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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi IJEBD (International Journal Of Entrepreneurship And Business Development) Informatik : Jurnal Ilmu Komputer Journal of Information Systems and Informatics bit-Tech International Journal of Advances in Data and Information Systems Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Robotics and Control Systems Jurnal Impresi Indonesia Literasi Nusantara Journal of International Conference Proceedings Jurnal Ilmiah Edutic : Pendidikan dan Informatika Signal and Image Processing Letters
Diyasa, I Gede Susrama Mas
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Fuzzy Time Series Cheng Optimasi Adaptive Particle Swarm Optimization (APSO) untuk Optimalisasi Prediksi Harga Beras di Kota Surabaya Ulayya, Yasmin; Idhom, Mohammad; Diyasa, I Gede Susrama Mas
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Harga beras rentan mengalami fluktuasi, berdampak signifikan pada kesejahteraan masyarakat, terutama kelompok berpendapatan rendah. Di Surabaya, kenaikan harga beras mendorong perlunya prediksi akurat untuk mitigasi dampak ekonomi. Penelitian ini bertujuan meramalkan harga beras menggunakan metode Fuzzy Time Series Cheng (FTS Cheng) yang dioptimalkan dengan Adaptive Particle Swarm Optimization (APSO) untuk menangani data non-linear dan fluktuatif. Data sekunder diambil dari Dinas Perindustrian dan Perdagangan Provinsi Jawa Timur (Siskaperbapo) periode 1 Januari 2023 hingga 31 Maret 2025, mencakup harga beras premium dan medium di Pasar Tambahrejo dan Pasar Wonokromo. Metode utama adalah FTS Cheng dengan optimasi APSO untuk meningkatkan akurasi prediksi. Model menunjukkan akurasi tinggi dengan MAPE (Mean Absolute Percentage Error) sangat rendah. Di Pasar Tambahrejo, MAPE beras premium 0,09% dan medium 0,00%. Di Pasar Wonokromo, MAPE premium 6,38% dan medium 0,85%. Optimasi APSO berhasil menurunkan MAPE, misalnya di Pasar Tambahrejo (premium turun 0,38%, medium turun 0,68%). Kombinasi FTS dan APSO menghasilkan prediksi harga beras yang presisi. Temuan ini dapat mendukung kebijakan stabilisasi harga, manajemen stok, dan perencanaan produksi beras lebih efektif, sekaligus meningkatkan stabilitas ekonomi rumah tangga.   Abstract Rice prices are prone to fluctuations, significantly impacting public welfare, especially low-income groups. In Surabaya, rising rice prices necessitate accurate predictions to mitigate economic impacts. This research aims to forecast rice prices using the Fuzzy Time Series Cheng (FTS Cheng) method optimized with Adaptive Particle Swarm Optimization (APSO) to handle non-linear and fluctuating data. Secondary data was obtained from the East Java Provincial Department of Industry and Trade (Siskaperbapo) for the period January 1, 2023, to March 31, 2025, covering premium and medium rice prices at Tambahrejo Market and Wonokromo Market. The main method is FTS Cheng with APSO optimization to improve prediction accuracy. The model demonstrates high accuracy with very low MAPE (Mean Absolute Percentage Error). At Tambahrejo Market, MAPE for premium rice is 0.09% and medium rice is 0.00%. At Wonokromo Market, MAPE for premium rice is 6.38% and medium rice is 0.85%. APSO optimization successfully reduces MAPE, for example at Tambahrejo Market (premium decreased by 0.38%, medium decreased by 0.68%). The combination of FTS Cheng and APSO produces precise rice price predictions. These findings can support price stabilization policies, stock management, and more effective rice production planning, while improving household economic stability.
Analysis Postponed VAT Feature on Invoicing Module of Odoo 16 using Rapid Application Development Permana, Eriko Indra; Diyasa, I Gede Susrama Mas; Swari, Made Hanindia Prami
EDUTIC Vol 12, No 1: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i1.28484

Abstract

The postponement of Value Added Tax (VAT) payment is a policy aimed at easing financial burdens for companies that frequently import goods, as it allows businesses to defer tax payments instead of prepaying them during imports, thereby improving cash flow and reducing operational costs. This study explores the implementation of VAT payment postponement in the Odoo 16 Invoicing module using the Rapid Application Development (RAD) method, chosen for its rapid iteration and prototyping capabilities to meet user needs and regulatory changes efficiently. By modeling an importing company’s business process in Odoo 16, the research implements and tests the VAT postponement feature, assessing its effectiveness in streamlining operations and enhancing financial flexibility. The study also evaluates the RAD method's efficiency in development and deployment, providing insights into the integration of fiscal policies with corporate IT systems to bolster operational performance and global competitiveness.
Bus Passenger Demand Forecasting Using A Hybrid ARIMA–MLP Model Moerrin, Naufal Baihaqi; Damaliana, Aviolla Terza; Diyasa, I Gede Susrama Mas
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3549

Abstract

Accurate passenger demand forecasting is crucial for operational planning and service reliability in public transportation systems. Despite the effectiveness of traditional models, existing approaches often struggle with nonlinear fluctuations in demand, which limits their ability to adapt to real-world variability. This study proposes a hybrid forecasting framework that combines the Autoregressive Integrated Moving Average (ARIMA) model with a Multi-Layer Perceptron (MLP) neural network for short-term passenger demand prediction. By using ARIMA to capture linear components like trend, seasonality, and autocorrelation, and MLP to model the residuals that contain nonlinear patterns, the proposed approach integrates the strengths of both models. This hybrid method addresses gaps in current forecasting techniques by improving adaptability and precision. Empirical analysis was conducted using daily passenger count data from Bus Trans Jatim during 2023–2024. Data preprocessing included exploratory time series analysis, variance stabilization, and outlier assessment to ensure compatibility with the modeling assumptions. Forecast performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the hybrid ARIMA–MLP model achieved a MAPE of 4.95%, outperforming the standalone ARIMA model in providing more adaptive and accurate short-term forecasts. These findings have practical implications for public transportation planning, enabling more responsive and efficient operations, particularly for forecasting demand fluctuations.
Co-Authors A, Denisa Septalian A. A. Ngurah Gunawan Achmad Junaidi Adila, Ma’ratul Agussalim, Agussalim Ali, Munawar Awang, Wan Suryani Wan Bambang Trigunarsyah, Bambang Basuki Rahmat Masdi Siduppa Damaliana, Aviolla Terza Dewi , Deshinta Arrova Dwi Arman Prasetya Erik Iman Heri Ujianto Erma Suryani Eva Yulia Puspaningrum Fahrudin, Tresna Maulana Faikul Umam Fatullah, Ryan Reynickha Fetty Tri Anggraeny Henki Bayu Seta Humairah, Sayyidah I Made Merdana Ibrahim, Mohd Zamri Bin idhom, Mohammad Karim, Mohammad Daniel Sulthonul Kartika Maulida Hindrayani Kusuma, Nugraha Varrel Kuswardana, Dendy Arizki Kuswardhani , Hajjar Ayu Cahyani Made Hanindia Prami Swari Moerrin, Naufal Baihaqi Mudjahidin NYOMAN DITA PAHANG PUTRA, NYOMAN P, Made Hanindia Permana, Eriko Indra Permanasari, Wahyu Melinda Prayoga, Aji Putra, Handie Pramana Riyantoko, Prismahardi Aji Rizky Mahendra, Rakha Sampurno, Ilham Ade Widya Saputra, Wahyu Syaifullah Jauharis Sari, Allan Ruhui Fatmah Sari, Refika Ayuna Sibarani, Michael Alexander Justin Audison Sugiarto S Trimono, Trimono Ulayya, Yasmin Viet Ha, Nguyen Yisti Vita Via Zahrah, Fathima Zamri, Mohd