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All Journal Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Informatika (JUPITA) SWAGATI: Journal of Community Service Journal of Multiscale Materials Informatics
Gustina Alfa Trisnapradika
Universitas Dian Nuswantoro
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Other

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Rekomendasi Paket Mata Pelajaran Pilihan (MPP) pada SMA Negeri 1 Kebumen Menggunakan Algoritma K-means Gustina Alfa Trisnapradika; Wildanil Ghozi; Yuminah Yuminah
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 2 (2023): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i2.8514

Abstract

Curriculum changes are needed to adapt education to the times. Since the covid-19 pandemic, face-to-face learning has been suspended. Online learning is an alternative used during a pandemic. This has an impact on learning loss so that the quality of learning decreases. Recovery of learning during the pandemic and post-pandemic Covid-19 is important to reduce the impact of learning loss on students. After the pandemic, the independent curriculum was launched which was a refinement of the 2013 curriculum which had only been implemented in several schools. The subject structure of the Merdeka curriculum for SMA level in Fese E or grade 10, all students get the same subjects. While in Phase F (grades 11 and 12), the subject structure is divided into 2 main groups, namely general subjects and elective subjects. Based on the provisions of the SMKA 2021-2022 curriculum structure, SMA Negeri 1 Kebumen prepares elective subjects (MPP) which are made up of 7 MPP packages. This study uses a clustering technique of student scores using the K-Means algorithm to obtain MPP package recommendations that suit student abilities. For each MPP package, clustering is carried out into 2 clusters with features in the form of predetermined subject scores. The result of this clustering is that each student gets a "yes" or "no" recommendation for each MPP package.
Pelatihan Foto Produk Berbasis Smartphone bagi Pengusaha Batik di Kampung Batik Rejomulyo Muhammad Rifki Ardian; Mirza Yuansyah; Gustina Alfa Trisnapradika; Lusi Noviani Prasetyo
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 6, No 3 (2023): September 2023
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v6i3.1573

Abstract

Pandemi covid-19 telah membangun budaya baru yaitu jual beli online. Hal ini juga terjadi di Kampung Batik Rejomulyo yang berpenduduk dengan mata pencaharian umumnya adalah pengusaha batik. Pergeseran penjualan konvensional menjadi online, menimbulkan masalah baru yaitu kesulitan mendapatkan foto produk yang baik dan memuaskan konsumen. Para pemilik toko batik memiliki pola pikir bahwa melakukan foto produk tidak bisa dengan smartphone yang dimiliki karena kualitasnya kurang baik. Padahal faktanya dengan pengetahuan tentang teknik dan pengalaman menggunakan smartphone untuk mengambil foto produk, kita bisa mendapatkan hasil foto yang mengagumkan. Kegiatan pengabdian ini dilakukan untuk berbagi pengetahuan dan pengalaman tentang teknik pengambilan foto produk dari smartphone termasuk teknik angle dan pemanfaatan cahaya.
SOSIALISASI USER INTERFACE WEBSITE JARICTKU.COM UNTUK MENDONGKRAK POPULARITAS BATIK SEMARANGAN DI KAMPUNG BATIK REJOMULYO Gustina Alfa Trisnapradika; Rizky Syah. Gumelar; Nico Ananda Febrian; Yudha Mulyana; Cornellius Adryan Putra Sumarjono
Jurnal Pengabdian Informatika Vol. 1 No. 4 (2023): JUPITA Volume 1 Nomor 4, Agustus 2023
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Batik Semarangan, memerlukan peningkatan popularitas dan apresiasi yang lebih luas. Sebagai upaya mendukung hal ini, website jarictku.com memanfaatkan teknologi Augmented Reality untuk memberikan fitur virtual try-on bagi pengguna, memungkinkan mereka untuk mencoba berbagai jenis Batik Semarangan secara virtual. Sosialisasi user interface ini sangat penting, khususnya di Kampung Batik Rejomulyo, pusat produksi Batik Semarangan. Sosialisasi ini bertujuan untuk memastikan bahwa masyarakat dapat memahami dan memanfaatkan fitur virtual try-on ini secara efektif. Diharapkan, dengan memahami dan memanfaatkan teknologi ini, Batik Semarangan dapat mendapatkan tempat yang lebih luas di hati masyarakat dan membantu melestarikan warisan budaya Indonesia.
Predicting Methanol Space-Time Yield from CO? Hydrogenation Using Machine Learning: Statistical Evaluation of Penalized Regression Techniques Harun Al Azies; Muhamad Akrom; Setyo Budi; Gustina Alfa Trisnapradika; Aprilyani Nur Safitri
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1341

Abstract

This study investigates the effectiveness of machine learning techniques, specifically penalized regression models Ridge Regression, Lasso Regression, and Elastic Net Regression in predicting methanol space-time yield (STY) from CO? hydrogenation data. Using a dataset derived from Cu-based catalyst research, the study implemented a comprehensive preprocessing approach, including data cleaning, imputation, outlier removal, and normalization. The models were rigorously evaluated through 10-fold cross-validation and tested on unseen data. Ridge Regression outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) of 0.7706, Mean Absolute Error (MAE) of 0.5627, and Mean Squared Error (MSE) of 0.5938. In comparison, Lasso and Elastic Net Regression models exhibited higher error metrics. Feature importance analysis revealed that Gas Hourly Space Velocity (GHSV) and Molar Masses of Support significantly influence catalytic activity. These findings suggest that Ridge Regression is a promising tool for accurately predicting methanol production, providing valuable insights for optimizing catalytic processes and advancing sustainable practices in chemical engineering.
Sosialisasi Website SEA Sebagai Sarana Pendukung Sekolah Perempuan Kreatif Batursari (SEKARI) Primavieri Rhesa Ardana; Gustina Alfa Trisnapradika; Dzaki Asari Surya Putra; Mustafid Kaisalana; Sri Winarno
ABDI: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 6 No 4 (2024): Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : Labor Jurusan Sosiologi, Fakultas Ilmu Sosial, Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/abdi.v6i4.981

Abstract

Desa Batursari merupakan desa yang berpenduduk 17.622 jiwa, dan terdapat 3.964 jiwa perempuan desa. Desa Batursari menghadapi beberapa masalah terkait perempuan, seperti pernikahan dini, jerat pinjaman online, kriminalitas, kekerasan seksual, serta banyaknya perempuan yang tidak bekerja. Untuk mengatasi permasalahan tersebut, tim PPK Ormawa HMTI membuat sekolah non formal yang ditujukan perempuan batursari bernama Sekolah Perempuan Kreatif Batursari (SEKARI) yang bertujuan sebagai upaya peningkatan keterampilan dan pengetahuan, dilengkapi juga dengan LMS bernama Sekari Edu Academy (SEA) sebagai sarana utama dalam akses materi pembelajaran. Metode yang digunakan yaitu ceramah, demo praktik, diskusi, dan evaluasi. Sosialisasi dilaksanakan selama 2 hari dengan tujuan untuk meningkatkan pengetahuan peserta mengenai fitur-fitur yang menunjang pembelajaran di Sekari yaitu pretest, postest, dan unduh materi pembelajaran. Hasil nilai pretest posttest ini diolah menjadi evaluasi pada kegiatan sosialisasi sehingga berdampak pada peningkatan pengetahuan dan keterampilan perempuan Desa Batursari.
Utilization of Alkane Hydrocarbons in Waste Management to Support a Green Economy Movement for Students GUSTINA ALFA TRISNAPRADIKA; NOOR AGENG SETIYANTO; SUPRIADI RUSTAD; MULJONO; MUHAMMAD NAUFAL
SWAGATI : Journal of Community Service Vol. 4 No. 1 (2026): March
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2026v4i1.2645

Abstract

Household waste management, particularly used cooking oil (waste cooking oil), remains an environmental issue due to low public awareness and limited understanding of sustainable waste management practices. Improper disposal of used cooking oil can lead to environmental pollution and pose health risks. This community service activity aims to improve students’ knowledge and skills in processing used cooking oil into value-added aromatherapy candles, while fostering environmental awareness and an entrepreneurial spirit based on the green economy. The method applied is Community Based Participatory Research (CBPR), conducted in two stages: delivery of theoretical material on the 3R concept (Reduce, Reuse, Recycle) and hands-on practice in processing used cooking oil. The activity was carried out at SMK Negeri 9 Semarang involving 23 students. The results indicate an increase in participants’ understanding, as shown by improvements in scores on 3R education (from 59.5 to 83.3) and used cooking oil processing (from 50 to 85). In addition, students were able to produce aesthetically pleasing aromatherapy candles with economic value potential.
Machine Learning-Assisted Discovery and Optimization of Sodium-Ion Batteries: A Review Gustina Alfa Trisnapradika; Harun Al Azies; Muhamad Akrom; Usman Sudibyo; Noor Ageng Setiyanto
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i1.15954

Abstract

Sodium-ion batteries (SIBs) have emerged as a promising alternative to lithium-ion batteries due to the natural abundance, low cost, and wide geographic availability of sodium resources. However, their practical implementation is hindered by challenges such as lower energy density, slower ion diffusion, and limited cycle stability. In recent years, machine learning (ML) has been increasingly applied to accelerate the discovery, design, and optimization of SIB materials and systems. This review provides a comprehensive overview of ML applications in sodium-ion battery research, including electrode material discovery, electrolyte optimization, performance prediction, and degradation analysis. Various ML techniques, such as supervised learning, unsupervised learning, and deep learning, are discussed in relation to their roles in materials informatics. Additionally, challenges such as data scarcity, model interpretability, and transferability are critically analyzed. Finally, future perspectives on integrating ML with high-throughput experiments and quantum computing are highlighted to guide next-generation sodium-ion battery research.
Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds Aprilyani Nur Safitri; Gustina Alfa Trisnapradika; Achmad Wahid Kurniawan; Wahyu AJi Eko Prabowo; Muhamad Akrom
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 Gustina Alfa Trisnapradika; Muhamad Akrom
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.
Synergizing Quantum Computing and Machine Learning: A Pathway Toward Quantum-Enhanced Intelligence Gustina Alfa Trisnapradika; Muhamad Akrom
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
Co-Authors Achmad Wahid Kurniawan Aprilyani Nur Safitri Bagas Aditya Mahendra Cornellius Adryan Putra Sumarjono Dzaki Asari Surya Putra Filmada Ocky Saputra Harun Al Azies Lusi Noviani Prasetyo Mirza Yuansyah Muhamad Akrom Muhammad Naufal Muhammad Rifki Ardian Muljono, - Mustafid Kaisalana Nico Ananda Febrian Noor Ageng Setiyanto, Noor Ageng Novianto Nur Hidayat Primavieri Rhesa Ardana Renjiro Azhar Pramono Rizky Syah Gumelar Rizky Syah. Gumelar Saraswati, Galuh Wilujeng Setyo Budi Sri Winarno SUPRIADI RUSTAD Usman Sudibyo Wahyu Aji Eko Prabowo Wildanil Ghozi Yudha Mulyana Yuminah Yuminah