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All Journal Journal of Information Systems Engineering and Business Intelligence Malcom: Indonesian Journal of Machine Learning and Computer Science
Rafi, Haris
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Computer Science & IT

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Model-based Decision Support System Using a System Dynamics Approach to Increase Corn Productivity Suryani, Erma; Rafi, Haris; Utamima, Amalia
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.139-151

Abstract

Background: As the population increases, the need for corn products also increases. Corn is needed for various purposes, such as food consumption, industry, and animal feed. Therefore, increasing corn production is crucial to support food availability and the food industry. Objective: The objective of this project is to create a model to increase corn farming productivity using scenarios from drip irrigation systems and farmer field school programs. Methods: A system dynamics approach is utilized to model the complexity and nonlinear behaviour of the corn farming system. In addition, several scenarios are formulated to achieve the objective of increasing corn productivity. Results: Simulation results showed that adopting a drip irrigation system and operating a farmer field school program would increase corn productivity. Conclusion: The corn farming system model was successfully developed in this research. The scenario of implementing a drip irrigation system and the farmer field school program allowed farmers to increase corn productivity. Through the scenario of implementing a drip irrigation system, farmers can save water use, thereby reducing the impact of drought. Meanwhile, the scenario of the farmer field school program enables farmers to manage agriculture effectively. This study suggests that further research could consider the byproducts of corn production to increase the profits of corn farmers.   Keywords: Corn Farming, Decision Support System, Modeling, Simulation, System Dynamics
Perbandingan Support Vector Machine dan Naïve Bayes untuk Klasifikasi Sentimen Ulasan E-Commerce: Comparison of Support Vector Machine and Naïve Bayes for E-Commerce Review Sentiment Classification Nurmadewi, Dita; Jailani, Zakiul Fahmi; Rafi, Haris; Anggoro, Dimas Aryo; Setiowati, Dewi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2648

Abstract

Ulasan pelanggan di platform e-commerce memuat informasi penting tentang pengalaman pengguna terhadap produk dan layanan. Namun, mengingat jumlahnya sangat besar, analisis manual tidak efisien. Klasifikasi sentimen berbasis machine learning dapat secara otomatis mengidentifikasi opini dari teks ulasan. Penelitian ini ingin melakukan perbandingan antara performa Support Vector Machine (SVM) dan Naïve Bayes dalam melakukan klasifikasi sentimen pada ulasan di platform e-commerce. Dataset terdiri atas 11.606 ulasan pelanggan yang bersumber dari repositori dataset publik. Tahap pra-pemrosesan mencakup case folding, tokenization, penghilangan stopword, serta stemming. Fitur teks ditampilkan memakai Term Frequency–Inverse Document Frequency (TF-IDF). Kinerja model dievaluasi berdasarkan skema 5-fold cross-validation menggunakan metrik accuracy, precision, dan recall, serta F1-score. Hasil eksperimen menemukan algoritma Support Vector Machine mempunyai performa lebih unggul jika dibandingkan dengan Naïve Bayes, di mana perolehan nilai accuracy masing-masing mencapai 0.8717 dan 0.8555. Temuan ini sekaligus menunjukkan Support Vector Machine mempunyai kemampuan generalisasi yang lebih baik dalam membedakan kelas sentimen pada data ulasan e-commerce berbasis teks.
Co-Authors Amalia Utamima Dimas Aryo Anggoro Erma Suryani Jailani, Zakiul Fahmi Nurmadewi, Dita Setiowati, Dewi