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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Abdi Insani Journal of Computer and Information System (J-CIS)
Putra, Ahmad Fatoni Dwi
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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PELATIHAN DESAIN GRAFIS SEBAGAI UPAYA PENINGKATAN PENGETAHUAN DAN KETERAMPILAN DALAM PEMASARAN KONTEN SEBAGAI PELUANG MENDAPATKAN PASSIVE INCOME BAGI KARANG TARUNA CIPTA RASA DAYA DI DESA KARANG SIDEMEN Syuhada, Fahmi; Saputra, Joni; Adipta, Marazaenal; Anggarista, Randa; Kumoro, Danang Tejo; Afriansyah, M.; Lonang, Syahrani; Putra, Ahmad Fatoni Dwi; Firdaus, Asno Azzawagama; Pratama, Ramadhana Agung; Yamin, Muhamad
Jurnal Abdi Insani Vol 12 No 5 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i5.2235

Abstract

The Community Partnership Empowerment activity aimed to enhance the skills and knowledge of the youth in Karang Sidemen Village, Central Lombok, in the field of digital creative economy, specifically through digital content marketing that can generate passive income. The PKM program is supported by the Directorate of Research, Technology, and Community Service through the BIMA 2024 program. The activities included a socialization session on the concept of the creative economy and technical training on using Adobe Illustrator, where participants were encouraged to market their creations on platforms like Shutterstock. The outcomes of this program showed an improvement in participants' graphic design skills, as evidenced by their ability to create logos, set up Shutterstock accounts, and independently upload their work. Additionally, this activity involved students under the Merdeka Belajar-Kampus Merdeka (MBKM) scheme, providing them with experiential learning outside the campus. In conclusion, this program successfully made a positive impact on digital literacy and the creative economy in the community and is expected to contribute to the village's economic sustainability through the empowerment of local potential in a sustainable manner.
Identifikasi Status Stunting menggunakan Metode Klasifikasi Pemrosesan Citra: Systematic Literature Review Putri, Mindi Richia; Putra, Ahmad Fatoni Dwi; Asmaul Husna; Arsan Kumala Jaya; Muhammad Ari Rifqi
Journal of Computer and Information System ( J-CIS ) Vol 8 No 1 (2025): J-CIS Vol. 8 No. 1 Tahun 2025
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jcis.v8i1.5061

Abstract

Stunting adalah masalah kesehatan yang signifikan di Indonesia yang memengaruhi pertumbuhan fisik, perkembangan kognitif, dan kualitas sumber daya manusia di masa depan. Laporan dari Organisasi Kesehatan Dunia (WHO) menyatakan bahwa prevalensi stunting di Indonesia mencapai 21,6% pada tahun 2022. Untuk mengklasifikasikan stunting, metode konvensional seperti pengukuran antropometri manusal masih digunakan, tetapi memiliki keterbatasan seperti bergantung pada tenaga medis, memiliki kemungkinan kesalahan, dan sulit diakses di daerah terpencil. Tujuan dari penelitian ini adalah untuk mengevaluasi teknologi dan pemrosesan citra sebagai alternatif untuk metode deteksi stunting yang lebih akurat dan efektif. Hasil penelitian menunjukkan bahwa teknologi dan algoritma seperti MediaPipe Pose memiliki akurasi 98,48%, Deep Neural Nets (DNN) 93,83%, dan Support Vector Machine (SVM) 91,1%. Algortima CNN lebih efektif dalam menganalisis gambar secara otomatis terutama untuk dataset besa dan algortima SVM efektif untuk dataset kecil-menengah dengan dukungan ekstraksi fitur. Peneliti merekomendasikan untuk menggabungkan kedua metode ini untuk membuat sistem deteksi stunting yang lebih cepat, akurat, dan efisien. Temuan ini diharapkan dapat berfungsi sebagai titik acuan penting dalam proses pengembangan inovasi di bidang kesehatan anak di Indonesia.
Optimizing Rain Prediction Model Using Random Forest and Grid Search Cross-Validation for Agriculture Sector Putra, Ahmad Fatoni Dwi; Azmi, Muhamad Nizam; Wijayanto, Heri; Utama, Satria; Wedashwara Wirawan, I Gede Putu Wirarama
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3891

Abstract

Agriculture, as a sector that is highly influenced by weather conditions, faces challenges due to increasingly unpredictable changes in weather patterns. The aim of this research is to create an optimal rainfall prediction model to help farmers create irrigation schedules, use fertilizer, and planting schedules, and protect plants from extreme weather events. The method used in this research to obtain the best rain prediction model is to use the random forest algorithm and the grid search cross-validation algorithm. Random Forest, known for its robustness and accuracy, emerged as a suitable algorithm for predicting rain. utilizing a substantial dataset from the West Nusa Tenggara Meteorology, Climatology, and Geophysics Agency covering the period 2000 to 2023. The data is then processed first to ensure its readiness for use. This process involves removing outlier data points, empty data entries, and unused features. After the preprocessing stage, the data underwent training using the Random Forest algorithm, resulting in an R-squared value of 0.1334. To obtain the optimal model, Grid Search Cross Validation is used. The results of this research obtained the best rain prediction model with an R-squared value of 0.0268. This model will be used to predict rain in the agricultural sector. This research concludes that we can get the best rain prediction model by combining Random Forest and Gird Search Cross-Validation. For further research, we can compare other rain prediction methods, add features, and combine datasets from a wider area.
Co-Authors Adipta, Marazaenal Afriansyah, M. Anggarista, Randa Arsan Kumala Jaya Asmaul Husna Asno Azzawagama Firdaus Azmi, Muhamad Nizam Heri Wijayanto Joni Saputra Kumoro, Danang Tejo Mindi Richia Putri Muhamad Yamin Muhammad Ari Rifqi Pratama, Ramadhana Agung Satria Utama, Satria Syahrani Lonang Syuhada, Fahmi Wedashwara Wirawan, I Gede Putu Wirarama