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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 Jurnal Algoritma 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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Pembuatan Desain Logo Sebagai Ikon Baru Bagi Kampung Batik Rejomulyo Semarang Trisnapradika, Gustina Alfa; Nusantara, Hanif Setia; Dewi, Dinar Pitania; Zahrah, Febrina Nabila; Widyawati, Femmi; Putra, Ricky Primayuda
Community : Jurnal Pengabdian Pada Masyarakat Vol. 3 No. 3 (2023): November : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/community.v3i3.416

Abstract

Kampung Batik Rejomulyo, sebuah kampung yang memiliki warisan seni budaya dan kerajinan batik yang kaya. Dalam konteks globalisasi dan persaingan ketat, pengembangan dan promosi kampung batik ini diperlukan untuk menjaga relevansi dan daya saingnya. Salah satu caranya melalui pembuatan desain logo yang akan dijadikan ikon baru kampung tersebut. Logo ini bertujuan untuk meningkatkan pengenalan produk batik semarangan, serta dapat menjadi aset berharga untuk strategi branding produk batik semarangan di Kampung Batik Rejomulyo. Harapan dari penggunaan logo baru ini dapat memberikan identitas yang kuat bagi Kampung Batik Rejomulyo, tidak hanya di tingkat lokal tetapi juga secara nasional maupun internasional.
Ecoprint: Upaya Mengurangi Paparan Digital pada Anak Melalui Sekolah Perempuan Kreatif Batursari Trisnapradika, Gustina Alfa; Amri, Sahrul; Ardiansyah, Nibras Bahy; Rozada, Akfi
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 2 (2024): Juli : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/community.v4i2.543

Abstract

Golden Age is an era for children which is most crucial in building character in the future. However, nowadays many children are exposed to gadgets from an early age. The role of parents is very important to facilitate creative and positive activities to divert children's addiction to gadgets. Ecoprint is one of the activities that can be done with children. So, the service team held a service in the form of ecoprint training which was attended by women from Desa Batursari who are members of the Batursari Creative Women's School. As a result, participants took part in the training enthusiastically and produced good creations. The hope is that this activity can be an idea for playing with children at home.
Peningkatan Potensi Ekonomi Digital Perempuan Desa Batusari Bersama Kampus Shopee Semarang Trisnapradika, Gustina Alfa; Nissa, Khoirul; Prayogo, Sandi Yudha; Rizky, Muhammad Ivan Khoirur
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 3 (2024): November : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/f62pf519

Abstract

For some, the role of housewives is often underestimated because it is called an unproductive role. In fact, housewives have many skills and capabilities in self-development if they find the right place. Desa Batursari has a population demographic with a high population of 35,229 people. Of the total population, 17,625 are women. This is also a high potential for carrying out various aspects of empowerment and increasing competence for women, especially housewives. Housewives in Desa Batursari have many skills in producing creative goods and processed food products but are constrained in marketing which is only by word of mouth. Therefore, the Community Service Team collaborated with the Kampus UMKM Shopee Semarang to provide appropriate digital marketing education and training for housewives in Desa Batursari. The activity was held with a presentation of the education followed by the practice of optimizing digital marketing in the marketplace. It is hoped that with this community service activity, housewives in Desa Batursari can increase the economic potential of themselves and their families.
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.
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 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 Kaisalana, Mustafid Kumoro, Imanuel Dimas Cahyo Liya Umaroh, Liya Moch Anjas Aprihartha Muhammad Akrom 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