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All Journal Techno.Com: Jurnal Teknologi Informasi Eksergi: Chemical Engineering Journal International Journal of New Media Technology Jurnal ULTIMATICS KOMPUTIKA - Jurnal Sistem Komputer Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Abdi Masya Community: Jurnal Pengabdian Pada Masyarakat NERO (Networking Engineering Research Operation) Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
Trisnapradika, Gustina Alfa
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Comparison of Linear and Non-Linear Machine Learning Algortima for Predicting the Effectiveness of Plant Extracts as Corrosion Inhibitors Mulyana, Yudha; Akrom, Muhamad; Trisnapradika, Gustina Alfa
IJNMT (International Journal of New Media Technology) Vol 11 No 1 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i1.3572

Abstract

This research aims to develop a Machine Learning (ML) model that can predict the corrosion inhibitor potential of plant extracts with high accuracy. Corrosion is a serious problem in industry because it can reduce the service life of materials and cause economic losses. This research focuses on the use of green inhibitors, especially plant extracts, which are considered environmentally friendly and have high anticorrosion efficiency. The dataset used includes molecular and physicochemical features of plant extracts. The ML model development process involves data normalization, selection of linear and non-linear ML algorithms, model training with k-fold crossvalidation, and model performance evaluation using regression metrics such as MSE, RMSE, MAE, and R2. Experiments compare various ML algorithms and show that the AdaBoost Regressor (ABR) model exhibits the best prediction performance with the highest R2 value of 0.993 and a low MSE of 0.002. These results provide new insights into the potential of ML models to predict effective corrosion inhibitors from plant extracts. The ABR model had a low prediction error, indicating high accuracy in predicting corrosion inhibition efficiency. In addition, the analysis of important features shows that two features, Conc and LUMO, have a significant influence on the ABR model. This research makes an important contribution to the development of effective prediction methods in the corrosion control industry. The ABR model can serve as a basis for designing more effective and environmentally friendly corrosion inhibitor materials, as well as a reference for other researchers in developing ML models that accurately predict the corrosion inhibition efficiency of plant extracts.
Investigasi Model Machine Learning Terbaik untuk Memprediksi Kemampuan Penghambatan Korosi oleh Senyawa Benzimidazole Akrom, Muhamad; Sumarjono, Cornellius Adryan Putra; Trisnapradika, Gustina Alfa
Komputika : Jurnal Sistem Komputer Vol. 13 No. 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.11048

Abstract

This research aims to investigate the corrosion inhibition performance of Benzimidazole compounds using a machine learning (ML) approach. The main challenge in developing ML is to obtain a model with high accuracy so that the prediction results are relevant and accurate to the actual properties of a material. In this research, we evaluate various linear and non-linear algorithms to obtain the best model. Based on the coefficient of determination (R2) and root mean square error (RMSE) metrics, it was found that the AdaBoost Regressor (ADA) model was the model with the best predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds. This approach offers a new perspective on the ability of ML models to predict effective corrosion inhibitors.
Investigasi Efisiensi Penghambatan Korosi Senyawa Quinoxaline Berbasis Machine Learning Adiprasetya, Vicenzo Frendyatha; Akrom, Muhamad; Trisnapradika, Gustina Alfa
Eksergi Vol 21, No 2 (2024)
Publisher : Prodi Teknik Kimia, Fakultas Teknologi Industri, UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/e.v21i2.10025

Abstract

Korosi memberikan kekhawatiran serius bagi sektor industri dan akademik karena mempunyai dampak negatif yang signifikan terhadap sejumlah bidang, termasuk perekonomian, lingkungan, masyarakat, industri, keamanan, dan keselamatan. Saat ini, banyak peminat topik pengendalian kerusakan bahan berbasis molekul organik. Quinoxaline mempunyai potensi sebagai inhibitor korosi karena tidak beracun, mudah diproduksi, dan efektif dalam berbagai kondisi korosif. Mengeksplorasi kemungkinan kandidat penghambat korosi melalui penelitian eksperimental adalah proses yang memakan waktu dan sumber daya yang intensif. Dengan menggunakan pendekatan machine learning (ML) berdasarkan model quantitative structure-property relationship (QSPR), kami mengevaluasi beragam algoritma linier dan non-linier sebagai model prediktif nilai corrosion inhibition efficiency (CIE) dalam penelitian ini. Kami menemukan bahwa, untuk kumpulan data senyawa quinoxaline, model non-linier Gradient Boosting Regressor (GBR) mengungguli keseluruhan model linier dan non-linier, serta hasil dari literatur dalam hal kinerja prediksi berdasarkan metrik root mean squared error (RMSE), mean squared error (MSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE) dan coefficient of determination (R2). Secara keseluruhan, penelitian kami memberikan sudut pandang baru tentang kapasitas model ML untuk memperkirakan kemampuan penghambatan korosi pada permukaan besi oleh senyawa organik quinoxaline.
PENYULUHAN PENINGKATAN EKSPOR BATIK MENUJU PASAR GLOBAL BERKELANJUTAN DI KAMPUNG REJOMULYO Pramono, Renjiro Azhar; Trisnapradika, Gustina Alfa; Adhy, Bagaskara Bayu; Prawesty, Ganis Fatimah Diaz; Sutrisno, Hendra; Putra, Ricky Primayuda
Abdi Masya Vol 4 No 2
Publisher : Pusat Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52561/abma.v4i2.308

Abstract

Batik Semarangan, sebagai produk budaya khas Semarang, memiliki potensi untuk mendapatkan popularitas dan apresiasi yang lebih luas. Dalam rangka mendukung tujuan ini, kami menjalin kerjasama dengan Lembaga Bea Cukai untuk mengembangkan dan memasarkan produk batik dari Kampung Rejomulyo ke pasar global yang berkelanjutan. Pengabdian ini berfokus pada aspek-aspek penting dalam bidang pengabdian kepada masyarakat, seperti pelatihan bagi para pemilik toko batik, peningkatan proses produksi, penerapan prinsip-prinsip Teknologi Tepat Guna (TTG), desain yang mengikuti tren, serta strategi penyebaran teknologi. Kerjasama yang kuat antara para pemilik toko batik dan Lembaga Bea Cukai membentuk suatu pola yang dapat memajukan industri kreatif lokal secara berkelanjutan. Hasil dari kegiatan ini memberikan dampak positif pada seluruh peserta serta pemilik toko batik yang hadir. Sebesar 77% peserta telah memahami dan mengetahui langkah-langkah dalam melakukan kegiatan ekspor, peraturan yang mengatur kegiatan ekspor, serta terbukanya peluang potensi baru untuk UMKM yang ada di Kampung Rejomulyo.
PENGOLAHAN LIMBAH MINYAK JELANTAH MENJADI LILIN AROMATERAPI BERNILAI JUAL DALAM GERAKAN 3R Kumoro, Imanuel Dimas Cahyo; Trisnapradika, Gustina Alfa; Rahma, Khalida Nur; Kartikadarma, Etika
Abdi Masya Vol 5 No 1
Publisher : Pusat Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52561/abdimasya.v5i1.363

Abstract

Indonesia is one of the largest waste-contributing countries in the world. This is no exception in Kelurahan Bandarharjo which has a large population so that household waste production is directly proportional to the population. The processing of household waste needs special attention, both from the household scale to the national scale. So far, household waste has only been collected at a TPS and ends up piled up in a TPA. This cannot continue because these piles of rubbish can become a source of pollution and disease for the community. Used cooking oil or jelantah is a type of waste that must be found in every household. So the service team carried out outreach activities and demonstrations on the processing used cooking oil waste or jelantah which was then processed into aromatherapy candles to turn waste into high-value selling products.. The method used in this service is in the form of socialization and demonstrations carried out in one day. As a result, the 3R (Reduce, Reuse, Recycle) movement provides an increase in public knowledge in managing the cleanliness of the earth. The team hopes that this training can also be disseminated in the surrounding district.
Perbandingan Model Machine Learning Terbaik untuk Memprediksi Kemampuan Penghambatan Korosi oleh Senyawa Benzimidazole Sumarjono, Cornellius Adryan Putra; Akrom, Muhamad; Trisnapradika, Gustina Alfa
Techno.Com Vol. 22 No. 4 (2023): November 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i4.9201

Abstract

Penelitian ini merupakan studi eksperimen untuk melakukan penyelidikan inhibitor korosi oleh senyawa Benzimidazole dengan melakukan pendekatan machine learning (ML). Karena korosi menyebabkan banyak kerugian yang timbul karena kehilangan material konstruksi, keselamatan kerja dan pencemaran lingkungan akibat produk korosi dalam bentuk senyawa yang mencemarkan lingkungan. Melakukan pendekatan ML adalah untuk mendapatkan model akurasi yang terbaik sehingga dapat digunakan untuk memprediksi dengan relevan dan akurat terhadap suatu material. Dalam penelitian ini, kami mengevaluasi algoritma ML dengan metode linear dan nonlinear dengan menggunakan metode k-fold cross-validation untuk membantu dalam mengukur performa model ML. Mengacu pada metrik coefficient of determination (R2) dan root mean square error (RMSE), kami menyimpulkan bahwa model AdaBoost regressor (ADA) merupakan model dengan performa prediksi terbaik dari eksperimen yang kami lakukan dari literatur untuk dataset senyawa benzimidazole. Keberhasilan model penelitian ini menawarkan perspektif baru tentang kemampuan model ML untuk memprediksi penghambat korosi yang efektif.  
Pelatihan Model Computational Thinking bagi Guru TK dan SD Gaussian Kamil School Semarang Trisnapradika, Gustina Alfa; Pertiwi, Ayu; Prabowo, Wahyu Eko Aji; Setiyanto, Noor Ageng; Putra Sumarjono, Cornellius Adryan
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.1888

Abstract

CT is the ability to think to solve problems whose solution is computing. CT abilities cannot possibly grow in an instant, CT knowledge and skills need time to grow and develop so that they produce as expected. CT training for kindergarten and elementary school teachers at Gaussian Kamil School will be carried out in several stages, starting with an introduction to the CT concept, practice questions, practicing CT using digital methods, and Unplugged. It is hoped that this training can improve teachers' CT abilities, so that teachers will infuse it with students. Students who receive CT from an early age are expected to be able to be independent and behave better because they are used to solving problems in the correct, fast and efficient way.
Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds Safitri, Aprilyani Nur; Trisnapradika, Gustina Alfa; Kurniawan, Achmad Wahid; Prabowo, Wahyu AJi Eko; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i1.10464

Abstract

The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors.
Comparison of Ridge and Kernel Ridge Models in Predicting Thermal Stability of Zn-MOF Catalysts Trisnapradika, Gustina Alfa; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i1.10542

Abstract

This study investigates machine learning-based quantitative structure-property relationship (QSPR) models for predicting the thermal stability of zinc metal-organic frameworks (Zn-MOF). Utilizing a dataset comprising 151 Zn-MOF compounds with relevant molecular descriptors, ridge (R) and kernel ridge (KR) regression models were developed and evaluated. The results demonstrate that the R model outperforms the KR model in terms of prediction accuracy, with the R model exhibiting exceptional performance (R² = 0.999, RMSE = 0.0022). While achieving high accuracy, opportunities for further improvement exist through hyperparameter optimization and exploration of polynomial functions. This research underscores the potential of ML-based QSPR models in predicting the thermal stability of Zn-MOF compounds and highlights avenues for future investigation to enhance model accuracy and applicability in materials science.
Analyzing Preprocessing Impact on Machine Learning Classifiers for Cryotherapy and Immunotherapy Dataset Setiadi, De Rosal Ignatius Moses; Islam, Hussain Md Mehedul; Trisnapradika, Gustina Alfa; Herowati, Wise
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-2

Abstract

In the clinical treatment of skin diseases and cancer, cryotherapy and immunotherapy offer effective and minimally invasive alternatives. However, the complexity of patient response demands more sophisticated analytical strategies for accurate outcome prediction. This research focuses on analyzing the effect of preprocessing in various machine learning models on the prediction performance of cryotherapy and immunotherapy. The preprocessing techniques analyzed are advanced feature engineering and Synthetic Minority Over-sampling Technique (SMOTE) and Tomek links as resampling techniques and their combination. Various classifiers, including support vector machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), XGBoost, and Bidirectional Gated Recurrent Unit (BiGRU), were tested. The findings of this study show that preprocessing methods can significantly improve model performance, especially in the XGBoost model. Random Forest also gets the same results as XGBoost, but it can also work better without significant preprocessing. The best results were 0.8889, 0.8889, 0.6000, 0.9037, and 0.8790, respectively, for accuracy, recall, specificity, precision, and f1 on the Immunotherapy dataset, while on the Cryotherapy dataset, respectively, they were 0.8889, 0.8889, 0.6000, 0.9037, and 0.8790. This study confirms the potential of customized preprocessing and machine learning models to provide deep insights into treatment dynamics, ultimately improving the quality of diagnosis.
Co-Authors Achmad Wahid Kurniawan Adhy, Bagaskara Bayu Adiprasetya, Vicenzo Frendyatha Akrom, Muhamad Amri, Sahrul Ardiansyah, Nibras Bahy Ardyansyah, Nibras Bahy Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Dewi, Dinar Pitania Etika Kartikadarma Gumelar, Rizky Syah Handayani, Margareta Valencia Suci Harun Al Azies Herowati, Wise Hidayat, Novianto Nur Ignatius Moses Setiadi, De Rosal Islam, Hussain Md Mehedul Kumoro, Imanuel Dimas Cahyo Liya Umaroh, Liya Moch Anjas Aprihartha Muhammad Naufal, Muhammad Muljono Muljono Mulyana, Yudha Muttaqin, Muhammad Al Ghorizmi Ningrum, Novita Kurnia Nissa, Khoirul Noor Ageng Setiyanto, Noor Ageng Nusantara, Hanif Setia Prabowo, Wahyu Aji Eko Prabowo, Wahyu Eko Aji Prakoso, Dwi Pramono, Renjiro Azhar Prawesty, Ganis Fatimah Diaz Prayogo, Sandi Yudha Putra Sumarjono, Cornellius Adryan Putra, Dzaki Asari Surya Putra, Maulana Damar Adhesyah Putra, Ricky Primayuda Putranto, Nicholaus Verdhy Rahma, Khalida Nur Rizky, Muhammad Ivan Khoirur Rohman, Adib Annur Rozada, Akfi Safitri, Aprilyani Nur Saraswati, Eka Rizky Anggi setiawan, nabila putri Shanty, Maritza Ardila Sumarjono, Cornellius Adryan Putra Supriadi Rustad Sutrisno, Hendra T. Sutojo Widyawati, Femmi Zahrah, Febrina Nabila