Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi bit-Tech Journal of Renewable Energy and Smart Device
Nani Krisnawaty Tachjar
Perbanas Institute
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Kompleksitas Proses dan Non-Konformitas Sebagai Faktor Keterlambatan Pada Alur Kerja Purchase-To-Pay Lengkap Pendekatan Machine Learning Lucia Sri Istiyowati; Pratiwi Rachmadi; Nani Krisnawaty Tachjar
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 2 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No2.pp801-809

Abstract

Invoice processing is a crucial component of a financial information system, particularly the accounts payable module integrated with an Enterprise Resource Planning (ERP) system. Delays in invoice processing are common, leading to financial penalties. This study employs a data-driven approach by integrating process mining and machine learning using the XGBoost algorithm to analyze the factors contributing to delays in the purchase-to-pay (P2P) workflow. Event log data from the BPI Challenge 2019 public dataset and refined to 3,471 complete cases. Process discovery and performance analysis were performed using the Disco tool, while an XGBoost binary classification model was developed with 16 process-level features extracted from the event logs. The model yielded an AUC-ROC of 0.6267 with a 5-fold cross-validation of 0.6382 and a standard deviation of 0.0099, indicating moderate discriminatory ability. Feature importance analysis identified process complexity (0.191), ideal workflow conformity (0.187), and invoice cancellation frequency (0.122) as the dominant delay factors, while document completeness contributed insignificantly. These findings suggest that in a complete P2P workflow, the cause of delay lies in the characteristics of process execution. The results of this study provide direction for developing accounts payable information systems with a focus on complexity reduction and real-time compliance monitoring.
Co-Authors Arief Rahman Hidayat Budi Indiarto Deden Prayitno Dwi Atmodjo WP Lely Priska D Tampubolon Lucia Sri Istiyowati M. Iman Wahyudi Pratiwi Rachmadi Winny Purbaratri