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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 Faktor Exacta 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 Global Science: Journal of Information Technology and Computer Science
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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Pelatihan Klasifikasi Data Menggunakan Naive Bayes untuk Mengembangkan Literasi Data di SMK Media Informatika Dwiasnati, Saruni; Devianto, Yudo; Yuliarty, Popy; Gunawan, Wawan
IRA Jurnal Pengabdian Kepada Masyarakat (IRAJPKM) Vol 3 No 1 (2025): April
Publisher : CV. IRA PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56862/irajpkm.v3i1.166

Abstract

Data processing skills are essential in the digital era, especially for vocational high school (SMK) students preparing for technology-driven careers. This community service activity aimed to enhance data literacy among SMK Media Informatika students through training in data classification using the Naive Bayes algorithm, a fundamental method in data science and machine learning. The algorithm was chosen for its simplicity, ease of understanding, and relevance in introducing probabilistic decision-making logic. The training was conducted interactively, covering basic data concepts, dataset visualization, and practical implementation using Python. The results showed improved student understanding of classification concepts and their application to real-world problems, such as user data category prediction. The activity also encouraged analytical thinking, awareness of valid data collection, and interest in data science. This training is expected to serve as a model for applied learning in vocational schools and support the development of data-oriented curricula at the vocational education level.
Implementasi Metode Interpolasi dan Analytical Hierarchy Process untuk Penerimaan Pegawai Devianto, Yudo; Sari, Yunita Sartika; Manikam, Ratna Mutu
Jurnal Ilmiah FIFO Vol 15, No 1 (2023)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2023.v15i1.006

Abstract

Setiap perusahaan, baik pemerintah maupun swasta, dari skala kecil, menengah, hingga besar, dapat bergantung pada teknologi informasi untuk memaksimalkan hasil dan mempermudah operasi. Kemudahan dan efisiensi sistem komputerisasi saat ini juga dapat diterapkan sebagai pendukung pengambilan keputusan dalam perekrutan pegawai di suatu departemen. Dengan melakukan simulasi matematis tentang cara kerja berbagai metode pendukung keputusan dalam memeringkat pelamar untuk ditempatkan di suatu bagian. Metode pendukung keputusan yang akan digunakan antara lain Interpolasi dan AHP. Hasil penelitian ini menunjukkan bahwa interpolasi lebih tepat digunakan untuk mendukung keputusan dengan kebutuhan yang membutuhkan perhitungan dengan nilai manfaat yang besar dan biaya yang rendah, sedangkan AHP lebih tepat digunakan untuk mendukung keputusan yang memiliki variasi atribut yang lebih banyak. Hasil yang diberikan lebih mengakomodir keputusan yang akan diambil karena dilakukan perbandingan satu per satu antara kriteria satu dengan kriteria lainnya. Metode ini juga sangat tepat digunakan jika pengambil keputusan tidak memiliki prioritas terhadap bobot penilaian yang akan dilakukan. Namun metode ini memerlukan langkah penyelesaian yang cukup panjang sehingga akan lebih sulit untuk diimplementasikan karena memerlukan ketelitian yang lebih dalam proses penentuan konsistensi logika.
Imbalanced Data NearMiss for Comparison of SVM and Naive Bayes Algorithms Gunawan , Wawan; Devianto , Yudo; Sari, Anggi Puspita
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The study aims to improve the diagnosis, management, and prevention of HIV/AIDS by using classification algorithms. The dataset used consists of 707,379 records and 89 columns. Data preprocessing includes removing irrelevant attributes, handling inconsistencies, and balancing the data using the NearMiss method, resulting in a balanced proportion of reactive and non-reactive HIV cases. Once the data is balanced, it is split into several ratios: 60:40, 70:30, 80:20, and 90:10. The classification models used in this study are Naive Bayes and SVM. The models are evaluated using the metrics Accuracy, Precision, Recall, and F1-Score. The results show that the SVM model achieves the highest accuracy of 82.6% with a 90:10 data split at a 6-fold value, and 82.2% with a 60:40 data split at a 5-fold value. On the other hand, Naive Bayes achieves the highest accuracy of 61.1% with a 60:40 data split.
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.
Penerapan Data Science untuk Mendukung Transformasi Digital UMKM di Kelurahan Kembangan Utara Dwiasnati, Saruni; Devianto, Yudo; Gunawan, Wawan; Yuliarty, Poppy
Kapas: Kumpulan Artikel Pengabdian Masyarakat Vol 4, No 2 (2025)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/ks.v4i2.4382

Abstract

The implementation of Data Science to support the digital transformation of SMEs (Small and Medium Enterprises) in Kelurahan Kembangan Utara aims to help local SMEs enhance their competitiveness through the utilization of digital technology. In the era of digitalization, SMEs need to adapt to changes in order to remain relevant and grow. Through the Data Science approach, this program focuses on utilizing data for market analysis, trend prediction, and business process optimization. Training provided to SME owners includes the application of data analysis algorithms, the creation of product recommendation systems, and the use of digital platforms that can improve operational efficiency and expand market reach. By integrating data-driven decision-making, SME owners can make more accurate decisions, increase sales, and open up new business opportunities. This program not only provides insights into the importance of digitalization but also equips participants with practical skills in using technologies relevant to the local market's needs. The expected outcome of this program is the improvement of the digital capacity of SMEs in Kelurahan Kembangan Utara, which in turn can contribute to the empowerment of the local economy.
Benchmarking Machine Learning Models for Large-Scale Loan Default Prediction Using Real Data Devianto, Yudo; Saragih, Rusmin; Cahyana, Yana
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 1 (2026): March: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i1.181

Abstract

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.
Co-Authors Aditya Wibowo Ahmad Fauzi Andika Bayu Shantya Budi ARIF, SUTAN MOHAMMAD Avrizal, Reza Baihaqi, Kiki Ahmad Bambang Sukowo Corry Coriah Haerudin Danny Manongga Dwiki Jatikusumo Eko Sediyono Eliyani Eliyani Eugenius Kau Suni Fauzi Ahmad Muda Galih Priambodo Galih Priyo Waseso Gugun Gunawan Gunawan, Wawan 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 Rusmin Saragih, Rusmin Saputra, Dony Ramadhan Sari, Anggi Puspita Sari, Yunita Sartika Saruni Dwiasnati Sony Wirdianto Saputra Sri Yulianto Joko Prasetyo Sumarto, Marco Alfan Susafaati, Susafaati Wawan Gunawan Wawan Gunawan Wawan Gunawan Yana Cahyana Yuliarty, Poppy