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FRUIT IMAGE CLASSIFICATION USING DEEP LEARNING ALGORITHM: SYSTEMATIC LITERATURE REVIEW (SLR) Mirwansyah, Dedy; Arief Wibowo
Multica Science and Technology Vol 2 No 2 (2022): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v2i2.356

Systematic literature review (SLR) research studies various classification models with deep learning algorithms on fruit with digital images. In recent years, computer vision and processing techniques are increasingly useful in the fruit industry, especially for quality and color inspection, sizing, and shape sorting applications. Research in this area demonstrates the feasibility of using a machine computer vision system to improve product quality. Utilizing deep learning in the field of image processing or digital image processing, Image Processing is used to assist humans in recognizing and/or classifying objects quickly, and precisely, and can process large amounts of data simultaneously. Classifying fruit through a computerized system using deep learning algorithms with CNN, MASK-RCNN, FASTER-RCNN, and SSD models. Developed on the multilayer perceptron (MLP) layer, the algorithm is processed into two-dimensional data, to the image and is capable of classifying images with larger classes.

Pelatihan Dan Pendampingan Pembelajaran Dalam Meningkatkan Kualitas Proposal Penelitian Pada Mahasiswa Kesehatan Indawati, Rachmah; Arief Wibowo; Dinana Izzatul Ulya; Tamara Nur Budiarti
MATAPPA: Jurnal Pengabdian Kepada Masyarakat Volume 7 Nomor 1 Tahun 2024
Publisher : STKIP Andi Matappa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31100/matappa.v7i1.3480

Background: Health is basic need of human life. Many health problems have not been solved. This requires collaboration and involves multi disciplines. One of sciences that forms public health is biostatistics. However, biostatistics is considered difficult by students. Besides that, knowledge about research is needed to solve health problems. The individual level, students write proposals late, proposals are not specific. In order to maximize students' abilities, they need to provide research concepts and data analysis. The aim of the service was provided training and learning mentoring to make research proposals. Methods: Activities are carried out for one semester. Target is seventh semester students. The activity provides training on research concepts and methodology. Results: Results show increased knowledge. This good knowledge is transferred into action (35 skripsi proposals). Conclusion: learning mentoring helps students master concepts and foster good attitudes and behavior towards the learning process

DEVELOPMENT OF A PREDICTIVE MODEL FOR EARLY CHILDHOOD LEARNING SUCCESS BASED ON ENSEMBLE LEARNING WITH INTEGRATION OF PSYCHOLOGICAL AND DEMOGRAPHIC DATA Zaqi Kurniawan; Rizka Tiaharyadini; Arief Wibowo
Jurnal Sistem Informasi Vol. 12 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i1.9956

Early chilhood learning serves as a crucial foundation for cognitive and emotional development, significantly influencing future academic success. The use of machine learning technologies presents chances to improve the effectiveness and scalability of educational practices in the digital age. By creating an ensemble learning-based model which includes both demographic and psychological data. This study overcomes the shortcomings of earlier research, which frequently ignores the psychological elements operating learning outcomes. The F1-Score, Accuracy, Precision, and Recall measures are used in this study to evaluate prediction using Random Forests and Gradient Boosting Machines. With an F1-Score of 89%, Accuracy of 92 %, Precision of 90%, and Recall of 88%, the Random Forest model exceeded Gradient Boosting, proving its ability to manage data complexity while finding a balance between precision and recall. The results show while demographic characteristics like age, gender, and parental occupation have little impact on early learning achievement, academic performance and attendance are the most important predictors. This emphasizes the necessity of focused tactics to improve academic achievement and classroom engagement. The study is limited by the representativeness of the dataset and the limited extent of psychological data, notwithstanding its contributions. To improve the interpretability and use of prediction models in early childhood education, future research should address these constraints by integrating qualitative methodologies, utilizing sophisticated machine learning techniques, and considering larger psychological factors

ANALISA FAKTOR YANG MEMPENGARUHI AUDIT TATA KELOLA TEKNOLOGI INFORMASI MENGGUNAKAN FRAMEWORK COBIT 2019 DAN VAL IT Riri Fajriah; Arief Wibowo
JURNAL SATYA INFORMATIKA Vol. 10 No. 1 (2025): JURNAL SATYA INFORMATIKA
Publisher : FAKULTAS TEKNIK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59134/jsk.v10i1.682

Era digitalisasi saat ini memberikan peluang kepada organisasi bisnis untuk dapat mengintegrasikan proses bisnis dengan dukungan perangkat teknologi yang sesuai dengan kebutuhan konsumen. Oleh karena itu, manajemen teknologi dan informasi tidak hanya sebatas sebagai dukungan operasional saja, namun menjadi salah satu upaya dalam mendukung keputusan strategis bisnis jangka panjang. Dampak dari hal ini banyak organisasi bisnis yang melakukan investasi pada perangkat teknologi dan informasi serta mengupayakan bagaimana manajemen dan tata kelola teknologi informasi di perusahaan bisa di evaluasi dengan baik agar dapat memaksimalkan keuntungan dan kontribusi bagi pencapaian tujuan bisnis. Pada penelitian ini akan dievaluasi dari beberapa penelitian sebelumnya terkait implementasi COBIT 2019 Framework dan VAL IT Framework 2.0 yang berfungsi dalam evaluasi bagaimana proses tata kelola dan investasi teknologi dan informasi bisa berjalan dengan tepat. Tujuan dari penelitian ini adalah menemukan research GAP analysis dari penelitian yang ada mengenai bagaimana penelitian lanjutan yang tepat terkait evaluasi tata kelola teknologi dan informasi di perusahaan. Hasil dari penelitian didapatkan bahwa agar dapat memberikan evaluasi secara komprehensif mengenai tata kelola teknologi informasi sebaiknya ditambahkan dengan proses identifikasi menggunakan IT Risk Management Framework agar bisa dinilai secara detail faktor resiko bisnis yang berkorelasi dengan manajemen dan investasi TI pada perusahaan.

ANALISA FAKTOR YANG MEMPENGARUHI AUDIT TATA KELOLA TEKNOLOGI INFORMASI MENGGUNAKAN FRAMEWORK COBIT 2019 DAN VAL IT Riri Fajriah; Arief Wibowo
JURNAL SATYA INFORMATIKA Vol. 10 No. 1 (2025): JURNAL SATYA INFORMATIKA
Publisher : FAKULTAS TEKNIK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59134/jsk.v10i1.682

Era digitalisasi saat ini memberikan peluang kepada organisasi bisnis untuk dapat mengintegrasikan proses bisnis dengan dukungan perangkat teknologi yang sesuai dengan kebutuhan konsumen. Oleh karena itu, manajemen teknologi dan informasi tidak hanya sebatas sebagai dukungan operasional saja, namun menjadi salah satu upaya dalam mendukung keputusan strategis bisnis jangka panjang. Dampak dari hal ini banyak organisasi bisnis yang melakukan investasi pada perangkat teknologi dan informasi serta mengupayakan bagaimana manajemen dan tata kelola teknologi informasi di perusahaan bisa di evaluasi dengan baik agar dapat memaksimalkan keuntungan dan kontribusi bagi pencapaian tujuan bisnis. Pada penelitian ini akan dievaluasi dari beberapa penelitian sebelumnya terkait implementasi COBIT 2019 Framework dan VAL IT Framework 2.0 yang berfungsi dalam evaluasi bagaimana proses tata kelola dan investasi teknologi dan informasi bisa berjalan dengan tepat. Tujuan dari penelitian ini adalah menemukan research GAP analysis dari penelitian yang ada mengenai bagaimana penelitian lanjutan yang tepat terkait evaluasi tata kelola teknologi dan informasi di perusahaan. Hasil dari penelitian didapatkan bahwa agar dapat memberikan evaluasi secara komprehensif mengenai tata kelola teknologi informasi sebaiknya ditambahkan dengan proses identifikasi menggunakan IT Risk Management Framework agar bisa dinilai secara detail faktor resiko bisnis yang berkorelasi dengan manajemen dan investasi TI pada perusahaan.

PELATIHAN ANALISIS DATA KATEGORI DALAM MENINGKATKAN PENGETAHUAN DAN KETERAMPILAN ANALISIS DATA BIDANG KESEHATAN Indawati, Rachmah; Arief Wibowo; Assaye Girma Mengistu; Antonius Yansen Suryadarma5; Surma Elisa Manihuruk
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 7 No 3 (2024): APTEKMAS Volume 7 Nomor 2 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36257/apts.v7i2.8442

In health sector, a lot of data is found categorical data. The advantage of this category data can do an 'assessment', 'association' and 'effect' of few variables. On the one hand, students do not explore using categorical data, yet understand comprehensively between one method and another statistical method and only focus on one particular method. The purpose is to conduct training to provide knowledge about the concept data and skills data analysis. The target audience is health students. In order for participants to have interest and the training process is fun, the method used is to provide practice from basic to advanced levels and are given repeatedly according to different cases. The results showed increase in participants knowledge about the concept 72.5% and skills data analysis 78.05%. Evaluation of process and instructor showed good. Giving repeated with different levels of ability can develop sensitivity to health issues and practice data analyze quickly and accurately. So, meaningful soft skill element that can be developed, namely being critical. The systematic material is create fun learning process, generate interest and want to learn. It can build cognitive abilities. Discussions help participants gain knowledge and develop soft skills, namely being able to communicate.

Pemanfaatan Generative Artificial Intelligence (GenAI) untuk Prediksi dan Analisis Bencana Alam Arief Wibowo; Asep Surahmat
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Disaster prediction and analysis are crucial components in mitigating the impacts of natural hazards such as floods, earthquakes, and landslides. Conventional systems often rely on deterministic models and limited historical data, which restrict their accuracy and adaptability to dynamic environmental changes. The emergence of Generative Artificial Intelligence (GenAI), particularly models based on deep learning and generative architectures such as Generative Adversarial Networks (GANs) and Diffusion Models, introduces new opportunities for synthetic data generation and predictive simulation. This study aims to explore the implementation of GenAI in disaster prediction and analysis by reviewing recent literature and practical applications in Indonesia. The proposed framework integrates multimodal data—including meteorological, seismic, and remote sensing data—into generative models to simulate disaster scenarios and improve early warning systems. The results indicate that GenAI can enhance data diversity, reduce bias in model training, and support real-time decision-making in disaster management. The study concludes that GenAI has strong potential to revolutionize disaster analytics and strengthen climate resilience through adaptive, data-driven insights. Thus, the output of this research is conceptual and focuses on designing a framework, while empirical testing forms the basis for further research development.

PENGELOMPOKAN TRANSAKSI KARTU DEBIT PERBANKAN MENGGUNAKAN ALGORITMA K-MEANS Iwan Irawan; Reza Rahman; Arief Wibowo
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3558

One of bank customers' most widely used non-cash payment methods is making payments to merchants using debit cards. The data generated from these transactions can be utilized effectively by banks. This study analyzes customer spending habits through debit card transactions, employing a data mining technique called K-means clustering. By identifying patterns in customer transactions, the research aims to assist business units in developing targeted product strategies. The analysis determined that four clusters were optimal, resulting in a tightly grouped dataset with an average distance of 5.764 from the respective cluster centers. Grouping nominal transactions based on the date and time of the transaction can provide valuable insights for bank management when considering customer fund allocation.

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