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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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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 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 : Jurnal Teknik Informatika 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.
Pemanfaatan Google Site dalam Pelatihan Pembuatan Website Sebagai Kegiatan Penunjang Edukasi Life Skills Pelajar SMA N 2 Mranggen Kabupaten Demak Herowati, Wise; Kurniawan, Achmad Wahid; Budi, Setyo; Muljono, Muljono; Rustad, Supriadi; Ignatius Moses Setiadi, De Rosal; Sutojo, T.; Trisnapradika, Gustina Alfa; Aprihartha, Moch Anjas
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2840

Abstract

Menghadapi persaingan kemampuan dan keterampilan terutama untuk generasi sekarang harusnya dihadapi dengan mempersiapkan pengetahuan yang mumpuni terutama kemampuan-kemampuan untuk menunjang life skills . Kemampuan tersebut perlu diperkuat sedari diri terutama pada jenjang pendidikan menengah atas atau jenjang SMA. Salah satu kemampuan yang dapat diasah pada jenjang pendidikan tersebut adalah pengetahuan dan kemampuan mengenai pembuatan sebuah website. Menciptakan sebuah website sering kali dianggap sulit dan membutuhkan kemampuan pemrograman khusus, hal ini menjadi tantangan tersendiri salah satunya bagi salah satu sekolah yakni SMA N 2 Mranggen Demak. Sebagai salah satu cara menyelesaikan tantangan tersebut, kegiatan PKM yang telah terlaksana ini memperkenalkan konsep dasar pembuatan website menggunakan Google Site. Diharapkan melalui kegiatan pelatihan tersebut para pelajar dapat memiliki keterampilan tambahan untuk menambah kemampuan guna menunjang life skills mereka
Pengenalan Sistem Pertanian Cerdas Untuk Konservasi Alam Dan Penghematan Energi dengan Metode Critical Thinking pada Siswa SD Islam Bintang Juara Ningrum, Novita Kurnia; Umaroh, Liya; Trisnapradika, Gustina Alfa; Naufal, Muhammad
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2708

Abstract

Indonesia sebagai negara kepulauan  terbesar di duniia terdiri dari wilayah daratan dan lautan dengan ribuan pulau kecil yang tersebar di seluruh kawasan perairannya. Akan tetapi pengelolaan ekosistem alam yang buruk selama beberapa tahun terakir berdampak pada kerusakan ekosistem alam yang dari tahun 2017-2021 terjadi deforetasi di pulau kecil mencapai 79% tiap tahunnya. Kerusakan akibat deforestasi tersebut tidak hanya merusak ekosistem alam juga merusak sumber energy yang tidak terbarukan yang disebabkan oleh penambangan dan pembukaan lahan hutan yang melanggar etika lingkungan. Oleh karenanya diterapkan metode critical thinking pada siswa. Berpikir kritis sangat dibutuhkan oleh anak sejak usia dini, utamanya tingkatan sekolah dasar. Dengan berpikir kritis, siswa menjadi lebih tajam dalam memahami permasalahan dan tetpat sasaran dalam menentukan solusi permasalahan yang ditemukan. Adanya smart farming merupakan salah satu pendekatan teknologi yang dapat dilakukan agar kerusakan ekosistem alam dapat dikendalikan. Pengenalan smart farming disampaikan dengan metode computational thinking yang  diikuti oleh 20 peserta yang terdiri dari 17 siswa dan 3 guru pengajar di SD Islam Bintang Juara.
Peningkatan Kapabilitas Perlindungan Diri Perempuan Desa Batursari Melalui Praktik Dasar Muay Thai Trisnapradika, Gustina Alfa; Saraswati, Eka Rizky Anggi; Muttaqin, Muhammad Al Ghorizmi; Prakoso, Dwi
Community : Jurnal Pengabdian Pada Masyarakat Vol. 5 No. 1 (2025): Maret : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/16fnb325

Abstract

Maraknya tawuran antar gangster di malam hari membuat masyarakat menjadi resah untuk beraktivitas dengan bebas. Hingga di era saat ini, perempuan masih menjadi objek atau target utama tindak kriminalitas akibat stigma bahwa perempuan adalah kaum yang lemah.  Desa Batursari memiliki populasi sebesar 35.229 jiwa dengan sebaran jumlah penduduk perempuan sebanyak 17.625 jiwa sehingga memiliki kerentanan menjadi korban kriminalitas yang tinggi. Oleh karenanya, Tim Pengabdi berkolaborasi bersama Dinas Perempuan dan Anak (DP3AP2KB) Provinsi Jawa Tengah dan perangkat Desa Batursari untuk mengadakan kegiatan edukasi dan praktik dasar Muay Thai sebagai bentuk antisipasi terhadap terjadinya kriminalitas. Hasilnya, terjadi 67,7% peningkatan pengetahuan dan kapabilitas kaum perempuan masyarakat Desa Batursari dalam bidang perlindungan diri.
Synergizing Quantum Computing and Machine Learning: A Pathway Toward Quantum-Enhanced Intelligence Trisnapradika, Gustina Alfa; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12947

Abstract

The convergence of quantum computing and artificial intelligence has introduced a new paradigm in computational science known as Quantum Artificial Intelligence (QAI). By leveraging quantum mechanical principles such as superposition, entanglement, and quantum parallelism, QAI aims to overcome the limitations of classical machine learning, particularly in handling high-dimensional data, complex optimization, and scalability issues. This paper presents a comprehensive review of foundational concepts in both classical machine learning and quantum computing, followed by an in-depth discussion of emerging quantum algorithms tailored for AI applications, such as quantum neural networks, quantum support vector machines, and variational quantum classifiers. We explore the practical implications of these approaches across key sectors, including healthcare, finance, cybersecurity, and logistics. Furthermore, we identify critical challenges related to hardware limitations, algorithmic stability, data encoding, and ethical considerations. Finally, we outline research directions necessary to advance the field, highlighting the transformative potential of QAI in shaping the next generation of intelligent technologies
IMPROVING AWARENESS OF INTERNET SECURITY AND ETHICS AMONG STUDENTS AT SMA NEGERI 2 MRANGGEN Naufal, Muhammad; Hidayat, Novianto Nur; Trisnapradika, Gustina Alfa; Al Azies, Harun
Jurnal Layanan Masyarakat (Journal of Public Services) Vol. 9 No. 2 (2025): JURNAL LAYANAN MASYARAKAT
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/.v9i2.2025.204-214

Abstract

This community service initiative aimed to enhance the awareness of internet security and ethics among high school students at SMA Negeri 2 Mranggen, Demak Regency, Central Java. The program utilized a structured methodology consisting of outreach, training, and evaluation stages conducted in a hands-on environment within the school's computer laboratory. The training covered key topics such as data security, phishing attacks, malware, and ethical internet use. The sessions were held in the school’s computer laboratory to provide hands-on experience. Each training session had a duration of 3×45 minutes, attended by 31 students, allowing comprehensive exploration of the material. Pre- and post-tests were administered to assess the effectiveness of the training. The results demonstrated a significant improvement in students' knowledge, with average scores increasing from 49 in the pre-test to 72.67 in the post-test. A paired t-test analysis confirmed this improvement as statistically significant, with a T-statistic of -13.971 and a P-value of 2.07 × 10-14. The findings highlight the program's success in raising awareness and equipping students with the skills to navigate the digital world safely and responsibly. This initiative underscores the importance of educational programs in fostering internet literacy and security awareness among young users. To build on these findings, it is recommended that similar training sessions to be conducted regularly to reinforce the concepts learned. Additionally, a long-term plan is proposed as a form of sustainability of this community service program, namely by expanding the training targets not only to students but also to teachers, housewives and children who are already accustomed to gadgets.
Development and Implementation of a Corrosion Inhibitor Chatbot Using Bidirectional Long Short-Term Memory Ardyansyah, Nibras Bahy; Putra, Dzaki Asari Surya; Putranto, Nicholaus Verdhy; Trisnapradika, Gustina Alfa; Akrom, Muhamad
IJNMT (International Journal of New Media Technology) Vol 12 No 1 (2025): Vol 12 No 1 (2025): 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.v12i1.3752

Abstract

This research delves into the intricate phenomenon of corrosion, a process entailing material degradation through chemical reactions with the environment, causing consequential losses across diverse sectors. In response, corrosion inhibitors are a proactive measure to counteract this deleterious impact. Despite their paramount significance, public awareness regarding corrosion and inhibitors remains limited, necessitating intensified educational efforts. The primary focus of this study is developing a Chatbot system designed to disseminate information on corrosion, inhibitors, and related topics. Employing the Machine Learning Life Cycle model, a deep learning approach, specifically the Bidirectional Long Short-Term Memory (BLSTM) architecture, is utilized to construct an optimized Chatbot model. Post-training evaluation of the BLSTM model reveals noteworthy performance metrics, including a remarkable 100% accuracy rate and a substantial 92% validation accuracy over 100 epochs. Training and validation losses are reported as 0.2292 and 0.9342, respectively. In conclusion, the BLSTM algorithm is an effective tool for training and enhancing Chatbot models, ensuring commendable corrosion awareness and inhibition performance.
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
Edukasi Pengelolaan Sampah Berbasis 3R: Jelantah Menjadi Lilin Aromaterapi bagi Perempuan Desa Batursari Trisnapradika, Gustina Alfa; Putra, Maulana Damar Adhesyah; Shanty, Maritza Ardila; Handayani, Margareta Valencia Suci
Jurnal Pengabdian Kepada Masyarakat (JPKM) TABIKPUN Vol. 6 No. 1 (2025)
Publisher : Faculty of Mathematics and Natural Sciences - Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpkmt.v6i1.190

Abstract

Sampah rumah tangga dan sejenisnya merupakan isu kritis di Kabupaten Demak, karena volumenya terus meningkat. Untuk mengatasi masalah ini, Tim Pengabdi melaksanakan program di Desa Batursari, Kabupaten Demak yang bertujuan untuk meningkatkan kesadaran dan keterampilan masyarakat dalam pengelolaan sampah dengan menerapkan konsep 3R (Reduce, Reuse, Recycle). Salah satunya adalah pengolahan minyak jelantah menjadi lilin aromaterapi, memberikan nilai tambah pada limbah rumah tangga yang sering diabaikan. Metode pelaksanaan program meliputi penyampaian materi, sosialisasi, dan pelatihan langsung kepada 38 peserta yang mayoritas merupakan ibu rumah tangga. Berdasarkan nilai pre-test dan post-test yang diukur sebelum dan sesudah kegiatan menunjukkan adanya peningkatan pemahaman peserta, dari rata-rata 50,21% sebelum pelatihan setelahnya menjadi 68,57%. Program ini berhasil meningkatkan pengetahuan dan keterampilan masyarakat dalam pengelolaan sampah melalui pembuatan lilin aromaterapi dan diharapkan dapat berkontribusi terhadap pengurangan sampah di TPA serta peningkatan kualitas lingkungan hidup di Kabupaten Demak.
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.
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