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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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Real-time business intelligence development using machine learning to increase the potential of the dairy goat milk business Primawati, Alusyanti; Sitanggang, Imas Sukaesih; Annisa, Annisa; Astuti, Dewi Apri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5612-5625

Abstract

The development of big data and real-time data warehouse (RTDW) technologies has transformed traditional business intelligence (BI) into real-time business intelligence (RTBI). The RTBI framework is developed in this study by incorporating machine learning-based real-time prediction features. The complexity of layer integration in the RTBI framework is a challenge in building RTBI. The development of RTBI was carried out in business areas that did not have RTBI from the beginning, such as the dairy goat milk business in Probolinggo, East Java. Another main reason is that the dairy goat milk business is a food alternative to cow's milk in Indonesia. The results of this study can contribute to increasing the potential value of the goat milk business. The research method was developed by adapting to the Kimball method and unified modeling language (UML). The real-time prediction feature with the long short-term memory (LSTM) algorithm is the main feature in the RTBI framework developed in the research. The calculation results of real-time predictive analysis latency successfully approached 0 milliseconds (ms), namely 9.35×10-5 ms. The application of RTBI in the dairy goat milk business was successfully built but the real data is very limited, so RTBI is less able to describe the movement of the business.
Convolutional neural network for estimation of harvest time of forage sorghum (sorghum bicolor) cultivar samurai-1 Suradiradja, Kahfi Heryandi; Sitanggang, Imas Sukaesih; Abdullah, Luki; Hermadi, Irman
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1730-1738

Abstract

One of the economic alternatives to improve the quality of ruminant feed is combining grass as the main feed with high-protein forages such as sorghum. To get a quality sorghum harvest during the period, it must be right when it has good biomass content, nutrients, and digestibility. The problem is that measuring quality in the laboratory has additional costs and time, which is not short, causing delays. An approach with machine learning using a convolutional neural network can be a better solution. This research uses a convolutional neural network algorithm with the right architecture to estimate sorghum harvest time from imaging results of unmanned aerial vehicles. The stages of this research include data collection, pre-processing, modeling, and finally, the evaluation stage. This research compares the results of several convolutional neural network (CNN) algorithm architectural models: simple CNN, ResNet50 V2, visual geometry group-16 (VGG-16), MobileNet V2, and Inception V3. The result is determining the CNN algorithm architectural model that can estimate sorghum harvest time with maximum accuracy. The best result is the simple CNN architectural model with an accuracy of 0.95. This research shows that the classification model obtained from the CNN algorithm with a simple CNN architecture is the choice model for estimating sorghum harvest time.
SIPP KARHUTLA: Implikasi Pembaharuan Kebijakan Syaufina, Lailan; Sitanggang, Imas Sukaesih; Purwanti , Endang Yuni
Policy Brief Pertanian, Kelautan, dan Biosains Tropika Vol 4 No 2 (2022): Policy Brief Pertanian, Kelautan dan Biosains Tropika
Publisher : Direktorat Kajian Strategis dan Reputasi Akademik IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/agro-maritim.0402.253-257

Abstract

Kebakaran hutan dan lahan (karhutla) di Indonesia terjadi setiap tahun dan memberikan dampak yang merugikan pada beberapa aspek kehidupan dan lingkungan. Patroli pencegahan karhutla merupakan kegiatan untuk mengobservasi kondisi rawan karhutla di lapangan, mendeteksi dini terjadinya kebakaran, dan melakukan sosialisasi pada stakeholder terkait. Pengembangan Sistem Informasi Patroli Pencegahan (SIPP) Karhutla bertujuan untuk memudahkan tim patroli pencegahan karhutla dan pengelola data patroli melaksanakan tugasnya dengan lebih efektif dan efisien. Sejak tahun 2021, SIPP Karhutla telah diterapkan oleh Manggala Agni di wilayah Sumatera dan telah memiliki payung hukum berupa Perdirjen Pengendalian Perubahan Iklim Kementerian Lingkungan Hidup dan Kehutanan ( KLHKKLHK). Kebijakan ini telah terbukti mendukung pelaksanaan patroli secara lebih efisien dan efektif efektif. Beberapa implikasi kebijakan tersebut telah teridentifikasi untuk disiapkan oleh KLHK.
Sistem Pendukung Keputusan Cerdas untuk Pemilihan Jenis Tanaman Pertanian Kota Ramadhan, Jeri; Hermadi, Irman; Sitanggang, Imas Sukaesih
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25982

Abstract

Urban farming activities have become increasingly popular to meet their food needs in urban areas. Jakarta as provinces with a population density of high, have a program of urban farming is developed by the farmers to urban Balkot Farm. This study aims to support system to develop a clever move the crop farm a town with the simple additive weighting (SAW). The methods used to obtain the highest related alternative plant assessed according to its parameters that affect the eligibility of land and a room with a variety of plants. The methodology used software development life cycle (SDLC) prototyping model consisting of five of the communication, quick plan, modeling quick design, construction of prototype deployment and delivery and feedback. Data collection method using interviews and the study of literature. Research results of a web application system that has an alternative menu, criteria, subcriteria, rating match and results. Smart decision support system based on black box testing and user acceptance testing successfully shows menus according to land criteria that will be used according to stakeholder needs.
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 : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

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

Abstract

Kondisi ekonomi nelayan tradisional masih berada di lingkaran kemiskinan sehingga diperlukan solusi untuk meningkatkan kesejahteraan. Salah satu solusi adalah dengan menggunakan teknologi informasi mengenai daerah penangkapan ikan, sehingga nelayan dapat menghemat bahan bakar dan menambah jumlah tangkapan. Informasi daerah penangkapan ikan dapat di tentukan dengan cara mengolah data citra satelit dan menggunakan teknologi machine learning. Penelitian ini bertujuan membuat model yang dapat melakukan menklasifikasi daerah penangkapan ikan menggunakan algoritma Random Forest dan Support Vector Machine menggunakan data citra satelit laut jawa dan sekitarnya dari tahun 2019-2021 dengan menggunakan parameter klorofil, suhu permukaan laut, salinitas, ketinggian dan suhu air laut. Hasil penelitian ini menunjukan parameter klorofil mempunyai peran paling besar sebesar 77.14% dalam menentukan daerah penangkapan ikan. Hasil nilai precision yang dihasilkan algoritma Support Vector Machine (99.83%) lebih tinggi dibanding dengan yang dihasilkan algoritma Random Forest (99.80%). Meski demikian model klasifikasi yang dihasilkan algoritma Random Forest mempunyai nilai accuracy (99.90%), recall (100%) dan F1 score (99.90%) yang lebih tinggi dibanding dengan yang dihasilkan algoritma Support Vector Machine dengan nilai accuracy (99.89%), recall (99.96%) dan 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 : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

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

Abstract

Penentuan tingkat kematangan tomat secara manual memiliki kelemahan karena standar yang subjektif dan memakan waktu. Penelitian ini bertujuan untuk mengidentifikasi kematangan tomat berbasis representasi warna Hue Saturation Value (HSV) menggunakan Principal Component Analysis (PCA) sebagai ekstraksi ciri dan K-Nearest Neighbor (KNN) untuk klasifikasi. Penelitian ini menggunakan 400 citra dengan resolusi spasial 400x400 yang dikelompokkan dalam 5 tingkat kematangan yaitu green, turning, pink, light red dan red. Data terbagi menjadi data latih dan data uji dengan rasio 80:20. Skenario yang diberlakukan merupakan pembagian data ruang warna yaitu Hue (H), Saturation (S), Value (V), Hue-Saturation (HS), Hue-Value (HV), Saturation-Value (SV) dan HSV. Nilai k sebagai tetangga pada KNN yang dijadikan sebagai skenario adalah 1, 3, 5, 7, 9 dan 11. Adapun nilai principal componen yang diterapkan sebesar 5, 10, 15 dan 65 dengan varian rasio 95%. Hasil penelitian menunjukkan bahwa dengan K=7 dan nilai PC =5 menghasilkan nilai akurasi tertinggi dengan persentase 94% pada pengujian HV. Hasil penelitian ini menunjukkan bahwa dengan klasifikasi data uji sebanyak 80 data citra, didapatkan hasil sebanyak 75 data hasil akurat dan 5 data yang tidak akurat.
Identification of Android APK malware through local and global feature extraction using meta classifier Herawan, Yoga; Sitanggang, Imas Sukaesih; Neyman, Shelvie Nidya
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1834-1849

Abstract

Android, the most widely used mobile operating system, is also the most vulnerable to malware due to its high popularity. This has significantly focused on Android malware detection in mobile security. While extensive research has been conducted using various methods, new malware’s emergence underscores this field’s dynamic nature and the need for continuous research. The motivation that drives malware developers to create Android malware constantly is the potential to access Android devices, thereby gaining access to sensitive user information. This study, which is a complex and in-depth exploration, aims to detect Android malware using a meta-classifier that combines the single-classifier light gradient boosting machine, support vector machine, and random forest. The process involves converting disassembled malware codes into grey images for global and local feature extraction. The classification accuracy is 97% at best on a malware dataset of 3,963 samples. The main contribution of this paper is to produce an Android APK malware detector model that works by combining multiple machine learning algorithms trained using the dataset resulting from local and global feature extraction algorithms.
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.
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