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All Journal Journal Digital Technology Trend
Dedi Dwi Saputra
Cyber University Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management

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Optimalisasi Proses Bisnis Inventaris Multigudang Menggunakan Business Process Model and Notation: Studi Kasus PT Satellite of Glow Raden Mochamad Issa Wirakusumah; Dedi Dwi Saputra
Journal Digital Technology Trend Vol. 5 No. 2 (2026): December
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jdtt.v5i2.464

Abstract

Fragmented inventory management in multi-warehouse environments causes recording errors, delayed stock synchronisation, and weak accountability. PT Satellite of Glow manages four separate warehouses relying on physical logbooks and isolated spreadsheets without a traceable audit trail. This study models and optimizes the company's inventory processes using Business Process Model and Notation (BPMN). A qualitative single-case study was conducted through direct observation and semi-structured interviews with four actor groups across the four warehouses. The workflow was modelled As-Is, evaluated using process redesign heuristics, and restructured into an automated To-Be model. Process performance was assessed by measuring the cycle times of ten high-frequency activities. The As-Is model revealed 18 manual activities, 12 of which were non-value-adding administrative tasks requiring identical data to be recorded three times. Redesigning the process via task elimination, automation, and database centralisation reduced the total cycle time from 150 to 30.5 minutes, achieving an efficiency gain of 79.67% with activity improvements ranging from 66.70% to 90.00%. Information-oriented activities attained higher efficiency (84.48%) than physical transactions (73.02%), confirming that physical handling defines the upper boundary of software-driven optimization. The To-Be model eliminates multi-tier recording and establishes automated audit-trail tracking.
Implementasi Machine Learning dengan Metode Text Mining dan Algoritma Naïve Bayes untuk Klasifikasi Keluhan Pelanggan pada Twitter Operator Telkomsel Dedi Dwi Saputra; Riza Fahlapi; Antonius Yadi Kuntoro; Taufik Asra; Hermanto Hermanto
Journal Digital Technology Trend Vol. 5 No. 2 (2026): December
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jdtt.v5i2.467

Abstract

Twitter is one of the most widely used social media platforms in Indonesia, and telecommunication companies such as PT Telekomunikasi Seluler (Telkomsel) utilize their official account, @Telkomsel, as a primary channel for customer interaction and complaint handling. Currently, incoming complaint tweets are still classified manually by the customer service division, a procedure that is time-consuming and prone to human inconsistency. This study implements a text mining approach integrating the Naïve Bayes algorithm, Adaptive Boosting (AdaBoost), and the Synthetic Minority Over-Sampling Technique (SMOTE) to automatically classify Indonesian-language tweets mentioning @Telkomsel into two classes: "Complaint" and "Non-Complaint." A dataset of 2,000 tweets was collected through crawling in RapidMiner, followed by a two-stage text preprocessing workflow utilizing Gataframework and RapidMiner. The classification model was validated using 10-fold cross-validation. The experimental results demonstrate that the combination of Naïve Bayes, AdaBoost, and SMOTE achieves an accuracy of 78.84%, precision of 79.90%, recall of 80.51%, and an AUC of 0.809. These findings confirm that the proposed framework is capable of effectively automating complaint detection to support more responsive customer service operations.
Co-Authors Antonius Yadi Kuntoro Hermanto Hermanto Raden Mochamad Issa Wirakusumah RIZA FAHLAPI Taufik Asra