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All Journal EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA Indonesian Physical Review
Rini Anggraini Pakpahan
Universitas Graha Nusantara Padangsidimpuan
Agriculture, Biological Sciences & Forestry Education Physics

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ANALISIS SPASIAL POTENSI ENERGI SURYA MENGGUNAKAN MACHINE LEARNING DAN SISTEM INFORMASI GEOGRAFIS Rini Anggraini Pakpahan; Sri Utami Kholila Mora Siregar; Erwina Azizah Hasibuan; Suryanti Suraja Pulungan
Eksakta : Jurnal Penelitian dan Pembelajaran MIPA Vol 11, No 2 (2026): Eksakta : Jurnal Penelitian dan Pembelajaran MIPA
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan, UM-Tapsel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/eksakta.v11i2.243–255

Abstract

Provinsi Sumatera Utara memiliki potensi energi surya yang signifikan, yang dapat membantu kemajuan sumber energi terbarukan; namun, untuk memanfaatkan potensi ini sepenuhnya, penting untuk melakukan analisis spasial yang tepat untuk menentukan lokasi ideal bagi proyek energi surya. Penelitian ini bertujuan untuk mengevaluasi potensi energi surya dengan menggabungkan teknik Machine Learning dengan SIG. Data yang digunakan meliputi GHI, suhu, curah hujan, tutupan awan, elevasi topografi, kemiringan, dan pemanfaatan lahan, yang bersumber dari NASA POWER, CHIRPS, SRTM, ESA WorldCover, dan BIG. Pendekatan yang diambil dalam penelitian ini melibatkan pra-pemrosesan data, analisis spasial berbasis SIG, pembuatan model Random Forest, dan penilaian model melalui metrik seperti RMSE, MAE, MAPE, dan R², diikuti dengan pemetaan potensi energi surya menggunakan teknik klasifikasi Jenks. Hasil penelitian menunjukkan bahwa model Random Forest memberikan akurasi prediksi yang patut dipuji, mencapai R² sebesar 0,89, RMSE sebesar 0,24, MAE sebesar 0,18, dan MAPE sebesar 4,82%. Analisis pentingnya fitur mengungkapkan bahwa GHI, tutupan awan, dan curah hujan merupakan faktor utama yang memengaruhi kemampuan energi surya. Penilaian spasial menunjukkan bahwa wilayah pesisir timur Sumatera Utara memiliki potensi energi surya yang relatif lebih tinggi dibandingkan wilayah pegunungan Bukit Barisan. Hasil ini menunjukkan bahwa kombinasi Random Forest dan SIG menawarkan wawasan spasial yang berharga dan dapat secara efektif membantu dalam pengambilan keputusan yang tepat terkait perencanaan dan pembangunan PLTS.
FIRST-PRINCIPLES STUDY OF Sn-DOPING EFFECTS ON THE ELECTRONIC AND OPTICAL PROPERTIES OF ZnO Rini Anggraini Pakpahan; Suryanti Suraja Pulungan
Indonesian Physical Review Vol. 9 No. 3 (2026)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ipr.v9i3.618

Abstract

In this work, the effects of substitutional Sn doping on the electronic and optical properties of ZnO were investigated using first-principles Density Functional Theory (DFT) calculations within the Generalized Gradient Approximation of Perdew–Burke–Ernzerhof (GGA-PBE). A 2 × 2 × 2 wurtzite ZnO supercell was employed, where Sn atoms substituted Zn atoms at concentrations of 6.25, 12.50, and 18.75% on Zn sites. Structural optimization was first carried out to obtain the relaxed atomic configurations before evaluating the electronic and optical responses of the doped systems. The calculated PBE band gap decreases from 0.82 eV for pure ZnO to 0.71, 0.55, and 0.38 eV, respectively, indicating a significant modification of the electronic structure after doping. Density of states (DOS) analysis indicates that Sn-derived electronic states emerge near the conduction-band region, leading to modifications of the band-edge structure and enhanced electronic transitions. Optical calculations further reveal increased low-energy absorption and a shift of the optical response toward the visible-light region after Sn incorporation, suggesting improved light-harvesting capability. Although the absolute band-gap values are underestimated compared with experiment, the observed trend provides meaningful insight into the role of Sn concentration in tuning ZnO properties. These results demonstrate that substitutional Sn doping is an effective strategy for tuning the electronic and optical properties of ZnO and improving its potential for visible-light optoelectronic and photovoltaic applications.
PREDIKSI KUAT TEKAN BATU BATA MENGGUNAKAN ALGORITMA MACHINE LEARNING BERBASIS DATA PRODUKSI Rini Anggraini Pakpahan; Sri Utami Kholila Mora Siregar; Suryanti Suraja Pulungan
Eksakta : Jurnal Penelitian dan Pembelajaran MIPA Vol 11, No 1 (2026): Eksakta : Jurnal Penelitian dan Pembelajaran MIPA
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan, UM-Tapsel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/eksakta.v11i1.%p

Abstract

Clay bricks are a very popular building material used in projects in Indonesia, with compressive strength as a key quality indicator. Laboratory compressive strength testing is destructive, time-consuming, and less practical for small and medium-scale industries. The study seeks to create a compressive strength predictive model utilizing production statistics and advanced machine learning algorithms and to compare manually molded and pressed bricks. The dataset consists of secondary production data, including clay composition, moisture content, molding pressure, firing temperature, and firing time, with compressive strength as the output variable. Three algorithms were applied: Linear Regression, Support Vector Regression, and Random Forest Regression. To assess model performance, utilized the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Study outcomes reveal that Random Forest Regression achieves the best performance, with an R² value of 0.89 and the lowest prediction errors. Results from the feature importance analysis demonstrate that molding pressure and firing time are the most influential factors affecting compressive strength. The predicted results are consistent with previous experimental studies reporting higher and more stable compressive strength in pressed bricks compared to manually molded bricks. This approach demonstrates strong potential as a data-driven decision-support tool for brick quality control.
Co-Authors Erwina Azizah Hasibuan, Erwina Azizah Sri Utami Kholila Mora Siregar Suryanti Suraja Pulungan Suryanti Suraja Pulungan