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All Journal Techno.Com: Jurnal Teknologi Informasi Eksergi: Chemical Engineering Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology Jurnal ULTIMATICS KOMPUTIKA - Jurnal Sistem Komputer Building of Informatics, Technology and Science Journal of Information Systems and Informatics Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Abdi Masya Jurnal Algoritma Community: Jurnal Pengabdian Pada Masyarakat NERO (Networking Engineering Research Operation) Journal of Future Artificial Intelligence and Technologies Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian Kepada Masyarakat
Trisnapradika, Gustina Alfa
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Comparison of Multilinear Regression and AdaBoost Regression Algorithms in Predicting Corrosion Inhibition Efficiency Using Pyridazine Compounds Mulyana, Yudha; Akrom, Muhamad; Trisnapradika, Gustina Alfa; Setiawan, Nabila Putri
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3809

Abstract

Abstract-Corrosion is a serious problem in various industries that leads to increased production costs, maintenance, and decreased equipment efficiency. The use of organic compounds as corrosion inhibitors has become an increasingly desirable solution due to their effectiveness and environmental friendliness. This study compares the performance of two machine learning algorithms, Multilinear Regression (MLR) and AdaBoost Regression (ABR), in predicting the corrosion inhibition efficiency (CIE) of pyridazine-derived compounds. The dataset used consists of molecular properties as independent variables and CIE values as targets. To measure the performance of the model, a k-fold cross-validation process was used, where the dataset was divided into equal subsets. Each iteration uses one subset as validation data, while the other subset as training data. Results show that the AdaBoost Regression model achieves higher accuracy (99%) than Multilinear Regression (98%) in predicting CIE. Important feature analysis showed that Total Energy (TE) and Dipole Moment (µ) were the most influential variables in the ABR model, highlighting their important role in inhibitor effectiveness. Model evaluation was performed with R2 and RMSE metrics, where nonlinear models such as ABR were shown to be superior in predicting corrosion inhibition efficiency. These findings support the use of nonlinear methods to improve the effectiveness of protecting industrial equipment from corrosion.
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 Teknik 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.
Perbandingan Algoritma NBC, SVM, Logistic Regression untuk Analisis Sentimen Terhadap Wacana KaburAjaDulu di Media Sosial X Rohman, Adib Annur; Trisnapradika, Gustina Alfa
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7261

Abstract

This research aims to analyze sentiment towards KaburAjaDulu discourse on X social media by utilizing Logistic Regression, Support Vector Machine (SVM), and Naive Bayes algorithms. Data was collected through a crawling process and resulted in 3,011 tweet data. Pre-processing stages include data cleaning, conversion of letters to lowercase, normalization, tokenization, stopword removal, and stemming. After preprocessing, the data was divided into two sentiment categories, namely positive and negative using a lexicon approach. The dataset is divided using an 80:20 scheme for training and test data, with feature representation utilizing the TF-IDF method. The modeling process is performed utilizing the three algorithms to be evaluated using accuracy, precision, recall, and f1-score metrics. As a solution to class inequality, the oversampling technique SMOTE (Synthetic Minority Over-sampling Technique) is applied. Based on the evaluation, it shows that before the application of SMOTE, Naive Bayes algorithm obtained 78.18% accuracy, 81.80% precision, 77.06% recall, and 77.35% f1-score; SVM obtained 85.63% accuracy, 86.49% precision, 85.68% recall, and 85.94% f1-score; while Logistic Regression obtained 83.05% accuracy, 85.31% precision, 82.47% recall, and 82.95% f1-score. After applying SMOTE, Naive Bayes improved to 81.90% accuracy, 82.27% precision, 81.67% recall, and 81.87% f1-score; SVM obtained 85.63% accuracy, 87.59% precision, 86.89% recall, and 87.13% f1-score; and Logistic Regression obtained 83.33% accuracy, 84.46% precision, 83.62% recall, and 83.88% f1-score. These findings prove that SVM has the most consistent and superior sentiment classification performance on this dataset, making an important contribution to the development of methods for analyzing people's views on social media platforms.
Optimasi model machine learning untuk prediksi inhibitor korosi berbasis augmentasi dataset senyawa n-heterocyclic menggunakan KDE Gumelar, Rizky Syah; Akrom, Muhamad; Trisnapradika, Gustina Alfa
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v%vi%i.27945

Abstract

This study aims to optimize a machine learning model to predict the corrosion inhibitor effectiveness of N-Heterocyclic compounds.  The main challenge in this modelling is the limited dataset due to the high cost and time required to collect experimental data. To overcome this problem, this research utilizes Kernel Density Estimation (KDE) as a data augmentation technique, generating virtual samples that improve dataset diversity and model predictive performance. The developed dataset includes 11 relevant chemical features such as HOMO, LUMO, and Gap Energy. Linear (MLR, Ridge, Lasso, and ElasticNet) and non-linear (KNR, Random Forest, Gradient Boosting, Adaboost, XGBoost) machine learning models were evaluated based on Root Mean Squared Error (RMSE) and coefficient of determination (R²). The results show that data augmentation using KDE improves prediction accuracy and stability, especially in non-linear models like Random Forest and XGBoost. The application of KDE proved effective in improving the performance of predictive models. It can be recommended as an augmentation method in similar studies that require additional data to improve prediction accuracy.Keywords: Machine Learning, Kernel Density Estimator (KDE), Corrosion Inhibitor, Dataset
Perbandingan Model Ensemble untuk Memprediksi Efisiensi Penghambatan Korosi Senyawa N-Heterosiklik Cahyana, Timothy Mulya; Akrom, Muhammad; Trisnapradika, Gustina Alfa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2247

Abstract

Penelitian ini mengevaluasi dan membandingkan efektivitas berbagai model regresi ensemble dalam memprediksi Corrosion Inhibition Efficiency (CIE) dari senyawa N-heterosiklik. Model-model yang dievaluasi adalah Extra Trees Regressor, Random Forest Regressor, Light Gradient Boosting Regressor, Gradient Boosting Regressor, Extreme Gradient Boosting Regressor, Adaptive Boosting Regressor, Bagging Regressor, dan Categorical Boosting Regressor, menggunakan fitur molekul seperti highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), celah energi (Delta E), momen dipol (mu), potensi ionisasi (I), afinitas elektron (A), keelektronegatifan (chi), kekerasan global (eta), kelembutan global (sigma), elektrofilisitas (omega), dan fraksi elektron yang ditransfer (Delta N). Di antara model yang dievaluasi, Extreme Gradient Boosting Regressor memberikan kinerja terbaik, dengan skor R-squared (R2) tertinggi sebesar 0.9776. Temuan ini menunjukkan efektivitas model ensemble dalam meningkatkan akurasi prediksi inhibisi korosi dan pentingnya pembelajaran mesin dalam mengembangkan inhibitor korosi yang lebih baik.
Optimasi Algoritma SVM dengan Teknik SMOTE dan Tuning Parameter pada Klasifikasi Balita Stunting Muttaqin, Muhammad Al Ghorizmi; Trisnapradika, Gustina Alfa
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8330

Abstract

Stunting in toddlers is a chronic nutritional problem that has long-term impacts on human resource quality, including cognitive development and vulnerability to diseases. Brebes Regency is one of the priority areas for stunting management in Indonesia. This study aims to optimize the performance of the Support Vector Machine (SVM) algorithm in classifying stunting status among toddlers by addressing data imbalance using the Synthetic Minority Oversampling Technique (SMOTE) and parameter tuning. A total of 9,598 anthropometric samples collected from several community health center in Brebes were processed through stages of data cleaning, label encoding, outlier handling, standardization, and class splitting, and then divided into training data (80%) and testing data (20%). Two models were compared: the baseline SVM model and the optimized SVM model, which integrates SMOTE and parameter tuning through GridSearchCV. The results showed that the baseline model achieved an accuracy of 98.31%, but the recall for the stunting class was only 89.19%. After applying SMOTE and parameter tuning, the model’s performance improved, achieving an accuracy of 99.78% and a recall for the stunting class of 98.46%. This improvement demonstrates that the use of SMOTE and parameter tuning is highly effective in enhancing the model’s sensitivity toward the minority class. Therefore, this study shows that a comprehensive optimization approach can effectively support early detection of stunting, making it a valuable tool for more targeted health intervention planning.
Peningkatan Kinerja Model Naïve Bayes untuk Analisis Sentimen Komentar Terkait “Sound Horeg” Menggunakan SMOTE dan Tuning Parameter Kaisalana, Mustafid; Trisnapradika, Gustina Alfa
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8554

Abstract

The phenomenon of “Sound Horeg” on online platforms has sparked diverse public sentiments, making sentiment analysis an essential tool for understanding public opinion. This study aims to classify user sentiments (positive/negative) related to “Sound Horeg” using the Naïve Bayes algorithm. The dataset used in this research exhibits significant class imbalance, with a predominance of negative sentiments. The methodology involves a series of text preprocessing stages, including case folding, tokenizing, normalization, lexicon-based sentiment labeling, stopword removal, stemming, and duplicate removal. The sentiment labeling process utilizes an Indonesian sentiment lexicon compiled from two sources lexicon_positif.csv and lexicon_negatif.csv containing predefined lists of words with positive and negative sentiment scores based on Indonesian public opinion lexicons. Subsequently, text features are extracted using the Term Frequency–Inverse Document Frequency (TF-IDF) method. To address data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is applied to the training data to balance the number of positive and negative samples. The Naïve Bayes model is then optimized using GridSearchCV to determine the best alpha value. Experimental results show that the unoptimized Naïve Bayes model achieved an accuracy of 73%, but struggled to classify minority classes (positive sentiments) due to data bias. After applying SMOTE and parameter tuning, the model’s performance improved significantly, demonstrating the effectiveness of these techniques in producing a more balanced and robust model. This study concludes that the Naïve Bayes algorithm, when optimized with SMOTE and hyperparameter tuning, is effective for Indonesian-language sentiment analysis, particularly on imbalanced datasets. Future work may include exploring other algorithms and employing broader sentiment lexicons and more complex linguistic features to further enhance model performance.
Multi-Tier Architecture Design for Scalable and Effective Non-Formal Learning: A Redesign of Serat Kartini Women's School LMS Ardana, Primavieri Rhesa; Trisnapradika, Gustina Alfa
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1341

Abstract

Non-formal education plays a vital role in empowering women in rural areas of Central Java, Indonesia. However, the existing Learning Management System (LMS) of Woman School Serat Kartini, built on a monolithic Laravel architecture, suffers from significant performance degradation and scalability limitations under growing user loads and shared hosting constraints. This leads to high latency, frequent session interruptions, and reduced participation, ultimately undermining learning effectiveness. This study redesigns the LMS using a multi-tier application architecture through the Design Science Research (DSR) methodology. The proposed blueprint separates the system into four independent tiers: Presentation (Next.js for users, React.js for administrators), Logic (Express.js for API Layer), Cache (Redis with cache-aside strategy), and Data (MySQL). The design artifacts include detailed architecture diagrams, ERD, use case, and sequence diagrams. Conceptual evaluation demonstrates that the multi-tier approach enhances modularity, reduces latency, supports horizontal scalability, and improves resource efficiency , ensuring reliable access for women learners with limited digital literacy and unstable internet connectivity. The redesigned LMS conceptually strengthens learning accessibility, engagement, and program sustainability in resource-constrained non-formal education contexts. This research is limited to the conceptual design phase without implementation or empirical testing.
Enhancing the Predictive Accuracy of Corrosion Inhibition Efficiency Using Gradient Boosting with Feature Engineering and Gaussian Mixture Model Amri, Sahrul; Akrom, Muhamad; Trisnapradika, Gustina Alfa
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11560

Abstract

Prediction The development of Quantitative structure property relationship (QSPR) models for predicting corrosion inhibition efficiency (IE) often faces challenges due to small datasets, which heightens the risk of overfitting and results in less reliable performance assessments. This research creates an entirely leakage-free modeling framework by combining per-fold preprocessing, augmentation of training-only data, and rigorous Leave-One-Out Cross-Validation (LOOCV). A set of 20 pyridazine derivatives was evaluated using 12 quantum-chemical descriptors, including HOMO, LUMO, ΔE, dipole moment, electronegativity, hardness, softness, and the electron-transfer fraction. An initial assessment showed that all baseline models lacking augmentation Gradient Boosting, Random Forest, SVR, and XGBoost demonstrated limited predictive power (R² < 0.20), revealing the dataset's inherently low information complexity.To enhance representation in the feature space, a multi-scale Gaussian Mixture Model (GMM) was used to generate chemically valid synthetic samples, with all components trained solely on the training subset from each LOOCV fold. This strategy consistently improved model performance. The two most successful configurations, XGBoost + GMM v2 and Random Forest + GMM v3, reached R² values of 0.4457 and 0.4108, respectively, along with significant decreases in RMSE, MAE, and MAPE. These findings illustrate that GMM-based generative augmentation effectively captures multicluster structures within the descriptor space while expanding the chemical variability domain in a controlled way.While the resulting R² values remain inadequate for high-precision quantitative predictions, the proposed methodology provides a solid basis for early-stage evaluation of corrosion inhibitors in situations with limited data. Future research will aim to integrate advanced DFT-derived descriptors, molecular graph representations, and tests against larger external datasets to enhance model generalizability.
“Sailing Beyond Limit” sebagai Analogi Latihan Keterampilan Manajemen Mahasiswa dalam Upaya Implementasi Peran Agent of Change Trisnapradika, Gustina Alfa; Juhara, Kanahaya Putri; Bahri, Alfino Kautsar; Ananta, Putri Rossa; Siregar, Nadia Itona; Ningrum, Novita Kurnia
Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian kepada Masyarakat Vol. 4 No. 1 (2026): Januari: Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian kepada Masyarakat
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/bumi.v4i1.1393

Abstract

Students have a strategic role as agents of change that requires adequate leadership and managerial skills. Formal learning in the classroom has not been able to fully develop these practical skills. This community service activity aims to improve the basic management skills of students at the Faculty of Computer Science, Universitas Dian Nuswantoro through Basic Student Management Skills Training (LKMM TD) with the theme Sailing Beyond Limits. The implementation method uses a Participatory Learning Action approach that includes problem identification, strategic planning, activity implementation, and joint evaluation. The activity was attended by 193 students from student organizations and general students. The learning process was carried out through lectures, case studies, discussions, group work, and pre- and post-tests. The evaluation results showed a significant increase in participant capacity, marked by an increase in the average score from 68.21 percent in the pre-test to 91.69 percent. LKMM TD activities effectively equip students with managerial, leadership, communication, and motivational control skills as provisions for carrying out the role of student agents of change.
Co-Authors Achmad Wahid Kurniawan Adhy, Bagaskara Bayu Adiprasetya, Vicenzo Frendyatha Amri, Sahrul Ananta, Putri Rossa Ardana, Primavieri Rhesa Ardiansyah, Nibras Bahy Ardyansyah, Nibras Bahy Ayu Pertiwi Bahri, Alfino Kautsar Budi, Setyo Cahyana, Timothy Mulya 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 Juhara, Kanahaya Putri Kaisalana, Mustafid Kumoro, Imanuel Dimas Cahyo Liya Umaroh, Liya Moch Anjas Aprihartha Muhammad Akrom Muhammad Naufal Muljono Muljono Mulyana, Yudha Muttaqin, Muhammad Al Ghorizmi Nadia Itona Siregar, Nadia Itona Ningrum, Novita Kurnia Nissa, Khoirul Noor Ageng Setiyanto, Noor Ageng Nusantara, Hanif Setia 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 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