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All Journal Rang Teknik Journal Jurnal Ilmu Fisika EDUKATIF : JURNAL ILMU PENDIDIKAN Menara Ilmu Pelita Eksakta Jurnal Engine: Energi, Manufaktur, dan Material Jurnal Vokasi Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Surya Teknika Pillar of Physics: Jurnal Berkala Ilmiah Fisika International Journal of Multidisciplinary Research and Literature (IJOMRAL) METAL : Jurnal Sistem Mekanik dan Termal
Ade Usra Berli
Universitas Muhammadiyah Sumatera Barat
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN) Leni, Desmarita; Yermadona, Helga; Usra Berli , Ade; Sumiati, Ruzita; Haris, Haris
SURYA TEKNIKA Vol 10 No 1 (2023): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v10i1.4843

Abstract

This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.
Analisis Pengaruh Komposisi Kimia dan Suhu Perlakuan Panas Terhadap Sifat Mekanik Baja Tahan Karat untuk Aplikasi Konstruksi Ade usra Berli; Desmarita Leni; Helga Yermadona
SURYA TEKNIKA Vol 10 No 2 (2023): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v10i2.6059

Abstract

A deep understanding of the mechanical properties of stainless steel is crucial for designing constructions that meet the requirements. However, to comprehensively understand the mechanical properties of stainless steel, sufficient testing is needed to gather data regarding its characteristics. In this research, an analysis was conducted on the Effect of Chemical Composition and Heat Treatment on the Mechanical Properties of Stainless Steel, using data from the Material Algorithm Project (MAP), which is a material database. The data was analyzed using descriptive statistics and Pearson correlation to observe the relationships between these variables. The research results indicate that chemical elements such as Cu and Ni have a positive correlation with elongation, indicating that higher concentrations of these elements lead to higher elongation of stainless steel. Furthermore, it was also found that temperature has a strong negative correlation with yield strength (YS) and ultimate tensile strength (UTS), with correlation values of -0.71 and -0.86, respectively. Further analysis revealed that water quenching resulted in better ultimate tensile strength compared to air quenching. This research demonstrates that experimental material testing datasets not only validate experiments but can also actively be used in the analysis and design of more effective materials.
Rancang Bangun Lengan Robot Pick And Place 6 Axis Menggunakan Arduino Mega Qadhar, Muhammad Aziz Al; Hariyadi, Hariyadi; Dwiharzandis, Aggrivina; Kesuma, Dytchia Septi; Berli, Ade Usra
Menara Ilmu : Jurnal Penelitian dan Kajian Ilmiah Vol 19, No 2 (2025): Vol 19 No. 02 JULI 2025
Publisher : LPPM Universitas Muhammadiyah Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31869/mi.v19i2.6908

Abstract

Penelitian ini dilatarbelakangi oleh kebutuhan industri terhadap sistem otomatis yang efisien, akurat, dan aman untuk menggantikan proses manual, serta memanfaatkan kemudahan teknologi seperti Arduino dalam sistem robotik. Tujuan penelitian ini adalah merancang dan membangun prototype lengan robot 6-axis dengan sistem pick and place yang dikendalikan oleh mikrokontroler Arduino Mega menggunakan metode inverse kinematic untuk memindahkan objek secara otomatis dan akurat. Penelitian menggunakan metode eksperimen rekayasa dengan tahap perancangan, pembuatan, dan pengujian sistem. Gerakan robot dikendalikan untuk mencapai posisi target secara tepat. Hasil pengujian menunjukkan sistem berfungsi stabil dengan akurasi tinggi. Tegangan pada Arduino Mega tercatat sebesar 4,929 VDC saat aktif, masih dalam toleransi ±5% dari 5 V. Sensor infrared berfungsi baik dengan respon logika digital stabil. Motor servo menerima suplai rata-rata 10,30 VDC, dengan tegangan terendah 10,14 V pada servo 5 saat proses peletakan objek. Ini menunjukkan beban lebih berat pada gripper. Secara keseluruhan, sistem dapat menjalankan tugas pick and place dengan baik meskipun masih memiliki keterbatasan ruang gerak karena bentuk prototipe. Keberhasilan ini menunjukkan potensi implementasi sistem robotik berbasis Arduino dalam otomasi industri.Kata Kunci: sistem otomatis, rekayasa, inverse kinematic, otomasi industri
Strengthening Data Literacy and Packaging Efficiency in Food MSMEs through Statistical Approaches in Koto Tangah District, Padang Mira Meilisa; Hariyadi Hariyadi; Fitri Kurnia; Dytchia Septi Kesuma; Ade Usra Berli; Rahmat Jordan; Praja Purnama Nusa; Muhammad Farhan Al Azfar
Pelita Eksakta Vol 9 No 1 (2026): Pelita Eksakta, Vol. 9, No. 1
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol9-iss1/333

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in the Indonesian economy, particularly in the food sector. However, many food MSMEs face challenges in managing supply risks, business data recording, and product packaging. This service program aims to strengthen data literacy, improve packaging efficiency, and assist in meeting business legalities for five food SMEs in Parupuk Tabing, Koto Tangah, Padang. Through the Participatory Action Research (PAR) approach, this program focuses on three main interventions: (1) data recording and stock management training, (2) introduction to vacuum packaging technology, and (3) fulfilling business legality requirements by obtaining NIB, PIRT, and halal certification.The results show significant changes, including increased business legality (all MSMEs now have NIB and PIRT), the use of vacuum packaging, and the implementation of regular recordkeeping. This program demonstrates that, in the context of MSMEs with limited data, the statistical approach begins with record-keeping habits and simple information systems as a foundation for further analysis.
Penggunaan Trainer Pembangkit Listrik Tenaga Uap Sebagai Media Pembelajaran Untuk Meningkatkan Pemahaman Siswa SMK Dhuafa Padang Desmarita Leni; Aggrivina Dwiharzandis; Helga Yermadona; Muchlisinalahuddin Muchlisinalahuddin; Ade Usra Berli; Rahma Wati; Muhammad Farhan
Reswara: Jurnal Pengabdian Kepada Masyarakat Vol 7, No 2 (2026)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v7i2.8018

Abstract

Kegiatan Program Kreativitas Mahasiswa (PKM) ini bertujuan meningkatkan pemahaman siswa Program Keahlian Teknik Mesin di SMK Dhuafa Padang mengenai konsep konversi energi dan prinsip kerja Pembangkit Listrik Tenaga Uap (PLTU) melalui penggunaan trainer sebagai media pembelajaran. Permasalahan mitra adalah keterbatasan media praktik sehingga pembelajaran PLTU masih bersifat teoritis. Kegiatan dilaksanakan melalui demonstrasi trainer PLTU, pendampingan penggunaan alat, serta evaluasi menggunakan metode pretest dan posttest. Materi yang diberikan meliputi prinsip kerja turbin uap, fungsi komponen utama PLTU, dan pengamatan parameter suhu, tekanan, serta putaran turbin. Hasil kegiatan menunjukkan bahwa penggunaan trainer mampu meningkatkan pemahaman dan minat belajar siswa terhadap materi pembangkitan listrik. Sebanyak 40 dari 65 siswa memberikan penilaian “Sangat Baik” dan 25 siswa “Baik” terhadap penggunaan trainer. Dengan demikian, trainer PLTU efektif digunakan sebagai media pembelajaran interaktif pada pendidikan vokasi
PREDICTION OF MECHANICAL PROPERTIES OF CAST TITANIUM BASED ON THE CHEMICAL ELEMENTS OF THE ALLOY USING A MACHINE LEARNING MODEL Desvita Irwan; Jana Hafiza; Desmarita Leni; Yassirli Amri; Ade Usra Berli
International Journal of Multidisciplinary Research and Literature Vol. 5 No. 3 (2026): INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND LITERATURE
Publisher : Yayasan Education and Social Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53067/ijomral.v5i3.426

Abstract

Cast titanium is widely used in advanced engineering applications due to its high strength-to-weight ratio and good corrosion resistance. However, its mechanical properties are highly sensitive to variations in the alloy's chemical composition, making conventional testing less efficient in terms of time and cost. This study aims to predict the mechanical properties of cast titanium, namely yield strength and tensile strength, based on the alloy's chemical composition using a machine learning approach. The dataset was obtained from the Materials Algorithms Project (MAP) with 100 cast titanium specimens developed through small and controlled variations in chemical composition to represent realistic manufacturing conditions. Modeling was performed using Decision Tree and Random Forest algorithms with data splitting schemes of 60:40, 70:30, and 80:20 and k-fold cross validation. Model performance evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²) metrics. The results of the Pearson correlation analysis showed that Al, V, and Fe-X elements have a strong positive correlation with mechanical properties, while Ti and O show a significant negative correlation, which is in line with the theory of titanium alloy metallurgy. The modeling results show that the Random Forest algorithm provides the best performance with lower prediction errors and better stability compared to Decision Tree. This study proves that the machine learning approach, especially the Random Forest algorithm, is effective in predicting the mechanical properties of cast titanium based on chemical composition, with the best performance shown by the RMSE value of 70.95 and MAE of 47.95, thus potentially supporting the design and optimization of cast titanium alloys based on data.
Analisis Laju Perpindahan Panas Pada Berbagai Material Dinding Bangunan Ade Usra Berli; Dytchia Septi Kesuma; Aggrivina Dwiharzandis; Wiwin Lovita; Desmarita Leni
Menara Ilmu : Jurnal Penelitian dan Kajian Ilmiah Vol 20, No 1 (2026): Vol 20 No. 01 JANUARI 2026
Publisher : LPPM Universitas Muhammadiyah Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31869/mi.v20i1.7579

Abstract

Efisiensi energi termal merupakan faktor kunci dalam perancangan bangunan di wilayah tropis, di mana suhu udara cenderung tinggi sepanjang tahun. Penelitian ini menganalisis karakteristik perpindahan panas pada tiga material dinding bangunan yang umum digunakan di Indonesia, yaitu beton, bata merah dan kayu, dengan memanfaatkan data suhu udara harian Kota Padang pada bulan Agustus 2025 yang dihimpun dari BMKG. Pendekatan kuantitatif digunakan dengan mengolah data suhu minimum, maksimum, dan rata-rata harian untuk menghitung laju aliran energi panas melalui mekanisme konduksi pada masing-masing material.Hasil pengolahan data menunjukkan bahwa suhu rata-rata bulanan mencapai 26,9 °C dengan standar deviasi 0,999 °C, menggambarkan kondisi termal yang relatif stabil di kawasan tropis lembap. Perhitungan laju aliran energi panas mengindikasikan bahwa beton memiliki nilai tertinggi sebesar 0,0249 kWh/m² per hari, diikuti oleh bata merah sebesar 0,0187 kWh/m², sementara kayu menunjukkan nilai terendah yaitu 0,0037 kWh/m². Total energi panas bulanan memperkuat pola yang sama, di mana beton mencapai 0,748 kWh/m², bata merah 0,561 kWh/m², dan kayu 0,112 kWh/m². Temuan ini menunjukkan bahwa beton merupakan penghantar panas yang paling besar, sedangkan kayu memiliki kemampuan isolasi panas paling baik di antara material yang dikaji.Secara keseluruhan, penelitian ini memberikan gambaran komparatif mengenai kinerja termal material dinding di iklim tropis dan dapat menjadi dasar dalam pemilihan material bangunan yang lebih efisien secara energi. Hasil studi ini diharapkan mendukung upaya peningkatan kenyamanan termal sekaligus optimalisasi penggunaan energi pada bangunan di kawasan tropis. Kata kunci: perpindahan panas, material bangunan, efisiensi termal, data BMKG, iklim tropis. 
Co-Authors Ade Ari Agasi Aggrivina Dwiharzandis Akrajas Ali Umar Akrajas Ali Umar Aksa, Monica Klauzia Aprinal Adila Asril Chichi Rahayu Dahyunir Dahlan Desmarita Leni Desvita Irwan Dytchia Septi Kesuma Firmansyah, Herian Fitri Kurnia Hariyadi Hariyadi Hariyadi, Hariyadi Helga Yermadona Jana Hafiza Kesuma, Dytchia Septi Meilisa, Mira Mira Meilisa Mira Meilisa Muchlisinalahuddin Muhammad Farhan Muhammad Farhan Al Azfar Muharni, Riza NURUL HIDAYAH Praja Purnama Nusa Qadhar, Muhammad Aziz Al Rahma Wati Rahmat Jordan Ruzita Sumiati Wiwin Lovita Yassirli Amri Yenni Darvina Yulkifli Yulkifli Yustini Zakirman