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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pertanian Tanaman Pangan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal TAM (Technology Acceptance Model) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Sitanggang, Imas S.
Bogor Agricultural University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science

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Perbandingan Kinerja LSTM dan Prophet untuk Prediksi Deret Waktu (Studi Kasus Produksi Susu Sapi Harian) Alusyanti Primawati; Imas Sukaesih Sitanggang; Annisa Annisa; Dewi Apri Astuti
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 3 (2023): Volume 9 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i3.72031

Abstract

Prediksi deret waktu dibutuhkan untuk menjawab pertanyaan bisnis dimasa depan  yang akurat sehingga perlunya membangun model prediksi yang memiliki kinerja bagus. Pendekatan machine learning seperti long short term memory (LSTM) dan Prophet menjadi popular saat ini untuk pemodelan prediksi deret waktu. Agribisnis susu segar saat ini salah satu studi kasus yang memerlukan peranan teknologi informasi seperti bisnis intelijen untuk memastikan ketersediaan pasokan susu dimasa depan. Upaya pertama yang perlu dilakukan adalah menyiapkan model prediksi yang tepat meskipun data awal yang dikumpulkan masih sedikit atau terbatas. Dataset produksi susu sapi selama 300 hari menjadi data penelitian yang dimodelkan kedalam LSTM dan Prophet. Keduanya dibandingkan kinerjanya terhadapa data terbatas. Hasilnya uji koefisien determinasi R2 keduanya yaitu 0.2, sehingga perlu dilakukan peningkatan kinerja melalui tahapan revise and enhance. Hasilnya, kedua model meningkat nilai R2 menjadi 0.3 dan LSTM lebih baik dari Prophet. Meskipun demikian perbedaan keduanya tidak terlalu signifikan dan peningkatan juga tidak berbeda terlalu jauh karena data susu memiliki pola multi-periode dengan tren berbeda signifikan. Periode 90 hari pertama adalah masa klimaks laktasi sedangkan periode kedua setelah 90 hari adalah masa intervensi peternak menurunkan hasil perah untuk persiapakan ternak kambing perah ke masa kawin dan bunting.
Classification of Pestalotiopsis sp. Leaf Fall Disease Severity in Rubber Plants using UAV Multispectral Vegetation Indices and 1-D Convolutional Neural Networks Solikin; Yeni Herdiyeni; Annisa; Lilik Budi Prasetyo; Tri Rapani Febbiyanti; Imas Sukaesih Sitanggang; Sri Nurdiati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7601

Abstract

Leaf-fall disease caused by Pestalotiopsis sp. is a major threat to rubber (Hevea brasiliensis) plantations because it suppresses photosynthetic activity, accelerates defoliation, and reduces latex productivity. In operational practice, severity assessment is still dominated by visual field inspection, which is subjective, time-consuming, costly, and difficult to standardize across large plantation areas. This study develops a disease severity classification model for Pestalotiopsis sp. using a Convolutional Neural Network (CNN) based on vegetation-index features derived from UAV multispectral imagery. The model classifies disease severity into four levels: L1 (Light Infection), L2 (Moderate Infection), L3 (Severe Infection), and L4 (Very Severe Infection). To represent temporal and biological variability in disease expression, multispectral data were collected from multiple rubber clones over two observation periods. Feature construction focused on NDRE, LCI, CI, NDVI_NDRE_Interaction, and GCI_Ratio, which capture chlorophyll-related and canopy condition responses to infection. Because severity classes were imbalanced, the Synthetic Minority Over-sampling Technique (SMOTE) was applied before model training. A one-dimensional CNN was then trained to learn nonlinear patterns among index-based predictors for multilevel severity classification. Hyperparameter tuning improved overall accuracy from 85.30% to 90.00%. Class-wise F1-scores changed from 0.91 to 0.94 (L1), 0.83 to 0.84 (L2), 0.75 to 0.88 (L3), and 0.97 to 0.84 (L4), with the largest improvement in L3 recall (0.67 to 0.94). These results indicate that the selected vegetation indices and interaction terms are informative predictors for objective and scalable disease severity classification under heterogeneous plantation conditions.
Logistic Regression Modeling of Peatland Fire Hotspots in Bengkalis District Using Integrated Environmental and Anthropogenic Drivers Nur Hayati; Imas Sukaesih Sitanggang; Lilik Budi Prasetyo; Lailan Syaufina
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1560

Abstract

Peatland fires occur almost annually in Bengkalis District, Riau Province, Indonesia, where peatlands cover about 65% of the area and contribute significantly to carbon emissions and regional haze, highlighting the need for improved fire risk prediction. This research aims to apply a probabilistic logistic regression approach to predict peatland fire hotspot occurrence and identify its key drivers. Hotspot data from 2015–2023 were derived from VIIRS satellite observations and classified into low (l), nominal (n), and high (h) confidence levels. Then hotspot confidence levels are classified into two scenarios: (1) the nh scenario (l = 0; n–h = 1) and (2) the h scenario (l–n = 0; h = 1), representing different fire thresholds. The predictor variable was modeled using anthropogenic and environmental, with multicollinearity testing to ensure model stability. The results show that the nh scenario performs better, with Nagelkerke R² = 0.0681, Hosmer–Lemeshow χ² = 5.7663, AUC = 0.69, and accuracy = 95.19%, indicating acceptable fit and moderate discrimination. Significant predictors include plantation land use, peat characteristics, and precipitation. These findings suggest that the approach can support peatland fire risk assessment, although further refinement is required.
Co-Authors Aditya Cipta Raharja Agus Buono Agus Buowo Ainuddin A. N Ainuddin A. N. Akhmad Faqih Alusyanti Primawati Andi Nurkholis Anissa Rezainy Annisa Annisa Annisa Annisa Puspa Kirana Baba Barus Deden Sumirat Hidayat Deny Ramdhany DEWI APRI ASTUTI Fitri Insani GONO SEMIADI Heru Sukoco Ikhsan Kurniawan Irman Hermadi Kahfi Heryandi Suradiradja Khaira, Ulfa Koentjoro, Bambang S. Lailan Syaufina Lailan Syaufina LILIK BUDIPRASETYO Luki Abdullah Makarim, Abdul Karim Marimin , Norwati Mustapha Norwati Mustapha Nur Hayati Prima Trie Wijaya Putri Thariqa Razali Yaakob Razali Yaakob Ruhul Amin Sanusi Sanusi Sobir Sobir Solikin Sri Nurdiati Suci Sri Utami Sutjipto Taufik Djatna Tri Rapani Febbiyanti Widyawati, Dewi Kania Wulandari Wulandari Yazid Aufar