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All Journal ComEngApp : Computer Engineering and Applications Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal Ilmiah FIFO Jurnal Pengabdian Pada Masyarakat InComTech: Jurnal Telekomunikasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) IJISCS (International Journal Of Information System and Computer Science) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Asia International Conference on Industrial Revolution for Polytechnic Education Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Systems Engineering and Information Technology Kapas: Kumpulan Artikel Pengabdian Masyarakat IRA Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Nasional
Yudo Devianto
Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Other

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Quantitative Analysis of Training Completion Using Multivariate Linear Regression Devianto, Yudo; Dwiasnati, Saruni; Gunawan, Wawan; Sumarto, Marco Alfan; Saputra, Dony Ramadhan
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8851

Abstract

The urgency of this research stems from the strategic need to monitor and evaluate the achievements of digital training implemented by various academies under government coordination, including VSGA, FGA, DEA, TA, and GTA. In the context of the national digital transformation program, the availability of an analytical model that can predict the success of participants in completing training is critically crucial to support the achievement of the Ministry’s Key Performance Indicators (KPIs). The purpose of this study is to develop a predictive model based on multivariate linear regression that combines two main variables, the percentage of participants accepted and the percentage of participants who participate in onboarding, to project the level of training completion. This model is expected to provide a quantitative and objective assessment of the effectiveness of digital training implementation in each academy. The targeted outputs of this study include the development of a predictive model with performance validation through the calculation of R², which yielded a value of 0.9448, as well as the provision of technical reports and data-driven recommendations for enhancing digital training governance. The Technology Readiness Level (TKT) of this study is at TKT 3, and there is evidence of conceptual validation of the predictive model based on real data collected from the implementation of the training. This stage marks the readiness of the research to continue developing the system model and implementing it on the training evaluation platform in the next stage.
Transformasi Digital UMKM Melalui Pelatihan Data Science: Studi Kasus di Kelurahan Kembangan Utara Devianto, Yudo; Sukowo, Bambang; Jatikusumo, Dwiki
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 3 (2025): Vol 8, No 3 (2025): SEPTEMBER 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i3.3075

Abstract

Kegiatan pengabdian kepada masyarakat ini dilaksanakan di Kelurahan Kembangan Utara, Jakarta Barat, dengan tujuan meningkatkan kapasitas pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) dalam memanfaatkan data science untuk manajemen usaha dan strategi pemasaran digital. Permasalahan utama yang dihadapi mitra adalah rendahnya literasi digital, kurangnya pencatatan berbasis data, serta strategi promosi yang masih konvensional sehingga berdampak pada daya saing dan pertumbuhan usaha. Metode kegiatan meliputi tahap persiapan, pelatihan, pendampingan, dan evaluasi. Materi pelatihan mencakup manajemen usaha berbasis data, pemasaran digital menggunakan Google Trends, Facebook Ads Manager, Instagram Insights, dan Google Analytics, serta pencatatan transaksi menggunakan Excel dan Google Sheets. Evaluasi dilakukan melalui pre-test, post-test, dan kuesioner kepuasan peserta. Hasil kegiatan menunjukkan adanya peningkatan signifikan dalam pemahaman dan keterampilan peserta, terbukti dari perbedaan hasil sebelum dan sesudah pelatihan, di mana mayoritas peserta mampu memahami dan mengimplementasikan strategi berbasis data. Respon positif peserta juga menunjukkan relevansi materi dengan kebutuhan sehari-hari. Program ini berkontribusi dalam memperkuat literasi digital, meningkatkan daya saing UMKM, dan mendorong terbentuknya ekosistem UMKM berbasis data. Kegiatan ini juga sejalan dengan program Merdeka Belajar Kampus Merdeka (MBKM) dan indikator kinerja utama (IKU) perguruan tinggi dalam mendukung kolaborasi dosen, mahasiswa, dan masyarakat.
Pemodelan Wilayah Titik Api Kebakaran Hutan Menggunakan Deep Learning Dwiasnati, Saruni; Devianto, Yudo; Arif, Sutan Mohammad; Avrizal, Reza
Jurnal Ilmiah FIFO Vol 16, No 1 (2024)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2024.v16i1.001

Abstract

Indonesia merupakan negara tropis yang mengalami kebakaran hutan setiap tahunnya. Kebakaran hutan terjadi disebabkan oleh durasi musim panas yang terlalu lama dari waktu semestinya. Hutan merupakan tempat tinggal berbagai jenis satwa dan fauna yang memiliki banyak kekayaan hayati yang dapat membuat mereka bertahan hidup. Sering terjadinya kebakaran hutan menjadi isu lingkungan yang dianggap krusial dan mendapatkan perhatian baik dari tingkat lokal maupun internasional. Penelitian yang dilakukan ini menyajikan kajian klasifikasi wilayah titik api kebakaran hutan menggunakan salah satu algoritma Deep Learning (DL) yaitu metode Convolutional Neural Network (CNN), hal ini sangat dibutuhkan untuk pendahuluan mengenai peringatan dini kebakaran hutan yang ada di daerah tersebut. Wilayah titik api kebakaran hutan yang digunakan dalam penelitian ini dikumpulkan dari daerah Nusa Tenggara Timur (NTT), terutama pulau-pulau seperti Sumba dan Timor. Metode CNN melibatkan dua langkah utama. Langkah pertama adalah pengklasifikasian gambar melalui proses feedforward. Langkah kedua adalah fase pembelajaran menggunakan teknik backpropagation. Model CNN yang digunakan dalam proses pelatihan dataset menguji citra dengan beberapa pengoptimal dan diperoleh hasil akurasi yang tinggi. Kemiripan area yang terbakar dengan fitur terang lainnya mengurangi kepastian deteksi kebakaran hutan. Hasil penelitian menunjukkan bahwa Model CNN yang digunakan Untuk deteksi dan segmentasi area terbakar menggunakan algoritma terpilih, kinerja terbaik dengan pembelajaran mendalam yang dilaporkan dalam literatur adalah 89%.Teknik yang diusulkan dilatih menggunakan wilayah varian (kumpulan data) dan mengevaluasi presisi berdasarkan ambang recall, dengan akurasi keseluruhan 89%.
Quantitative Analysis of Training Completion Using Multivariate Linear Regression Devianto, Yudo; Dwiasnati, Saruni; Gunawan, Wawan; Sumarto, Marco Alfan; Saputra, Dony Ramadhan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8851

Abstract

The urgency of this research stems from the strategic need to monitor and evaluate the achievements of digital training implemented by various academies under government coordination, including VSGA, FGA, DEA, TA, and GTA. In the context of the national digital transformation program, the availability of an analytical model that can predict the success of participants in completing training is critically crucial to support the achievement of the Ministry’s Key Performance Indicators (KPIs). The purpose of this study is to develop a predictive model based on multivariate linear regression that combines two main variables, the percentage of participants accepted and the percentage of participants who participate in onboarding, to project the level of training completion. This model is expected to provide a quantitative and objective assessment of the effectiveness of digital training implementation in each academy. The targeted outputs of this study include the development of a predictive model with performance validation through the calculation of R², which yielded a value of 0.9448, as well as the provision of technical reports and data-driven recommendations for enhancing digital training governance. The Technology Readiness Level (TKT) of this study is at TKT 3, and there is evidence of conceptual validation of the predictive model based on real data collected from the implementation of the training. This stage marks the readiness of the research to continue developing the system model and implementing it on the training evaluation platform in the next stage.
Detection of Rice Leaf Pests Based on Images with Convolution Neural Network in Yollo v8 Fauzi, Ahmad; Baihaqi, Kiki Ahmad; Pertiwi, Anggun; Devianto, Yudo; Dwiasnati, Saruni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2008

Abstract

Detection of rice leaf pests is important in agriculture because it can help farmers determine appropriate preventive measures. One method that can be used to detect rice leaf pests is digital image processing technology. In this research, proof of suitability for solving this case was carried out between the Convolutional Neural Network (CNN) algorithm which was run offline with R-CNN and YOLOv8 for detecting rice leaf pests. At the data preparation stage, images of rice leaves were taken from various sources with a total of 100 images taken from website data and 10 images taken from the research site. Next, preprocessing and data augmentation are carried out to improve image quality and increase data variation. At the model training stage, a training and evaluation process is carried out using two types of algorithms, namely R-CNN and YOLOv8. The accuracy of the testing results using the same data using Yolov8 obtained 87.0% accuracy and 79% precision, while using R-CNN the results obtained were 85% for accuracy and 75% for precision with data divided into 80 training data 20 validation data and 10 testing data. Labeling the dataset uses Makesensei which has been completely standardized, with the resulting parameters being the spots on rice leaves.
Co-Authors Aditya Wibowo Ahmad Fauzi Andika Bayu Shantya Budi ARIF, SUTAN MOHAMMAD Avrizal, Reza Baihaqi, Kiki Ahmad Bambang Sukowo Bambang Sukowo Corry Coriah Haerudin Dwiasnati, Saruni Dwiki Jatikusumo Eliyani Eliyani Eugenius Kau Suni Fauzi Ahmad Muda Galih Priambodo Galih Priyo Waseso Gugun Gunawan Herry Derajad Wijaya Hidayat, Rahmat Rian Inda Wulan Irawati Khatin Faisah Muhammad Reza Ramdani Nur Septiyani Aglis Paradilla Shabilla Pertiwi, Anggun Popy Yuliarty, Popy Ratna Mutu Manikam Saputra, Dony Ramadhan Sari, Anggi Puspita Sari, Yunita Sartika Saruni Dwiasnati Saruni Dwiasnati Saruni Dwiasnati Saruni Dwiasnati Saruni Dwiasnati Sony Wirdianto Saputra Sukowo, Bambang Sumarto, Marco Alfan Wawan Gunawan Wawan Gunawan Wawan Gunawan