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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) TEKNIK INFORMATIKA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Telematika JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH MIND (Multimedia Artificial Intelligent Networking Database) Journal CSRID (Computer Science Research and Its Development Journal) Building of Informatics, Technology and Science Jurnal Mantik Jurnal Manajemen, Ekonomi Sains Jurnal Varian JURNAL PIONIR INFOKUM Journal of Applied Data Sciences Jurnal Ipteks Terapan : research of applied science and education International Journal of Research in Vocational Studies (IJRVOCAS) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Jurnal Pengabdian Masyarakat dan Riset Pendidikan SISFOTENIKA Proceeding Of International Conference On Education, Society And Humanity
Wanayumini
Universitas Asahan
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Optimizing Disaster Response: A Systematic Review of Time-Dependent Cumulative Vehicle Routing in Humanitarian Logistics Dedy Hartama; Wanayumini Wanayumini; Irfan Sudahri Damanik
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29686

Abstract

Effective delivery of aid during disasters is crucial for mitigating impacts and ensuring well-being. A major challenge in humanitarian logistics is optimizing vehicle routing to maximize efficiency and minimize delivery times. which included 50 studies published between 2012 and 2022. We used the prism method to guide the process of choosing a study, which started from 200 Abstract which is identified and ends with 50 appropriate studies for in -depth analysis. This systematic literature review (SLR) examines the Time-Dependent Cumulative Vehicle Routing Problem (TDCVRP) in humanitarian logistics, identifying VRP variants, their applications, and effectiveness in disaster scenarios. Using a comprehensive search and PRISMA guidelines, the review highlights the importance of optimization models and advanced algorithms. Applications include aid delivery, evacuation management, and facility location optimization, though challenges like computational complexity and reliance on real-time data persist. The review identifies research gaps and suggests future research should focus on integrating advanced methods and improving practical applicability in disaster responses.
SEGMENTASI CITRA PADA CITRA ASLI BUAH JERUK BERDASARKAN NILAI THRESHOLDING Simangunsong, Dame Lasmaria; Wanayumini, Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 1 (2025): February 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i1.2491

Abstract

Abstract: This research aims to determine "Image Segmentation of Original Images of Orange Fruit Based on Thresholding Values". Thresholding value image segmentation is an image processing method that separates objects and backgrounds in an image based on differences in brightness levels. Image regions that tend to be dark will be made darker (perfect black with an intensity value of 0), while image regions that tend to be light will be made brighter (perfect white with an intensity value of 1). Therefore, the output of the segmentation process using the thresholding method is a binary image with a pixel intensity value of 0 or 1.The author conducted this research to obtain data by researching and concentrating on writings or sources identified with the problem being studied. Literary research can be obtained by searching for journals, ebooks taken from the internet that plan to find current hypotheses with the problem being studied.Based on the results of determining the thresholding value in determining the binary value, the threshold value is determined as a reference. If the matrix value is smaller than the threshold value then the result is 0 and if the matrix value is greater than the threshold value then the result is 1. Keywords: Thresholding Value, Image Segmentation, Orange Fruit Abstrak: Penelitian ini bertujuan untuk mengetahui “Segmentasi Citra Pada Citra Asli Buah Jeruk Berdasarkan Nilai Thresholding”. Yang mana Segmentasi citra nilai thresholding adalah metode pengolahan citra yang memisahkan objek dan background dalam suatu citra berdasarkan perbedaan tingkat kecerahannya. Region citra yang cenderung gelap akan dibuat semakin gelap (hitam sempurna dengan nilai intensitas sebesar 0), sedangkan region citra yang cenderung terang akan dibuat semakin terang (putih sempurna dengan nilai intensitas sebesar 1). Oleh karena itu, keluaran dari proses segmentasi dengan metode thresholding adalah berupa citra biner dengan nilai intensitas piksel sebesar 0 atau 1.Penelitian ini penulis lakukan untuk memperoleh data dengan meneliti dan berkonsentrasi pada tulisan atau sumber yang diidentifikasi dengan masalah yang diteliti. Penelitian kepustakaan bisa didapat dengan cara mencari jurnal, ebook yang diambil dari internet yang berencana untuk menemukan hipotesis saat ini dengan masalah yang diteliti.Berdasarkan Dari hasil penentuan nilai thresholding dalam menetukan nilai biner, maka sebagai acuannya ditentukan dengan nilai dari threshold nya. Jika nilai matriks lebih kecil dari nilai threshold maka hasilnya bernilai 0 dan jika nilai matriks lebih besar dari nilai threshold maka hasilnya bernilai 1. Kata kunci: Nilai Thresholding, Segmentasi Citra, Buah Jeruk
Online Shop Product Sales Prediction Using Multilayer Perceptron Algorithm Erica Rian Safitri; Lili Tanti; Wanayumini Wanayumini
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44286

Abstract

This study aims to develop a predictive model for forecasting product sales using the Multilayer Perceptron (MLP) algorithm. The model's performance was evaluated using key metrics, including the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score. The model achieved an MAE of 0.861, an MSE of 9.521, and an impressive R² score of 0.999, demonstrating its ability to accurately predict product sales with minimal error. Feature correlation analysis identified key variables related to the target prediction, which is the number of products ready for shipment, underscoring the importance of feature selection in enhancing model performance. Prediction results revealed variability among product sales, with products like Foodpak Matte 245 (Code 49) predicted to sell approximately 244.31 units, while others like Stiker Kertas (Code 90) showed lower sales forecasts. The findings suggest that strategic interventions may be necessary to boost sales for underperforming items and capitalize on the demand for popular products. Future improvements, such as optimizing the network architecture, experimenting with activation functions and optimization algorithms, and incorporating external factors such as market trends, could further enhance the model’s accuracy and predictive power. Overall, the MLP model demonstrates strong potential for product sales forecasting, providing valuable insights for business decision-making.
Impact of Hyperparameter Tuning on CNN-Based Algorithm for MRI Brain Tumor Classification Muhammad Nasri Gea; Wanayumini Wanayumini; Rika Rosnelly
JURNAL TEKNIK INFORMATIKA Vol 18, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44147

Abstract

This study examines the impact of hyperparameter tuning on the performance of Convolutional Neural Networks (CNN) in classifying brain tumors using MRI images. The dataset, sourced from Kaggle, underwent preprocessing techniques such as normalization, augmentation, and resizing to enhance consistency and diversity. The study evaluates five hyperparameter configurations, analyzing their effects on classification accuracy, precision, recall, and F1-score. The optimal configuration (batch size: 16, epochs: 10, learning rate: 0.001) achieved an accuracy of 86%, precision of 81%, recall of 85%, and an F1-score of 0.83. Other configurations showed trade-offs, where larger batch sizes increased recall but reduced precision. These findings emphasize the importance of careful hyperparameter tuning to optimize medical imaging classification performance.
DEVELOPMENT OF SKIN CANCER PIGMENT IMAGE CLASSIFICATION USING A COMBINATION OF MOBILENETV2 AND CBAM Juni Ismail; Lili Tanti; Wanayumini Wanayumini
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6541

Abstract

Skin cancer is one of the most common types of cancer worldwide, making early detection a crucial factor in improving patient recovery rates. This study compares three classification methods for pigmented skin cancer images using a combination of VGG16 with CBAM, MobileNetV2 with CBAM, and a hybrid VGG16-MobileNetV2 approach with transfer learning. The dataset used in this study is the Skin Cancer ISIC - The International Skin Imaging Collaboration (HAM10000) from Kaggle, which consists of 10,015 images covering seven types of skin cancer. After balancing, the dataset was reduced to 2,400 images with three main classes: Actinic Keratosis (AKIEC), Basal Cell Carcinoma (BCC), and melanoma (MEL), each containing 800 images. This study involves data preprocessing stages such as augmentation, normalization, and image resizing to ensure optimal data quality. The model training process was conducted using the Adam optimizer, a batch size of 16, and an Early Stopping mechanism to prevent overfitting. Evaluation results indicate that the MobileNetV2 with CBAM model achieved the best performance with a validation accuracy of 86%, followed by the VGG16-MobileNetV2 combination at 77%, while VGG16 with CBAM experienced overfitting with an accuracy of 54%. Additionally, the best-performing model demonstrated a precision of 86.53% and a recall of 86.46%, highlighting its superior stability in detecting skin cancer compared to previous single-model approaches. With these results, the developed system can serve as an effective tool for medical professionals in performing early and more accurate skin cancer diagnoses
Implementasi Metode ID3 dalam Faktor Penentuan Nilai Akhir Mahasiswa pada Mata Kuliah ElisaBeth S, Noprita; Nasution, Ammar Yasir; Alfitra, Andra; Sumantri, Ekoliyono Wahyu; Rahma, Intan Dwi; Harahap, Sarwedi; Wanayumini, Wanayumini
SISFOTENIKA Vol. 14 No. 1 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v14i1.416

Abstract

Setiap Perguruan Tinggi mempunyai peraturan untuk setiap pembelajaran, demikian juga dalam hal pemberian nilai akhir dari suatu mata kuliah yang dilakukan oleh dosen, tentunya dalam memberikan penilaian harus objektif dan sesuai dengan aturan yang telah ditetapkan, dalam pemberian nilai terkadang dosen ada memberikan nilai secara acak dan tidak tentu. Untuk membantu dosen dalam pemberian nilai yang baik berdasarkan aturan yang telah ditetapkan maka penelitian ini dibuat agar para dosen tidak ragu memberikan nilai akhir terhadap setiap mahasiswa. Penelitian ini menggunakan metode ID3 dan perhitungan pengolahan nilainya untuk log menggunakan Microsoft Excel serta aplikasi yang digunakan untuk pengujian adalah RapidMiner dengan kriteria yang digunakan adalah presensi, tugas, quiz, UTS dan UAS. Hasil dari penelitian ini RapidMiner dapat memetakan faktor atau menentukan komponen nilai akhir mahasiswa dengan baik dan cepat sehingga para dosen dapat mengikutinya sebagai panduan dalam hal pemberian nilai akhir mahasiswa pada suatu mata kuliah
REAL - TIME FACE DETECTION USING MATLAB HAAR CASCADE ALGORITHM Jannah, Miftahul; Wanayumini, Wanayumini; Ardana, Abdul Aziz; Selase, Septinur; Nurliana, Nurliana
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 3 (2025): Juni 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3692

Abstract

Abstract: Face detection remains a challenging task in computer vision due to real-world factors such as uneven lighting, varying viewpoints, distance, and occlusion. This study aims to develop and evaluate a real-time facial feature detection application (detecting face, eyes, nose, and mouth) using MATLAB and a webcam. Detection is performed using the Viola-Jones Cascade Classifier method through the vision.CascadeObjectDetector function. Key parameters that were adjusted include the MergeThreshold (ranging from 4 to 50 depending on the feature) and MinSize (based on estimated feature size within the frame). However, this study does not include tuning of other parameters such as FalseAlarmRate, which constitutes a limitation of the employed method. The adjustment of these parameters proved significant in improving detection accuracy and robustness under varying lighting conditions. Nevertheless, the system still encounters difficulties in detecting facial features in the presence of occlusion. This study also has the potential to serve as a foundation for further developments in face recognition, emotion detection, or biometric authentication.            Keywords: computer vision; haar cascade; MATLAB Abstrak: Deteksi wajah merupakan tantangan dalam visi komputer karena dipengaruhi oleh kondisi nyata seperti pencahayaan tidak merata, sudut pandang, jarak, dan obstruksi. Penelitian ini bertujuan untuk mengembangkan dan menguji aplikasi deteksi fitur wajah secara real-time (wajah, mata, hidung, dan mulut) menggunakan MATLAB dan kamera webcam. Deteksi dilakukan dengan metode Viola-Jones Cascade Classifier melalui fungsi vision.CascadeObjectDetector. Parameter penting yang disesuaikan adalah MergeThreshold (antara 4 hingga 50 tergantung fitur), MinSize (mengikuti estimasi ukuran fitur dalam frame). Namun, penelitian ini tidak mencakup penyesuaian parameter lain seperti FalseAlarmRate, yang menjadi salah satu keterbatasan metode yang digunakan. Penyesuaian parameter ini terbukti signifikan dalam meningkatkan akurasi deteksi dan ketahanan terhadap variasi kondisi pencahayaan. Namun, sistem masih mengalami kesulitan mendeteksi fitur wajah jika terjadi obstruksi. Penelitian ini juga berpotensi menjadi dasar untuk pengembangan lebih lanjut dalam face recognition, emotion detection, atau biometric authentication.  Kata kunci: visi computer; haar cascade; MATLAB 
MACHINE LEARNING FOR CLASSIFICATION OF IKM PROGRAMS AT THE DEPARTMENT OF INDUSTRY AND TRADE OF LANGKAT REGENCY Adelina, Mimi Chintya; Wanayumini, Wanayumini; Situmorang, Zakarias
PROCEEDING OF INTERNATIONAL CONFERENCE ON EDUCATION, SOCIETY AND HUMANITY Vol 2, No 1 (2024): Second International Conference on Education, Society and Humanity
Publisher : PROCEEDING OF INTERNATIONAL CONFERENCE ON EDUCATION, SOCIETY AND HUMANITY

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

Abstract

This research attempts to address these challenges by constructing a classification model using the Naive Bayes algorithm. Naive Bayes is a statistical algorithm that can be employed to predict the probability of membership in a class based on the available data. This method can assist the Department of Industry and Trade of Langkat Regency in selecting targeted programs and identifying SMEs (Small and Medium Enterprises) with potential success. The research will involve the collection and analysis of data regarding SMEs in Langkat Regency, including information about the industry type, geographic location, and business formality status. This data will be utilized to train the Naive Bayes classification model to predict the potential success of programs offered by the Department of Trade and Industry. Consequently, it is anticipated that this model can aid in more effective and efficient decision-making in the management of SME programs
THE ROLE OF CONVOLUTIONAL NEURAL NETWORK (CNN) AND RECURRENT NEURAL NETWORK (RNN) ON LEADERSHIP AND WORKFORCE AGILITY IN UMSU POSTGRADUATE PROGRAMS Triwanda, Eri; Wanayumini, Wanayumini; ayadi, B. Herawan H
PROCEEDING OF INTERNATIONAL CONFERENCE ON EDUCATION, SOCIETY AND HUMANITY Vol 2, No 1 (2024): Second International Conference on Education, Society and Humanity
Publisher : PROCEEDING OF INTERNATIONAL CONFERENCE ON EDUCATION, SOCIETY AND HUMANITY

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

Abstract

Convolutional Neural Network (CNN) is a development of Multilayer Perceptron (MLP) designed to process and classify data. Recurrent Neural Network (RNN) is an artificial neural network architecture known for its good performance as it processes input data sequentially. In a study conducted by Sugiharto et al., the Recurrent Neural Network method was found to have an accuracy rate of 65%, with an average macro precision of 0.59, an average macro recall of 0.62, and an average macro F1-score of 0.60. The weighted average precision was 0.67, the weighted average recall was 0.65, and the weighted average F1-score was 0.65. Both Convolutional Neural Network and Recurrent Neural Network can be used for research in organizational management, especially in the postgraduate program at Universitas Muhammadiyah Sumatera Utara. The development of artificial intelligence-based systems can also assist management in providing better services. This research describes the implementation of Convolutional Neural Network (CNN) with Recurrent Neural Network (RNN) to examine the roles of Leadership and Workforce Agility in organizational agility within the postgraduate program at UMSU. The analysis results draw conclusions regarding the best values for accuracy, precision, recall, and F-measure between the Convolutional Neural Network (CNN) and Recurrent Neural Network algorithms
CLASSIFICATION OF K-NEAREST NEIGHBOR (K-NN) AND CONVOLUTIONAL NEURAL NETWORK (CNN) FOR THE IDENTIFICATION OF BRONCHITIS DISEASE IN TODDLERS USING GLCM FEATURE EXTRACTION BASED ON THORAX X-RAY IMAGES Nasution, M. Fachrurrozi; Wanayumini, Wanayumini; Roesnelly, Rika
PROCEEDING OF INTERNATIONAL CONFERENCE ON EDUCATION, SOCIETY AND HUMANITY Vol 2, No 1 (2024): Second International Conference on Education, Society and Humanity
Publisher : PROCEEDING OF INTERNATIONAL CONFERENCE ON EDUCATION, SOCIETY AND HUMANITY

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Abstract

K-Nearest Neighbor (K-NN) is a classification method that seeks the majority class from the k-nearest neighbors of a sample to be classified. Meanwhile, Convolutional Neural Network (CNN) is a type of artificial neural network specifically designed to recognize patterns in image data. The features are then extracted using GLCM (Gray Level Co-occurrence Matrix) from Thorax X-Ray images. This research aims to develop two classification approaches, namely K-Nearest Neighbor (K-NN) and Convolutional Neural Network (CNN), to detect bronchitis disease in toddlers based on Thorax X-Ray images. Feature extraction based on the Gray Level Co-occurrence Matrix (GLCM) is used to transform images into numerical features that can be processed by classification algorithms. The results from both methods will be combined based on various evaluation metrics, such as accuracy, precision, recall, F1-score, etc
Co-Authors Ade Clinton Sitepu Ade Clinton Sitepu Adelina, Mimi Chintya Al Ayyub, Muhammad Azwar Alfitra, Andra Amanda, Windi Winona Andi Zulherry Annas Prasetio Annas Prasetio Ardana, Abdul Aziz Arjuna Ginting ayadi, B. Herawan H B. Herawan Hayadi Dedy Hartama Dedy Hartama Desi Irfan Desi Irfan Devy Pratiwi Dini Farhatun Doughlas Pardede Elisabeth S, Noprita Erica Rian Safitri Erlina Erlina Gea, Muhammad Nasri Habib Satria Hanani Hutabarat, Jamina Harahap, Sarwedi Hartama, Dedy Hartono Hartono Hasibuan, Cici Cahyati Husin Sariangsah Ichsan Firmansyah Indra Mawanta Indra Swanto Ritonga Irfan Sudahri Damanik Ismail, Juni isnaini, fitri JAKA KUSUMA Juni Ismail Karina Andriani Khoirunsyah Dalimunthe Lili Tanti Lili Tanti Lubis, Cindy Paramitha lvindra, Farhan A M yoggi saputra M. Ari Iskandar Margolang, Khairul Fadhli Masri Wahyuni Mhd Fauzan Yafi Miftahul Jannah Muhammad Fachrurrozi Nasution Muhammad Nasri Gea Muhammad Sadikin Muhammad Sayid Amir Ali Lubis Muhammad Zarlis Mutiara S. Simanjuntak Nasution, Ammar Yasir Novendra Adisaputra Sinaga NURLIANA NURLIANA P.P.P.A.N.W. Fikrul Ilmi R.H. Zer Prasetya, Hardi Rahma, Intan Dwi Rika Rosnelly Rika Rosnelly Rika Rosnelly RIKA ROSNELLY Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly Rika Rosnelly, Rika Roesnelly, Rika Rohima, Rohima Roslina Roslina, Roslina Roslina, Roslina Sartika Mandasari Selase, Septinur Sihombing, Rotua Simangunsong, Dame Lasmaria Sri Ayu Rosiva Srg Sugeng Riyadi Sugeng Riyadi Sumantri, Ekoliyono Wahyu T S Gunawan Tambunan, Fazli Nugraha Tammamah Lubis, Hartati Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Triana Puspa handayani Triwanda, Eri Vicky Rolanda Wardana, Revo Wulandari, Wulandari Yuni Franciska Br Tarigan Zakarias Situmorang Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.