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All Journal CESS (Journal of Computer Engineering, System and Science) SCIENCE TECH: Jurnal Ilmiah Ilmu Pengetahuan dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Prosiding National Conference for Community Service Project Journal of Information System and Technology (JOINT) Conference on Management, Business, Innovation, Education and Social Sciences (CoMBInES) Conference on Community Engagement Project (Concept) Conference on Business, Social Sciences and Technology (CoNeScINTech) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer INOVTEK Polbeng - Seri Informatika
Mangapul Siahaan
Universitas Internasional Batam
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences Other

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PERANCANGAN SISTEM MANAGEMENT GUDANG BERBASIS WEB DENGAN EXTREME PROGRAMMING Mangapul Siahaan; Sudy
JURNAL ILMIAH BETRIK Vol. 14 No. 03 DESEMBER (2023): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v14i03 DESEMBER.170

Abstract

Information technology has become very important in the dissemination and reception of information, especially through web-based media, in an era of increasingly advanced and modern technology. Both organizations and individuals need information technology to increase efficiency and effectiveness in various aspects of operations and services provided. Warehouse management has become essential for an effective supply chain in an increasingly competitive business world. Business operations are greatly influenced by warehouse management, which plays a vital role in the storage, management, and distribution of goods. Although web-based warehouse management systems allow real-time monitoring, the main problem is responding to frequently changing requirements. To overcome this problem, this research uses the Extreme Programming (XP) method, an approach that is highly responsive and suitable for constantly changing environments and unstable customer needs. The XP method enables close collaboration between warehouse owners, suppliers and developers, ensuring that the software developed is of high quality. This research found a web-based warehouse management application system that can help companies and MSMEs manage their warehouses better. Management of goods, suppliers, ordering goods, transactions, reports and products and several important features in this system. Blackbox testing methods have ensured that all features function properly, increasing confidence in the use of the system. This study helps us understand how information technology can help improve warehouse management in a dynamic and competitive business context. With the help of information technology and XP methods, warehouse management becomes more responsive, flexible and able to better meet customer requirements. This is an important step towards success in the rapidly evolving business world.
Perancangan Sistem Prediksi Harga Saham Berbasis Website Menggunakan Algoritma Hybrid (ARIMA-LSTM) Handyca Yeng; Mangapul Siahaan
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 1: April 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i1.1620

Abstract

Stock investment, known as a high-risk, high-return instrument, has gained significant attention during the pandemic with a notable 44.42% increase in novice investors in Sidoarjo City. This research focuses on developing a web-based stock price prediction system utilizing a hybrid algorithm (ARIMA-LSTM) and integrating the Extreme Programming method in its development. Quantitative stock price data were obtained from Yahoo Finance. The research outcome is an implemented system that meets user requirements. More importantly, the system is capable of providing stock price predictions that closely align with actual data for the period from 2018 to 2022. Model evaluation employing Mean Squared Error (MSE) yielded a value of 0.0078, Mean Absolute Error (MAE) with a value of 0.556, and Mean Absolute Percentage Error (MAPE) at 0.412, which is equivalent to 41.89%. These evaluation results indicate that the hybrid ARIMA-LSTM model performs well, delivering accurate predictions. This research has the potential to benefit investors, financial analysts, and stock market stakeholders, enabling more informed and efficient decision-making.Keywords: Stock Prediction; Hybrid Algorithm; Extreme Programming; Web-Based System; ARIMA-LSTM.AbstrakInvestasi saham, yang dikenal sebagai instrumen high risk, high return, menjadi sorotan selama pandemi dengan peningkatan signifikan investor pemula sebesar 44.42% di Kota Sidoarjo. Penelitian ini berfokus pada pengembangan sistem prediksi harga saham berbasis website dengan memanfaatkan algoritma hybrid (ARIMA-LSTM) dan mengintegrasikan metode Extreme Programming dalam pengembangannya. Data harga saham yang digunakan diperoleh melalui Yahoo Finance secara kuantitatif. Hasil penelitian ini adalah sistem yang berhasil diimplementasikan dan sesuai dengan kebutuhan pengguna. Lebih penting, sistem ini mampu memberikan prediksi harga saham yang mendekati data aktual untuk periode tahun 2018 hingga 2022. Evaluasi model menggunakan MSE (Mean Squared Error) dengan nilai 0.0078, MAE (Mean Absolute Error) dengan nilai 0.556, MAPE (Mean Absolute Percentage Error) dengan nilai 0.412 yaitu 41.89%. Hasil evaluasi ini menunjukkan bahwa model hybrid ARIMA-LSTM berkinerja baik, memberikan prediksi yang akurat. Penelitian ini berpotensi memberikan manfaat bagi para investor, analis keuangan, dan pemangku kepentingan pasar saham, memungkinkan pengambilan keputusan yang lebih informasi dan efisien. 
Analysis of Rice Yield Prediction with Mlpregressor and Long Short-Term Memory Models Sunoto; Mangapul Siahaan
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wnpm3846

Abstract

This research aims to analyse and compare the accuracy of rice productivity prediction using Multi-Layer Perceptron  Regressor (MLPRegressor) and Long Short-Term Memory (LSTM) models. The data used comes from the Badan Pusat Statistik (BPS) for the period 2018-2023, covering rice productivity from 34 provinces in Indonesia. The study employed six different architectural models for each model, with training data using the 2018-2020 period and testing data for 2021-2023. The results show that the LSTM model with 2-42-42-42-1 architecture achieved the highest accuracy rate of 94.12% with MSE 0.00305660, while the MLPRegressor model with 2-22-1 architecture achieved 91.18% accuracy with MSE 0.00471975. These results indicate that LSTM performs slightly better in predicting rice productivity, which can be used as a reference for agricultural planning and food policy in Indonesia.
Co-Authors Ahmad Mawardi Lubis Ariq Bimantoro Balinda Oca Rosalia Christopher Harsana Jasa Daniel Arnoldi Gultom Darius Angtony Frank Lurich Handyca Yeng Indasari Deu Jason Angelo Ong Jennifer Jennifer Justin Justin Kelvin Kurniawan Kevin Anderson Melissa Valentino Rosiana Mikhael Chendra Muhammad Ridho Alfarizi Muhammad Sulton Maulana Oktavina Oktavina Restu Maulana Nashuha Ricky Hartanto Roy Valentino Chandra Ryan Kenidy Satria Lim Sudy Sunoto Tevin Lim Vendhy Vendhy Vincent Octarian Vianto Wahyu Yudianto