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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 Jurnal Teknik Informatika (JUTIF) 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 Jurnal Komtika (Komputasi dan Informatika)
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.
Implementasi Algoritma Random Forest Berbasis Machine Learning Untuk Prediksi Klon Kopi Unggul Febriansyah, Febriansyah; Nurmaleni, Nurmaleni
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i2.15227

Abstract

The significant increase in coffee prices in recent years has not been matched by optimized production, particularly in major coffee-producing regions such as Pagar Alam City. One of the main challenges is farmers’ limited capacity to determine the most suitable coffee clone for their environmental conditions. This study aims to develop an intelligent system based on machine learning to predict superior coffee clones that can improve productivity and support food security. The Random Forest algorithm was applied using the CRISP-DM framework, consisting of business understanding, data understanding, data preparation, modelling, evaluation, and deployment stages. The dataset comprised environmental variables (altitude, rainfall, soil pH, soil type, pest resistance, and production) and coffee clone labels (Clone1–Clone4). Experimental results indicate that the model achieved an average accuracy of approximately 75% under 5-fold cross-validation, with altitude and rainfall identified as the most influential factors in clone selection. The predictive system was implemented in Python and can be further developed into web- or mobile-based applications. This study demonstrates the potential of artificial intelligence in optimizing coffee production, enhancing farmers’ welfare. Kenaikan harga kopi yang signifikan dalam beberapa tahun terakhir belum diimbangi dengan produksi yang optimal, terutama di wilayah penghasil kopi utama seperti Kota Pagar Alam. Salah satu tantangan utama adalah keterbatasan kemampuan petani dalam menentukan klon kopi yang paling sesuai dengan kondisi lingkungannya. Penelitian ini bertujuan untuk mengembangkan sistem cerdas berbasis machine learning guna memprediksi klon kopi unggul yang dapat meningkatkan produktivitas dan mendukung ketahanan pangan. Algoritma Random Forest diterapkan dengan menggunakan kerangka kerja CRISP-DM, yang meliputi tahap pemahaman bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, dan penerapan. Dataset yang digunakan mencakup variabel lingkungan seperti ketinggian, curah hujan, pH tanah, jenis tanah, ketahanan terhadap hama, serta data produksi, dengan label klon kopi (klon1–klon4). Hasil eksperimen menunjukkan bahwa model yang dibangun mencapai rata-rata akurasi sekitar 75% menggunakan metode 5-fold cross-validation, dengan ketinggian dan curah hujan teridentifikasi sebagai faktor paling berpengaruh dalam pemilihan klon. Sistem prediksi ini diimplementasikan menggunakan Python dan dapat dikembangkan lebih lanjut menjadi aplikasi berbasis web atau mobile. Penelitian ini menunjukkan potensi kecerdasan buatan dalam mengoptimalkan produksi kopi, meningkatkan kesejahteraan petani, serta memperkuat ketahanan pangan nasional.
Improving the Accuracy of Stunting Prediction in Children in Pagar Alam City Using XGBoost Feature Selection and K-Nearest Neighbor Classification Putrawansyah, Ferry; Idris, Mohd. Yazid; Febriansyah, Febriansyah
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5473

Abstract

Stunting remains a major public health concern in Indonesia, including in Pagar Alam City. Early identification of at-risk children is essential to enable timely interventions and reduce long-term developmental consequences. However, predictive models such as K-Nearest Neighbor (K-NN) often experience reduced accuracy when faced with irrelevant features and imbalanced class distributions. This study integrates feature selection using Extreme Gradient Boosting (XGBoost) to enhance the predictive performance of K-NN in assessing stunting risk. Child growth data obtained from local health facilities were analyzed to build an initial baseline model, which exhibited limited accuracy due to excessive attributes and class imbalance. Through feature-importance analysis, XGBoost identified key predictors including sex, age, weight, and height. The optimized dataset was then used to retrain the K-NN model. Evaluation using accuracy, precision, recall, and F1-score demonstrated an improvement in accuracy from 85.63% to 93.72%. Beyond the computational results, this research provides significant contributions to the field of health informatics. The integration of XGBoost and K-NN offers an efficient analytical mechanism suitable for clinical decision support systems, particularly for data-driven screening in primary healthcare settings. The optimized, lightweight model can be embedded into health information systems to support child growth monitoring, strengthen evidence-based policymaking, and assist healthcare workers in targeting interventions more effectively. This approach can be replicated across other regions, supporting nationwide efforts to reduce stunting prevalence.
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 Ferry Putrawansyah Hamzah Hasyim Inda Anggraini Indah Yuliana Intan Juwita Kasman Kasman Kasman Lesi Anggraini Mohd. Yazid Idris Muntari, Siti Nanda S. Prawira Nasrullah, Abdi Nely Murniati Nurmaleni, Nurmaleni Rahmatillah Razak Sasmita Sasmita Syerina Raihatul Jannah Tri Susanti Tri Susanti Widya Lionita