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All Journal Engineering, Mathematics and Computer Science Journal (EMACS) Eduvest - Journal of Universal Studies Jurnal Pengabdian Barelang
Alfannisa Annurrullah Fajrin
Universitas Putera Batam
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Forecasting the Items Consumption in the Hotel Storage with the Autoregressive Integrated Moving Average Method Christopher Chandra; Alfannisa Annurrullah Fajrin; Cosmas Eko Suharyanto
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 1 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i1.6979

Abstract

In this era, hotel has storage as a storing space for every kind of items. Items stored in the storage are items being used for the needs of the staffs, also for the needs of hotel’s operational. The item consumption is running smoothly with resupply. However, there are often mistakes in resupplying the items. For preventing those several mistakes, a reference is needed to be used for controlling the amount of items arrival (monthly) with minding the amount of items in the storage should be. The reference to be used is the forecast of the item consumption every month. Forecasting was being done with Autoregressive Integrated Moving Average (ARIMA) method. There are five steps needed to build the ARIMA model, such as plot identification, model identification, model estimation, choosing the best model, and prediction (forecast). The input variable to be used in this research is the rime series from the data of storage’s item consumption starts from January 2018 until October 2020, and the output variable is the result of the prediction of item consumption in the next period, such as in November to December 2020. The results is subtracted with the number of items left in storage to obtain the minimum amount of item to be entered for the month.
Pembinaan Pembuatan Laporan Keuangan Dan Pemasaran Online Pada UKM Rafflesia Kota Batam Tukino Tukino; Syahril Effendi; Alfannisa Annurrullah Fajrin; Nanda Harry Mardika
Jurnal Pengabdian Barelang Vol 4 No 2 (2022): Jurnal Pengabdian Barelang Vol 4 No 3 2022
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jpb.v4i2.5534

Abstract

Pelaksanaan kegiatan pengabdian kepada masyarakat yang akan dilaksanakan berupa pembinaan Pembuatan Laporan Keuangan dan Pemasaran Online Usaha Kecil Menengah Bank Sampah Mandiri. Berdasarkan hasil wawancara dilapangan bahwa UKM Rafflesia ini memiliki permasalahan untuk melakukan pemasaran hasil produksi dan pencatatan keuangan. Metode yang digunakan dalam pembinaan UKM Rafflesia yang akan diberikan yaitu metode survei, metode ceramah, metode diskusi dan metode latihan. Hasil dari pengabdian UKM merasakan masih perlu penyesuaian terhadap Pembuatan Laporan keuangan yang berstandar akuntansi keuangan entitas tanpa akuntabilitas publik, sudah ada pemisahan laporan keuangan yang telah biasa disusun oleh UKM melalui keuntungan usaha dan kas pribadi serta telah tersedianya laman media sosial UKM yang dijalankan peserta dan pahamnya mengenai yang harus dilakukan dengan laman tersebut untuk memaksimalkan penjualan
Corpus Development and Pos Tagging Evaluation for Riau Malay Dialect Using Hidden Markov Model in A Low-Resource Setting Rifky Akbar Vetian; Koko Handoko; Andi Maslan; Alfannisa Annurrullah Fajrin
Eduvest - Journal of Universal Studies Vol. 6 No. 4 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i4.53066

Abstract

Natural Language Processing (NLP) plays a crucial role in enabling machines to process and understand human language. One of the fundamental tasks in NLP is Part-of-Speech (POS) tagging, which serves as the foundation for various downstream applications such as parsing, information extraction, and machine translation. However, the development of POS tagging models for low-resource languages remains a significant challenge due to the limited availability of annotated corpora. This study aims to develop a POS-tagged corpus for Bahasa Melayu Dialek Riau (BMDR) and evaluate the performance of a Hidden Markov Model (HMM) as a baseline approach for POS tagging. The dataset consists of approximately 600 sentences with around 10,000 tokens, which were manually annotated and validated using Inter-Annotator Agreement. The annotated corpus was then divided into training and testing sets with a ratio of 80:20. Experimental results show that the HMM model achieved an accuracy of 86.8%, with precision, recall, and F1-score values of 85.9%, 85.3%, and 85.6%, respectively. The results indicate that HMM remains a competitive approach for POS tagging in low-resource language settings. Error analysis reveals that lexical ambiguity, Out-of-Vocabulary (OOV) words, and limited training data are the primary factors affecting model performance. This research contributes by providing the first annotated POS corpus for BMDR, evaluating the effectiveness of HMM in a low-resource context, and offering insights into linguistic challenges in regional languages. Future work may explore larger datasets and advanced deep learning models to improve tagging performance.
Co-Authors Andi Maslan Christopher Chandra Handoko, Koko NANDA HARRY MARDIKA Rifky Akbar Vetian Suharyanto, Cosmas Eko Syahril Effendi tukino, tukino