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All Journal Jurnal Simetris Bulletin of Electrical Engineering and Informatics SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Informatik : Jurnal Ilmu Komputer Jurnal Telematika Jurnal Mantik SULUH: Jurnal Abdimas Computer Science and Information Technologies Jurnal Abdimas Bina Bangsa International Journal of Basic and Applied Science Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT ABDIKOM
Ridwan Raafi'udin
Universitas Pembangunan Nasional Veteran Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Other

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Performance comparison of algorithms in the classification of fresh fruit types based on MQ array sensor data Hananto, Bayu; Raafi'udin, Ridwan
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9070

Abstract

Accurate classification of fresh fruit types is essential in the agricultural sector for ensuring quality control, minimizing waste, and enhancing food safety across the supply chain. This study evaluates the performance of four machine learning algorithms—artificial neural network (ANN), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF)—in classifying fruit freshness based on data obtained from electronic noses equipped with MQ array sensors. Experiments were conducted using a comprehensive dataset comprising various fruit combinations, and model performance was assessed using accuracy, precision, recall, and F1 score metrics. Results indicate that the RF algorithm achieved the highest accuracy (100%) and precision (1.00), demonstrating superior performance in both classification accuracy and computational efficiency. ANN and KNN also performed well, with accuracies of 96.80% and 97.10%, respectively, while LR yielded a lower but still effective accuracy of 91.16%. Statistical analysis confirms that RF's superior performance is statistically significant when compared to the other algorithms. These findings suggest that RF is the most effective algorithm for fruit freshness classification using electronic nose data, offering fast and reliable results that are well-suited for integration into real-time monitoring systems in agricultural and food retail applications.
Pelatihan Canva dalam Peningkatan Kapasitas Pembuatan Materi Ajar Interaktif pada Guru di TK Islam Al Azkar Dewi, Catur Nugrahaeni Puspita; Raafiudin, Ridwan; Indriana, Intan Hesti; Theresa, Ria Maria
Jurnal Pengabdian kepada Masyarakat Bidang Ilmu Komputer Vol 3 No 2 (2025): Jurnal Pengabdian Kepada Masyarakat Bidang Ilmu Komputer (ABDIKOM)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/abdikom.v3i2.11966

Abstract

Desain materi ajar yang menarik memiliki peran penting dalam meningkatkan efektivitas pembelajaran, khususnya untuk anak usia dini. Salah satu solusi praktis yang dapat digunakan guru adalah platform desain Canva, yang menyediakan berbagai fitur user-friendly untuk menciptakan materi pembelajaran visual yang menarik. Penelitian ini mengevaluasi hasil pelaksanaan pelatihan desain pembelajaran menggunakan Canva melalui data form evaluasi yang dikumpulkan dari peserta pelatihan guru TK Islam Al Azkar. Hasil evaluasi menunjukkan peningkatan signifikan dalam kemampuan peserta menggunakan Canva secara mandiri. Mayoritas peserta memberikan respon positif dan menyatakan bahwa pelatihan ini sangat bermanfaat. Diharapkan pelatihan ini dapat berkelanjutan untuk terus meningkatkan kompetensi guru dalam menciptakan media pembelajaran digital yang menarik dan efektif.
Utilization of Electronic Nose to Detect Quality of Meat in the Beef Ribs section Hananto, Bayu; Widiyanto, Didit; Raafi'udin, Ridwan
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 21, No 1 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v21i1.27066

Abstract

This study analyzes the use of Electronic Nose (E-Nose) in detecting the quality of beef on the ribs. This experiment used a variety of gas sensors, and found a significant pattern related to rib meat quality. There are three sensors, namely MQ137, MQ5, and MQ6, which show the value is inversely proportional to the other sensors. An increase in the value of this sensor indicates a decrease in the quality of the ribs. Furthermore, MQ8 gave the highest score in the "Good" and "Excellent" categories, while MQ5 and MQ6 gave the highest score in the "Equal" and "Not Eligible" categories. The analysis revealed that E-Nose has the ability to recognize changes in aroma associated with changes in the quality of rib meat. These results show that E-Nose can provide objective and fast information about the quality of beef in the ribs, which can support the food industry in decision making and product quality control. Further research is needed to optimize the use of sensors and validate this technology in various storage conditions and types of beef.
Kegiatan Training of Trainers (ToT) Pengelolaan Desa Cerdas Digital Bagi Aparatur Pemerintah Desa Rawa Panjang Kabupaten Bogor Solihin, Indra Permana; Triwahyono, Bambang; Wibisono, M. Bayu; Raafi’udin, Ridwan; Wirawan, Rio
Jurnal Pengabdian kepada Masyarakat Bidang Ilmu Komputer Vol 2 No 1 (2023): Jurnal Pengabdian Kepada Masyarakat Bidang Ilmu Komputer (ABDIKOM)
Publisher : Fakultas Ilmu Komputer

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

Abstract

Kemajuan teknologi informasi telah memengaruhi cara layanan masyarakat di daerah pedesaan. Salah satu konsep yang berkembang adalah "Smart Village" (Desa Cerdas), yang menggabungkan teknologi dalam berbagai aspek pembangunan pedesaan. Dalam kerangka Program Peningkatan Pemerintahan dan Pembangunan Desa (P3PD), Kementerian Desa Pembangunan Daerah Tertinggal dan Transmigrasi (Kemendesa PDTT) telah mendorong penggunaan teknologi informasi dan komunikasi sebagai salah satu prioritas dalam alokasi dana desa. Hal ini bertujuan untuk mencapai Sustainable Development Goals (SDGs) di tingkat desa. Training of Trainer (ToT) mengenai pengelolaan desa cerdas ini diberikan kepada aparatur pemerintah desa dengan harapan mereka dapat menjadi instruktur yang melatih pemangku kepentingan di desa. Hasil dari pelatihan ini mencakup kemampuan peserta untuk mengelola proses penyusunan dokumen secara digital, mulai dari tingkat RT hingga tingkat yang lebih tinggi. Hal ini memungkinkan percepatan dan efisiensi dalam penyampaian surat dengan memanfaatkan teknologi tanda tangan digital. Selain itu, hasil pelatihan ini diharapkan akan memungkinkan adopsi layanan digital yang terintegrasi secara daring pada tahun 2024. Sebagai hasilnya, setiap layanan kepada masyarakat akan berfokus pada penerapan konsep "Smart Village" dengan layanan persuratan digital yang terintegrasi secara daring, dengan tujuan memberikan pelayanan yang optimal kepada masyarakat.
Digital Empowerment:Improving Dasawisma's Capabilities in Online Marketing and Sales Through the Marketplace: Digital Empowerment: Peningkatan Kapabilitas Dasawisma Dalam Pemasaran Dan Penjualan Online Melalui Marketplace Raafi’udin*, Ridwan; Rosmawarni, Neny; Dewi, Catur Nugrahaeni Puspita; Edyana, Fajar
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 6 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i6.22099

Abstract

This community service activity aims to improve the capabilities of dasawisma women in RW 10, Ciracas Village, Ciracas District, East Jakarta in digital marketing and online sales through the marketplace platform. Through a series of practical trainings, participants have been given an in-depth understanding of digital marketing concepts, effective online marketing strategies, and the use of various features in the marketplace platform. In addition, legal aspects, online security, and network development are also the focus, providing a strong foundation for participants to run an online business sustainably. With the aim of empowering the local economy, this activity is expected to help dasawisma women take advantage of the potential of the local economy and increase their contribution to local economic development, as well as this activity helps the government technically in relation to the community directly. This activity was attended by 120 participants and as many as 85 have provided feedback from the material and experience that has been given. The training material was delivered in lectures and discussions. For the lecture session, it was delivered by presenting material about digital marketing through the marketplace and comparing it in a conventional way. Broadly speaking, participants are able to accept and understand the use of digital marketing technology through the marketplace. Furthermore, the results of the implementation of community service activities were evaluated using a SWOT analysis with results showing a significant increase in understanding of online security and marketing. With the results of the training that show for the better, the community, especially dasawisma women, is ready to develop businesses both managed independently and business groups managed under the auspices of local residents.
PERANCANGAN TAMPILAN APLIKASI UJIAN BERBASIS KOMPUTER UNTUK UJIAN HARIAN SEKOLAH MENENGAH ATAS Nugrahaeni Puspita Dewi, Catur; Raafi'udin, Ridwan
ILKOM Jurnal Ilmiah Vol 10, No 3 (2018)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v10i3.375.298-305

Abstract

Information and communication technology develops and forces users to always improve the implementation of our technology. Almost in society both in the industry in business competition and education sector to improve educational services. Increasing number of students, it must be required to use technology in managing their daily educational activities. Today there are many high schools that implement computer based test, only limited for training and taking of national final exams. So, with a short time there are still many students who are not familiar with the form of computer based test. Although these schools already have computer laboratories. In this research, the tim will try to design a computer based test system for schools that already have computer laboratories to be able to produce a daily test model independently. This research will produce a model graphic user interface (GUI) that can support the daily exam process in school and is expected to help students to deal with other computer based test models. To produce a User Friendly display model, many survey of students and teachers was conducted on the form of the test display with various features, such as photos, clocks, countdown timers, formulas, and standard values or constants.
Meningkatkan Produktivitas Tenaga Pendidik dan Kependidikan di SDN Kebagusan 01 melalui Penerapan Teknologi AI Bayu Hananto; Catur Nugrahaeni PD; Ridwan Raafi’udin; Fajar Edyana; Nurhafifah Matondang
Abditeknika Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): April 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/abditeknika.v6i1.11298

Abstract

Beban kerja administratif yang tinggi sering mengurangi waktu guru dan tenaga kependidikan untuk fokus pada kegiatan pembelajaran, sehingga diperlukan strategi pemanfaatan teknologi kecerdasan buatan (AI) untuk meningkatkan produktivitas kerja. Kegiatan Pengabdian kepada Masyarakat ini bertujuan meningkatkan pemahaman dan keterampilan guru serta tenaga kependidikan SDN Kebagusan 01 dalam memanfaatkan AI untuk mendukung tugas administratif dan penunjang pembelajaran. Metode pelaksanaan dibagi menjadi tiga tahap: (1) eksplorasi melalui survei dan observasi untuk memetakan kebutuhan dan literasi teknologi; (2) elaborasi melalui seminar, pendampingan, dan tugas mandiri yang mengarahkan peserta mempraktikkan penggunaan AI (seperti ChatGPT, Gemini, Copilot, dan Scite) dalam alur kerja harian; serta (3) evaluasi melalui kuesioner dan monitoring dan evaluasi (MONEV). Kuesioner diisi oleh 19 responden (guru dan tenaga kependidikan). Hasil menunjukkan 78,9% responden berada pada tingkat kemampuan teknologi menengah dan 68,4% menggunakan AI minimal beberapa kali dalam seminggu. Sebanyak 73,7% menyatakan AI dapat menghemat waktu kerja dan 63,2% menilai AI meningkatkan kualitas hasil kerja. Namun, 68,4% responden masih merasa perlu pelatihan khusus, dengan kendala utama berupa keterbatasan infrastruktur, perangkat, dan variasi kemampuan individu. Kegiatan ini terbukti meningkatkan kesiapan dan pemanfaatan AI di sekolah, sekaligus memberikan dasar bagi perancangan program lanjutan yang lebih terstruktur untuk mengoptimalkan produktivitas guru dan tenaga kependidikan.   High administrative workloads often reduce the time of teachers and education staff to focus on learning activities, so a strategy for utilizing artificial intelligence (AI) technology is needed to increase work productivity. This Community Service activity aims to improve the understanding and skills of teachers and education staff of SDN Kebagusan 01 in utilizing AI to support administrative tasks and support learning. The implementation method is divided into three stages: (1) exploration through surveys and observations to map the needs and technology literacy; (2) elaboration through seminars, mentoring, and self-directed tasks that direct participants to practice the use of AI (such as ChatGPT, Gemini, Copilot, and Scite) in daily workflows; and (3) evaluation through questionnaires and monitoring and evaluation (MONEV). The questionnaire was filled out by 19 respondents (teachers and education staff). The results showed that 78.9% of respondents were at the intermediate level of technological proficiency and 68.4% used AI at least a few times a week. As many as 73.7% stated that AI can save work time and 63.2% assessed that AI improves the quality of work results. However, 68.4% of respondents still felt the need for special training, with the main obstacles being limited infrastructure, devices, and variations in individual abilities. This activity has been proven to increase the readiness and utilization of AI in schools, as well as provide a basis for designing more structured advanced programs to optimize the productivity of teachers and education staff.
Metaheuristic Optimized Fuzzy Ensemble for Maize Seed Quality Prediction Using Vis/NIR Spectroscopy Ridwan Raafiudin; Ali Khumaidi; Indra Permana Solihin; Erik Mulyana
International Journal of Basic and Applied Science Vol. 15 No. 1 (2026): Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v15i1.886

Abstract

Maize (Zea mays) seed quality assessment is essential for supporting agricultural productivity and sustainable seed management. This study proposes a non-destructive machine learning framework for predicting maize seed quality using portable Visible/Near-Infrared (Vis/NIR) spectroscopy. The framework integrates NIPPY-based spectral preprocessing, metaheuristic wavelength selection, and fuzzy ensemble learning to handle spectral noise, multicollinearity, and nonlinear relationships in small-sample spectral data. Informative wavelengths were selected using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Two fuzzy ensemble models were developed: a Fuzzy Residual-Corrected Ensemble that refines predictions through residual-based fuzzy correction, and an RF+XGB Fuzzy Ensemble that combines Random Forest and XGBoost outputs using confidence-based fuzzy weighting. The models were evaluated for Moisture Content (MC), Germination Rate (GR), and Electrical Conductivity (EC) using repeated cross-validation, variability measures, and statistical validation. The proposed fuzzy ensemble models achieved R² values ranging from 0.8249 to 0.8689 and showed performance comparable to the strongest Random Forest baseline. Statistical comparison indicated that the main contribution of the fuzzy ensemble framework lies not in large gains in mean accuracy, but in prediction stability, residual correction, and uncertainty-aware modeling. SHAP-based explainability further identified physiologically meaningful wavelength regions, including visible pigment-related bands and near-infrared moisture-related bands. The dataset consists of 800 maize seed samples from four varieties under laboratory conditions, which limits generalization to field environments. Future work will focus on multi-location validation, domain adaptation, and real-time implementation. Overall, the proposed framework provides a statistically validated and interpretable approach for portable Vis/NIR-based maize seed quality prediction.
Effects of hyperparameter tuning on random forest regressor in the beef quality prediction model Ridwan Raafi'udin; Yohanes Aris Purwanto; Imas Sukaesih Sitanggang; Dewi Apri Astuti
Computer Science and Information Technologies Vol 6, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i2.p159-168

Abstract

Prediction models for beef meat quality are necessary because production and consumption were significant and increasing yearly. This study aims to create a prediction model for beef freshness quality using the random forest regressor (RFR) algorithm and to improve the accuracy of the predictions using hyperparameter tuning. The use of near-infrared spectroscopy (NIRS) in predicting beef quality is an easy, cheap, and fast technique. This study used six meat quality parameters as prediction target variables for the test. The R² metric was used to evaluate the prediction results and compare the performance of the RFR with default parameters versus the RFR with hyperparameter tuning (RandomSearchCV). Using default parameters, the R-squared (R²) values for color (L*), drip loss (%), pH, storage time (hour), total plate colony (TPC in cfu/g), and water moisture (%) were 0.789, 0.839, 0.734, 0.909, 0.845, and 0.544, respectively. After applying hyperparameter tuning, these R² scores increased to 0.885, 0.931, 0.843, 0.957, 0.903, and 0.739, indicating an overall improvement in the model’s performance. The average performance increase for prediction results for all beef quality parameters is 0.0997 or 14% higher than the default parameters.
Effects of Semi-Automated Preprocessing in The Beef Freshness Prediction based on Near Infrared Spectroscopy Ridwan Raafi'udin; Yohanes Aris Purwanto; Imas Sukaesih Sitanggang; Dewi Apri Astuti
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.15142

Abstract

This study investigates the application of near-infrared spectroscopy (NIR) within the wavelength range of 1350–2550 nm to predict key quality parameters of beef, specifically focusing on tenderloin cuts. The quality indicators assessed include drip loss, color, pH, moisture content, storage duration, and total plate count (TPC) as a measure of microbial load. Predictive modeling was conducted using three machine learning algorithms: Partial Least Squares (PLS), Support Vector Regression (SVR), and Random Forest Regressor (RFR). To enhance model accuracy, a semi-automated preprocessing pipeline was employed utilizing the Nippy library. This library integrates several spectral preprocessing techniques including Savitzky-Golay filtering, Standard Normal Variate (SNV), Robust Normal Variate (RNV), Local Standard Normal Variate (LSNV), as well as clipping, resampling, baseline correction, and smoothing.  Among the models developed using raw spectral data, the RFR model exhibited the highest performance, achieving coefficient of determination (R²) values of 0.82 for drip loss, 0.65 for color, 0.67 for pH, 0.61 for moisture content, 0.81 for storage duration, and 0.76 for TPC. Post preprocessing, the predictive accuracy improved significantly with R² values increasing to 0.89, 0.82, 0.87, 0.85, 0.91, and 0.90 respectively for the same parameters. These findings underscore the potential of combining advanced machine learning techniques with robust preprocessing methods to enhance the non-destructive, rapid assessment of beef quality parameters. This approach offers a promising tool for quality control in the meat processing industry, facilitating more efficient and accurate monitoring of product standards.
Co-Authors Ali Khumaidi Ali Khumaidi ali khumaidi, ali Andhika Octa Indarso Catur Nugrahaeni Puspita Dewi DEWI APRI ASTUTI Didit Widiyanto Edyana, Fajar Erik Mulyana Fajar Edyana Hananto, Bayu Henki Bayu Seta Imas Sukaesih Sitanggang Indra Permana Solihin Indra Permana Solihin Intan Hesti Indriana Jayanta Jayanta Matondang, Nurhafifah Mohamad Bayu Wibisono Mohammad Asaduzzaman Rasel Muhammad Adrezo Mulyantini, Sri Musthofa Galih Pradana Neny Rosmawarni Noor Falih Nugrahaeni Puspita Dewi, Catur Nurul Afifah Arifuddin Prananto, Muhammad Teguh RAHAYU, FAJAR Ria Maria Theresa Sitanggang, Imas S. Tri Rahayu Triwahyono, Bambang Wirawan, Rio Yohanes Aris Purwanto