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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal INKOM Proceedings of KNASTIK Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal technoscientia Jurnal Teknologi Prosiding SNATIKA Vol 01 (2011) JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Information Technology and Computer Science Emerging Science Journal IJID (International Journal on Informatics for Development) CCIT (Creative Communication and Innovative Technology) Journal JISKa (Jurnal Informatika Sunan Kalijaga) JURNAL TEKNOLOGI TECHNOSCIENTIA Journal of Applied Data Sciences Makara Journal of Science Jurnal Informatika dan Multimedia JEECS (Journal of Electrical Engineering and Computer Sciences) Madani: Multidisciplinary Scientific Journal
Retantyo Wardoyo
Universitas Gadjah Mada
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network Budiarsa, Rahmat; Wardoyo, Retantyo; Musdholifah, Aina
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3256-3265

Abstract

Face recognition entails detecting and identifying facial attributes. Mask region convolutional neural network (R-CNN) method is a prominent approach, while prior research predominantly delved into refining loss functions and perfecting object and face detection, recognizing, and identifying faces using imperfect data remained relatively unexplored. This study focuses on an occluded dataset comprising Indonesian faces, wherein 'occluded' denotes facial data that lacks complete visibility-encompassing instances where objects obscure faces or are partially cropped. This investigation involves a deliberate experiment that tailors the intersection over union (IoU) of the region proposal network (RPN) to suit the nuances of occluded Indonesian faces, thereby augmenting accuracy in recognition and segmentation tasks. The innovation IoU in the strategic utilization of Anchors, which involves the exclusion of anchors falling beyond the image borders to optimize computational efficiency. The outcomes of this research are striking; it showcases a remarkable 14.75%, 10.9%, and 12.97% surge based on mean average precision (mAP), mean average recall (mAR), and F1-Scores compared to the conventional Mask R-CNN approach. Notably, our proposed model elevates the average accuracy by 10% to 15% and decreases running time by 21%, a noteworthy enhancement compared to the preceding model. This progress is substantiated by validation utilizing 300 instances dataset, reinforcing the robustness of our approach.
Feature selection based on chi-square and ant colony optimization for multi-label classification Widians, Joan Angelina; Wardoyo, Retantyo; Hartati, Sri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3303-3312

Abstract

Text classification is widely used in organizations with large databases and digital documents. In text classification, there are many features, most of which are redundant. High-dimensional features impact multi-label classification performance. Feature selection is a data processing technique that can overcome this problem. Feature selection techniques have two major approaches: filter and wrapper. This paper proposes a hybrid filter-wrapper technique combining two algorithms: Chi-square (CS) and ant colony optimization (ACO). In the first stage, CS is used to reduce the number of irrelevant features. The ACO method is in the second stage. The ACO is applied to select the efficient features and improve classifier performance. The experiment results show that CS-ACO, CS-grey wolf optimizer (GWO), CS, and without feature selection (FS) have a micro F1-score based multinomial naïve Bayes classifier including 80%, 79.75%, 79.64% and 77.78%. The result indicates that the CS-ACO algorithm is suitable for solving multi-label classification problems.
Classification of clove types using convolution neural network algorithm with optimizing hyperparamters Tempola, Firman; Wardoyo, Retantyo; Musdholifah, Aina; Rosihan, Rosihan; Sumaryanti, Lilik
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5533

Abstract

This study uses clove imagery by classifying it according to ISO 2254-2004 standards: whole, headless, and mother clove. This type of clove will affect the quality and economic value when it has been dried. For this reason, it is necessary to take a first step to control cloves' quality. One way is to classify it from the start. This research will utilize the convolution neural network algorithm and compare it with model transfer learning and modified VGG16 architecture on clove images. In addition, research is also looking for the most optimal hyperparameter. The results of this study indicate that the application of convolution neural network (CNN) to clove images obtains an accuracy value of 84% using a hyperparameter of 50 epochs, a learning rate of 0.001, and a batch size of 16. Meanwhile, for the application of transfer learning VGG16, Resnet50, MobileNetV2, InceptionV3, DensetNet151, and modified VGG16 have respectively each of the highest accuracy including 95.70%, 76.15%, 96.89%, 98.07%, 98.96%, and 99.11%.
Image dermoscopy skin lesion classification using deep learning method: systematic literature review Nugroho, Arief Kelik; Wardoyo, Retantyo; Wibowo, Moh Edi; Soebono, Hardyanto
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6077

Abstract

Classifying skin lesions poses a significant challenge due to the distinctive characteristics and diverse shapes they can exhibit, particularly in identifying early-stage melanoma. To address the shortcomings of the prior method, a neural network-driven strategy was introduced to differentiate between two types of skin lesions based on dermoscopic images. This new approach comprises four key stages: i) initial image processing, ii) skin lesion segmentation, iii) feature extraction, and iv) classification using deep neural networks (DNNs). Computers can also provide more accurate diagnosis results. In the review process, the articles are analyzed and summarized to contribute to developing methods or application development in skin lesion diagnosis. The stages include defining the relevant theory, input data, methods used (architecture and modules), training process, and model evaluation. This review also explores information based on trends and users, emphasizing the skin lesion segmentation process, skin lesion classification process, and minimal datasets as recommendations for future research.
Utilizing Support Vector Machine and Dimensionality Reduction to Identify Student Learning Styles within the Felder-Silverman Model Hananto, Andhika Rafi; Musdholifah, Aina; Wardoyo, Retantyo
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.337

Abstract

This research explores the impact of questionnaire structure on the accuracy of learning style classification, focusing on the optimization of the Felder-Silverman Learning Style Model (FSLSM) using advanced machine learning techniques. By employing Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction, the study identifies and retains the most informative variables from the original 44-question FSLSM instrument. These refined features are then processed through a Support Vector Machine (SVM) algorithm to evaluate classification performance across various core-to-secondary item ratios. Results indicate that the most optimal configuration—produced through the combined PCA-t-SNE reduction—achieved a peak accuracy of 89.54%, surpassing other configurations and highlighting the effectiveness of selective question modeling. This approach not only enhances prediction accuracy but also introduces a more efficient and streamlined FSLSM formula, reducing redundancy without compromising diagnostic precision. The study contributes to educational data mining by presenting a data-driven strategy for learning style assessment and offers practical implications for the development of adaptive, personalized learning systems grounded in statistically validated models.
Chicken Weight Prediction in Close House Farm Using Fuzzy Method Pratama, Kharis Suryandaru; Wardoyo, Retantyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 4 (2024): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.96406

Abstract

This study aims to predict the weight of chicken on a close house farm using the Fuzzy Logic method by implementing the LUKASEWICZ method. The data used in this study are the factors that affect the weight of the chicken including the number of chickens entering, the initial weight of the chicken, the temperature of the cage, the humidity of the cage, the quantity of water, the quantity of feed, and air circulation (wind speed) in the cage. The results of the calculation of Fuzzy with the łukasiEwicz method of these factors can be used to predict the chicken boboy during the harvest period and according to the weight set during the harvest period. The accuracy of the prediction value with the Absolute Percentage Error (MAPE) mean test shows the value of 5,3981%. It was concluded that the calculation of fuzzy with the łukasiewicz method can be used to predict the weight of chicken during the harvest period.
Review of Intent Diversity in Information Retrieval : Approaches, Models and Trends Mustakim, M; Wardoyo, Retantyo; Mustofa, Khabib
Journal of Information Technology and Computer Science Vol. 3 No. 2: November 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (392.012 KB) | DOI: 10.25126/jitecs.20183259

Abstract

The fast increasing volume of information databases made some difficulties for a user to find the information that they need. Its important for researchers to find the best method for challenging this problem. user intention detection can be used to increase the relevancies of information delivered from the information retrieval system. This research used a systematic mapping process to identify what area, approaches, and models that mostly used to detect user intention in information retrieval in four years later. the result of this research identified that item-based approach is still the most approach researched by researchers to identify intent diversity in information retrieval. The used of item-based approach still increasing from 2015 until 2017. 34% paper used topic models in their research. It means that Topic models still the necessary models explored by the researchers in this study.
Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning Wardoyo, Retantyo; Yuniarti, Wenty Dwi
IJID (International Journal on Informatics for Development) Vol. 9 No. 1 (2020): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09105

Abstract

The phenomenon of the rapid transfer of learning to online systems, such as e-Learning, has occurred massively. Institutions must ensure that student assessments run well. The characteristics of learning in e-Learning require an appropriate assessment method. The fuzzy logic method can be an option. Research shows that fuzzy logic is capable of providing flexible and objective performance evaluation. Fuzzy logic is a method that can overcome the uncertainty of transparency and objectivity of student assessments. In general, fuzzy logic applications are carried out by standards. Modification is an attempt to reveal the flexibility and to optimize the use of fuzzy logic. This study presents an analysis of fuzzy logic modification for the assessment of Algorithm and Data Structures courses held in e-Learning. These modifications include (i) modification of the parameter score with score compatibility, (ii) consequent modification of the fuzzy rules and (iii) modification of the implication process. The study results show that although the use of fuzzy logic requires more complicated procedures and tools, it can present various kinds of assessment as an option for educators to assess students in e-Learning.
Sistem Pendukung Keputusan Dalam Menentukan Lokasi Pembangunan Pelabuhan Pantai Berbasis AHP (Study Kasus: PT. Pilar Dasar Membangun Provinsi Sulawesi Utara) Lausu, Suwandi; Wardoyo, Retantyo
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 6 (2025): July 2025
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.16792391

Abstract

Indonesia is one of the archipelagic countries in the world that has two-thirds of its territory is water and is located in a strategic location because it is at the crossroads of world trade routes, in addition Indonesia also has a wide variety of ethnic groups scattered throughout Indonesia. So the need for a port is very important for every area and city in Indonesia. However, many ports built in Indonesia pay little attention to the location where the port will be built. So the ports in Indonesian waters are not long enough. To overcome the problem a computer system in the form of a decision support system determining the location of port construction is required. This study aims to build a system for decision support in determining the location of coastal port construction. The method used was AHP by comparing the parameters present at each port location. parameters include, Soil, Access, Hinterland, Land, Waves and Structure of the soil. So that the port to be built can work properly.
Co-Authors Abdul Wahid Adiananda Adiananda Agus Harjoko Ahmad Ashari Ahmad Asharit Aina Musdholifah Aina Musdholifah Albert Dian Sano Anastasya Latubessy Andeka Rocky Tanaamah Andika Kurnia Adi Pradana Andriyani, Widyastuti Anny Kartika Sari Arief Kelik Nugroho, Arief Kelik Azhari Azhari Azhari Azhari Azhari Subanar Bambang Sugiantoro Bambang Sugiantoro Bangun Wijayanto Bernard Renaldy Suteja Budiarsa, Rahmat Christian Dwi Suhendra Clara Hetty Primasari Danang Lelono Decky Hendarsyah Desyandri Desyandri Djemari Mardapi Doni Setyawan E. Elsa Herdiana Murhandarwati Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Edi Winarko Edi Winarko Enny Itje Sela Gede Angga Pradipta, Gede Angga Hananto, Andhika Rafi Hardyanto Soebono Herri Setiawan Herri Setiawan I Made Agus Wirawan I Made Agus Wirawan Ida Ayu Putu Sri Widnyani Istiyanto, Jazi Eko Jazi Eko Istiyanto Jazi Eko Istiyanto Jazi Eko Istiyanto Joan Angelina Widians, Joan Angelina Khabib Mustofa Khairunnisa Khairunnisa Kusrini Kusrini Lausu, Suwandi Lilik Sumaryanti M Mustakim M.Cs S.Kom I Made Agus Wirawan . Moh Edi Wibowo Muhamad Munawar Yusro Muhammad Fakhrurrifqi Muhammad Mukharir Munakhir Mudjosemedi Mustakim, M Nola Ritha NUR HASANAH Peggi Sri Astuti Pratama, Kharis Suryandaru Purba, Susi Eva Maria Purwo Santoso Putri Elfa Mas`udia Rahman Erama Rahmat Budiarsa Ramos Somya Rika Rosnelly Rosa Delima Rosihan Rosihan, Rosihan Santoso, Purwo Silmina, Esi Putri Sri Andayani Sri Hartati Sri Hartati Sri Hartati Sri Hartati Sri Kusrohmaniah, Sri Sri Kusumadewi Sri Mulyana Subahar, Subahar Subanar . Suryo Guritno Suryo Guritno Suryo Guritno Tempola, Firman Tenia Wahyuningrum Wenty Dwi Yuniarti, Wenty Dwi Wibowo, Moh Edi Winarko, Edi Wiwiet Herulambang Yayi Suryo Prabandari