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All Journal IJID (International Journal on Informatics for Development) Jurnal Teknologi Terapan Jurnal Abdi Insani Syntax: Journal of Software Engineering, Computer Science and Information Technology Media Jurnal Informatika Jurnal Pengabdian Masyarakat IPTEK Industrial Research Workshop and National Seminar Journal Of Artificial Intelligence And Software Engineering
Hayati, Hashri
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Journal : Media Jurnal Informatika

 1 
DEVELOPMENT OF A SALES FORECASTING APPLICATION USING THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE METHOD WITH EXTERNAL INPUT (ARIMAX) Fauziyyah, Aulia Aziizah; Brahmana, Jonanda Pantas Agitha; Simatupang, Paulina Lestari; Soewono, Eddy Bambang; Hayati, Hashri
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5693

Abstract

that also operates in the culinary industry through the Pempek Duo brand. In the operational business of the culinary sector, PT Selada has developed the Mireta Point of Sale (POS) system as a transactional and reporting tool. However, the existing system has not been equipped with a transaction history data analysis feature to predict sales trends. This condition makes it difficult for the company to identify which products are best-selling and which ones are less popular. This development aims to create a sales forecasting feature based on the Autoregressive Integrated Moving Average with Exogenous Input (ARIMAX) method in the Mireta POS system. The ARIMAX model was chosen because it can incorporate external variables into the prediction calculations, in this case, holiday factors. The development was carried out using a waterfall approach which includes the stages of requirements analysis, system design, model implementation, and accuracy testing. The data used consists of the sales transaction history of Pempek Duo products from January 2022 to February 2023, which has been grouped by week, as well as holiday data as an external variable. The model evaluation results show that the best parameter combination is ARIMAX(1,0,2) with a Mean Absolute Error (MAE) value of 4.3333. This value indicates an average prediction error of 4 sales packages per week. With this feature, Mireta POS can provide more accurate sales predictions, making it easier for the company to identify the best-selling and least popular products.
Application of Named Entity Recognition (NER) in Job Vacancy Matching Using an Ontology-Based Approach (Case Study: Information Technology Sector) Gunawan , Rizki; Hodijah, Ade; Taqwim , Muhammad Ikhsan Maulana; Siti Ababil , Afyar; Setijohatmo, Urip Teguh; Wulan, Sri Ratna; Alifi , Muhammad Riza; Sari , Aprianti Nanda; Hayati, Hashri
Media Jurnal Informatika Vol 17 No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5675

Abstract

The dissemination of job vacancies through online platforms still faces limitations in understanding the semantic relationships between the skills possessed by job seekers and the qualifications required by a job position. This mismatch results in an inefficient search process and longer search times. This study aims to develop a semantic-based job vacancy recommendation application (talent matching) using a skill ontology approach. One of the main challenges in developing the ontology is the lack of standardized data structures in job vacancy postings, particularly in the job description section. To address this issue, Named Entity Recognition (NER) techniques are applied to automatically extract skill entities from job description texts. The extracted results are then classified into a taxonomy structure using SkillsGPT, thereby forming a hierarchical skill concept model semantically represented within the ontology using Protégé. The matching process between user skills and job qualifications is conducted through semantic similarity calculations employing the Sánchez Similarity method. Job vacancy data are collected via web scraping, while system development follows the Rational Unified Process (RUP) methodology and is evaluated using Black Box testing. Evaluation results demonstrate that the developed system is capable of providing semantically relevant job vacancy recommendations according to the user's skill profile. Therefore, this study contributes both theoretically and practically to the development of ontology-based recommendation systems, particularly in the automated modeling of skill taxonomies from unstructured data.
Development of A Sales Forecasting Application Using The Autoregressive Integrated Moving Average Method With External Input (ARIMAX) Fauziyyah, Aulia Aziizah; Brahmana, Jonanda Pantas Agitha; Simatupang, Paulina Lestari; Soewono, Eddy Bambang; Hayati, Hashri
Media Jurnal Informatika Vol 17 No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5693

Abstract

PT Selada Indonesia Produktif is an information technology company that also operates in the culinary industry through the Pempek Duo brand. In the operational business of the culinary sector, PT Selada has developed the Mireta Point of Sale (POS) system as a transactional and reporting tool. However, the existing system has not been equipped with a transaction history data analysis feature to predict sales trends. This condition makes it difficult for the company to identify which products are best-selling and which ones are less popular. This development aims to create a sales forecasting feature based on the Autoregressive Integrated Moving Average with Exogenous Input (ARIMAX) method in the Mireta POS system. The ARIMAX model was chosen because it can incorporate external variables into the prediction calculations, in this case, holiday factors. The development was carried out using a waterfall approach which includes the stages of requirements analysis, system design, model implementation, and accuracy testing. The data used consists of the sales transaction history of Pempek Duo products from January 2022 to February 2023, which has been grouped by week, as well as holiday data as an external variable. The model evaluation results show that the best parameter combination is ARIMAX(1,0,2) with a Mean Absolute Error (MAE) value of 4.3333. This value indicates an average prediction error of 4 sales packages per week. With this feature, Mireta POS can provide more accurate sales predictions, making it easier for the company to identify the best-selling and least popular products.
Co-Authors Ade Hodijah Alifi , Muhammad Riza Asri Maspupah Brahmana, Jonanda Pantas Agitha Djoko Cahyo Utomo Lieharyani Eddy Bambang Soewono Fauziyyah, Aulia Aziizah Firdaus, Lukmannul Hakim Gelar, Trisna Gunawan , Rizki Iwan Awaludin Muhammad Riza Alifi Muhammad Rizqi Sholahuddin Mulia, Syelvie Ira Ratna Putriadhinia, Salma Syawalan Sari , Aprianti Nanda Sari, Aprianti Nanda Setijohatmo, Urip Teguh Simatupang, Paulina Lestari Siti Ababil , Afyar Syakrani, Nurjannah Taqwim , Muhammad Ikhsan Maulana Transmissia Semiawan Wonoseto, Muhammad Galih Wulan, Sri Ratna