaun Pakan Ternak Ruminansia Dengan Algoritma Random Forest Pada Kabupaten Lumajang Marlina, Dwi; Sitanggang, Imas Sukaesih; Annisa, Annisa; Astuti, Dewi Apri
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.15967

Abstract

Informasi mengenai ketersediaan lahan hijauan pakan ternak ruminansia pada tutupan lahan memerlukan data spasial yang akurat, salah satunya dapat diperoleh melalui teknologi penginderaan jauh. Citra satelit Landsat 8 mampu menyediakan informasi mengenai tutupan lahan, termasuk lahan hijauan, badan air, pemukiman, industri, dan jalan. Citra satelit tidak hanya menginformasikan lahan hijauan saja tetapi dapat menginformasikan tutupan lahan seperti badan air, pemukinan, industri, dan jalan. Oleh karena itu, diperlukan proses klasifikasi tutupan lahan untuk mengindentifikasi area yang berfungsi sebagai sumber hijauan pakan ternak ruminansia. Identifikasi ini penting untuk mengetahui ketersediaan pakan, yang selanjutnya dapat digunakan sebagai dasar dalam memprediksi biomassa vegetasi. Penelitian ini bertujuan untuk mengklasifikasi tutupan lahan hijauan yang berperan sebagai pakan ternak ruminansia. Metode yang digunakan adalah algoritma random forest dengan memanfaatkan citra satelit Landsat 8 untuk wilayah , Kabupaten Lumajang pada periode tahun 2018 hingga 2022. Hasil klasifikasi menghasilkan tiga kelas utama lahan hijaua, yaitu perkebunan, pertanian/sawah, dan semak belukar. Model klasifikasi yang dibangun mencapai tingkat akurasi sebesai 93%. Berdasarkan hasil analisis, rat-rata lahan hijauan di Kabupaten Lumajang terdiri atas lahan perkebunan sebuas 23.865,78 ha, pertanian/sawah seluas 18.363,21 ha, dan semak belukar seluas 949,98 ha. Hasil penelitian menunjukkan bahwa lahan hijauan di Kabupaten Lumajang didominasi oleh perkebunan, sehingga daerah ini memiliki potensi yang baik untuk pengembangan hijauan sebagai pakan ternak ruminansia. Ketersediaan lahan yang luas diharapkan dapat mendukung usaha peternakan dan pengelolaan sumber daya pakan di wilayah tersebut.
FIRE SPOT IDENTIFICATION BASED ON HOTSPOT SEQUENTIAL PATTERN AND BURNED AREA CLASSIFICATION Sitanggang, Imas Sukaesih; Istiqomah, Nalar; Syaufina, Lailan
BIOTROPIA Vol. 25 No. 3 (2018): BIOTROPIA Vol. 25 No. 3 December 2018
Publisher : SEAMEO BIOTROP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11598/btb.2018.25.3.676

Abstract

Indonesia has the world's largest tropical peatlands of about 14.9 million hectares that have important life support roles. However, fire frequently occurs in peatlands. According to experts and field forest firefighters, fire hotspots that appear in a sequence of two to three days at the same location have a high potential of becoming a forest fire. This study aimed to determine the sequential patterns of hotspot occurrences, classify satellite image data and identify the fire spots. Fire spot identification was done using hotspot sequence patterns that were overlaid with burned area classification results. Sequential pattern mining using the Prefix Span algorithm was applied to identify sequences of hotspot occurrence. Maximum Likelihood method was applied to classify Landsat 7 satellite images toward identifying burned areas in Pulang Pisau and Palangkaraya in Central Kalimantan and Pontianak in West Kalimantan. Sequence patterns were overlaid with image classification results. The study results show that in Pulang Pisau, 26.19% of sequence patterns are located in burned areas and 72.62% sequence patterns were found in the buffer of burned area within a radius of one kilometer. As for Palangkaraya, there were 62.50% sequence patterns located in burned areas and 87.50% sequence patterns in the buffer of burned area within the radius of one kilometer. In total, there were 72.62% and 87.50% fire hotspots recorded in Pisau and Palangkaraya, respectively, which are strong indicators of peatland fires.
Analisis Spasial-temporal Titik Panas dan PM2.5 di Riau, Jambi, dan Sumatera Selatan Lukman, Yasmin; Sitanggang, Imas Sukaesih; Hardhienata, Medria Kusuma Dewi
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 12 No. 2 (2025)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.12.2.212-227

Abstract

Kebakaran hutan dan lahan (karhutla) di Indonesia berkontribusi signifikan terhadap penurunan kualitas udara melalui peningkatan konsentrasi PM2.5. Penelitian ini menganalisis pola spasial-temporal sebaran titik panas dan estimasi konsentrasi PM2.5 di Provinsi Riau, Jambi, dan Sumatera Selatan selama Agustus–Oktober 2023. Data titik panas MODIS dianalisis menggunakan algoritma ST-DBSCAN dengan pengaturan parameter jarak spasial, jarak temporal, dan jumlah minimum titik untuk mengidentifikasi klaster kebakaran. Estimasi PM2.5 diperoleh dari konversi Aerosol Optical Depth (AOD) MODIS menggunakan model empiris. Hasil menunjukkan bahwa ST-DBSCAN efektif dalam mengidentifikasi klaster titik panas, dengan kepadatan klaster tertinggi teramati di Provinsi Sumatera Selatan. Rata-rata estimasi PM2.5 tercatat sebesar 50,51 µg/m³ di Provinsi Riau, 48,16 µg/m³ di Provinsi Jambi, dan 41,59 µg/m³ di Provinsi Sumatera Selatan. Konsentrasi PM2.5 tertinggi terjadi di Provinsi Riau pada bulan Oktober dan melampaui ambang batas pedoman kualitas udara WHO. Temuan ini menegaskan adanya keterkaitan kuat antara dinamika spasial-temporal karhutla dan peningkatan polusi udara, serta menunjukkan potensi pendekatan ini dalam mendukung analisis risiko lingkungan dan kesehatan.
Development of Post Fire Severity Assessment Module in Indonesian Forest and Land Fire Prevention Patrol System Sitanggang, Imas Sukaesih; Hidayat, Assad; Syaufina, Lailan
Jurnal Manajemen Hutan Tropika Vol. 32 No. 1 (2026)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.32.1.97

Abstract

The severity of forest and land fires is a crucial indicator for assessing their impact on ecosystems, particularly vegetation and soil. The assessment results serve as a foundation for forest and land restoration, rehabilitation, and conservation efforts. This study employs a deep learning algorithm to develop a forest and land fire severity assessment module. The CNN model used is MobileNetV2 that has an accuracy of 88.8%. The smart module is integrated into the Indonesian Forest and Land Fire Prevention Patrol Mobile Application and follows the Software Development Life Cycle approach in its development. Field observation images are input to the CNN module in the mobile application. The module then analyzes the fire severity and classifies it into very light, light, moderate, severe, and very severe categories. Testing results indicate that the module accurately predicts fire severity based on established assessment standards. The optimal time for capturing images is a few days after the fire, during daylight hours, to ensure the majority of images depict burned areas. Additionally, the findings highlight that lighting conditions and image quality significantly influence the accuracy of severity predictions. Further development is required to enhance the module's compatibility and flexibility, enabling its use across various devices.
Hyperparameter tuning of MobileNetV2 on forest and land fire severity classification Hidayat, Assad; Sitanggang, Imas Sukaesih; Syaufina, Lailan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp964-972

Abstract

Forest and land fires pose significant environmental challenges, causing economic and ecological damage depending on their severity. This study proposes a deep learning-based classification model to assess fire severity using the MobileNetV2 architecture. A dataset of 560 post-fire images was categorized into five severity levels, with dataset preprocessing involving resizing, rescaling, and image augmentation. To enhance model performance, K-means clustering was applied for balanced data distribution across classes. The model was trained using grid search for hyperparameter tuning, with the optimal combination being a batch size of 8, learning rate of 0.0001, and dropout of 0.3. Training was conducted in 50 epochs, and evaluation using the confusion matrix demonstrated an accuracy of 85%, precision of 86%, and recall of 81%. The results indicate that MobileNetV2 effectively classifies post-fire severity levels, offering a reliable tool for post-disaster assessment. This study highlights the significance of dataset preprocessing and hyperparameter tuning in improving model accuracy. Future research should explore alternative architectures and expand the dataset to enhance model generalization. These findings can aid authorities in assessing fire impact, supporting mitigation strategies, and improving post-fire land management.
Forest and Peatland Fire Severity Assessment at Siak Regency, Riau Province using Sentinel-2 Imagery Afina, Fakhri Sukma; Syaufina, Lailan; Sitanggang, Imas Sukaesih
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 11 No 4 (2021): Journal of Natural Resources and Environmental Management
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.11.4.621-630

Abstract

Siak Regency, Riau Province is one of the most forest and land fire-prone regencies in Indonesia. Most of the fires occur in peatland areas which contributes to the transboundary haze pollution in the region. Despite limited studies, fire severity assessment is an essential step in post-fire activities to estimate ecological impacts and economic impacts and law enforcement. This study aims to estimate fire severity using Sentinel-2 imagery at Siak Regency, Riau Province. The methods applied Normalized Burn Ratio on Sentinel-2 Imagery as an identification model based on reflectance value for 2019 imagery. The study revealed that burned areas in Siak Regency could be classified into four fire severity classes: low fire severity, moderate-low fire severity, moderate-high fire severity, and high fire severity. High fire severity was found mainly at Sungai Apit and Mempura Districts.
Co-Authors -, Rachmawati Abdul Rahman Saleh Abdul Wakhid Aditia Yudhistira Afina, Fakhri Sukma 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 Dwi Purwantoro Sasongko Eddy Prasetyo Nugroho Efendi, Zuliar Erliza Hambali Febriyanti Bifakhlina Firman Ardiansyah Hardhienata, Medria Kusuma Dewi Hari Agung Adrianto Hasibuan, Lailan Sahrina Hefni Effendi Hendra Rahmawan Hendra Rahmawan Herawan, Yoga Heru Sukoco Hidayat, Assad HUSNUL KHOTIMAH I Nengah Surati Jaya Ikhsan kurniawan Irman Hermadi Istiqomah, Nalar Ivan Maulana Putra Khairani Krisnanto, Ferdian Kurnianto, Andi Lailan Syaufina Lilis Syarifah Luki Abdullah Lukman, Yasmin Marlina, Dwi Medria Kusuma Dewi Hardhienata Miftah Farid Mohammad, Farid mufti, abdul Muhammad Abrar Istiadi Muhammad Asyhar Agmalaro Muhammad Murtadha Ramadhan Nia Kurniati Peggy Antonette Soplantila Prasetyo Nugroho, Eddy 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 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 Surjono Hadi Sutjahjo 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