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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 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 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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Prediction of Solar Radiation Data for Garlic Production in Magelang Regency Using Long Short-Term Memory Safrudin, Muhammad Safrul; Sitanggang, Imas Sukaesih; Adrianto, Hari Agung; Aini, Syarifah
JOIN (Jurnal Online Informatika) Vol 9 No 2 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i2.1113

Abstract

Garlic importation in Indonesia is frequently carried out to meet the high domestic market demand. To reduce dependency on imports, the development of local garlic production is crucial. This study aims to determine the optimal solar radiation for garlic growth using the Long Short-Term Memory (LSTM) algorithm. This algorithm was selected due to its ability to analyze time-series data and predict long-term patterns. The LSTM model was trained with the Adam optimizer, using a configuration of 1000 epochs, a batch size of 6, and a dropout rate of 2.0 to prevent overfitting. The model evaluation results show an indicating good accuracy with a RMSE of 0.1020, a Mean Squared Error (MSE) of 0.0104, and a correlation coefficient of 0.740, although it still has limitations in capturing extreme data fluctuations. The study found that in Magelang Regency especially in the sub-districts of Windusari, Grabag, Ngablak, Pakis, Dukun, Kaliangkrik, and Kajoran have optimal solar radiation for garlic cultivation between March and May, with a radiation range of 380 W/m² to 440 W/m². These findings provide valuable guidance for farmers in determining the optimal planting period, potentially enhancing local garlic production and reducing import dependency.
Application of Random Forest Algorithm to Analyze the Confidence Level of Forest Fire Hotspots in Riau Peatland Unik, Mitra; Sukaesih Sitanggang, Imas; Syaufina, Lailan; Surati Jaya, I Nengah
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 15 No 2 (2025): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
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.15.2.255

Abstract

Forest fires pose a significant challenge in Riau Province, Indonesia, especially in peatland areas. This study employs the Random Forest (RF) algorithm to analyze the confidence levels of hotspots, aiming to predict potential fire occurrences and improve fire management strategies. The research focuses on peatlands spanning 3.86 million ha, using key variables such as NDVI, surface temperature, and peat thickness derived from satellite data. The model achieved an average AUC of 0.732 and a classification accuracy of 70.3%, with medium-confidence hotspots demonstrating the best predictive performance (AUC: 0.707, F1-score: 0.804). However, the model struggled with low-confidence hotspots, reflecting challenges in distinguishing less prominent patterns in the data. Compared to other methods, RF demonstrates strong potential in handling complex environmental datasets, making it a valuable tool for hotspot prediction. This study contributes to understanding forest fire risks in peatlands and provides actionable insights for improving preparedness and mitigation efforts.
Effects of hyperparameter tuning on random forest regressor in the beef quality prediction model Raafi'udin, Ridwan; Purwanto, Yohanes Aris; Sitanggang, Imas Sukaesih; Astuti, Dewi Apri
Computer Science and Information Technologies Vol 6, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i2.p159-168

Abstract

Prediction models for beef meat quality are necessary because production and consumption were significant and increasing yearly. This study aims to create a prediction model for beef freshness quality using the random forest regressor (RFR) algorithm and to improve the accuracy of the predictions using hyperparameter tuning. The use of near-infrared spectroscopy (NIRS) in predicting beef quality is an easy, cheap, and fast technique. This study used six meat quality parameters as prediction target variables for the test. The R² metric was used to evaluate the prediction results and compare the performance of the RFR with default parameters versus the RFR with hyperparameter tuning (RandomSearchCV). Using default parameters, the R-squared (R²) values for color (L*), drip loss (%), pH, storage time (hour), total plate colony (TPC in cfu/g), and water moisture (%) were 0.789, 0.839, 0.734, 0.909, 0.845, and 0.544, respectively. After applying hyperparameter tuning, these R² scores increased to 0.885, 0.931, 0.843, 0.957, 0.903, and 0.739, indicating an overall improvement in the model’s performance. The average performance increase for prediction results for all beef quality parameters is 0.0997 or 14% higher than the default parameters.
The modeling of earthquake disaster mitigation in Bulukumba Regency: A stakeholder approach Ahmad, Despry Nur Annisa; Tarigan, Suria Darma; Tjahjono, Boedi; Sitanggang, Imas Sukaesih; Sakti, Harry Hardian
Journal of Degraded and Mining Lands Management Vol. 12 No. 4 (2025)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2025.124.8247

Abstract

Bulukumba Regency, located along the Walanae Fault and within a seismic gap zone, indicates the potential for future earthquake recurrence. However, the regional and community capacity to address earthquake hazards remains weak, as evidenced by the lack of regulations accommodating earthquake studies in Bulukumba. This study aimed to design an earthquake mitigation model based on a stakeholder approach in Bulukumba Regency. The methodology employed MACTOR (Matrix of Alternatives for Choice and Trade-Offs), utilizing survey and questionnaire data. The output is a framework for policymakers in earthquake mitigation activities. The results suggested two effective alternative models: (i) a stakeholder formulation model based on role capacity and (ii) a time segmentation model for stakeholder involvement in earthquake mitigation. Based on these two models, it is essential to establish strong coordination and collaboration among these actors in order to minimize the impact of disasters on both the community and the environment.
Exploration of Data Handling Techniques to Improve PM2.5 Prediction Using Machine Learning Unik, Mitra; Sitanggang, Imas Sukaesih; Syaufina, Lailan; Jaya, I Nengah Surati
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.25687

Abstract

Particulate matter (PM₂.₅) is one of the most dangerous air pollutants because it can penetrate the respiratory system and cause serious health problems. Amidst the limitations of a real-time and comprehensive air quality monitoring system, a data-driven predictive approach is needed that can accurately project PM₂.₅ concentrations. This study aims to develop a PM₂ concentration prediction model using the Random Forest Regressor (RFR) algorithm optimised through a series of data pre-processing techniques. The pre-processing techniques include outlier detection with four methods (Isolation Forest, Autoencoder ANN, OCSVM, IQR) and missing value handling using three approaches (Spline Cubic Interpolation, Nearest Point Interpolation, Data Removal). The daily data used covered 12 environmental variables (including rainfall, temperature, relative humidity, AOD, and NDVI) from the period of March 2022 to March 2023, with PM₂.₅ as the target. The RFR model was built with 100 decision trees and 10-fold cross-validation to improve accuracy. Results showed the combination of IQR (outlier detection) and data deletion (missing values) produced the best performance with RMSE 0.082, MAE 0.027, and R² 0.886. The most influential variables were temperature (TEMP), relative humidity (RHU), and evapotranspiration (ET). This research contributes to the development of an accurate air quality prediction model, supporting the mitigation of PM₂.₅ pollution impacts on public health
PENGARUH MEDIA SOSIAL TERHADAP SITASI PUBLIKASI INTERNASIONAL KARYA ILMIAH INDONESIA BIDANG PERTANIAN DENGAN PENDEKATAN ALTMETRICS Ibrahim, Cecep; Sukaesih Sitanggang, Imas; Sukoco, Heru
BACA: Jurnal Dokumentasi dan Informasi Vol. 40 No. 1 (2019): BACA: Jurnal Dokumentasi dan Informasi (Juni)
Publisher : Direktorat Repositori, Multimedia, dan Penerbitan Ilmiah - Badan Riset dan Inovasi Nasional (BRIN Publishing)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.baca.v40i1.456

Abstract

The purpose of this study was to measure the impact of Indonesia research especially in agriculture published in international Scopus journals using Altmetrics. This research method consisted of problem identification, data collection, data preprocessing, Altmetrics approach analysis, and final analysis. The data of this study were obtained from Scopus.com citation metadata by writing the Agriculture keyword and Indonesian affiliation that the limited year from 2015-2017. Altmetrics data is obtained from Altmetric.com; Altmetrics Explorer for Librarian by extracting DOIs from each publication of scientific work. Then the data is analyzed by the Altmetrics approach, namely Facebook Coverage and Mention Rate. This study performed an analysis based on Altmetrics data share to know the popularity Indonesian research in Scopus journal and analyzed the correlation between Citation data Indonesian research in Scopus journal and Altmetrics data share of Altmetric.com. This study analyzed the impact of 4484 Indonesia research articles published by Scopus journals in the field of agriculture through Altmetrics and compared it with bibliometrics. The result showed that Coverage and Mention Rate of social media only were below 30% which was not too significant in the content discussed, view & reader and mention on social media.
Pembangunan Model Prediksi Potensi Kebakaran Hutan dan Lahan Menggunakan Algoritma Machine Learning Berdasarkan Data Patroli Santoso, Angga Bayu; Sitanggang, Imas Sukaesih; Hardhienata, Medria Kusuma Dewi
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1180

Abstract

Indonesia allocates 120 million hectares or 64% of its land area as forest areas. Indonesia's forests continue to experience deforestation; one of the causes is forest and land fires (karhutla). The government conducts forest and land fire prevention through integrated patrols with the Forest and Land Fire Prevention Patrol Information System (SIPP Karhutla) facility for patrol data management. However, the patrol data are still primarily used for data observation and simple spatial analysis in the spatial module. Patrol data has not been used for further forest and land fire prevention studies. Based on these problems, this research aims to build a prediction model of potential forest and land fires using SVM, Random Forest, and XGBoost algorithms and compare model performance to get the best model. The preprocessing stage uses the SMOTE-ENN method to handle data class imbalance, and the k-fold cross-validation stage and hyperparameter tuning use the random search method. The confusion matrix evaluation method to see the model performance in terms of accuracy is XGBoost (94.81%), Random Forest (90.23%), SVM-linear (79.58%), SVM-polynomial model (73.99%), SVM-rbf (74.26%), and SVM-sigmoid (35.04%). Therefore, the best prediction model is XGBoost (94.81%) with boosting technique. The results of this study have implications for helping early prevention of forest and land fires on the islands of Sumatra and Kalimantan.
SOIL MOISTURE PREDICTION MODEL IN PEATLAND USING RANDOM FOREST REGRESSOR Taihuttu, Helda Yunita; Sitanggang, Imas Sukaesih; Syaufina, Lailan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2505-2516

Abstract

Soil moisture is one of the factors that has recently become the focus of research because it is strongly correlated with forest and land fires, where low soil moisture will increase drought and the incidence of forest and land fires. For this reason, this study aims to create a prediction model for soil moisture as an early prevention of fires in peatlands using the Random Forest Regressor (RFR) algorithm. RFR is used because of its ability to predict values and its resistance to overfitting and outliers. A dataset covering soil moisture, precipitation, temperature, maturity, and peat thickness was collected from August 2019 to December 2023. The data includes soil moisture, precipitation, temperature, maturity, and peat thickness. The data were divided into 80% for modeling and 20% for testing. Model performance was optimized through random search CV, resulting in significant prediction accuracy R-squared: 0.914, MAE: 0.0081, MSE: 0.0007, RMSE: 0 .0271, and MAPE: 0.969. These findings demonstrate the effectiveness of RFR in soil moisture prediction and pave the way for more appropriate and timelier implementation of fire mitigation strategies.
Estimation Model of Nutritional Content Based on Broiler Feed Images Using Convolutional Neural Network and Random Forest Mufti, Abdul; Sitanggang, Imas Sukaesih; Neyman, Shelvie Nidya; Abdullah, Luki
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.28682

Abstract

Purpose: This research aims to develop an intelligent model to estimate the nutritional content of broiler chicken feed based on feed images to assist farmers in selecting the best broiler feed and quickly verifying its quality to meet requirements. Methods: The methodology of this research includes literature study, data collection, data preprocessing, image classification, model evaluation, integration of CNN and random forest models, and estimation of nutritional content based on feed images. We collected 99 samples of broiler chicken feed from online stores in various regions of Indonesia, particularly Java. Next, we took pictures with a smartphone and analyzed the nutritional content using near-infrared spectroscopy. Preprocess the data by enhancing the dataset (color space and data augmentation). We use Convolutional Neural Network (CNN) for the classification of broiler feed images. The performance of the CNN model is evaluated using a confusion matrix. We integrate CNN and Random Forest Regressor (RFR) to estimate nutritional content from the features of broiler feed images. Result: The performance evaluation shows that the CNN (VGG-16) model is 0.9744% accurate and the RFR model has the highest R2 value of 0.8018. The benefits of this research include faster, more efficient, and automated feed quality measurement compared to traditional methods; maintaining feed quality standards; and avoiding health risks for livestock. Novelty: This research introduces an intelligent model to estimates the nutritional content of broiler feed images by integrating a CNN model with an RFR.
Transformasi Kerangka Hukum Lingkungan Indonesia melalui Next Generation Framework: Evaluasi Normatif-Praktis Tata Kelola Terpadu Mohammad, Farid; Sutjahjo, Surjono Hadi; Effendi, Hefni; Sitanggang, Imas Sukaesih; Sasongko, Dwi P
Bina Hukum Lingkungan Vol. 10 No. 1 (2025): Bina Hukum Lingkungan, Volume 10, Nomor 1, Oktober 2025
Publisher : Asosiasi Pembina Hukum Lingkungan Indonesia (PHLI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24970/bhl.v10i1.473

Abstract

ABSTRAK Hukum lingkungan di Indonesia telah mengalami berbagai transformasi selama empat dekade terakhir, terutama dalam bidang kebijakan dan peraturan Analisis Dampak Lingkungan (AMDAL). Namun, perubahan ini sering mencerminkan pergeseran yang meningkat ke arah kekakuan administrasi, yang mengorbankan tujuan substantif keberlanjutan dan kualitas layanan publik. Studi ini menerapkan Next Generation Framework (NGF), alat evaluatif komprehensif yang dikembangkan oleh Fonseca dan Gibson (2020) untuk melakukan meta-evaluasi terhadap lima peraturan lingkungan utama Indonesia yang dikeluarkan antara tahun 1986 dan 2021. Melalui analisis konten kualitatif dan penilaian berbasis ahli dari 50 elemen praktik baik di sepuluh kategori NGF, penelitian ini mengungkapkan kesenjangan kelembagaan kritis dalam rasionalitas hukum, integrasi keberlanjutan, mekanisme partisipatif, dan fleksibilitas adaptif. Temuan menunjukkan bahwa sementara peraturan terbaru menekankan perampingan prosedural dan integrasi digital, mereka secara bersamaan mengabaikan landasan normatif seperti keadilan lingkungan jangka panjang, hak-hak adat, dan tata kelola yang responsif. Penelitian ini menempatkan NGF dalam kerangka hukum normatif-praktis, memposisikannya sebagai alat diagnostik yang berharga untuk reformasi kelembagaan. Pada akhirnya, studi ini mengusulkan reorientasi desain hukum dalam tata kelola lingkungan yang menyelaraskan maksud normatif, praktik administrasi, dan responsif sosial-ekologis dalam pemberian layanan publik. Kata kunci: next generation framework (NGF); tata kelola lingkungan; evaluasi hukum normatif; kelembagaan; environmental impact assessment (EIA).   ABSTRACT Environmental law in Indonesia has undergone multiple transformations over the last four decades, particularly in the realm of Environmental Impact Assessment (EIA) policies and regulations. However, these changes often reflect an increasing shift toward administrative rigidity, compromising the substantive goals of sustainability and public service quality. This study applies the Next Generation Framework (NGF, a comprehensive evaluative tool developed by Fonseca and Gibson (2020) to conduct a meta-evaluation of five key Indonesian environmental regulations issued between 1986 and 2021. Through qualitative content analysis and expert-based scoring of 50 good practice elements across ten NGF categories, this study reveals critical institutional gaps in legal rationality, sustainability integration, participatory mechanisms, and adaptive flexibility. Findings show that while recent regulations emphasize procedural streamlining and digital integration, they simultaneously neglect normative foundations such as long-term environmental justice, indigenous rights, and responsive governance. The research situates NGF within a normative-practical legal framework, positioning it as a valuable diagnostic tool for institutional reform. Ultimately, the study proposes a reorientation of legal design in environmental governance one that harmonizes normative intent, administrative practice, and socio-ecological responsiveness in public service delivery. Keywords: next generation framework (NGF); environmental governance; normative legal evaluation; institutional reform; environmental impact assessment (EIA).
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 Dwi Purwantoro Sasongko Eddy Prasetyo Nugroho Efendi, Zuliar Fakhri Sukma Afina Febriyanti Bifakhlina Firman Ardiansyah Hardhienata, Medria Kusuma Dewi Hari Agung Adrianto Hasibuan, Lailan Sahrina Hefni Effendi 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 Mohammad, 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 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