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All Journal PROSIDING SEMINAR NASIONAL Jurnal Statistika Universitas Muhammadiyah Semarang Value Added : Majalah Ekonomi dan Bisnis Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Variance : Journal of Statistics and Its Applications Abdimas UNWAHAS : Jurnal Pengabdian Masyarakat UNWAHAS Jurnal Litbang Edusaintech Journal of Data Insights Prosiding Seminar Nasional Unimus Emerging Statistics and Data Science Journal JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Fatkhurokhman Fauzi
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Brent Crude Oil Price Forecasting using the Cascade Forward Neural Network Fatkhurokhman Fauzi; Dewi Ratnasari Wijaya; Tiani Wahyu Utami
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5052

Abstract

Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, crude oil will still be a contributor to the gross domestic product in 2021. The excessive consumption of fuel oil (BBM) in Indonesia has resulted in a scarcity of crude oil, especially diesel. Forecasting the price of Brent crude oil is an important effort to anticipate fluctuations in the price of fuel oil. The cascade-forward neural network (CFNN) method is proposed to forecast fuel prices because of its superiority in fluctuating data types. The data used in this research is the price of Brent crude oil in the period January 2008 to December 2022. The CFNN method will be evaluated using the mean absolute percentage error (MAPE) to choose the best architectural model. The best Architectural Model is used to predict the next 12 months. After 10 architectural model trials, 2-6-1 became the best model with a MAPE data training value of 6.3473% and MAPE data testing of 9.4689%. Forecasting the results for Brent crude oil for the next 12 months tends to experience a downward trend until December 2023.
A Data-Driven Comparison of Linear Mixed Model and Mixed Effects Regression Tree Approaches for Dairy Productivity Analysis Achmad Fauzan; Fatkhurokhman Fauzi; Rhendy K P Widiyanto; Khairil Anwar Notodiputro; Bagus Sartono
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6751

Abstract

Dairy productivity studies often involve hierarchical and longitudinal data structures that violate the assumptions of linear regression. This study compares two modeling approaches, Linear Mixed Model (LMM) and Mixed Effects Regression Tree (MERT), in predicting dairy productivity based on the 2024 National Dairy Productivity Survey data. Predictive performance was evaluated using MSEP, RMSEP, MAD, and MAPE, with MERT consistently outperforming LMM in accuracy and robustness. Permutational Multivariate Analysis of Variance (PERMANOVA) test results reinforced this finding, yielding a pseudo-F value of 224.7 and a p-value of 0.001, indicating statistically significant differences in model performance. Key predictors of MERT model included farm altitude, the previous week’s milk production, and the amounts of concentrate feed given, which are part of significant predictor variables in LMM. This finding underscores MERT’s superiority in modeling complex agricultural datasets while providing interpretable insights through data-driven segmentation. The study advocates policy focus in sustainable milk production as well as the availability of high quality of feed and altitude-based dairy farms location to improve milk productivity. Should these focuses implemented by the industry, combined with the MBG Program, Indonesia would be progressing better towards achievement of SDGs Goal 2 and 3.
Co-Authors - Tarsan Achmad Fauzan Agung Subakti Nuzulullail Ahmad Amrullah B Alfidha Rahmah Alifia Puspita Sari Anggun Erya Santika Anisa Ma'u Luthfi Anita Retno Indriani Ardelita Ika Fadhlillah Bagus Sartono Dannu Purwanto Dewi Ratnasari Wijaya Dwi Agustina Eny Winaryati Erika Siva Aulia Fatikha Adha Fahreza Fauziah Rahma Hanif Nur Ibrahim Hasbi Assidiqi Iis Widya Harmoko Indah Manfaati Nur Indra Firmansyah Lia Aryanti Sholekhah Litasya Shofwatillah Moh Yamin Darsyah Moh. Yamin Darsyah Nida Faoziatun Khusna Oktavia Sri Banowati Putri Ayu Firnanda Qonita Syalsabilla Handayani Rahma Nurmalita Rhendy K P Widiyanto Rizma Novinda Puteri RR. Ella Evrita Hestiandari Safril Ahmadi Sanmas Salmaa Fauziah Septi Winda Utami Shinta Amaria Soffi Amalia Nur Kholifah Syifa Aulia Tiani Wahu Utami Tiani Wahyu Utami Wiwit Putri Nur Izzaturrohmah Yan Nazala Bisoumi Yuni Nurkuntari