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All Journal JOIV : International Journal on Informatics Visualization Jurnal ULTIMA InfoSys Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Journal of Information Systems and Informatics PROFICIO: Jurnal Pengabdian Masyarakat Jurnal Informatika Ekonomi Bisnis
Johan, Monika Evelin
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Humanities Automotive Engineering Computer Science & IT Economics, Econometrics & Finance Education Mechanical Engineering Public Health Social Sciences Other

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PENGEMBANGAN E-LEARNING SYSTEM SD STRADA KARAWACI Desanti, Ririn Ikana; Suryasari; Wella; Johan, Monika Evelin; Faza, Ahmad
PROFICIO Vol. 5 No. 1 (2024): PROFICIO: Jurnal Abdimas FKIP UTP
Publisher : FKIP UNIVERSITAS TUNAS PEMBANGUNAN SURAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36728/jpf.v5i1.3037

Abstract

Learning Management System (LMS) has become one of the media used to support online teaching learning activities. LMS has a wide range of functions such as administration, documentation, and reporting. The condition of the Covid-19 pandemic in 2020 has caused SD Strada Karawaci to find a way to keep providing school supplies to the students. The Information Systems Study Program – Universitas Multimedia Nusantara (IS-UMN) has a strong commitment to make a positive contribution to the community, one of which is through community service activities (PKM). IS- UMN organizes PKM activities with one of its goals to improve literacy of information technology of the community. In general, the activities of PKM IS-UMN are divided into two stages: system development and training. Therefore, in the first phase of the activities of PKM IS-UMN will design an e-learning system for elementary school of Strada Karawaci. The PKM team conducted in-depth research to understand the user needs (requirements) and conducted interviews with teachers and students. In addition, the development phase of the e-learning system is also carried out with consideration of the curriculum and school learning methods.
Empowering Pregnancy Risk Assessment: A Web-Based Classification Framework with K-Means Clustering Enhanced Models Wongso, Bernard Pratama; Johan, Monika Evelin; Fianty, Melissa Indah
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.568

Abstract

This study aims to determine whether there is an increase in accuracy results for predicting pregnancy risk with a classification algorithm that goes through and without going through the clustering stage. After that, compare which classification algorithm gets the best improvement. This study uses the K-Means clustering approach, as well as the SVM, Naive Bayes, and K-Nearest Neighbor (KNN) classification algorithms. The pregnancy risk dataset used comes from the UCI Machine Learning Repository. Evaluation metrics used include accuracy, precision, recall, and F1-score. The results of the study revealed that the K-Means model with KNN provided the highest performance compared to the other two, with an accuracy of 79.53% and an average F1-score of 0.8. The implementation of K-Means resulted in an increase in accuracy of 0.4%, 1.57%, and 2.76% on KNN, SVM, and Naive Bayes respectively, which confirms the impact of clustering in improving classification performance. The resulting model can be used in real-time via a website built using the Flask API, and offers tools that can help health practitioners to plan treatments effectively and minimize the risk of pregnancy.
Application of Clustering-Based Data Mining for the Assessment of Nutritional Status in Toddlers at Community Health Centers Fianty, Melissa Indah; Johan, Monika Evelin; Aulia, Azka; Veronica, Mella Margareta
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.586

Abstract

Nutritional status is a crucial foundation for human health and development. Global facts indicate serious challenges in ensuring adequate nutrition, and the situation is no different in Indonesia. This research collected data from the Kelapa Dua Tangerang community health center and utilized data mining techniques with the k-means clustering algorithm to delve deeper into the nutritional status of toddlers. The research findings revealed that nearly 37.3% of toddlers experience issues with abnormal height or weight, as well as poor nutritional conditions, highlighting the importance of careful and timely intervention. With regular health monitoring by community health centers and active parental involvement, actions can be taken to support the optimal growth and development of these children. The results of this research provide a strong understanding to address malnutrition issues, which will ultimately support the formation of a healthier and more promising future generation in Indonesia.
Development of Web-based Application for Private School Tuition Fee Management with Prototyping Model Wiratama, Jansen; Johan, Monika Evelin; Sobiyanto, Sobiyanto; Wijaya, Matthew Chandra; Sugara, Victor Ilyas
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.588

Abstract

Private schools need help in handling school fees and financial processes. Traditional manual payment systems result in data processing issues, delayed financial reporting, and complications from misplaced records. Late fee payments threaten school income, which is crucial for staff salaries. Modern solutions are imperative. Desktop applications have limitations, requiring installation on specific devices, leading to compatibility concerns. This research opts for a web-based application. It employs prototyping models and predictive abilities using the Naïve Bayes algorithm. The web-based application aims to streamline fee management and predict payment delays, enhancing financial transaction management while prioritizing data security through database encryption. This web-based solution aligns with private schools' operational needs, simplifying payments and increasing late payment prediction accuracy. Extensive black-box testing validated its suitability, satisfying administrative staff needs. Four test cases gained administrative team approval. This innovation empowers private schools to optimize operations and financial management. In summary, the research tackles critical financial challenges private schools face by introducing a web-based application that simplifies payment processes, enhances accuracy in predicting late payments, and aligns seamlessly with administrative needs.
Web-Based Assignment Information System Serves to Improve Economic Research at Universities and Public Services Susanto, Meitio; Johan, Monika Evelin; Sulaiman, Agus; Fianty, Melissa Indah
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 1 (March 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i1.824

Abstract

Community Outreach is one of the Tridharma of Higher Education which is the obligation of lecturers in Indonesia. The Institute for Research and Community Outreach (LPPM) at Universitas Multimedia Nusantara (UMN) is an institution that supports and facilitates Community Outreach activities, starting from submitting proposals, monitoring implementation, assignment letters, and completion reports. There are several procedures such as filling in data on a form which can be accessed using the Linktree link. This makes it difficult for LPPM admins to manually recap data by checking the forms that have been submitted. Even though there is a Research and Community Outreach (RCOS) website, there is no feature for Community Outreach services yet. This research develops the RCOS LPPM website at UMN by adding features for Community Outreach services. By using the Agile development method which focuses on developers, existing software, customers, and change requirements, the web-based information system created is expected to further complement the digitalization and automation of LPPM UMN service features, making it easier for both lecturers and admins in the process.
Focus Group Discussion Validasi Aplikasi Pelayanan Kesehatan Publik Berbasis Teknologi Blockchain bagi Klinik di Kota Depok Fernando, Erick; Winanti, Winanti; Prabowo, Yulius Denny; Tjahjana, David; Johan, Monika Evelin
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 8, No 2 (2025): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v8i2.7986

Abstract

Kegiatan Focus Group Discussion (FGD) dilakukan di kota Depok dengan melibatkan 23 orang peserta untuk memvalidasi aplikasi pelayanan kesehatan public berbasis teknologi blockchain. FGD dilakukan dengan tujuan untuk memvalidasi aplikasi pelayanan kesehatan public berbasis teknologi blockchain yang dikembangkan oleh lima orang dosen dari tiga perguruan tinggi di Tangerang dan Jakarta. Metode yang digunakan untuk FGD adalah diskusi dan tanya jawab secara langsung dengan domain expert (Bidan, Dokter, tenaga medis dan pengelola klinik). Terdapat dua belas point penting dalam kegiatan FGD yang dijadikan sebagai bahan masukan dan perbaikan dalam pengembangan aplikasi dan ke dua belas point tersebut menjadi hasil FGD. Kedua belas point tersebut telah dicatat dan dirangkum serta dikonfirmasi kepada peserta. Hasil FGD menjadi bahan pertimbangan dan perbaikan aplikasi layanan kesehatan public berbasis teknologi blockchain yang siap untuk dipresentasikan ke pemangku kepentingan dan sebagai bahan pelaporan akhir program hibah research fundamental yang didanai oleh Dikti pada tahun 2024. Harapannya aplikasi pelayanan kesehatan public berbasis teknologi blockchain ini dapat diimplementasikan oleh stakeholder dan menjadi satu data kesehatan dengan tingkat keamanan tinggi
Implementation of Convolutional Neural Network Algorithm for Apple Leaf Disease Classification Widjaya, Ageng Cahyo; Wijaya, Kimi Axel; Soegono, Joaquin Noah; Varrel, Primus Kartika; Johan, Monika Evelin
ULTIMA InfoSys Vol 16 No 1 (2025): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v16i1.3842

Abstract

Apple leaf diseases can cause significant economic losses to apple farmers. Early detection and treatment of apple leaf diseases are essential to minimize crop losses. However, traditional methods for detecting apple leaf diseases, such as manual visual inspection by experts, can be time-consuming and laborious. Therefore, this study aims to develop a robust and efficient method for detecting diseases in apple tree leaves using Convolutional Neural Networks (CNNs). By using deep learning, the disease detection process becomes automated, saving time and resources. The CRISP-DM methodology was used in conducting this study. The results of the CNN model's performance in predicting disease types have a high level of accuracy and can be used as a model for detecting disease types in apple plant leaves.
Web-Based Deep Learning Approach to Identifying AI-Generated Anime Illustration Johan, Monika Evelin; Wong, Richard Faustine; Godata, Gempar Bambang; Wijaya, Westley; Haezer, Eben
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.2899

Abstract

As technology advances rapidly in artificial intelligence, the dominance of generative artificial intelligence (AI) images becomes increasingly evident in art, design, and the creative industry. However, the generative AI has processed numerous images from the Internet, including copyrighted content, trademarks, and artists' illustrations, which pose legal risks. Consequently, the manual tasks involved in managing and classifying these images have become more complex and time-consuming. Therefore, this research proposes the application of deep learning techniques, specifically Convolutional Neural Network (CNN), to automate the process of classifying AI-generated illustrations. The research was conducted by the Cross-Industry Standard Process for Data Mining (CRISP-DM) method. Initially, the study began with a literature review to describe the state-of-the-art in image detection. Then, a dataset of illustrations was collected from the Pixiv website using web scraping techniques. After data cleaning, separation, and augmentation, three pre-trained models were created and compared on 1200 training data and evaluated against 400 testing and 400 validation data. From the evaluation, the model using MobileNet V3 Large architecture achieved an impressive 94% accuracy, outperforming MobileNet V2 and Inception V3 architectures, respectively by 3% and 5%. Thus, the implementation of CNN holds the promise of providing an efficient solution for identifying and classifying various types of AI anime illustrations, benefiting consumers and artists practically. Future research could consider incorporating additional data categories and variations to further enhance the model's ability to distinguish between AI-generated and human-made illustrations.
Implementation of Customer Segmentation Model using K-Means and DBSCAN for Fashion Industry Product Transaction William, William; Johan, Monika Evelin
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.2978

Abstract

The use of online marketplaces is rapidly expanding in Indonesia, particularly within the fashion industry. To develop effective marketing strategies, it is essential to understand consumer behaviour through customer segmentation. With a deeper understanding of consumer behaviour, XYZ company, which is engaged in the fashion industry, can improve the effectiveness of marketing strategies and respond to consumer needs more accurately to achieve a significant increase in sales. This study aims to implement a customer segmentation model using clustering methods with machine learning algorithms, specifically K-Means and DBSCAN, following the CRISP-DM Data Mining Framework for data processing. The research utilizes purchasing transaction data from XYZ fashion industry, applying pre-processing techniques such as Standard Scaler and PCA before clustering. The K-Means and DBSCAN algorithms are implemented and evaluated using Silhouette Score and Davies-Bouldin Index matrices. Results show that the K-Means algorithm outperformed DBSCAN, achieving an optimal cluster number of k=7 with a Silhouette Score of 0.549 and a Davies-Bouldin Index of 0.593, compared to DBSCAN's Silhouette Score of 0.29 and Davies-Bouldin Index of 0.92. The final implementation involves creating a dashboard that automatically processes data and generates clusters to support customer segmentation decisions. The model was deployed through a simple website using FastAPI for backend Python execution and React with TypeScript for the front end. Future studies could address limitations by incorporating recent datasets to improve model accuracy, exploring alternative algorithms like Gaussian Mixture Models (GMM) for additional insights, and focusing on robust deployment strategies for real-world applications within the fashion industry.
Co-Authors Aulia, Azka Erick Fernando B311087192 Faza, Ahmad Fianty, Melissa Indah Godata, Gempar Bambang Haezer, Eben Ririn Ikana Desanti Sobiyanto, Sobiyanto Soegono, Joaquin Noah Sugara, Victor Ilyas Sulaiman, Agus Suryasari Susanto, Meitio Tjahjana, David Varrel, Primus Kartika Veronica, Mella Margareta Wella Widjaya, Ageng Cahyo Wijaya, Kimi Axel Wijaya, Matthew Chandra Wijaya, Westley William William WINANTI, WINANTI Wiratama, Jansen Wong, Richard Faustine Wongso, Bernard Pratama Yulius Denny Prabowo, Yulius Denny