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All Journal Jurnal Teknik Mesin Rekayasa Mesin Prosiding Semnastek JURNAL INTEGRASI Jurnal Rekayasa Material, Manufaktur & Energi Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Journal of Dynamics Logista: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal TEMAPELA JTM - Jurnal Teknik Mesin Jurnal Teknik Mesin Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Engine: Energi, Manufaktur, dan Material Jurnal Vokasi Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Surya Teknika Jurnal Pengabdian dan Peningkatan Mutu Masyarakat (JANAYU) Teknika Sains : Jurnal Ilmu Teknik Journal of Energy, Material, and Instrumentation Technology Jurnal Teknik Mesin International Journal of Multidisciplinary Research and Literature (IJOMRAL) METAL : Jurnal Sistem Mekanik dan Termal Dulang: Jurnal Pengabdian Kepada Masyarakat Industrial Research Workshop and National Seminar Jurnal Andalas: Rekayasa dan Penerapan Teknologi International Journal of Innovation in Mechanical Engineering and Advanced Materials Proceeding Applied Business and Engineering Conference Jurnal Polimesin IJIMCE : International Journal of Innovation in Mechanical Construction and Energy IJIS Edu : Indonesian Journal of Integrated Science Education JAPEPAM, Jurnal Pengabdian kepada Masyarakat
Ruzita Sumiati
Politeknik Negeri Padang
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Seleksi Fitur Berdasarkan Korelasi Pearson dalam Pemodelan Efisiensi Energi Bangunan Desmarita Leni; Aggrivina Dwiharzandis; Ruzita Sumiati; Haris Haris; Sicilia Afriyani
TEKNIKA SAINS Vol 8, No 2 (2023): TEKNIKA SAINS
Publisher : Universitas Sang Bumi Ruwa Jurai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24967/teksis.v8i2.2525

Abstract

Prediksi beban pemanasan dan pendinginan bangunan merupakan langkah penting untuk perencanaan dan pengelolaan sistem energi. Hal ini, tidak terlepas dari berkontribusi beban pemanasan dan pendinginan bangunan yang menyumbang 30% dari total konsumsi energi global. Penelitian ini bertujuan untuk menerapkan metode seleksi fitur berdasarkan korelasi Pearson dalam pemodelan prediksi beban pemanasan dan pendinginan bangunan menggunakan Artificial Neural Network (ANN). Korelasi Pearson digunakan untuk menganalisis hubungan antara variabel input dan variabel target. Fitur-fitur yang memiliki korelasi signifikan dengan variabel target digunakan sebagai dataset untuk pelatihan model, sedangkan yang tidak memiliki korelasi signifikan dihapus dari dataset pelatihan. Evaluasi dilakukan menggunakan metrik evaluasi seperti Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan R-squared untuk mengukur tingkat keakuratan dan kinerja model dalam memprediksi beban pemanasan dan pendinginan. Hasil pemodelan menunjukkan bahwa seleksi fitur berdasarkan korelasi Pearson menghasilkan prediksi yang sangat akurat untuk beban pemanasan dan pendinginan bangunan. Model ini menunjukkan kinerja yang baik selama pelatihan dan validasi dengan Cross Validation (CV) menggunakan k = 10. Hasil evaluasi model diperoleh nilai MAE 0.457, RMSE 0.628, dan R-squared 0.996 untuk beban pemanasan, sedangkan untuk beban pendinginan diperoleh nilai MAE sebesar 1.163, RMSE 1.74, dan R-squared 0.967. Hasil ini mengindikasikan bahwa seleksi fitur dengan korelasi Pearson dapat dijadikan pendekatan yang efektif untuk meningkatkan performa model prediksi menggunakan machine learning, terutama dalam konteks prediksi beban pemanasan dan pendinginan bangunan.
Comparative analysis of energy-efficient air conditioner based on brand Adriansyah Adriansyah; Desmarita Leni; Ruzita Sumiati
Jurnal POLIMESIN Vol 21, No 4 (2023): August
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v21i4.3625

Abstract

The availability of numerous air conditioners in the market with various brands and types often leads consumers to be unaware that the purchased air conditioner may be inefficient in terms of energy usage. This research aims to determine the most energy-efficient air conditioner based on the brand of air conditioners available in the market. The research method consists of four stages: data collection, data preprocessing, data analysis, and interpretation of results and conclusions. The data used in this study was obtained from the database of the Directorate General of New, Renewable, and Energy Conservation (EBETKE), which consists of 11 AC brands sold in the market. Data analysis was performed using data distribution analysis techniques, standard deviation calculations, and correlation analysis between variables, such as the Pearson's correlation coefficient. The results of this study show that the AC brand with the highest average efficiency value is Mitsubishi Electric, with a value of 16.36 Energy Efficiency Ratio (EER), while the AC brand with the lowest average efficiency value is GREE, with a value of 5.640 (EER). Each AC brand has a different average efficiency value, with significant variations. From the correlation heatmap results, the AC power does not appear to significantly affect the AC efficiency value, where AC with lower power tends to have higher efficiency values, but there are also AC with high power and high efficiency values. Additionally, the cooling capacity value also appears to have a small effect on the AC efficiency value, where AC with lower cooling capacity tends to have higher efficiency values. However, some AC brands have high cooling capacity values but also have high efficiency values. This study also shows a moderate correlation between the AC efficiency value and the AC's annual energy consumption value, where AC with higher efficiency values tends to have lower annual energy consumption values.
Modeling Mechanical Component Classification Using Support Vector Machine with A Radial Basis Function Kernel Ruzita sumiati; Moh. Chamim; Desmarita Leni; Yazmendra Rosa; Hanif Hanif
Jurnal Teknik Mesin Vol 16 No 2 (2023): Jurnal Teknik Mesin
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/jtm.16.2.1250

Abstract

The process of identification and classification of products in the era of modern manufacturing industries has become a crucial pillar in enhancing efficiency, productivity, and product quality. In this research, the modeling of manufacturing product classification, such as mechanical components consisting of four classes: bolts, washer, nuts, and locating pin, was conducted. The proposed model in this study is the Support Vector Machine (SVM) with Radial Basis Function (RBF). The dataset used consists of digital images of mechanical components, with each component having 400 samples, resulting in a total of 1600 samples. The dataset is divided into training and testing data, with 300 samples for each component in the training set, and 100 samples removed from the training set for external testing as model validation. The best model parameters were determined using grid search by varying the parameter values of C and γ (gamma). The model was evaluated using K=3 fold cross-validation, and external testing utilized a confusion matrix to calculate Accuracy, Precision, Recall, and F1-Score values. The research results indicate that the SVM model with the RBF kernel, using the combination of C=10 and γ=scale, achieves the best performance in classifying the four mechanical components. This is evident from the training results of the model, which were able to obtain evaluation metrics such as Accuracy of 94.17%, Precision of 0.94, Recall of 0.94, and F1-Score of 0.94. Meanwhile, the validation results with new data not present in the training dataset show that the model can achieve evaluation metrics with an Accuracy of 93%, Precision of 0.93, Recall of 0.93, and F1-Score of 0.93. These results are consistent with the training performance, indicating that the SVM model with the RBF kernel excels in classifying digital images of mechanical components, such as bolts, nuts, washer, and locating pin.
Rancang Bangun Alat Ukur Torsi dan Putaran Untuk Pengujian Turbin Savonius Pada Wind Tunnel Berbasis mikrokontroler Ruzita sumiati; Uyung Gatot S. Dinata; Dendi Adi Saputra; Riswan Riswan; Fahri Triharyono; Rahil Abde Andika; Fharel Abdillah
Jurnal Teknik Mesin Vol 17 No 1 (2024): Jurnal Teknik Mesin
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/jtm.17.1.1414

Abstract

The objective of this research is to design and develop a torque measurement device using a braking system and a rotational speed measurement tool for a Savonius turbine shaft, applied in a wind tunnel, with data acquisition controlled by an Arduino Uno microcontroller. The methodology employed in this research is the design and build method. The testing results indicate that the torque measurement device controlled by the Arduino Uno functions effectively. Comparing the results of braking force measurements using manual methods and data acquisition revealed a 2, 231 % error. Additionally, the rotational speed measurements using a tachometer and those using an encoder controlled by the Arduino Uno showed a small error of 0,59 %. Data were continuously monitored on a laptop screen during testing. Thus, this device can be utilized as an auxiliary measurement tool to assess the performance of a Savonius turbine
The Influence of Heatmap Correlation-based Feature Selection on Predictive Modeling of Low Alloy Steel Mechanical Properties Using Artificial Neural Network (ANN) Algorithm Leni, Desmarita; Sumiati, Ruzita; Adriansyah; Angelia, Nike; Nofriyanti, Elsa
Journal of Energy, Material, and Instrumentation Technology Vol 4 No 4 (2023): Journal of Energy, Material, and Instrumentation Technology
Publisher : Departement of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jemit.v4i4.203

Abstract

This study aims to evaluate the influence of heatmap correlation-based feature selection on predictive modeling of low alloy steel mechanical properties using an artificial neural network (ANN) algorithm. Heatmap correlation was used to determine the chemical elements most correlated to the low alloy steel mechanical properties, such as Yield strength (YS) and Tensile strength (TS). There were 15 input variables of chemical elements in this study, and after feature selection, 11 input variables were obtained for YS, and 13 input variables were obtained for TS. The ANN model was validated using K-fold 10 cross-validation and evaluated using loss metric, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results showed that modeling with feature selection was able to improve the YS prediction, with a decrease in value of 6.83% in MAE and 4.97% in RMSE, while the TS prediction decreased by 16.46% in MAE and 18.34% in RMSE after feature selection. These results indicate that the use of feature selection provides better performance compared to the model without feature selection, and heatmap correlation can be used as an alternative to improve model performance in predictive modeling of low alloy steel mechanical properties using the ANN algorithm.  
Perbandingan Alogaritma Machine Learning Untuk Prediksi Sifat Mekanik Pada Baja Paduan Rendah Leni, Desmarita; kusuma, Yuda Perdana; Sumiati, Ruzita; ., Muchlisinalahuddin; ., Adriansyah
Rekayasa Material, Manufaktur dan Energi Vol 5, No 2: September 2022
Publisher : Fakultas Teknik UMSU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/rmme.v5i2.11407

Abstract

The development of industrial technology encourages companies to be selective in determining the mechanical properties of materials, one of which is low-alloy steel. The purpose of knowing the mechanical properties of low alloy steel is to support the success of a construction product, transportation, machine elements, and so on. Heat treatment of metal is one of the test methods to determine the mechanical properties of steel by heating the steel at a certain temperature. The selection of low alloy steel composition has various variations to be applied so as to obtain the desired mechanical properties. The mechanical properties of low-alloy steel are strongly influenced by the composition contained in the steel. If the composition of the steel is added to a new element, the mechanical properties of the steel will change, so it needs to be retested. This research uses machine learning modeling to predict the mechanical properties of low-alloy steels based on their chemical compositions. This study compares three algorithms, namely decision tree (DT), random forest (RF), and artificial neural network (ANN), where the ANN algorithm has better performance by producing an RMSE value of 6.187 with training cycle parameter settings of 30.000, learning rate 0.007, momentum 0.9, and size of hidden layer 9.
Perancangan Aplikasi Berbasis Web Sebagai Alat Pendukung Keputusan Dalam Memilih Ac Hemat Energi ., Maimuzar; Sumiati, Ruzita; ., Haris; Leni, Desmarita; Dwiharzandis, Aggrivina
Rekayasa Material, Manufaktur dan Energi Vol 6, No 2: September 2023
Publisher : Fakultas Teknik UMSU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/rmme.v6i2.16255

Abstract

The increase in global energy demand has driven the need for efficient solutions in selecting energy-efficient air conditioners (ACs). This research focuses on designing a web-based application as a decision support tool for choosing energy-efficient ACs. Energy-efficient labeled AC data is obtained from the Directorate General of New and Renewable Energy and Energy Conservation (EBTKE) website. This database is processed according to the system's requirements, where each AC brand is evenly represented to prevent dominance by a few brands. There are 11 different AC brands in this dataset, and each brand has 10 data samples. The web-based application is developed using the Python programming language with the Streamlit framework. This application allows users to compare various AC brands by considering power, annual energy consumption, efficiency value, and electricity cost. In the application design, users can select AC brands according to their needs, set the operating duration, choose the AC efficiency level, and select the inverter AC type. The application presents comparisons in the form of bar charts, making it easy for users to understand the differences in AC characteristics. The average results from the efficiency comparison of each AC brand reveal that Daikin achieves the highest efficiency at 16.36 Energy Efficiency Ratio (EER), while the GREE brand has the lowest efficiency at 5.83 EER. This application can assist consumers and industrial AC stakeholders in making decisions to choose energy-efficient ACs according to their needs.
Analisis Perbandingan Metode Quenched Terhadap Sifat Mekanik Baja Tahan Karat Austenitik Berbasis Big Data Leni, Desmarita; ., Islahuddin; ., Mulyadi; ., Hendra; Sumiati, Ruzita
Rekayasa Material, Manufaktur dan Energi Vol 7, No 2: Juli 2024
Publisher : Fakultas Teknik UMSU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/rmme.v7i2.18025

Abstract

A profound understanding of the mechanical properties of stainless steel is crucial in modern industrial applications. However, comprehensively understanding the mechanical properties of stainless steel requires sufficient testing to gather data on its characteristics. In this research, an analysis was conducted on the influence of chemical composition and heat treatment on the mechanical properties of stainless steel, utilizing data from the Material Algorithm Project (MAP), which is a material database. The dataset comprises 986 samples, encompassing 11 chemical elements, variations in quenching, and 3 mechanical properties of stainless steel. The data was analyzed using descriptive statistics and Pearson correlation to examine the relationships between these variables. The research results indicate a negative correlation of -0.35 between temperature and Yield Strength (YS) in samples of steel undergoing air cooling. The YS values in this treatment can reach 300 MPa at temperature combinations ranging from 1300 K to 1330 K, with heating times ranging from 180 seconds to 420 seconds. Meanwhile, water cooling exhibits a wide range of cooling times and relatively high temperatures. Combinations of time between 1400 seconds and 2200 seconds and temperatures between 1300 K and 1400 K result in YS ranging from 240 MPa to 260 MPa. This research suggests that experimental material testing datasets not only play a passive role in validating an experiment but can also be actively utilized in the analysis and design of materials more effectively.
Evaluasi Pemodelan Augmentasi Data Sifat Mekanik Aluminium Menggunakan Generative Adversarial Networks Leni, Desmarita; Berli, Ade Usra; Kesuma, Dytchia Septi; Haris, Haris; Sumiati, Ruzita
Jurnal Engine: Energi, Manufaktur, dan Material Vol. 8 No. 1 (2024)
Publisher : Proklamasi 45 University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30588/jeemm.v8i1.1645

Abstract

Materials informatics is a new approach in material science that integrates information technology and material science to optimize the discovery of new materials more efficiently and innovatively. In materials informatics, experimental and simulation data are combined with data-driven methods such as big data, data augmentation, and machine learning to gain a deeper understanding of material properties. However, limitations in the availability of samples with desired characteristics and the lack of accurate experimental data pose challenges in materials informatics. In this study, we attempt to address these challenges by modeling the augmentation of mechanical properties of aluminum using Generative Adversarial Networks (GAN). GAN is used to generate synthetic data of aluminum's mechanical properties that closely resemble experimental data. This modeling is trained using experimental testing data consisting of aluminum's mechanical properties and chemical elements in the alloy, obtained from the material database. The dataset comprises 9 chemical element variables in the aluminum alloy and 2 mechanical property variables. The synthetic data generated from the modeling is evaluated using descriptive statistics, Pearson correlation, and Kolmogorov-Smirnov (KS) test to assess the extent to which the synthetic data resembles the original data. The evaluation results indicate that the distribution of synthetic data is similar to the original data. The Pearson correlation results show that most variables of chemical elements and mechanical properties of aluminum in the synthetic data have a correlation that is quite similar to the original data. The KS test results also indicate that the distribution of synthetic data does not significantly differ from the distribution of the original data. This indicates that the synthetic data generated has a high resemblance to the experimental data, enabling its use in materials informatics research. Thus, modeling the augmentation of aluminum's mechanical property data using GAN provides a significant contribution to expanding data availability in material science.
THE EFFECT OF TEMPERATURE AND ROASTING TIME ON CHANGES IN THE CHARACTERISTICS AND PHYSICAL PROPERTIES OF SOLOK ARABIKA COFFEE BEANS K, Arwizet.; Leni, Desmarita; Peng, Lim Hooi; Sumiati, Ruzita; Kusuma, Yuda Perdana
International Journal of Multidisciplinary Research and Literature Vol. 3 No. 1 (2024): 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.v3i1.202

Abstract

High-temperature coffee roasting is a key initiator of the degradation of complex compounds in coffee beans, ultimately producing the desired taste and aroma for coffee enthusiasts. The aim of this research is to explore the influence of temperature and roasting duration on the changes in the mechanical properties of coffee beans using the conduction heat method. In this study, 500 grams of dried Arabica coffee with an initial moisture content of 12% were placed in a roasting apparatus equipped with a roasting machine. The heat source used was a gas stove, where the surface temperature of the roasting chamber was kept constant through a thermocouple temperature measuring device. The roasting process was carried out for 15 minutes at surface temperatures of 160°C, 180°C, 200°C, and 220°C, respectively. The final moisture content for each surface temperature was 3.72%, 3.65%, 2.13%, and 1.81%. Identification of the degree of roasting was conducted through the evaluation of the physical properties of coffee beans, including color, weight loss, moisture content, texture, and bean density. The decrease in hardness and density could be modeled using kinetic equations, while the color change was indicated by a decrease in the L, a, and b values. The research results confirm that roasting temperature significantly impacts the changes in the mechanical properties of coffee beans. The minimum temperature required to achieve satisfactory roasting levels is 180°C, while roasting at 200°C for 15 minutes produces coffee beans with optimal roasting levels. These findings provide new insights into optimizing the coffee roasting process to achieve the desired quality of coffee beans
Co-Authors ., Adriansyah ., Islahuddin ., Maimuzar ., Muchlisinalahuddin Abdul Aziz Ade Usra Berli Adriansyah Adriansyah Adriansyah Afifah Afifah, Afifah Afriyani, Sicilia Aggrivina Dwiharzandis Aidil Zamri Aidil Zamri Alfi, Rizki Andrew Kurniawan Vadreas Angelia, Nike Anissa Vivia Fidela Arifian, Naf'an Arwizet Arwizet, Arwizet Asmed Asmed Budiman, Dadi Bukhari Camim, Moh. Candra Mayana, Hendri Dandi Ilham Delffika Canra Desmarita Leni Doni Marzuki Efiandi, Nota Elvando andha elvaris manalu Fahri Reza Fahri Triharyono Fajar Pradana Fajri Arsyah, Ahmad Hasnul Fanni Sukma Fardinal Fardinal Fardinal, Fardinal Fathir Alqodri Fharel Abdillah Fharel Abdillah Fitri Adona Gani Pratama Genta Ramadeto Ghandy Junne Putra Gusriwandi Gusriwandi Hamdani, Rifki Hamdani, Rifqi Hamzah Putra Hanif Hanif Hanif Hanif Haris ., Haris Haris Haris Haris Haris, Haris Helga Yermadona Hendra . Ikbal Ilham, M. Irwan Irwan Jana Hafiza Kesuma, Dytchia Septi Khairul Amri Khairul Amri Khan, Sharif kusuma, Yuda Perdana Lega Putri Utami M. Luthfi Artia Maimuzar Maimuzar Meiki Eru Putra Moh. Chamim Muchlisinalahuddin Mulyadi Mulyadi . Mulyadi Mulyadi Nandi Pinto NARO, AULIA Nasirwan Nasirwan Nasrullah Nasrullah Nofriadi Nofriadi Nofriyandi, R Nofriyanti, Elsa Nota Effiandi Nusyirwan Nusyirwan Nusyirwan Peng, Lim Hooi Rahil Abde Andika Rahmi, Nurfitri Rajimar Suhal Hasibuan Hasibuan Rakiman Rakiman Rakiman Rakiman Ridho Pratama Fajri Riezky Idvi Alfitra Rina Rina Rina Rina Rina, Rina Rino Sukma Riswan Riswan Rivanol Chadry Robby Novrizal Rudianto Rudianto Saputra M, Dendi Adi Seno, Abdi Sicilia Afriyani Sir Anderson Siska Angraini Rikosa Rikosa Suliono Suliono Tri Ego Wiranata Usra Berli , Ade Uyung Gatot Syafrawi Dinata Veny Selviyanty Verdian, Riza Yanziwar Yanziwar Yazmendra Rosa Yogi Kogama Yuda Perdana Kusuma Yuhefizar Yuhefizar Yuli Yetri Yuliarman Yuliarman Yuliarman Yuliarman Yusri Mura