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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Indonesian Journal of Applied Informatics Jurnal Informatika dan Rekayasa Perangkat Lunak E-Link: Jurnal Teknik Elektro dan Informatika Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika
Rudi Kurniawan
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Pemberdayaan Kelompok Tani melalui Sistem Informasi Pertanian Berbasis Android Riri Narasati; Rudi Kurniawan; Fikri Firmansyah; Gilang Ramadhan
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Farmer group empowerment is one of the important strategies in increasing agricultural productivity and farmer welfare. This research aims to develop and implement an Android-based agricultural information system as a medium for empowering farmer groups in Nganjuk Regency. The system is designed to provide access to agricultural information in a fast, accurate, and relevant manner, including weather information, commodity prices, cultivation techniques, and consultation with agricultural extension workers. The research method used is descriptive qualitative research with a case study approach. The system development process refers to the Waterfall model which includes the stages of analysis, design, implementation, testing, and maintenance. The implementation results show that the use of an Android-based information system significantly increases farmer group involvement in agricultural activities, accelerates the decision-making process, and improves access to agricultural information resources. In addition, the system facilitates two-way communication between farmers and extension workers, which was previously constrained by time and location limitations. The conclusion of this research is that information technology, especially Android applications, has great potential in empowering farmer groups in a sustainable manner. It is hoped that this system can be adopted more widely by other regions as an information technology-based farmer empowerment solution.
Pelatihan Video Editing untuk Siswa dan Pemuda dalam Industri Kreatif Digital Rudi Kurniawan; Ryan Hamonangan; Hilda Novia Ramadani; Ibnu Fajri
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

In the rapidly growing digital era, the need for skills in the creative industry is very important, especially in the field of video editing. Video editing training for students and youth aims to improve competence and practical skills that are relevant to current industry needs. This training activity was organized by Karang Taruna Kelurahan Lolu Selatan, in collaboration with the Digital Talent Indonesia Course and Training Institute (LKP), and targeted students and youth in Palu City who have an interest in digital creative fields. The training method used a hands-on approach with materials covering the basics of video editing, introduction to software, shooting techniques, and audio visual editing. The results of this training showed an increase in participants' knowledge and skills in using editing software and understanding the video production process. In addition, participants also obtained certificates as a form of recognition of the competencies obtained. This program is expected to be the first step in preparing the younger generation to compete in the world of work, especially in the creative industry sector. In conclusion, video editing training not only provides the benefits of technical skills, but also encourages creativity, innovation, and sustainable youth empowerment.
Peningkatan Kinerja SVM pada Klasifikasi Sentimen Ulasan iPusnas Berbasis Imbalance Handling Akmal Mustafa; Rudi Kurniawan; Bani Nurhakim; Puji Pramudya Marta; Khaerul Anam
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 1 (2026): Edisi April
Publisher : Universitas Harapan Medan

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Abstract

This study investigates sentiment classification of user reviews on the iPusnas digital library application to provide an objective overview of service quality and user experience. Numerous complaints related to login failures, application crashes, and issues in accessing digital books indicate the need for a computational approach capable of processing large volumes of user feedback. The proposed method integrates Natural Language Processing (NLP) techniques with the Support Vector Machine (SVM) algorithm. The workflow consists of collecting 2,000 reviews, applying text cleaning and normalization, tokenization, stopword removal, stemming, rating-based sentiment annotation, and feature extraction using TF-IDF. The dataset was divided using a train–test split for model training and evaluation. Experimental results show that the SVM model achieves 90.1% accuracy, demonstrating strong performance in detecting negative sentiments and moderate performance for positive sentiments due to class imbalance. These  findings highlight the effectiveness of NLP and SVM for extracting user perceptions and indicate the potential of this model as a decision-support tool for improving iPusnas application services. Overall, the study contributes to the advancement of digital service innovation in Indonesia.
Klasifikasi Tingkat Kesejahteraan Masyarakat Desa Cikuya Berdasarkan Data Sosial Ekonomi Menggunakan Algoritma Nive Bayes Ramdan Irawan; Rudi Kurniawan; Bani Nurhakim; Arif Rinaldi; Fathurrahman
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.203

Abstract

Penentuan tingkat kesejahteraan masyarakat memiliki peran penting dalam proses penyaluran bantuan sosial di tingkat desa. Namun, pendataan berbasis observasi manual masih menghadirkan potensi bias subjektif dan ketidakkonsistenan dalam pengambilan keputusan. Penelitian ini bertujuan mengembangkan model klasifikasi tingkat kesejahteraan masyarakat Desa Cikuya menggunakan algoritma Naïve Bayes sebagai pendekatan berbasis data yang lebih objektif. Tahapan penelitian meliputi pengumpulan data sosial ekonomi, pra-pemrosesan, encoding variabel kategorik, normalisasi variabel numerik, pelatihan model Gaussian Naïve Bayes, serta evaluasi menggunakan metrik akurasi, precision, recall, dan f1-score. Hasil penelitian menunjukkan bahwa model menghasilkan akurasi sebesar 98,33%, yang menunjukkan performa klasifikasi yang sangat baik. Analisis lebih lanjut mengindikasikan bahwa variabel pendapatan dan kondisi fisik rumah memiliki peranan paling dominan dalam membedakan kategori kesejahteraan. Model yang dikembangkan tidak hanya berfungsi sebagai alat klasifikasi, tetapi juga dapat dimanfaatkan sebagai sistem pendukung keputusan bagi pemerintah desa untuk menilai status kesejahteraan masyarakat secara lebih cepat, konsisten, dan bebas bias subjektif. Penelitian ini memberikan kontribusi pada pemanfaatan teknologi pembelajaran mesin dalam pemetaan kesejahteraan masyarakat, meskipun masih memiliki keterbatasan pada jumlah variabel dan cakupan data lokal. Temuan ini diharapkan dapat menjadi dasar pengembangan sistem penyaluran bantuan yang lebih tepat sasaran dan transparan.
Evaluasi Pengaruh Kualitas Data Terhadap Performa Model Machine Learning Menggunakan Pendekatan Data-Centric AI Bisma Mahendra; Martanto; Denni Pratama; Ahmad Faqih; Rudi Kurniawan
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.211

Abstract

Penelitian ini mengevaluasi pengaruh kualitas data terhadap performa model machine learning menggunakan pendekatan Data-Centric Artificial Intelligence (DCAI). Eksperimen dilakukan pada Titanic Dataset dengan membandingkan Random Forest dan Support Vector Machine (SVM) dalam tiga skenario penanganan missing values, yaitu Drop Missing, Mean Imputation, dan No Imputation. Kinerja model dievaluasi menggunakan metrik Accuracy, F1 Score, dan Area Under Curve (AUC). Hasil menunjukkan bahwa intervensi kualitas data memberikan dampak signifikan terhadap performa model. Random Forest mencapai performa terbaik pada skenario Drop Missing dengan Accuracy 0.813, F1-Score 0.758, dan AUC 0.859, sedangkan SVM memperoleh Accuracy tertinggi sebesar 0.822 pada skenario Mean Imputation. Uji statistik Paired t-Test menunjukkan tidak terdapat perbedaan performa yang signifikan secara statistik antara kedua model (p-value > 0.05). Temuan ini menegaskan bahwa peningkatan kualitas data lebih berpengaruh terhadap kinerja model dibandingkan pemilihan algoritma, sehingga mendukung paradigma Data-Centric AI.
Enhancing Face Authentication for Online Examination Systems Using Median Filtering and MobileNetV2 Dadang Sudrajat; Dian Ade Kurnia; Rudi Kurniawan; Othman bin Mohd; Maulana Sujarwadi; Salman Alfarizi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7185

Abstract

Digital transformation in higher education is driving the uptake of online tests, which require academic integrity, security, and robust user experience. In the context of authentication of users, deep learning based face recognition, in particular the Convolutional Neural Network (CNN) architectures, such as MobileNetV2, combined with intermediate filter, promises to deliver a consistent performance across a wide range of devices and imaging environments. However, there are limited comprehensive studies evaluating the final integration of the median filter and MobileNetV2 in high-value test scenarios. This study contributes by proposing an effective end-to-end Face Authentication Pipeline, assessing the median impact of filtering on MobileNetV2 performance, and validating it with a prototype application. The authentic face dataset was collected using the Teachable Machine, preprocessed with cropping, resizing, and median filtering, and then augmented through rotation, shift, shear, zoom, reversal, and brightness adjustment. The MobileNetV2 model was trained with Adam in a stepwise manner, starting with 0.001 and then 0.0001 for 20 epochs in a batch size of 32, and was evaluated for accuracy, precision, recall, and F1 score. Results show that the accuracy curve has remained stable at almost 95 percent during the 20th epoch; most grades achieved 1.00 in both precis, recall and F1, with some classings showing a limited decrease due to facial similarity or expression differences. These findings confirm that MobileNetV2 median filtering can be the basis for an effective, accurate and ready to integrate face recognition in online testing applications on a wide range of devices.
Optimalisasi Klasterisasi Tenaga Kesehatan Menggunakan K-Means dan Davies Bouldin Indexs Ayura Yufita; Rudi Kurniawan; Yudhistira Arie Wijaya; Tati Suprapti
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.96645

Abstract

Abstrak : Optimalisasi model pengelompokan data tenaga kesehatan adalah langkah strategis untuk memahami pola dan karakteristik kelompok data tertentu. Tujuan dari  penelitian ini adalah untuk  mendapatkan nilai K optimal menurut Davies Bouldin Indeks (DBI), mendapatkan nilai iterasi yang diperlukan oleh algoritma K-Means Clustering untuk mencapai hasil yang optimal, dan menentukan jenis metrik apa yang akan menghasilkan nilai (DBI) yang paling kecil. Hal ini  penting karena penelitian ini membantu perencanaan distribusi tenaga kesehatan yang lebih efisien di wilayah Jawa Barat denfan menghasilkan klaster optimal berbasis K-Means dan Optimize Parameter Grid. Penggunaan metode Knowledge Discovery in Database (KDD), yang mencakup proses pemilihan, praproses, transformasi, data mining, dan interpretasi/ evaluasi hasil. Hasil penelitian ditunjukkan pada iterasi 1-10 menggunakan K=2 dengan nilai DBI terendah sebesar 0,377.====================================================Abstract : Optimisation of health worker data clustering model is a strategic step to understand the patterns and characteristics of certain data groups. The objectives of this study are to obtain the optimal K value according to the Davies Bouldin Index (DBI), obtain the iteration value required by the K-Means Clustering algorithm to achieve optimal results, and determine what type of metric will produce the smallest (DBI) value. This is important because this research helps to plan a more efficient distribution of health workers in the West Java region by producing optimal clusters based on K-Means and Optimise Parameter Grid. The use of Knowledge Discovery in Database (KDD) method, which includes the process of selection, preprocessing, transformation, data mining, and interpretation/evaluation of results. The results showed in iterations 1-10 using K=2 with the lowest DBI value of 0.377.
Application of Support Vector Machine for Classification of Toddlers Nutritional Status Based on Anthropometric Data Mohamad Alif Subhi; Rudi Kurniawan; Bani Nurhakim
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1844

Abstract

Stunting remains a major health issue in Indonesia, especially among toddlers. This study aims to classify the nutritional status of toddlers (stunted and non-stunted) using anthropometric data from the Kaggle public dataset with the Support Vector Machine (SVM) algorithm. This dataset includes data on the height, weight, age, and gender of toddlers. It should be emphasized that the data does not originate from the Ciherang Bandung Posyandu, but rather the Posyandu is used only as a context for the potential application of the developed model. The process includes data acquisition, preprocessing (including normalization and data balancing using SMOTE), SVM model training, and evaluation with accuracy, precision, recall, F1-score, and ROC-AUC. The model was trained with an 70:30 data split and optimal parameters (C=1.0, gamma=0.01, kernel=RBF). The results showed high performance, indicating that this model can support early detection of stunting and the implementation of decision support systems in public health services.
Optimizing Sentiment Analysis on the Linux Desktop Using N-Gram Features Muhamad Taufiq Hidayat; Rudi Kurniawan; Tati Suprapti
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12255

Abstract

Linux, or GNU/Linux, is a widely used open-source operating system built on the Linux kernel that is available for anyone to use, known for its security and privacy advantages. With advancements in information technology, protecting privacy has become increasingly challenging due to data extraction practices done by major tech companies. This has encouraged some Mastodon users to switch to Linux, with many expressing their opinions on using Linux as their main operating system. This research seeks to analyze the sentiments of Mastodon users toward Linux through sentiment analysis to understand whether the trend is predominantly positive, negative, or neutral. The methodology used includes collecting data with the help of the Mastodon.py library which then gets manually labelled with the assistance of a linguistic expert as well as a linguistic rule proposed by previous research. The text mining process includes preprocessing steps which includes feature extraction with n-Gram to gain the most optimized result as well as employing feature selection using TF-IDF. The Naïve Bayes algorithm is employed for text classification. The entire process of data analysis is conducted with the help of AI Studio (RapidMiner) software. The results show that the highest-performing model for sentiment analysis is achieved with an n-gram value of 3, revealing user sentiment polarity towards Linux on Mastodon as follows: 42% positive, 28% negative, and 30% neutral. The sentiment analysis model has an accuracy of 63%, with a precision of 70%, recall of 80%, and an f1-score of 74% which shows that this method is able to optimize the sentiment analysis process. 
Pendampingan Inovasi Dan Diversifikasi Produk Berbasis Kebutuhan Pasar Bagi UMKM Kota Cirebon Khaerul Anam; Rudi Kurniawan; Nurul Fathonah; Pratama Putra Sanjaya
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 5 : Juni (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in supporting economic growth and employment generation in Indonesia. However, many MSMEs, particularly in the food and culinary sector, continue to face challenges such as limited product variety, low utilization of digital technologies, and insufficient understanding of market needs. This Community Service Program aimed to enhance the capacity of MSMEs in product innovation and market-oriented product diversification in Cirebon City. The program employed a participatory approach consisting of five stages: partner needs assessment, market needs survey, product innovation workshops, product development mentoring, and evaluation. The participating MSMEs included Dana Kinasih, Syukuri, Torani Sumoer Makmur, Sambal Anakku, and Rumah Kukus. The results demonstrated significant improvements in both innovation capacity and business competitiveness. The total number of products increased from 8 to 16, average monthly revenue rose from IDR 3,000,000 to IDR 4,500,000, and the number of new customers increased by 25%. Furthermore, pre-test and post-test evaluations indicated substantial improvements in participants’ understanding of product innovation, digital marketing, e-commerce, and the application of Artificial Intelligence (AI) in business. Market survey findings revealed that consumers preferred products with modern packaging (85%), diverse flavor variants (70%), and convenient online purchasing options (75%). These findings suggest that market-oriented product innovation and diversification mentoring is an effective approach to improving the competitiveness and sustainability of MSMEs in Cirebon City.
Co-Authors Achmad Fikri Adbdul Muhyi Ade Awaludin Aditia Ahmad Faqih Ahmad Haekal Susanto Ahmad Muhaimin Ahmad Rai Fatkaozi Akmal Mustafa Arif Rinaldi Ayura Yufita Bani Nurhakim Bisma Mahendra Cep Lukman Rohmat Dadang Sudrajat Denni Pratama Dian Ade Kurnia Dodi Solihudin Edi Wahyudin Fathurrahman Fatihanursari Dikananda Fikri Firmansyah Fitriani Agustina Gilang Ramadhan Hilda Novia Ramadani Ibnu Fajri Khaerul Anam Martanto . Maulana Sujarwadi Mohamad Alif Subhi Muhamad Taufiq Hidayat Muhammad Hilmy Naufan Muhammad Khoerul Ikhwan Narasati, Riri Narasati Nurul Fathonah Odi Nurdiawan Othman bin Mohd Pratama Putra Sanjaya Puji Pramudya Marta Raditya Danar Dana Ramdan Irawan Rifki Hermawan Ryan Hamonangan Salman Alfarizi Tati Suprapti Taufik Hidayat Wartumi Wartumi Windy Astuti Yudhistira Arie Wijaya Yudis Firmansyah