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All Journal Jurnal Informatika dan Teknik Elektro Terapan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Khatulistiwa Informatika Jurnal Informatika Universitas Pamulang Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Magister Pendidikan IPA JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Informasi Kaputama (JSIK) Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) JTIK (Jurnal Teknik Informatika Kaputama) JISA (Jurnal Informatika dan Sains) Infotek : Jurnal Informatika dan Teknologi Yumary: Jurnal Pengabdian kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Jurnal Abdimas Bina Bangsa Ngabdimas : Pengabdian Pada Masyarakat Jurnal Dinamika Informatika (JDI) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Ngabdimas Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Febriansyah .
Institiut Teknologi Pagar Alam
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Peningkatan Kemampuan Mahasiswa ITPA dalam Analisis Data Pertanian melalui Pelatihan Data Mining dengan Google Colab Febriansyah; Muntari, Siti; S Prawira, Nanda
Jurnal Pengabdian Magister Pendidikan IPA Vol 8 No 2 (2025): April-Juni 2025
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpmpi.v8i2.11745

Abstract

In the era of precision agriculture and information digitalization, the ability to manage and analyze large-scale data (big data) has become a strategic competency, especially in addressing the challenges of modern agriculture. One of the main issues faced by vegetable farmers in the partner community area is the difficulty in accurately predicting harvest yields due to the lack of data-driven analysis based on historical records. In fact, substantial data on production, climate, and market prices are available but have not been optimally utilized, either by farmers or by agricultural students as future professionals in the field. Initial observations indicate that students of the Institut Teknologi Pagar Alam (ITPA) lack sufficient understanding and skills in applying data mining methods to extract meaningful information from agricultural data. This community service activity was designed to improve data literacy and technical skills among ITPA students through training on data mining techniques using Google Colab. Google Colab was chosen as it supports Python programming execution in a cloud computing environment without the need for local software installation, and it enables collaboration and efficiency in processing large datasets. The training involved 10 students, divided into two sessions covering an introduction to data mining concepts, agricultural dataset processing, and the implementation of classification and clustering algorithms. Post-training evaluation showed a significant improvement in both conceptual understanding and practical abilities among participants. This training is expected to enable students to become drivers of digital transformation in the agricultural sector through more strategic use of data.
Pengembangan Produk Olahan Hasil Pertanian Tidak Layak Jual Pepaya APeS dan Pisang KeMPeS Febriansyah, Febriansyah; Oktavianus, Donny; Nasrullah, Abdi
Yumary: Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 2 (2023): Desember
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/yumary.v4i2.2445

Abstract

Purpose: This research aims to develop value-added processed products from previously unsellable agricultural produce, namely papaya and bananas, including Papaya-based Abon, Permen (candies), and Selai (jams) or APeS, and Banana-based Keripik (chips), Molen (banana fritters), Permen (candies), and Sale (sugar-coated snacks) or KeMPeS, within the Small and Medium Enterprises (UMKM) sector of Banana Chips in Penantian Village. The research primarily focuses on the utilization of digital marketing strategies as the key tool for marketing these products. Methodology/approach: The research method employed product development experiments and the implementation of digital marketing strategies. Data collection involved surveys, interviews, observations, and market analysis. The results indicate that the developed papaya-based APeS and banana-based KeMPeS products have successfully enhanced the value of previously unsellable agricultural produce. Furthermore, the implementation of digital marketing strategies has proven effective in increasing the visibility and sales of UMKM Banana Chips' products in the digital marketplace. Results/findings: The results indicate that the developed papaya-based APeS and banana-based KeMPeS products have successfully enhanced the value of previously unsellable agricultural produce. Furthermore. Limitation: This research suggest that UMKM in similar regions can leverage similar strategies to develop processed agricultural products and enhance market access through digital platforms. Contribution: This research offers a positive contribution to the development of UMKM in Penantian Village and provides valuable insights into the utilization of digital marketing to support local economic growth.
Implementasi Metode Naive Bayes untuk Klasifikasi Kondisi Gizi Balita Febriansyah, Febriansyah
Jurnal Informatika Universitas Pamulang Vol 9 No 2 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i2.39676

Abstract

The determination of a toddler's nutritional status involves calculating weight and height based on age. Naïve Bayes is a machine learning algorithm for classification problems in data mining that utilizes probability mathematics (also known as Bayes' theorem) to distinguish between different classes. This system is designed to facilitate the nutrition staff at the Pajar Bulan Village Health Center in more accurately storing data and automatically determining the nutritional status of toddlers. The system is developed using the Rapid Application Development (RAD) method, which comprises three phases: requirements planning, design workshop, and implementation. The classification system for toddler nutritional status using the Naive Bayes algorithm aims to provide more accurate information to address malnutrition in toddlers. The data processing with the Naive Bayes algorithm results in the development of a system for classifying the nutritional status of toddlers at the Pajar Bulan Village Health Center.
SISTEM PENUNJANG KEPUTUSAN PEMILIHAN USTAD USTADZAH TERBAIK PADA MTS DEMPO DARUL MUTTAQIEN Febriansyah, Febriansyah; Muntari, Siti
Jurnal Khatulistiwa Informatika Vol 11, No 2 (2023): Periode Desember 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jki.v11i2.20269

Abstract

Tujuan penelitian ini adalah untuk membuat suatu sistem penunjang keputusan pemilihan ustad / ustadzah terbaik pada Mts Dempo Darul Muttaqien Pagar Alam, permasalahan yang dihadapi saat ini Untuk pemilihan ustad / ustadzah di Mts dilakukan dengan cara semi manual dengan menggunakan kalkulator sebagai alat hitung untuk menjumlahkan nilai dan Ms.Word tempat untuk memasukkan nilai-nilai kriteria pada tabel yang telah di tentukan oleh kepala madrasah dalam pemilihan ustad / ustadzah terbaik pada Mts Dempo Darul Muttaqien. Dengan metode ini tidak begitu efektif yang dikarnakan besar kemungkinan kesalahan dalam penjumlahan nilai-nilainya. Sistem penunjang keputusan ialah sistem informasi yang berbasis komputer dalam membantu manusia untuk pengambilan suatu keputusan, suatu sistem baru yang lebih efektif dan sudah terkomputerisasi, dimana dengan adanya sistem penunjang keputusan ini maka akan ada database untuk menyimpan data ustad / ustadzah, data kriteria dan data hasil penilaian yang tersimpan di database. Metode Simple Addictive Weighting (saw) adalah metode terbobot yang akan digunakan dalam sistem untuk menghitung nilai bobot setiap atribut kemudian dilanjutkan prangkingan yang akan menyeleksi alternatif terbaik dari sejumlah alternatif. Untuk perancangan Sistem ini dirancang dengan metode UML (Unified Modeling Language) dan Aplikasi Axure. Bahasa pemrograman yang digunakan yaitu PHP dan MySQL sebagai database-nya. Pengujian sistem ini menggunakan  blackbox testing. sistem penunjang keputusan ini nantinya akan di implementasikan pada MTS agar mempermudah tim penilai pada Mts dalam pengolaan data dan menginputan data untuk menentukan ustad / ustadzah yang menjadi terbaik pada Mts Dempo Darul Muttaqien kota Pagar Alam.
PENERAPAN METODE ASOSIASI DATA MINING PADA E-COMMERCE TOKO NADHIRA Inda Anggraini; Febriansyah
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 7 No. 2 (2023): Volume 7, Nomor 2, Juli 2023
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v7i2.105

Abstract

The purpose of this problem is to produce e-commerce using the association data mining method with the FP-growth algorithm to determine the products that appear most frequently. It also aims to make it easier for buyers and visitors to find products that are most frequently accessed by visitors. At the Nadhira Batik shop, the sales process is conventional, namely visitors come directly to the store and sort the clothing products to be purchased for the payment process, which is also done directly to the cashier. The media used for sales are also still limited, especially for payments that cannot be made through the system. The method used is the association method with the FP-Growth algorithm while the system development method used is waterfall development with the stages of analysis, design, coding, testing and implementation. The results of this study are e-commerce using the FP-Growth data mining association algorithm to determine the products that appear most frequently.
Co-Authors Abdi NAsrullah Anggriani, Yesi Pitaloka Anggun Budiastuti Arif, Alfis Asep Syaputra Asep Syaputra Dedi Setiadi Donny Oktavianus, Donny Edowinsyah Efan Efan Fenny Etrawati Hamzah Hasyim Inda Anggraini Indah Yuliana Intan Juwita Kasman Kasman Kasman Lesi Anggraini Muntari, Siti Nanda S. Prawira Nasrullah, Abdi Nely Murniati Rahmatillah Razak Sasmita Sasmita Syerina Raihatul Jannah Tri Susanti Tri Susanti Widya Lionita