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All Journal Jurnal Teknologi dan Manajemen Sistem Industri Journal of Novel Engineering Science and Technology Journal of Innovative and Creativity
Nayla Farikha Zahra
Universitas Bojonegoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Integrated Prediction Model for Normal and Recycled Aggregate Concrete Strength Using Ensemble Learning Techniques Sujiat; Eko wahyu Abryandoko; Ocha Silvia Kencana; Nayla Farikha Zahra
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.2092

Abstract

Recycled aggregate concrete (RAC) is a sustainable alternative construction material to reduce natural resource exploitation and manage construction and demolition waste. However, predicting the mechanical performance of RAC remains a challenge due to the high variability of recycled aggregate properties. The purpose of this study is to develop a machine learning model to predict the compressive strength of recycled aggregate-based concrete and compare its performance with normal concrete. The dataset used consists of 2165 samples (1600 normal concrete and 565 recycled aggregate concrete) collected from various scientific publications. Three tree-based machine learning algorithms (Random Forest, XGBoost, and LightGBM) were implemented and optimized using RandomizedSearchCV with 5-fold cross-validation. The results showed that LightGBM provided the best performance with R² = 0.92, MAE = 2.45 MPa, and RMSE = 3.52 MPa on the test set. This model is able to predict the compressive strength of normal concrete (R² = 0.92) and recycled aggregate concrete (R² = 0.91) with almost the same accuracy, indicating strong generalization. Feature importance analysis revealed that curing age, cement content, and water content are the most important factors in compressive strength prediction, while for RAC, recycled aggregate water absorption (WRCA) also makes a significant contribution. Error analysis shows that residuals are random and normally distributed without systematic bias. This model can reliably predict concrete compressive strength in the range of 20-60 MPa with an average error of ±3-4 MPa and can be integrated into mix proportioning design software to improve the efficiency of the design process and support the use of sustainable construction materials.
Pemberdayaan Kelompok Tani Melalui Pelatihan Pembuatan Pupuk Organik Cair Berbasis Urin Sapi Sebagai Alternatif Pupuk Kimia Nayla Farikha Zahra; Nanik Fadilaton Nafisa; Dilla Yesita Sari; Meilisa Rusdiana Surya Efendi
Journal of Innovative and Creativity (Joecy) Vol. 6 No. 2 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i2.13720

Abstract

Penggunaan pupuk kimia secara terus-menerus oleh petani di Desa Tondomulo, Kecamatan Kedungadem, Kabupaten Bojonegoro berpotensi menurunkan kesuburan tanah dan meningkatkan biaya produksi pertanian, sementara urin sapi yang dihasilkan dari populasi ternak yang besar di desa tersebut belum dimanfaatkan secara optimal. Permasalahan tersebut mendorong dilaksanakannya program pengabdian kepada masyarakat melalui pelatihan pembuatan pupuk organik cair (POC) berbasis urin sapi bagi kelompok tani. Program ini bertujuan meningkatkan pengetahuan dan keterampilan 30 peserta, terdiri atas 10 ketua dan 20 anggota kelompok tani se-Desa Tondomulo, dalam memproduksi POC secara mandiri sebagai alternatif substitusi sebagian pupuk kimia. Metode yang digunakan meliputi persiapan, penyuluhan, praktik pembuatan POC, evaluasi, tindak lanjut, dan pengukuran keberhasilan program, dengan menghadirkan pemateri dari Wilayah Kerja Perlindungan Tanaman Pangan dan Hortikultura (Wilker PTPH) Bojonegoro. Evaluasi dilakukan melalui observasi dan wawancara yang dianalisis secara deskriptif kualitatif. Hasil kegiatan menunjukkan peningkatan pemahaman peserta mengenai kandungan hara urin sapi dan tahapan pembuatan POC, kehadiran dan partisipasi aktif peserta selama kegiatan, penguasaan keterampilan praktik pembuatan POC oleh sebagian besar peserta, serta komitmen peserta untuk menerapkan POC pada lahan masing-masing. Peserta menunjukkan respons positif terhadap materi dan praktik yang diberikan. Dengan demikian, pelatihan pembuatan POC berbasis urin sapi berpotensi menjadi alternatif solusi yang mendukung efisiensi biaya produksi sekaligus memperkuat pemberdayaan masyarakat di sektor pertanian.
Peramalan Jumlah Kunjungan Pengguna Pelayanan Statistik Terpadu menggunakan Metode Time Series di Badan Pusat Statistik: Nayla Farikha Zahra, Heru Prastiyono, Agung Firdausi Ahsan Nayla Farikha Zahra; Heru Prastiyono; Agung Firdausi Ahsan
Jurnal Teknologi dan Manajemen Sistem Industri Vol. 4 No. 1 (2025): Jurnal Teknologi dan Manajemen Sistem Industri (JTMSI) - MARET
Publisher : Universitas Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56071/jtmsi.v4i1.2078

Abstract

This study addresses the forecasting of visitor numbers to the Pelayanan Statistik Terpadu (PST) unit at the Badan Pusat Statoistic (BPS) Bojonegoro office using a time series approach. Forecasting visitor volume is essential for supporting service capacity planning, resource allocation, and enhancing the operational efficiency of data-driven public services. The study compares three forecasting methods—Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters Exponential Smoothing, and Prophet—using monthly visitor data from January 2020 to May 2025. The dataset was divided into a training set (January 2020–December 2024) and a test set (January–June 2025). Model evaluation was conducted using R², Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) metrics. The results indicate that the Prophet model delivered the best performance, achieving an RMSE of 1.44, MAE of 1.34, and MAPE of 0.91—outperforming both Holt-Winters and SARIMA. Prophet's superior performance is attributed to its ability to simultaneously model long-term downward trends and annual seasonal patterns. The findings demonstrate that an adaptive, time series-based forecasting approach can support data-driven decision-making for regional public statistical services.
Co-Authors Agung Firdausi Ahsan Dilla Yesita Sari Effendi, Meilisa Rusdiana Surya EKO WAHYU ABRYANDOKO Heru Prastiyono Nanik Fadilaton Nafisa Ocha Silvia Kencana Sujiat