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All Journal Jurnal Simantec Jurnal Ilmiah Kursor ELINVO (Electronics, Informatics, and Vocational Education) Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Network Engineering Research Operation [NERO] Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Journal of Information Systems and Informatics JoMMiT : Jurnal Multi Media dan IT JUSIFOR : Jurnal Sistem Informasi dan Informatika International Journal of Integrative Sciences Signal and Image Processing Letters
Fifin Ayu Mufarroha
Fakultas Teknik, Universitas Trunojoyo Madura
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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Traditional Herbal Medicine Production Information System Based on Prototyping Method Yunitarini, Rika; Fitrianto, Hambali; Mufarroha, Fifin Ayu; Koeshardianto, Meidya
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.112

Abstract

Indonesia is the country with the second largest biodiversity in the world after Brazil. Indonesia's biodiversity is very rich, both on land and at sea, and is one of the most important in the world. The benefits of Indonesia's biodiversity is as a natural resource that plays an important role one of them in the production of traditional herbal medicine. Madura Island in East Java, Indonesia, is famous for its natural resources and respected Madurese herbal medicine, internationally recognized for its efficacy in addressing health and beauty issues. The increasing demand for traditional herbal medicine products motivates the industry to improve production efficiency, prioritizing effective management and optimal utilization of raw material stocks. This research aims to manage the production needs of traditional herbal medicine by identifying information needs and developing a Production Information System using the Laravel framework to meet industry needs. This research will evaluate the impact of the system on the production process and the management of raw material needs in the traditional herbal medicine sector. The expected results include a positive contribution to the industry, better production performance, and improved handling of raw material stocks. The integration of the Laravel framework is expected to improve production performance and provide features for the traditional herbal medicine industry. In conclusion, this research seeks to offer a customized and effective solution for the traditional herbal medicine industry, addressing the increasing market demand through the optimization of production processes and management practices.
KLASIFIKASI JENIS REMPAH PENGHASIL MINYAK ATSIRI MENGGUNAKAN METODE MACHINE LEARNING Fifin Ayu Mufarroha; Doni Abdul Fatah
Jurnal Simantec Vol 11, No 1 (2022): Jurnal Simantec Desember 2022
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v11i1.19743

Abstract

Rempah-rempah merupakan bahan alami gacorqq yang digunakan dalam berbagai industri, seperti kuliner, obat-obatan, kosmetik, dan industri parfum. Minyak atsiri yang dihasilkan dari rempah-rempah memiliki nilai ekonomi yang tinggi dan digunakan dalam berbagai aplikasi industri. Penelitian ini bertujuan untuk mengenali pola dalam data rempah-rempah dan mengklasifikasikan jenis rempah-rempah ke dalam jenis yang tepat berdasarkan gacorqq karakteristik dari setiap rempah. Metode K-NN dipilih karena kesederhanaannya dan kemampuannya dalam menghasilkan akurasi yang baik. Penelitian ini gacorqq melibatkan beberapa tahap, antara lain pengumpulan data rempah-rempah, ekstraksi fitur dari data, dan pengenalan jenis rempah menggunakan metode K-NN. Penelitian ini menggunakan 125 dataset, yang terdiri dari 25 data untuk setiap jenis rempah, yaitu Lengkuas, Temulawak, Kencur, Jahe, dan Kunyit. Hasil klasifikasi metode K-NN dalam 3 skenario, yaitu dengan nilai K = 1, 3, dan 6. Hasil klasifikasi terbaik diperoleh pada nilai K = 3, dimana hasil akurasi sebesar 96%. Hasil akurasi ini dapat digunakan untuk mengevaluasi kinerja model klasifikasi yang dikembangkan.Dengan adanya metode ini, diharapkan dapat membantu dalam pengenalan rempah-rempah yang berkualitas, pengendalian kualitas produk yang mengandung rempah-rempah, serta pengembangan produk baru dalam berbagai industri.Kata kunci: Rempah, Klasifikasi, Machine Learning, K-NN.
Co-Authors Achmad Jauhari Achmad Jauhari, Achmad Ahmad Try Bayu Al Haq Akhmad Tajuddin Tholaby Andharini Dwi Cahyani Ari Kusumaningsih Bachtiar, Dimas Bain Khusnul Khotimah Bain Khusnul Khotimah Dafid, Ach Devie Rosa Anamisa Doni Abdul Fatah Firli Irhamni Fitrianto, Hambali Ifandia, Alfian Mahendra Ika Oktavia Suzanti Indah Agustien Sirajuddin Indah Pratiwi Khusnul Fatimah Koeshardianto, Meidya Linawati Linawati Moh. Yusril Ihza Maulana Mohammad Yanuar Hariyawan Muhammad Amfahtori Wijarnoko Muhammad Yusuf Mulaab Mulaab Nuraini, Dinah Okie Maria Amul Husnah Rachmad Masbadi Hatullah Nurnaryo Rika Yunitarini Septiyanto, Triyas Siradjuddin, Indah Agustien Sukri, Hanifudin Violina, Dina Wahyu Indra Kustina Wanda, Isnaini Septyana Yeni Kustiyahningsih Yudha Dwi Putra Negara, Yudha