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All Journal Information System for Educators and Professionals : Journal of Information System KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Teknologi Informasi dan Multimedia Jurnal Accounting Information System (AIMS) INFORMATIKA Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) AMMA : Jurnal Pengabdian Masyarakat Akuntansi: Jurnal Riset Ilmu Akuntansi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS)
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STMIK IKMI Cirebon
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Pemberdayaan Wirausaha Melalui Rancangan Ekosistem Bisnis Berbasis Platform Digital Martanto; Mulyawan; Arif Budi Setiawan; Betran Renaldi
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 3 : April (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Micro, small, and medium enterprises (MSMEs) play a vital role in the economy but often face challenges in developing their businesses in the digital era. This Community Partnership Program aims to empower entrepreneurs through the design of a business ecosystem based on digital platforms. The activities include situational analysis of partner MSMEs, designing a suitable business ecosystem model, training on the utilization of digital platforms for various business aspects (marketing, operations, and management), and assistance in implementing the designed ecosystem. It is expected that, through this program, partner MSMEs can improve efficiency, expand market reach, enhance customer interaction, and ultimately achieve sustainable business growth through the utilization of an integrated digital ecosystem.
Pelatihan Pola Dan Segmentasi Citra Bagi Dosen Kopertip Indonesia Untuk Mendukung Penelitian Multidisiplin Mulyawan; Nana Suarna; Gildan Jaya Muhammad Ramadhan; Muhammad Alfian Nur Rahmat
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 3 : April (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Pattern recognition and image segmentation are visual data analysis techniques with broad applications in various research fields. This Community Service Program aims to provide training on pattern recognition and image segmentation for lecturers of Kopertip Indonesia. This training seeks to enhance lecturers' understanding and ability to apply these techniques as tools to support multidisciplinary research. The training material includes the fundamentals of image processing, various pattern recognition methods, image segmentation algorithms, and case studies of applications in cross-disciplinary research contexts. It is hoped that this activity can encourage the improvement of quality and interdisciplinary research collaboration within Kopertip Indonesia.
Pendampingan Pembukuan Sederhana Untuk Pedagang Pasar Tradisional Mulyawan; Khaerul Anam; Abdul Muhyi; Achmad Fajar
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Traditional market vendors play a vital role in supporting local economies and contribute significantly to community economic activities. However, most of them have not implemented proper bookkeeping systems, resulting in difficulties in managing cash flow, calculating profits, and accessing formal financial services. This program aims to provide simple bookkeeping assistance to traditional market traders to help them gain basic financial record-keeping skills. The program involved initial observation, development of a simple bookkeeping module, in-person training sessions, and hands-on assistance in daily financial recording. The bookkeeping system was tailored to the needs of small-scale businesses, covering records of income, expenses, and profit-loss, using notebooks and easy-to-understand paper forms. The results indicated an increased awareness among traders about the importance of financial documentation and their ability to independently create basic financial reports. Additionally, some vendors expressed interest in saving and accessing banking services as part of improved financial management. This initiative had a positive impact on enhancing the financial literacy of traditional traders and encouraged the development of a more organized business administration culture. In the future, such mentoring programs are expected to continue periodically and evolve toward the digitalization of simple bookkeeping systems via mobile applications.
Pengelolaan Sampah Berbasis Teknologi Informasi untuk Masyarakat Perkotaan Mulyawan; Khaerul Anam; Daffa Ezra Pratama; Dini Andriyani
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Urban waste management faces significant challenges due to the increasing volume of waste, inadequate infrastructure, low public awareness, and limited use of technology. This community service program aims to develop an information technology-based solution to create a more efficient, environmentally friendly, and community-engaged waste management system. Through the development of a mobile application, residents can report waste conditions in real-time, monitor waste sorting, and access waste collection schedules. The program also includes community training, provision of waste sorting facilities, and educational campaigns to raise environmental awareness. The implementation has shown significant improvements in public participation in waste sorting, reduction in the volume of waste sent to landfills, and overall improvement in environmental quality. Furthermore, this initiative contributes to human resource empowerment through training in technology use and waste management. The success of this program demonstrates that integrating information technology with public education can be an effective solution to urban waste management challenges.
ADAPTIVE CLASS WEIGHTING DAN AUGMENTATION UNTUK KLASIFIKASI BATIK KERATON Witriyani Witriyani; Dian Ade Kurnia; Yudhistira Arie Wijaya; Mulyawan Mulyawan; Irfan Ali
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 6 No. 1 (2026): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v6i1.1516

Abstract

This study aims to improve the performance of Batik Keraton motif classification on an imbalanced dataset through the integration of adaptive class weighting and data augmentation within a transfer learning framework. The dataset consists of 1,799 images across four classes (Kawung, Mega Mendung, Parang, Truntum), preprocessed to 224×224 pixels and split stratifiedly into training, validation, and test sets (80/10/10). Three transfer learning architectures—ResNet50V2, VGG16, and EfficientNetB0—were evaluated with adaptive class weighting and geometric augmentation to enhance minority-class representation. The results indicate that ResNet50V2 with pretrained weights achieved the best performance, reaching a test accuracy of 92.78%, macro precision of 93.13%, macro recall of 92.79%, and a macro F1-score of 92.83%. Adaptive class weighting improved sensitivity toward minority classes, while augmentation contributed to model stability and generalization. These findings demonstrate that combining adaptive weighting and augmentation effectively enhances Batik Keraton motif classification under imbalanced data conditions.  
Analisis Dan Prediksi Risiko Kelahiran Bayi Menggunakan K-Means Dan Deep Neural Network (DNN) Mukhlisin Ilahudin; Nana Suarna; Agus Bahtiar; Mulyawan; Irfan Ali
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.206

Abstract

Risiko kelahiran bayi merupakan indikator penting dalam evaluasi kesehatan ibu dan anak sehingga diperlukan pendekatan analitis yang mampu mengidentifikasi pola risiko secara akurat. Penelitian ini bertujuan menganalisis dan memprediksi risiko kelahiran bayi dengan mengintegrasikan metode K-Means dan Deep Neural Network (DNN). Dataset yang digunakan terdiri dari 983 data rekam medis ibu hamil yang telah melalui tahap pengumpulan data, pembersihan, dan preprocessing meliputi normalisasi, encoding variabel kategorikal, penanganan outlier, serta seleksi fitur. Metode K-Means digunakan untuk mengelompokkan data berdasarkan kemiripan karakteristik klinis guna membentuk representasi pola risiko awal, yang selanjutnya digunakan sebagai fitur tambahan pada model DNN. Model DNN dirancang menggunakan beberapa hidden layer dengan fungsi aktivasi ReLU dan regularisasi dropout. Hasil pengujian menunjukkan bahwa model menghasilkan akurasi sebesar 61,93% dan nilai ROC AUC sebesar 0,6402, yang mengindikasikan performa moderat dalam memprediksi risiko kelahiran bayi. Stabilitas kurva loss dan akurasi menunjukkan proses pelatihan yang berjalan dengan baik tanpa overfitting signifikan. Secara praktis, model ini berpotensi digunakan sebagai alat bantu awal bagi tenaga kesehatan dalam mengidentifikasi ibu hamil dengan risiko kelahiran lebih tinggi sehingga dapat dilakukan pemantauan dan intervensi lebih dini.
Improving the School Type Clustering Model on the Foundation Using the K-Means Algorithm (Case Study: Kebon Kelapa Al-Ma'rifah, Cirebon Regency) Hanifah Nur Aulia; Martanto; Arif Rinaldi Dikananda; Mulyawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.739

Abstract

This study aims to improve the school type grouping model at the Kebon Kelapa Al-Ma'rifah Foundation, Cirebon Regency, using the K-Means algorithm. Data-based grouping is very important in supporting efficient education management, especially in environments that have various types of schools such as Madrasah Aliyah (MA), Vocational High School (SMK), Madrasah Tsanawiyah (MTs), and Madrasah Ibtidaiyah (MI). The data used comes from the New Student Registration (PPDB) dataset for the 2023–2024 school year, with demographic attributes such as name, place of birth, gender, and time of school entry. The evaluation of clustering quality was carried out using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. The results show that the optimal number of clusters is K=5 with the lowest DBI value of 0.201, which results in compact and well-separated clusters. The implementation of the K-Means algorithm helps the foundation understand the distribution pattern of students based on attributes such as gender, region, and entry time. This research provides practical benefits, including more targeted resource allocation, improved quality of education, and efficiency in school management. In addition, this research contributes to the development of data mining models in the education sector and opens up opportunities for the exploration of additional attributes such as academic achievement and socioeconomic conditions. Further research is suggested to use alternative algorithms such as K-Medoids or DBSCAN.
The Improvement of Indonesian Film Genre Clustering Model Using the K-Means Algorithm in Film Production Decision-Making Wiratriyana; Martanto; Arif Rinaldi Dikananda; Mulyawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.765

Abstract

The Indonesian film industry is expanding rapidly, but understanding audience preferences remains a significant challenge for producers. This study aims to cluster Indonesian films by genre and synopsis using the K-Means algorithm to aid in marketing strategies and content development. The dataset comprises 1,271 Indonesian film entries, including attributes like release year, genre, synopsis, and user ratings. The research follows the Knowledge Discovery in Databases (KDD) framework, which involves data selection, preprocessing, transformation, clustering with K-Means, and evaluation using the Elbow method to identify the optimal number of clusters. The results show that the K-Means algorithm successfully grouped the films into three clusters: drama, horror, and others. The analysis indicates that drama films dominate the high-rating cluster, while horror films are more commonly found in the low-rating category. The use of Principal Component Analysis (PCA) in the visualization aids in interpreting the clustering results, providing a clearer view of the data distribution. These findings highlight the potential for improving film production strategies by aligning content with audience preferences. By understanding genre patterns and ratings, producers can make more informed decisions in marketing and content development.
Development of Educational Game for Introduction Animal Types Using the ADDIE Method Smart Apps Creator In Improving Knowledge Students Azzahra Rizky Artoti; Martanto; Arif Rinaldi Dikananda; Mulyawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.777

Abstract

The development of technology in education opens up opportunities for innovation to create interactive learning media, especially for early childhood. This research aims to develop educational games based on Smart Apps Creator using the ADDIE method to introduce animal species to Al-Washliyah kindergarten students. The method used is ADDIE, consisting of five stages, namely: Analysis, Design, Development, Implementation, and Evaluation. in this study conducted validity, reliability, normality, homogeneity, and anova tests to measure the effectiveness of this learning media. The results showed that this animal species recognition educational game succeeded in improving student understanding with an average score before the use of learning media of 59.2% increasing to 87.73% after using learning media. Validity and reliability tests show that this learning media meets the criteria of effective, easy-to-use, and interesting learning media.
Analisis Kelompok Lansia Berdasarkan Kategori Usia Dengan Metode K-Means Clustering Lela Lailatul Kaamilah; Mulyawan Mulyawan
Akuntansi Vol. 2 No. 2 (2023): Juni: Jurnal Riset Ilmu Akuntansi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/akuntansi.v2i2.234

Abstract

The application of k-means can be used to group the number of elderly people based on age category. By using this algorithm, groups of elderly residents who have the same age characteristics can be determined. Elderly (elderly) is the share of the population aged 60 years and over, the health department categorizes the elderly based on their age level, namely: early elderly, late elderly, and elderly). The increasing number of elderly people worldwide has created problems in managing the welfare of the elderly population. Inaccurate information regarding the distribution of the number of elderly people in each age category makes it difficult to make the right decisions to improve the welfare of the elderly population. In this case, the application of K-Means clustering can be used as a tool in grouping the number of elderly people based on age category. With this algorithm, data can be grouped quickly and efficiently, so that it can assist in making appropriate decisions to improve the welfare of the elderly population. However, the K-Means Clustering algorithm is only used as a tool, and must be strengthened with proper analysis and recommendations. In the village of Cimari, Cikoneng District, Ciamis Regency, in terms of grouping the elderly based on age categories, there are limitations in managing data on the elderly population, namely by manual method which requires quite a long time. The design method used is data collection: the number of elderly people and their age category. This data was obtained from data from Cimari villagers Based on the grouping results using K-Means Clustering on the grouping of the elderly population by age category, the Davies Bouldin results were 0.263, cluster 0 contained 119 items, cluster 1 contained 101 items, and cluster 2 contained 80 items.
Co-Authors Abdul Muhyi Achmad Fajar Ade Rizki Rinaldi Arif Budi Setiawan Arif Rinaldi Dikananda Azzahra Rizky Artoti Betran Renaldi Cep Lukman Rohmat Daffa Ezra Pratama Dian Ade Kurnia Dini Andriyani Dwi Teguh Afandi Edi Wahyudin Hanifah Nur Aulia Hilman Rifa'i Iin Irfan Ali Kaslani Khaerul Anam Lela Lailatul Kaamilah Martanto . Muhammad Alfian Nur Rahmat Mukhlisin Ilahudin Nana Suarna Nanda Satria Halim Pratama Narasati, Riri Narasati Nining Rahaningsih Nisa Dieanwati Nuris Nisa Dienwati Nuris Raditya Danar Dana Ramadhan, Gildan Jaya Muhammad Ryan Hamonangan Sandy Eka Permana Shalsa Fadillah Rahmadenta Syarif Maulana Yaasin Tati Suprapti Umi Hayati Utami Aryanti Wiratriyana Witriyani Witriyani Yudhistira Arie Wijaya