cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ana Tsalitsatun Ni'mah
Contact Email
ana.tsalits@nuris.ac.id
Phone
+6285366622280
Journal Mail Official
admin@nuris.ac.id
Editorial Address
STAI Nurul Islam Mojokerto YPP Nurul Islam Pungging Mojokerto (Kampus 2) Jl. Raya PP Nurul Islam Pungging, Tunggal Pager - Pungging Kabupaten Mojokerto Kodepos 61384
Location
Kab. mojokerto,
Jawa timur
INDONESIA
Sains Data Jurnal Studi Matematika dan Teknologi
Published by Yayasan Pondok Pesantren Nurul Islam Mojokerto
ISSN : 2986903X     EISSN : 2986903X     DOI : https://doi.org/10.52620/sainsdata
Core Subject : Science, Education, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Mathematics Physics
Sains Data Jurnal Studi Matematika dan Teknologi, published by the STAI Nurul Islam Mojokerto. Its a biannual refereed journal concerned with the practice and processes of mathematics and technologies. It provides a forum for academics, practitioners and community representatives to explore issues and reflect on practices relating to the full range of engaged activity. This journal is a peer-reviewed online journal dedicated to the publication of high-quality research focused on research and best pratices. The mission of Sains Data Jurnal Studi Matematika dan Teknologi is to serve as the premier peer-reviewed, interdisciplinary journal to advance theory and practice related to all forms of social science. This includes highlighting innovative endeavors; critically examining emerging issues, trends, challenges, and opportunities; and reporting on studies of impact in the areas of mathematics and technologies.
Arjuna Subject : Ilmu Komputer (semua kategori) - Teori Komputasi dan Matematika
Articles 42 Documents
 1   2   3   4   5 
A Comparative Study of Holt-Winters Exponential Smoothing Models for Forecasting Palm Oil Production at PT XYZ Difta Alzena Sakhi; Karina Auralia; Muhammad Nasrudin; Masti Fatchiyah Maharani
Sains Data Jurnal Studi Matematika dan Teknologi Vol 4, No 2: July-December 2026
Publisher : Institut Nurul Islam Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52620/sainsdata.v4i2.420

Abstract

Monthly palm oil production fluctuates due to seasonal patterns and long-term trends, making accurate forecasting essential for operational planning in plantation companies. This study aims to compare seven Holt–Winters Exponential Smoothing models with different trend and seasonal component configurations to forecast the monthly palm oil production of PT XYZ using a univariate approach. The analysis is based on 135 observations covering the period from January 2015 to March 2026. The performance of each model configuration was evaluated using the last 12 months as the test set and assessed based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the Holt–Winters model with a multiplicative trend and additive seasonality consistently achieved the best forecasting performance, with a MAPE of 5.71%, an MAE of 284.89 tons, and an RMSE of 331.83 tons, substantially outperforming the other six model configurations. The selected model was subsequently used to generate production forecasts for the next 12 months, providing a basis for managerial decision-making in harvest planning, mill capacity management, workforce allocation, and sales strategy.
Penerapan Metode Fine Tuning BRIO (Bringing Order to Abstractive Summarization) pada Model mBART untuk Peringkasan Abstraktif Berita Berbahasa Indonesia Devina Marsya Rani; I Nyoman Saputra Wahyu Wijaya; I Made Putrama; Ni Putu Novita Puspa Dewi
Sains Data Jurnal Studi Matematika dan Teknologi Vol 4, No 2: July-December 2026
Publisher : Institut Nurul Islam Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52620/sainsdata.v4i2.456

Abstract

Meningkatnya jumlah berita pada media daring menyulitkan pembaca memperoleh informasi secara cepat karena keterbatasan waktu untuk membaca seluruh artikel. Kondisi ini mendorong pengembangan sistem peringkasan teks abstraktif berbasis Transformer yang mampu menghasilkan ringkasan baru dengan tetap mempertahankan informasi penting. Model mBART memiliki kemampuan baik dalam berbagai tugas pemrosesan bahasa alami, namun masih memerlukan fine tuning agar sesuai dengan karakteristik data dan tugas spesifik. Hingga saat ini, penerapan metode BRIO pada model mBART untuk tugas peringkasan abstraktif berita berbahasa Indonesia masih belum banyak dilaporkan, sehingga efektivitas pendekatan tersebut belum diketahui secara memadai. Oleh karena itu, penelitian ini bertujuan mengevaluasi efektivitas BRIO sebagai metode fine-tuning pada model mBART untuk peringkasan abstraktif berita berbahasa Indonesia. Penelitian ini menggunakan dataset Liputan6 yang terdiri atas 26.903 pasangan artikel dan ringkasan. Evaluasi dilakukan menggunakan ROUGE, METEOR, Novel Unigram, dan Compression Ratio. Hasil menunjukkan ROUGE-1 sebesar 48,39%, ROUGE-2 sebesar 32,41%, ROUGE-L sebesar 41,72%, ROUGE-L sebesar 41,72%, METEOR sebesar 41,52%, Novel Unigram sebesar 0,0557, dan Compression Ratio sebesar 6,93. Hasil tersebut menunjukkan bahwa penerapan BRIO mampu menghasilkan ringkasan yang tetap mempertahankan informasi utama dengan tingkat pemadatan yang baik, sehingga berpotensi menjadi pendekatan fine tuning untuk pengembangan sistem peringkasan berita berbahasa Indonesia.

Page 5 of 5 | Total Record : 42

 1   2   3   4   5 

Filter by Year

2023 2026


Reset
Filter By Issues
All Issue Vol 4, No 2: July-December 2026 Vol 4, No 1: January - June 2026 Vol 3, No 2: July-December 2025 Vol 3, No 1: January - June 2025 Vol 2, No 2: July-December 2024 Vol 2, No 1: January - June 2024 Vol 1, No 2: July-December 2023 Vol 1, No 1: January - June 2023