Computer Science, Artificial Intelligence, Signal Processing, Computer Science Applications
22
Scopus Publications
586
Scholar Citations
11
Scholar h-index
12
Scholar i10-index
Scopus Publications
SOWA Method Framework: New Algorithm for Criteria Weight Balancing with a Hybrid Subjective and Objective Approach Sumanto, Aditya Lapu Kalua, Fintri Indriyani, Rakhmadi Irfansyah Putra, Ayuni Asistyasari, Adhie Thyo Priandika International Journal of Information Engineering and Electronic Business, 2026 This research proposes the implementation of the subjective and objective weighting approach (SOWA) method as a new approach in determining the criteria weights that combines subjective assessments from experts and data-driven objective calculations. The criteria weights generated from the SOWA method are then used in various multi-criteria decision-making (MCDM) methods, such as simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS), multi-objective optimization on the basis of ratio analysis (MOORA), grey relational analysis (GRA), multi-attribute utility theory (MAUT), weighted aggregated sum product assessment (WASPAS), weighted product (WP), simple multi-attribute rating technique (SMART), multi-attributive border approximation area comparison (MABAC), and Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA), to evaluate and rank alternatives. The research results show that the SOWA method is capable of producing balanced and representative weights, as well as consistent alternative rankings across MCDM methods. Sensitivity analysis of the ranking results indicates that all methods yield identical ranking results, signifying a high level of stability and reliability of the generated weights. These findings demonstrate that the SOWA method can serve as a solid foundation in decision support systems, particularly in the context of candidate selection or evaluation based on multiple criteria.
Comparison of SVM and KNN for BEMD-Based Texture Analysis in Huanglongbing Disease Severity Classification Rachmat Adi Purnama, Dwiza Riana, Mochamad Wahyudi, Sumanto Sumanto Beyond Technology Summit on Informatics International Conference Bts I2c 2025, 2025 Oranges are one of the tropical fruit commodities with high economic value and play an important role in the global agricultural sector. However, their productivity is threatened by Huanglongbing (HLB), the most destructive citrus disease worldwide. This disease is caused by phloem-limited bacteria of the genus Candidatus Liberibacter and is transmitted by the insect vector Diaphorina citri. HLB symptoms include irregular mottling of leaves, stunted growth, and small, misshapen, and bitter fruits, ultimately leading to yield decline and even plant death. This study aimed to analyze and classify the severity levels of HLB using the Bidimensional Empirical Mode Decomposition (BEMD) method combined with the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms. BEMD is used to extract texture features from three Intrinsic Mode Functions (IMF), which are merged through an image fusion process to produce a more informative composite image. The dataset consisted of 1,000 images of citrus leaves divided into four classes (healthy, mild, moderate, and severe). The test results show that SVM achieved an accuracy of 89% and KNN 88%, an improvement over previous research, which only achieved 77% accuracy using two IMFs. Further studies are recommended to add color and shape features to increase accuracy and support the implementation of real-time diagnostic systems in the field.
Real-Time Skin Disease Classification on Android Using Mobilenetv2 Muhammad Rafi Muttaqin, Delvia Dianita Trianasari, Yusuf Muhyidin, Sumanto Icaisd 2025 2025 International Conference on Advanced Information Scientific Development Artificial Intelligence Advancing Research and Computational Innovations for Global Welfare Proceedings, 2025 Skin diseases are prevalent, and timely diagnosis is not always accessible. This study investigated on-device screening using a lightweight convolutional neural network for mobile health. A MobileNetV2 classifier was trained on a curated multisource dataset with five classes: eczema, fungal infection, acne, normal skin, and an explicit non-skin category. The images were resized, normalized, and augmented. The models were optimized using Adam and a learning rate schedule. Performance was assessed using accuracy, precision, recall, F1-score, and confusion analyses. The best model was converted to TensorFlow Lite and deployed on Android for the field tests. The model achieved a 0.97 test accuracy with high per-class F1-scores and perfect non-skin rejection. The learning curves showed stable convergence with a minimal training-validation gap. On-device inference provides low-latency predictions while maintaining data on the handset. Confusion occurred mainly among visually similar dermatological classes, suggesting targeted augmentation and threshold calibration. A MobileNetV2-based pipeline with a non-skin safeguard enables accurate, privacy-preserving, realtime screening on smartphones. This approach supports early triage in resource-limited settings and offers a practical ac-curacy-efficiency balance for point-of-care deployment. Future work should broaden taxonomies, improve fairness across skin tones and devices, and extend external validation on a population scale.
Rhegmatogenous Retinal Detachment Eye Disease Detection For Digital Image Contrast Enchancement Analysis Using Clahe Method Indarti Indarti, Kurniabudi Kurniabudi, Mochamad Wahyudi, Sumanto Sumanto Beyond Technology Summit on Informatics International Conference Bts I2c 2025, 2025 Rhegmatogenous Retinal Detachment (RRD) is becoming more common, which emphasizes the need for trustworthy and easily available diagnostic methods that can increase picture visibility and detection precision. This study suggests a web-based program that combines automatic RRD recognition from fundus images with the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique for digital image contrast augmentation. The proposed system preprocesses retinal images using CLAHE to improve contrast and detail visibility before applying classification and segmentation analysis. Diagnostic performance has significantly improved, according to an evaluation employing 1,700 fundus pictures. With a Dice coefficient of 95.0%, CLAHE increased segmentation accuracy to 90.0% and improved the Intersection over Union (IoU) score from 68.0% to 78.4% (+10.4%). The model outperformed the baseline by 6.9% in classification, achieving 94.2% accuracy, 91.8% precision, 89.5% recall, and an AUC-ROC of 0.953. Technical examination revealed a 1.81× contrast improvement, SNR of 20.45 dB, and MSE of 0.0334, accompanied by a little 0.4-second increase in computing time.
Development of an Interactive Web App for Image Segmentation Using Otsu and LAB K-Means Sriyadi, Jufriadif Na’am, Mochamad Wahyudi, Sumanto Beyond Technology Summit on Informatics International Conference Bts I2c 2025, 2025 This study introduces a web-based image segmentation tool designed for embryo detection in egg images using the Otsu, K-Means, LAB K-Means, and Otsu + K-Means methods. The system aims to support the practical, interactive, and automated evaluation of segmentation accuracy in real time. A dataset of 150 egg images, categorized as infertile, fertile, or highly fertile, was used. The segmentation performance was evaluated using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Intersection over Union (IoU). The experimental results show that the hybrid Otsu + K-means method achieved the highest segmentation accuracy, with an average MSE of 0.012, RMSE of 0.109, and IoU of 0.90, outperforming the other techniques. Its superiority stems from combining global thresholding stability with local clustering refinement, which produces clearer and more consistent embryo boundaries. The web-based system not only allows batch testing but also supports individual image testing, making it efficient for educational, research, and practical purposes. The combination of thresholding and clustering techniques was effective in identifying embryo regions under various lighting and color conditions. This study demonstrates the potential of web-based image processing technology to enhance the precision and accessibility of embryo detection systems.
Lightweight YOLOv8 Model for Detecting Downy Mildew on Golden Alisha Melon Leaves Hendra Supendar, Jufriadif Na’am, Mochamad Wahyudi, Sumanto Beyond Technology Summit on Informatics International Conference Bts I2c 2025, 2025 Early detection of downy mildew, a major disease affecting melon crops, is crucial to minimize yield losses and support sustainable precision agriculture. This study aims to develop an automatic detection model for downy mildew on Golden Alisha melon leaves using a computer vision approach based on the YOLOv8 architecture. A custom dataset was collected from real agricultural fields in Serang, Indonesia, consisting of a relatively small number of healthy and infected leaves that were manually annotated by experts. To mitigate the limited dataset size and enhance model generalization, the data were preprocessed through resizing, normalization, and extensive augmentation to generate diverse image variations that better reflect real-field conditions. The YOLOv8 model was trained with tuned hyperparameters to balance accuracy and computational efficiency. Experimental results show that the proposed model achieved outstanding performance, with a Precision of 0.983, Recall of 0.93, mAP@50 of 0.995, and mAP@50–95 of 0.9, indicating high detection accuracy and robustness under varying lighting conditions. These results demonstrate that the YOLOv8- based model can accurately identify and localize infected areas in real time, providing a reliable tool for early disease diagnosis and effective crop management in the field, while also highlighting the need for future work involving larger and more diverse datasets.
Detection of Apple Fruit Diseases Using Deep Learning Sumanto, Andi Saryoko, Sumarna, Sriyadi, Hafis Nurdin, Ahmad Yani Icaisd 2025 2025 International Conference on Advanced Information Scientific Development Artificial Intelligence Advancing Research and Computational Innovations for Global Welfare Proceedings, 2025 Apples are among the most widely consumed fruits worldwide; however, their production is significantly affected by diseases such as scab, rot, and blotch, which lead to severe economic losses in the apple industry. Traditional visual inspection by experts is costly, time-consuming, and inconsistent, highlighting the need for an automated detection system to address these issues. With the advancement of deep learning, convolutional neural networks (CNNs) have become an effective tool for classifying plant diseases. In this study, a prototype apple disease classification system was developed using image processing techniques and the ResNet-50 architecture implemented in a Python environment. The Plant Pathology Apple Dataset was used for training and testing, with preprocessing applied to optimize the image quality before classification. The experimental results demonstrated that the number of epochs significantly influenced the system performance, with an accuracy ranging between 98% and 100% across 10 epoch configurations. Although fluctuations due to overfitting were observed, ResNet50 showed superior performance compared with traditional algorithms, indicating its strong potential for real-world agricultural applications.
Development of a Decision Support System Based on New Approach Respond to Criteria Weighting Method and Grey Relational Analysis: Case Study of Employee Recruitment Selection Dyah Ayu Megawaty, Damayanti Damayanti, Sumanto Sumanto, Permata Permata, Dandi Setiawan, Setiawansyah Setiawansyah International Journal on Informatics Visualization, 2025 The purpose of this research is to propose a new approach in the criteria weighting method using the RECA method, the RECA method can help provide a systematic and structured framework for determining criteria weights in multi-criteria decision making. The determination of weights using the RECA method is to increase objectivity and accuracy in the candidate assessment and selection process by determining the appropriate weight for each criterion based on responses and assessments from experts or stakeholders. Testing the RECA Method with Multi Attribute Decision Making (MADM) techniques is an important step in measuring the effectiveness of the RECA Method in the context of multi-criteria decision making. Ranking tests using Spearman correlation between the RECA method and other methods such as SAW with a correlation value of 1, MOORA with a correlation value of 0.9636, MAUT with a correlation value of 0.9515, WP with a correlation value of 0.891, SMART with a correlation value of 0.9636, and TOPSIS with a correlation value of 0.8788 show a high level of rank consistency between the RECA method and these methods. This indicates that the RECA Method has a strong ability to generate similar candidate rankings with other methods, validating its reliability and consistency in the context of multi-criteria decision making. Implications for further research include exploring the application of the RECA method in different decision-making contexts other than recruitment, such as performance evaluation, project selection, or supplier selection. Further research could investigate the integration of the RECA method with other decision-making methods or algorithms to improve its performance and applicability in complex decision environments. Comparative studies with larger sample sizes and diverse datasets can provide deeper insights into the effectiveness and reliability of the RECA method compared to other methods.
Potato Disease Classification: A Review Andi Saryoko, Sumanto, Elin Panca Saputra, Samudi, Evita Fitri, Linda Marlinda Icaisd 2025 2025 International Conference on Advanced Information Scientific Development Artificial Intelligence Advancing Research and Computational Innovations for Global Welfare Proceedings, 2025 Potatoes are one of the world's main foodstuffs after rice, wheat, and corn. They are also an important horticultural commodity with considerable domestic trade and export value. Potatoes are prioritized in agricultural development because they demonstrate strong competitiveness compared to other vegetables. In Indonesia, the role of potatoes continues to increase, both as fresh produce and as processed products. In the future, potato commodities are expected not only to serve as vegetables but also as an alternative source of carbohydrates, thereby contributing to food security. Potato cultivation in Indonesia is concentrated in highland areas (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1,000-3,000 \mathrm{m}$</tex> above sea level), with production centers located in Central Java, West Java, East Java, South Sulawesi, North Sumatra, West Sumatra, and Jambi. The objective of this research is to identify the most widely used approaches for potato disease prediction and to examine how these strategies have evolved over time. Both qualitative and quantitative methodologies are employed, with the Scopus database serving as the primary source for scientific journal publications. A total of 113 papers, published between 2020 and 2025, are analyzed. Bibliometric research is conducted using the bibliometrix library and the functions available in Biblioshiny. The findings indicate that potato disease prediction involves the use of diverse technologies. Many supervised machine learning techniques have been applied, with deep learning-particularly Convolutional Neural Networks (CNNs) emerging as the most extensively used approach for forecasting potato diseases. This rapidly advancing field of computer science highlights the importance of interdisciplinary collaboration, attracting researchers worldwide who are eager to contribute to this area of study.
Decision Support System for Internet Service Selection Using the Logarithmic Least Squares Weighting and Weighted Product Method Muhammad Najib Dwi Satria, Setiawansyah, Erliyan Redi Susanto, Ajeng Savitri Puspaningrum, Sumanto 2024 Beyond Technology Summit on Informatics International Conference Bts I2c 2024, 2024 Choosing the right internet service is an important challenge in today's digital era, especially for users who need reliability and optimal performance according to specific needs. This article proposes a decision support system (DSS) for internet service selection using a combination of Logarithmic Least Squares Weighting (LLSW) and Weighted Product Method (WPM) methods. The LLSW method is used to objectively determine the weight of the criteria based on the available data, while the WPM is used to evaluate and rank the available internet service alternatives. The purpose of the study is to implement a decision support system that can help users in choosing the best internet service by considering various important criteria objectively by combining LLSW and WPM methods to create an effective evaluation model for internet service selection. The study considers a variety of important criteria, such as data transfer speed, subscription fees, connection stability, number of devices, and customer service. The recommendation results showed that Biznet Home, First Media, and Indihome received the highest scores, indicating that these two services may be the top choice for users based on the assessment criteria used. The test results show that this decision support system is able to provide accurate and reliable recommendations in choosing the internet service that best suits the needs of users. This system is expected to help consumers in making more informed and effective decisions.
From Traditional to Innovation: Large Language Models in Fisheries Data Extraction Fitra Septia Nugraha, Irwansyah Saputra, Heru Triana, Mochamad Wahyudi, Ummu Radiyah, Arfhan Prasetyo, Muhammad Faisal, Juarni Siregar, Sumanto Proceeding 2024 International Conference on Information Technology Research and Innovation Icitri 2024, 2024
Real-Time Detection of Huanglongbing (HLB) Disease in Citrus Leaves Using Enhanced YOLO V8 Algorithm S Sumanto, RA Purnama, H Supendar, A Christian, KA Querio Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 23 (1), 1-7 , 2026 2026
Analisis Klaster Tingkat Stres Generasi Z Berdasarkan Pola Tidur dan Aktivitas Media Sosial Menggunakan Metode K-Means Clustering IH Putra, A Nurrahman, SA Saputra, S Sumanto, I Budiawan, R Pakpahan Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) 10 (1), 465-471 , 2026 2026
Implementasi Lightweight Neural Network Berbasis YOLOv8n untuk Klasifikasi Sampah Real-Time MH Ali, S Sulaiman, R Ardiyansyah, S Sumanto, G Taufiq, JT Kumalasari Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) 10 (1), 449-453 , 2026 2026 Citations: 1
Analisis Kinerja Model Yolov8 Berbasis Roboflow pada Deteksi Sampah Plastik Non-Plastik Otomatis MA Alghiffary, Y Saputra, SN Ali, S Sumanto, G Taufig, JT Kumalasari Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) 10 (1), 454-459 , 2026 2026 Citations: 1
Klasifikasi Penyakit Daun Tanaman Berbasis Citra Menggunakan Convolutional Neural Network Data Augmentation BD Suci, M Musfiroh, SP Sefriani, S Sumanto, R Pakpahan, I Budiawan Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) 10 (1), 439-448 , 2026 2026
Analisis Tren Popularitas Musik Spotify Menggunakan Chi-Square, Regresi Linear & Anova QAA Sentanu, MA Alamsyah, M Rivaldi, S Sumanto, G Taufiq Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) 10 (1), 460-464 , 2026 2026
Decision Support System for Determining Strategic Warehouse Locations Using a Combination of the WENSLO Weighting and RAWEC Method J Wang, S Setiawansyah, T Ardiansah, F Ulum, S Sumanto JUTI: Jurnal Ilmiah Teknologi Informasi, 165-184 , 2026 2026 Citations: 3
Implementasi Data Mining Pada Gangguan Tidur Berdasarkan Gaya HidupMenggunakan Metode K-Means Clustering IO Zalmi, S Faatin, Y Yunardus, S Sumanto, I Budiawan, R Pakpahan Jurnal Ilmiah Sistem Informasi 5 (1), 143-155 , 2026 2026
Comparison of Objective Weighting Methods in SAW and Their Effect on Alternative Ranking Results J Wang, S Setiawansyah, S Sumanto Jurnal Masyarakat Informatika 17 (1), 89-112 , 2026 2026 Citations: 2
Prediksi Penyakit Jantung Menggunakan Algoritma Machine Learning Berdasarkan Indikator Kesehatan J Karo-Karo, AR Syakir, R Raihan, S Sumanto, I Budiawan, R Pakpahan, ... Jurnal Ilmiah Sistem Informasi 5 (1), 84-95 , 2026 2026 Citations: 2
Penerapan Algoritma Random Forest Untuk Prediksi Tingkat Stres Dari Aktivitas Media Sosial MH Umar, RDA Pangaribuan, R Primadana, S Sumanto, I Budiawan, ... Jurnal Media Informatika 6 (6), 3113-3122 , 2025 2025 Citations: 1
Development of an Interactive Web App for Image Segmentation Using Otsu and LAB K-Means J Na’am, M Wahyudi 2025 2nd Beyond Technology Summit on Informatics International Conference … , 2025 2025
Lightweight YOLOv8 Model for Detecting Downy Mildew on Golden Alisha Melon Leaves H Supendar, J Na’am, M Wahyudi 2025 2nd Beyond Technology Summit on Informatics International Conference … , 2025 2025
Rhegmatogenous Retinal Detachment Eye Disease Detection For Digital Image Contrast Enchancement Analysis Using Clahe Method I Indarti, K Kurniabudi, M Wahyudi, S Sumanto 2025 2nd Beyond Technology Summit on Informatics International Conference … , 2025 2025
Comparison of SVM and KNN for BEMD-Based Texture Analysis in Huanglongbing Disease Severity Classification RA Purnama, D Riana, M Wahyudi, S Sumanto 2025 2nd Beyond Technology Summit on Informatics International Conference … , 2025 2025
Analisis Pendeteksian dan Klasifikasi Produk di Lingkungan Supermarket Menggunakan Dataset Roboflow TA Yamani, A Rofiqi, MI Fauzan, S Sumanto, G Taufiq, K Kumalasari Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan … , 2025 2025
Analisis Perbandingan Algoritma Random Forest, SVM, dan Logistic Regression untuk Menentukan Model Terbaik Prediksi Penyakit Diabetes A Firgiawan, FN Andriansyah, RN Ramadhan, Sumanto, I Budiawan, ... Jurnal Teknik Informatika Dan Teknologi Informasi 5 (3), 113–130 , 2025 2025
Application of Support Vector Machine Algorithm For Classification of Sleep Disorders ASB Tsabitah Raihanah Putri,Putri Nur Utamy,Mochamad Wahyudi, Sumanto Journal of Artificial Intelligence and Engineering Applications 5 (2025) , 2025 2025
MADM Decision Support Systems in Healthcare Optimizing Outcomes with AI and Analytics S Nani Purwati, Gunawan Budi Sulistyo, Noor Hasan, Sri Kiswati, Paulus Tofan ... 2025
Sistem Pendukung Keputusan: Konsep dan Implementasi Metode Multi-Attribute Decision Making Setiawansyah, Y Nuryaman, AL Kalua, AR Isnain, Sumanto 2025
MOST CITED SCHOLAR PUBLICATIONS
Rancang Bangun Sistem Pemenuhan Kebutuhan Gizi Pada Orang Sakit Berbasis Android MA Rizky, AS Puspaningrum, ER Susanto J. Inform. Dan Rekayasa Perangkat Lunak 4 (3), 319-325 , 2023 2023 Citations: 230
Pelatihan pembuatan media pembelajaran untuk meningkatkan kualitas pembelajaran bagi guru sekolah dasar R Rumidjan, S Sumanto, S Sugiharti Abdimas Pedagogi: Jurnal Ilmiah Pengabdian kepada Masyarakat 1 (1), 77-81 , 2017 2017 Citations: 57
Model Naïve Bayes Classifiers For Detection Apple Diseases S Sumanto, Y Sugiarti, A Supriyatna, I Carolina, R Amin, A Yani 2021 9th International Conference on Cyber and IT Service Management (CITSM … , 2021 2021 Citations: 24
K-means algorithm for clustering the location of accident-prone on the highway D Puspitasari, M Wahyudi, M Rizaldi, A Nurhadi, K Ramanda, Sumanto Journal of Physics: Conference Series 1641 (1), 012086 , 2020 2020 Citations: 19
Supplier selection very small aperture terminal using AHP-TOPSIS framework S Sumanto, K Indriani, LS Marita, A Christian J. Intell. Comput. Heal. Informatics 1 (2), 39 , 2020 2020 Citations: 15
Development of a Decision Support System Based on New Approach Respond to Criteria Weighting Method and Grey Relational Analysis: Case Study of Employee Recruitment Selection DA Megawaty, D Damayanti, S Sumanto, P Permata, D Setiawan, ... JOIV: International Journal on Informatics Visualization 9 (1), 314-323 , 2025 2025 Citations: 13
Decision Support System Perspective Using Entropy and Multi-Attribute Utility Theory in the Selection of the Best Division Head Setyawan, Sumanto JURNAL MEDIA INFORMATIKA BUDIDARMA 8 (2), 1001-1009 , 2024 2024 Citations: 12
Viola-Jones Algorithm for Face Detection using Wider Face Dataset S Sumanto, B Wijonarko, M Qommarudin, A Sudibyo, P Widodo, ... 2022 10th International Conference on Cyber and IT Service Management (CITSM … , 2022 2022 Citations: 12
Analisis kelayakan kredit rumah menggunakan metode Naive Bayes untuk mengurangi kredit macet S Sumanto, LS Marita, L Mazia, TW Ratnasari Applied Information System and Management (AISM) 4 (1), 17-22 , 2021 2021 Citations: 12
Sistem Informasi Koperasi Berbasis Web D Pribadi, R Wajhillah, A Wibowo, A Supiandi, S Sumanto Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat 1 (2) , 2018 2018 Citations: 12
Profile Matching Untuk Pemilihan Produk Asuransi Terbaik S Sumanto Jurnal Informatika Merdeka Pasuruan 5 (1), 464918 , 2020 2020 Citations: 11
Deteksi dan Prediksi Cerdas Penyakit Paru-Paru dengan Algoritma Random Fores D Kurniawan, M Wahyudi, L Pujiastuti, S Sumanto Indonesian Journal Computer Science 3 (1), 51-56 , 2024 2024 Citations: 10
Alternatif Pemilihan Supplier Barang IT VSAT Terbaik dengan Metode Technique For Order Preference By Similarity To an Ideal Solution (TOPSIS) S Sumanto, S Sumarna Jurnal Informatika Merdeka Pasuruan 4 (1), 465411 , 2019 2019 Citations: 9
Sistem Pendukung Keputusan Rekomendasi Hotel Bintang Tiga Menggunakan Kombinasi Entropy dan Combine Compromise Solution AD Wahyudi, S Sumanto, S Setiawansyah, A Yudhistira Bulletin of Artificial Intelligence 3 (1), 16-25 , 2024 2024 Citations: 8
An Entropy-Assisted COBRA Framework to Support Complex Bounded Rationality in Employee Recruitment RR Oprasto, J Wang, AFO Pasaribu, S Setiawansyah, R Aryanti, ... Bulletin of Computer Science Research 5 (3), 207-218 , 2025 2025 Citations: 7
From traditional to innovation: Large language models in fisheries data extraction FS Nugraha, I Saputra, H Triana, M Wahyudi, U Radiyah, A Prasetyo, ... 2024 International Conference on Information Technology Research and … , 2024 2024 Citations: 7
Pengembangan Sistem Deteksi Objek Botol Real-Time dengan YOLOv8 untuk Aplikasi Vision D Triyanto, M Zidan, M Wahyudi, L Pujiastuti, S Sumanto Indonesian Journal Computer Science 3 (1), 44-50 , 2024 2024 Citations: 7
Decision support system perspective using entropy and multi-attribute utility theory in the selection of the best division head MW Arshad, S Sumanto, S Setiawansyah J. Media Inform. Budidarma 8 (2), 1109-1119 , 2024 2024 Citations: 6
Peningkatan Mutu Sekolah Melalui Implementasi Perpustakaan Digital ER Susanto, R Rusliyawati, A Sucipto, A Wantoro, A Sulistiawati Journal of Engineering and Information Technology for Community Service 1 (2 … , 2022 2022 Citations: 6
Sistem Pendukung Keputusan Pemilihan Paket Umroh Menggunakan Metode AHP Pada PT. Shabilla Eraldo Utama. B Santoso, H Harianto, S Sumanto 2019 Citations: 6
