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All Journal bit-Tech Journal of Renewable Energy and Smart Device
Nani Krisnawaty Tachjar
Perbanas Institute
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Sistem Informasi Terintegrasi dengan System Development Life Cycle untuk Efisiensi Pengelolaan Data Bengkel Anjany Arief Rahman Hidayat; Nani Krisnawaty Tachjar
bit-Tech Vol. 7 No. 1 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i1.1611

Abstract

Sistem Informasi saat ini lumrah digunakan, dan dapat membantu mempermudah hidup manusia. Hanya saja umumnya sistem informasi ini masih banyak yang bersifat stand-alone application, yang berarti belum terintegrasi dengan aplikasi atau modul lainnya. Khususnya usaha yang masih termasuk Usaha Mikro, Kecil dan Menengah (UMKM). Pencatatan data dengan menggunakan metode konvensional seringkali menimbulkan kesalahan dalam pembuatan laporan yang ada, proses pengolahan data transaksi penjualan, pengolahan data barang dan juga  pengolahan data pelanggan. Beberapa hal tersebut menjadi permasalahan di bengkel Anjany, sehingga pada akhirnya laporan yang dikeluarkan tidak akurat untuk mengambil langkah selanjutnya. Jika hal tersebut terus dilakukan, bukan tak mungkin akan menimbulkan konsekuensi kerugian. Oleh karena itu peneliti melakukan pengamatan, wawancara, dan juga mencari studi literatur untuk mengumpulkan data yang mendukung penelitian. Kemudahan pada proses pengolahan data, pengelolaan data pelanggan dan juga stok barang, mempermudah pengolahan data transaksi penjualan, meminimalisir kesalahan dalam pencatatan dan pembuatan laporan adalah tujuan penelitian ini. Agar lebih terarah, penelitian ini menggunakan metode Software Development Life Cycle (SDLC) untuk mengembangkan sistem, khususnya menggunakan model waterfall dalam pengembangan sistem pengelolaan dan penjualan di bengkel Anjany. SDLC dipilih karena memiliki beberapa keuntungan yang sesuai untuk pengembangan sistem ini. Sedangkan pemodelan sistem menggunakan Unified Modelling Language (UML) yang lebih mudah diterima oleh pengguna.
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 Arief Rahman Hidayat Budi Indiarto Deden Prayitno Dwi Atmodjo WP Lely Priska D Tampubolon M. Iman Wahyudi Winny Purbaratri