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All Journal Telematika MKOM Journal of Renewable Energy and Smart Device
Budi Indiarto
Perbanas Institute
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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KLASIFIKASI SMS SPAM DENGAN METODE NAIVE BAYES CLASSIFIER UNTUK MENYARING PESAN MELALUI SELULAR Budi Indiarto
Telematika MKOM Vol 8, No 2 (2016): Jurnal Telematika MKOM Vol. 8 No. 2 September 2016
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (194.114 KB)

Abstract

SMS atau Short Message Service (Layanan Pesan Singkat) merupakan sarana komunikasi yang masih dipergunakan oleh banyak orang yang menggunakan telepon genggam. SMS adalah sebuah layanan pesan singkat yang berisi 160 karakter per pesan yang dikirimkan antar pengguna  telepon genggam. Orang dapat dengan mudah mengirimkan dan menerima SMS tanpa ada batasan. SMS tidak hanya digunakan untuk mengirimkan pesan antar pengguna yang sudah mengenal satu sama lain, tetapi juga digunakan oleh mereka yang tidak dikenal untuk berbagai kepentingan, seperti menawarkan berbagai macam produk dan jasa bahkan  SMS juga digunakan untuk tindakan yang tidak sesuai dengan norma hukum yang mengarah pada tindak pidana. SMS yang tidak diinginkan oleh penerima dikategorikan sebagai spam atau junk SMS atau SMS sampah. Untuk menyaring SMS yang masuk ke inbox SMS, filter spam bisa dibuat. Salah satu algoritma yang dapat dipergunakan untuk menyaring SMS adalah algoritma Naive Bayes Classifier. Dengan menggunakan algoritma penyaring SMS spam diharapkan dapat digunakan untuk menyaring SMS yang masuk ke inbox pengguna, sehingga pengguna dapat memilih SMS yang bermanfaat saja yang akan dibaca dan/atau ditindaklanjuti.
Hybrid Rule-Based and Anomaly Detection Model for Wholesale Sales Risk Classification Dwi Atmodjo WP; Winny Purbaratri; Lely Priska D Tampubolon; Nani Krisnawaty Tachjar; Deden Prayitno; Budi Indiarto; M Iman Wahyudi
Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v4i1.1184

Abstract

Large-scale wholesale transaction systems face increasing risks from suspicious purchasing patterns, abnormal customer behavior, and operational inconsistencies, while conventional rule-based methods may fail to identify previously unseen patterns. This study develops a decision-level hybrid risk classification model that combines expert-derived business rules with post-hoc anomaly decisions from Isolation Forest and DBSCAN. The modules are orchestrated in Apache Airflow and executed through a scheduled daily DAG for batch transaction monitoring. The model was evaluated retrospectively using 12,476 wholesale transactions recorded over 12 months in a single company. The business rules and ground-truth labels were elicited from the same expert pool; therefore, the separation between model development and evaluation was implemented at the data level through independent training, validation, and test subsets. The Hybrid Rule + Isolation Forest configuration achieved the highest accuracy at 87.5%, compared with 74.3% for the authors' rule-based baseline. The resulting 13.1-percentage-point gain should be interpreted as an internal ablation result rather than a comparison with a state-of-the-art external model. For operational efficiency, the automated workflow processed a batch of 1,000 transactions in 42 seconds, compared with approximately 3 hours of manual processing. These findings suggest that combining interpretable business rules with anomaly detection at the decision level can improve risk classification while retaining operational transparency. However, because the evaluation used data from only one wholesale company and shared expert sources for rules and labels, validation across independent companies and expert groups is required before broader generalization.
Co-Authors Deden Prayitno Dwi Atmodjo WP Lely Priska D Tampubolon M. Iman Wahyudi Nani Krisnawaty Tachjar Winny Purbaratri