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Dashboard Monitoring Pemesanan Produk Percetakan Dengan Pendekatan Key Performance Indikator Putri Diana Nopianti; Susan Dian Purnamasari; Andri Andri; Muhamad Ariandi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1155

Printing is a complex industry with processes involving many variables and stages. In this context, it is important to have an effective tool to monitor and manage printing product orders. Dashboard monitoring with a key performance indicator (KPI) approach can be the right solution for monitoring operational performance and identifying areas that need improvement. This study aims to create a KPI-based monitoring dashboard for printing product orders that is relevant to business goals. The current printing order process, transaction records and order results are still being processed using manual methods. Summarized in the order recap sheet made by the cashier every month. The process of analysis of achievement for ordering so far has not been carried out. From the analysis process that has not been carried out, there are obstacles including making the printing press not know the bidding position directly, the number of orders for each printing product whether it reaches the order target or not. His suggestion is to make an order monitoring dashboard to be able to see which position the target is in and what position the current offer is in. The most effective solution to find out is the Monitoring Dashboard for Printing Product Orders with the Key Performance Indicator Approach). The goal is to make it easier for the owner to meet the orders used, namely the concept of the KPI approach. The dashboard monitoring of orders aims to monitor the achievement of printing targets. The methodology used in this study includes the stages of Business Justification, Business Drivers, Business Issues Analysis, Cost-Benefit Analysis, and Risk Assessment. The needs analysis process involves engaging KPIs that are relevant to ordering printing products, based on the company's deep understanding of business processes and goals. Dashboard design involves selecting metrics, setting up informative visual displays,. The developed dashboard provides an overview of printing product orders as a whole. This KPI approach in dashboard monitoring provides significant benefits for printing companies. By monitoring orders through relevant metrics, companies can identify and implement process improvements needed to increase order efficiency, and improve product quality. In addition, this dashboard can also find out if you don't know the position of the offer directly or the number of orders for each printing product whether it reaches the order target or not. You can use this dashboard to monitor orders to be able to see between the target and the current advertising conditions which position is in the right place. set on accurate and up-to-date data.

Peramalan Kebutuhan Obat Menggunakan Metode Single Exponential Smoothing Amelia Vebrianti; Muhammad Nasir; Jemakmun Jemakmun; Andri Andri
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4267

Ogan Ilir Regional General Hospital (RSUD) tries to maintain the availability of medicines to meet patient needs. The Pharmacy Department of Ogan Ilir Regional Hospital always monitors existing drug stocks before placing orders with distributors of pharmaceutical supplies. The drug procurement process is carried out in a planned manner to reduce the risk of loss from the method of drug procurement and to identify the need for highly used drugs. This research aims to determine the forecasting of drug needs using the Single Exponential Smoothing method. The data used is drug use data for the period 2021 to 2022. Based on the research results, it was found that based on the highest MAPE score it was at 22% for alpha 0.1, and the highest MAPE was at 12% for alpha 0.5. These results show that the prediction of drug use is successful and has good value because the MAPE score does not exceed 20%.

Peramalan Kebutuhan Obat Menggunakan Metode Single Exponential Smoothing Amelia Vebrianti; Muhammad Nasir; Jemakmun Jemakmun; Andri Andri
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4381

Ogan Ilir Regional General Hospital (RSUD) tries to maintain the availability of medicines to meet patient needs. The Pharmacy Department of Ogan Ilir Regional Hospital always monitors existing drug stocks before placing orders with distributors of pharmaceutical supplies. The drug procurement process is carried out in a planned manner to reduce the risk of loss from the method of drug procurement and to identify the need for highly used drugs. This research aims to determine the forecasting of drug needs using the Single Exponential Smoothing method. The data used is drug use data for the period 2021 to 2022. Based on the research results, it was found that based on the highest MAPE score it was at 22% for alpha 0.1, and the highest MAPE was at 12% for alpha 0.5. These results show that the prediction of drug use is successful and has good value because the MAPE score does not exceed 20%.

Implementasi Algoritma BERTopic Berbasis IndoBERT untuk Pemodelan dan Analisis Trending Topik Berita Online pada Dashboard Website M. Wirayuda; Ari Muzakir; Wydyanto Wydyanto; Andri Andri
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10951

The growing volume of online news makes it difficult for the Sumatera Ekspres editorial team to manually track popular topics in a timely manner. This study implements the BERTopic algorithm based on IndoBERT to automatically model trending topics of online news, following the CRISP-DM stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data were collected through web scraping from eight national news portals and YouTube, then processed through case folding, cleaning, tokenization, and stopword removal before being modeled using IndoBERT as the embedding, UMAP for dimensionality reduction, HDBSCAN for clustering, and c-TF-IDF for keyword extraction, complemented by a trend_score metric to identify topics that are genuinely gaining momentum. The main contribution of this study lies in integrating more heterogeneous multi-source data than prior work and introducing the trend_score metric as a more accurate indicator of momentum, realized as a ready-to-use dashboard for the editorial team. Testing across four data periods (1, 7, 14, and 30 days) shows that the model consistently produces topics of good coherence and diversity quality. The results were implemented into a website-based dashboard built with FastAPI and SQLite that presents trending topic visualizations, word cloud, and news headline recommendations, which has been tested and confirmed to function as required, making it suitable as a decision-support tool for the editorial team to monitor popular issues quickly, efficiently, and in a data-driven manner.

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