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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Angkasa: Jurnal Ilmiah Bidang Teknologi
Taufiq Rizaldi
Department of Information Technology, Politeknik Negeri Jember
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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An End-to-End Machine Learning Pipeline for Online Purchase Intention Prediction Using Random Forest and MLOps Practices Akas Bagus Setiawan; Hendra Yufit Riskiawan; Hermawan Arief Putranto; Taufiq Rizaldi; Rachmad Andri Atmoko
Angkasa: Jurnal Ilmiah Bidang Teknologi Vol 18, No 1 (2026): Februari
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/angkasa.v18i1.3841

Abstract

Predicting online shoppers' purchase intention is a key issue in e-commerce because it directly affects conversion and marketing effectiveness. The main focus of this article is a Random Forest purchase-intention model accompanied by an end-to-end MLOps implementation to ensure production readiness. The dataset used is Online Shoppers Intention with 12,330 samples and 18 features representing administrative, informational, and product-related characteristics, along with behavioral metrics. Preprocessing includes missing-value imputation, numerical feature standardization, categorical feature encoding, and outlier removal using the z-score method. The model is optimized with GridSearchCV and 3-fold cross-validation. Test results show 91.38% accuracy with 73.60% precision, 56.64% recall, and 64.02% F1-score for the positive class. MLOps implementation uses MLflow for experiment tracking, Prometheus-Grafana for monitoring, and a GitHub Actions-based CI/CD pipeline for deployment automation. Overall, the Random Forest model delivers strong predictive performance on e-commerce data and is supported by an MLOps pipeline that improves reproducibility, deployment, and production monitoring
Land Suitability Assessment with TOPSIS in the Agrarian Sector Arvita Agus Kurniasari; Trismayanti Dwi Puspitasari; Pramuditha Shinta Dewi Puspitasari; Taufiq Rizaldi; I Gede Wiryawan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 3 (2026): JULY (ON PROGRESS)
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i3.2606

Abstract

Sustainable development for agricultural productivity and environmental conservation necessitates proper land use and utilization planning. Land suitability analysis is a key tool in evaluating land potential by considering various aspects such as altitude, solar radiation, rainfall, humidity, temperature, wind speed and soil pH. Appropriate decision-making in this context requires processing and analysis of diverse datasets, often involving complex multi-criteria evaluations. Among the different methods used for making decisions based on multiple criteria, the Technique for Order Preference by Similarity to Ideal Solution, or TOPSIS, is special because it is easy to use, works well, and can handle both numbers and words as types of criteria. This research presents the development of a web-based Land Suitability Assessment with TOPSIS in the Agrarian Region of Bondowoso Regency. This system is built using PHP as a server-side language, utilizing web technology for wider accessibility and real-time user interaction supported by a Geographic Information System.. Using weather parameter data with a resolution of 0.50 x 0.50 (approximately 55 km x 55 km per grid), this Decision support system shows an accuracy level of 82.60% with comparative data for 2023. This system is expected to facilitate more effective and efficient decision-making in sustainable land use planning.
Co-Authors Arvita Agus Kurniasari Hendra Yufit Riskiawan Hermawan Arief Putranto I Gede Wiryawan Pramuditha Shinta Dewi Puspitasari Rachmad Andri Atmoko Setiawan, Akas Bagus Trismayanti Dwi Puspitasari