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All Journal Syntax Jurnal Informatika Format : Jurnal Imiah Teknik Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Mantik Penusa J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research ICIT (Innovative Creative and Information Technology) Journal CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal of Intelligent Decision Support System (IDSS) Community Development Journal: Jurnal Pengabdian Masyarakat SENADA : Semangat Nasional Dalam MengabdI IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika) Reputasi: Jurnal Rekayasa Perangkat Lunak Vertex SENADA : Semangat Nasional Dalam Mengabdi Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Sistem Informasi dan Teknologi Informasi
Embun Fajar Wati
Universitas Bina Sarana Informatika
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Multivariate Long Short-Term Memory (LSTM) Algorithm for Spatial-Temporal Agricultural Productivity Time Series Forecasting Embun Fajar Wati; Anggi Puspita Sari; Tuslaela Tuslaela
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 2 (2026): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i2.356

Abstract

Accurate forecasting of crop productivity is fundamental to contemporary food security planning, yet conventional predictive models frequently underperform when confronted with the multivariate, spatial, and temporal intricacies inherent in agronomic datasets. This study presents a robust deep learning framework leveraging a multivariate Long Short-Term Memory (LSTM) network to forecast yields of principal food crops. The model was developed using a panel dataset from 12 districts in Chhattisgarh and Madhya Pradesh, India (2010–2017), comprising area, production, and yield observations for multiple competing crops. Rigorous preprocessing protocols included the application of separate StandardScalers to mitigate matrix inversion issues, and the derivation of land-allocation features to capture spatial interactions among crops. A lightweight LSTM architecture stabilized by gradient clipping was employed to enhance convergence and prevent exploding gradients. Empirical results demonstrate that the multivariate LSTM notably outperforms simple baseline estimators by effectively modeling non-linear relationships and district-level yield heterogeneity, attaining an RMSE of 494.70 Kg/ha and an R² of 0.8031. These findings suggest that spatial anthropogenic indicators—particularly the allocation of land across commodities—serve as informative proxies for reliable yield prediction in contexts lacking comprehensive weather-sensor data
A Web-Based Student Grade Management Information System for SMP IP YAKIN West Jakarta Ermelinda Angela Amfotis; Embun Fajar Wati
Jurnal Sistem Informasi dan Teknologi Informasi Vol. 3 No. 1 (2025): December 2025
Publisher : LP2M Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/justinfo.v3i1.3216

Abstract

The world of technology is currently developing very rapidly in line with the great need for information. Currenly the computers has taken a very important role in any work related to data processing, assessment is very important in a learning, assessment is carried out by the teachers to determine the ability of students to master the learning objevtives that have been set. At the SMP IP YAKIN West Jakarta where every work on storing grades is stiil done manually. Judging from this working system, the author sees a lot of inefficiencies and effectiveness or inaccuracy. From this research, the author wants to design an information system application processing student grades at SMP IP YAKIN West Jakarta which aims to produce software. The waterfall research method is used in this study. In data collection, there are two types that need to be collected, namely primary data and secondary data, primary data is the result of observations and interviews from relevant sources, while secondary research data uses the technique of collecting learning data or reading experts. This research resulted in a student grade  processing application system, which is designed to help check grades quickly and precisely. Help in the search for student grades because of the grades stored in the database.
Unsupervised Machine Learning Based DSS for Land Profiling and Disease Risk Mitigation in Smart Farming Embun Fajar Wati; Elvi Sunita; Andi Diah Kuswanto
Vertex Vol. 15 No. 2 (2026): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/na9y0b02

Abstract

Decision Support Systems (DSS) in smart farming require methodologies capable of representing the holistic complexity of agricultural ecosystems. This study proposes a DSS framework based on unsupervised machine learning, specifically K-Means clustering, to automatically segment land profiles using IoT sensor records. The dataset consists of 500 global sensor data points covering seven essential environmental variables: soil moisture, pH, temperature, rainfall, humidity, sunlight duration, and the NDVI index. Through Principal Component Analysis (PCA) for dimensionality reduction and Silhouette Score evaluation, the system successfully identified and mapped seven land profiles with distinct microclimatic characteristics. Cross-tabulation analysis further demonstrates the principal novelty of this DSS, namely its ability to classify land into "Safe Zones" (Clusters 0, 3, and 4), which are characterized by Mild disease status and are suitable for Soybean, Cotton, and Maize, as well as "High-Risk Zones" (Clusters 1, 2, 5, and 6), which consistently correspond to Severe disease status. These findings indicate that a DSS based on environmental clustering is substantially more effective for crop selection recommendations and disease prevention than conventional predictive approaches. Ultimately, this framework provides farmers with actionable insights to optimize productivity and minimize agricultural risk
Co-Authors Andi Diah Kuswanto Andi Diah Kuswanto Kuswanto Anggi Puspita Sari Anggi Puspita Sari Anggi Puspita Sari Anggi Puspitasari, Anggi Arief Fadholi Arvin Anggoro Kusumo Budi Sudrajat Elvi Sunita Elvi Sunita Perangin-angin Elvi Sunita Sunita Enok Tuti Alawiah Ermelinda Angela Amfotis Istikharoh, Istikharoh Jaya, Tri Ika Kiki Indayani Lukman Hakim Luthfi Indriyani Martua Hami Siregar Maryadi, Dedi Mike Indarsih Mike Indarsih, Mike Nur Apriyani Nur Indah Kurniawati Perangin-Angin, Elvi Sunita Raudah Nasution Rudianto, Biktra Sari, Anggi Puspita SUSANTI Tri Ika Jaya Tri Ika Jaya Kusumawati Tuslaela Tuslaela Tuslaela Yunan Maulana Putra