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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] Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control 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 Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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Peramalan Curah Hujan Berbasis Jaringan Syaraf Tiruan untuk Optimalisasi Musim Tanam Padi Bachtiar, Dimas; Pratiwi, Indah; Jauhari, Achmad; Yusuf, Muhammad; Mufarroha, Fifin Ayu; Anamisa, Devie Rosa
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 1 (2025): JUSIFOR - Juni 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i1.5862

Abstract

Sektor pertanian berperan penting dalam kesejahteraan masyarakat Indonesia, terutama dalam produksi beras. Namun, hasil panen beras sangat bergantung pada faktor iklim, khususnya curah hujan yang mempengaruhi jadwal tanam dan produktivitas. Penelitian ini bertujuan mengembangkan model prediksi curah hujan menggunakan Jaringan Syaraf Tiruan Backpropagation (JST) untuk mengoptimalkan musim tanam padi di Bangkalan, Madura. Data curah hujan harian dari Januari 2019 hingga Juli 2024 diproses, dinormalisasi, dan digunakan sebagai input pada JST. Setelah pengujian berbagai konfigurasi, model terbaik diperoleh dengan learning rate 1.0, lima hidden layer, dan sepuluh neuron, menghasilkan MAPE sebesar 0,3912. Model ini memprediksi bahwa musim tanam padi di Bangkalan dapat dimulai pada bulan Oktober karena tingkat curah hujan memenuhi ambang batas yang diperlukan. Penelitian ini memberikan wawasan bagi pemangku kepentingan dalam merencanakan jadwal tanam yang efektif untuk mengurangi risiko gagal panen.
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 Mufarroha, Fifin Ayu; Abdul Fatah, Doni
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.
Revealing Stunting Risk Patterns through Comparative Analysis of Hierarchical and Deep Embedded Clustering Mufarroha, Fifin Ayu; Rahmat, Abdullah Basuki; Husni; Rachmad, Aeri; Lestari, Vivin Ayu; Dwiyanti, Tasya; Maulana, Malik
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2555

Abstract

Stunting remains a significant health issue in Indonesia due to its long-term impact on human resource quality and economic productivity. Despite various intervention programs, disparities in stunting rates between regions remain high, particularly in areas with diverse socioeconomic conditions. This study aims to identify patterns and group regions based on stunting risk levels using two machine learning approaches: Hierarchical Clustering (HC) and Deep Embedded Clustering (DEC). The data used are aggregated data from toddler measurements, including the number of toddlers measured, the number of stunting cases, and the percentage of stunting in the 2020–2024 period. The analysis was conducted by comparing the cluster results from the two methods. The HC method is implemented using an Agglomerative Clustering approach with the Ward linkage criterion, while DEC uses a layered autoencoder architecture optimized through Kullback–Leibler divergence. To assess cluster quality, the study uses the Silhouette Score metric. The results showed that HC produced the highest Silhouette score of 0.5430, while DEC reached 0.4874, with a year-on-year performance trend. These findings indicate that HC excels in clustering stability, while DEC is more adaptive to data complexity and nonlinear patterns. The combination of the two has the potential to support the formulation of more comprehensive, data-driven policies to identify and address stunting-prone areas.
Co-Authors Achmad Jauhari Achmad Jauhari, Achmad Aeri Rachmad 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 Dwiyanti, Tasya Firli Irhamni Fitrianto, Hambali Husni Ifandia, Alfian Mahendra Ika Oktavia Suzanti Indah Agustien Sirajuddin Indah Pratiwi Khusnul Fatimah Koeshardianto, Meidya Linawati Linawati Maulana, Malik Moh. Yusril Ihza Maulana Mohammad Yanuar Hariyawan Muhammad Amfahtori Wijarnoko Muhammad Yusuf Mulaab Mulaab Nuraini, Dinah Okie Maria Amul Husnah Rachmad Masbadi Hatullah Nurnaryo Rahmat, Abdullah Basuki Rika Yunitarini Septiyanto, Triyas Siradjuddin, Indah Agustien Sukri, Hanifudin Violina, Dina Vivin Ayu Lestari Wahyu Indra Kustina Wanda, Isnaini Septyana Yeni Kustiyahningsih Yudha Dwi Putra Negara, Yudha