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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal PkM MIFTEK
Zulkarnaen, Ade Iskandar
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Humanities Computer Science & IT Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Meningkatkan Strategi Pemasaran di Desa Sindangprabu Purnamasari, Ismi; Nurfadillah, Rifa Sri; Hamidah, Sabrina; Oktavia, Yayu; Fajar, Hanafi Arya; Muttaqin, Dadan Ahmad Badar; Maulidiyana, Maya; Ulumudin, Jamil; Nurani, Pelita Maulida; Taufik, Risman; Akihari, Bobby Dwitama Eden; Fazri, Muhammad Insan Kamil; Agriyanto, Ari; Zulkarnaen, Ade Iskandar; Latif, Abdul; Najib, Rafi; Fauzi, Khasbi Aqsal; Nurhayati, Mila; Agistina, Aini; Prasetya, Yoga Budi; Alamsyah, Restu
Jurnal PkM MIFTEK Vol 4 No 1 (2023): Jurnal PkM MIFTEK
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.4-1.1318

Abstract

The development of the business in Sindang Prabu Village really encourages us to deepen more on how to improve the marketing strategy of businesses owned by local residents in the neighborhood. The Sindang Prabu village area has many resources that can be used to improve small businesses, especially in marketing. The purpose of this research itself is to explain how a good marketing strategy can increase the level of sales of products owned by entrepreneurs. The method used in this research is qualitative method. The sample used in this study is a micro business owned by local residents. The functions and materials used in this research study are intended to find out how to do or a good marketing strategy in order to increase sales of micro-entrepreneurs' products. The results of the analysis from conducting a survey directly show that the lack of promotion carried out by business owners makes the marketing value unsatisfactory of the product and also the constraints on the lack of auxiliary tools such as machines in the production process become a significant influence in the marketing process carried out by business owners , it can be seen that promotions and also tools in the production process can be the right solution in increasing the marketing of products owned by these micro entrepreneurs.
Perancangan Model Convolutional Neural Network Pada Aplikasi Pengenalan Aksara Sunda Berbasis Mobile Kurniadi, Dede; Zulkarnaen, Ade Iskandar; Mulyani, Asri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 5: Oktober 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Mendikbudristek pada tahun 2022 menjadikan 38 bahasa daerah menjadi objek revitalisasi, salah satunya yaitu bahasa Sunda. Hal tersebut dikarenakan sebagian besar  bahasa daerah di Indonesia kondisinya terancam punah dan kritis. Hilangnya bahasa daerah juga mengancam keberadaan aksara lokal yang menjadi bagian integral dari warisan budaya. Dalam upaya memperkenalkan serta mendukung revitalisasi di bidang kebahasaan, pengembangan aplikasi media pembelajaran berbasis mobile dapat menjadi solusi yang efektif. Hal ini didasarkan bahwa perangkat smartphone memiliki pengguna yang luas di kalangan masyarakat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan model Convolutional Neural Network (CNN) untuk pengenalan aksara Sunda pada perangkat berbasis mobile. Model CNN diterapkan pada fitur belajar menulis untuk mengenali tulisan tangan aksara Sunda dan memberikan feedback kepada pengguna. Penelitian ini menggunakan metode Machine Learning Lifecycle (MLLC) dimana tahapan yang dilakukan meliputi problem definition, data, model, dan production system. Penelitian dimulai dengan pembuatan dataset tulisan tangan digital, yang kemudian digunakan untuk melatih model klasifikasi menggunakan arsitektur CNN VGG-16. Dataset yang berhasil dibuat sebanyak 7500 gambar yang terdiri dari aksara Sunda swara, ngalagena, dan ngalagena serapan. Model yang dihasilkan dari proses pelatihan dengan total 10 epoch memperoleh akurasi sebesar 99%, sementara pada data testing memperoleh akurasi rata-rata sebesar 83%. Pada tahap akhir pengujian, model diimplementasikan pada prototype aplikasi pengenalan aksara Sunda berbasis mobile dan dapat dengan baik mengklasifikasi aksara Sunda. Hasil dari penelitian ini yaitu berupa model pengenalan aksara Sunda yang dapat diterapkan pada aplikasi berbasis mobile. Melalui pembuatan model dan prototype aplikasi pengenalan aksara Sunda, penelitian ini ikut berkontribusi pada digitalisasi aksara Sunda serta menyediakan landasan untuk pengembangan dan penelitian lanjutan dalam penerapan CNN pada aplikasi berbasis mobile.   Abstract In 2022, the Minister of Education, Culture, Research and Technology made 38 regional languages ​​the object of revitalization, one of which is Sundanese. This is because most regional languages ​​in Indonesia are endangered and critical. The loss of regional languages ​​also threatens the existence of local scripts, which are an integral part of cultural heritage. To introduce and support revitalization in the language field, the development of mobile-based learning media applications can be an effective solution. This is based on the fact that smartphone devices have a wide user base in society. This study aims to design and implement a Convolutional Neural Network (CNN) model for Sundanese script recognition on mobile-based devices. The CNN model is applied to the learning-to-write feature to recognize Sundanese handwriting and provide user feedback. This study uses the Machine Learning Lifecycle (MLLC) method, where the stages include problem definition, data, models, and production systems. The study began with creating a digital handwriting dataset, which was then used to train a classification model using the CNN VGG-16 architecture. The successfully created dataset was 7500 images consisting of Sundanese swara, ngalagena, and ngalagena absorption scripts. The model produced from the training process with 10 epochs obtained an accuracy of 99%, while the testing data obtained an average accuracy of 83%. In the final stage of testing, the model was implemented on a mobile-based Sundanese script recognition application prototype and could classify Sundanese script well. The results of this study are in the form of a Sundanese script recognition model that is applied to a mobile-based application prototype. By creating a model and prototype of a Sundanese script recognition application, this research contributes to the digitalization of Sundanese script and provides a foundation for further development and research in the application of CNN to mobile base applications.
Co-Authors Abdul Latif Agistina, Aini Agriyanto, Ari Akihari, Bobby Dwitama Eden Alamsyah, Restu Asri Mulyani Dede Kurniadi Fajar, Hanafi Arya Fauzi, Khasbi Aqsal Fazri, Muhammad Insan Kamil Hamidah, Sabrina Maulidiyana, Maya Muttaqin, Dadan Ahmad Badar Najib, Rafi Nurani, Pelita Maulida Nurfadillah, Rifa Sri Nurhayati, Mila Oktavia, Yayu Prasetya, Yoga Budi Purnamasari, Ismi Taufik, Risman Ulumudin, Jamil