Article Per Year (5 Year)
p-Index From 2021 - 2026
7.996
P-Index
This Author published in this journals
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 Jurnal Intelek Dan Cendikiawan Nusantara SISFOTENIKA Proceeding Of International Conference On Education, Society And Humanity
Wanayumini
Universitas Asahan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

Published : 64 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 64 Documents
Search

 3   4   5   6   7 
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

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

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
PREDIKSI KEBUTUHAN TENAGA PENDIDIK BERDASARKAN PERTUMBUHAN JUMLAH SISWA/I MENGGUNAKAN METOE MONTE CARLO Hasibuan, Cici Cahyati; Wanayumini, Wanayumini
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4095

Abstract

Abstract: This study aims to predict the need for educators at the junior high school level in Asahan Regency based on student growth using the Monte Carlo method. This process is done by generating random numbers through the Linear Congruential Method (LCM) and mapping them into the probability distribution of the number of students based on historical data. The prediction results show the number of junior high school students in 2026 reached 41,161 people. Based on the ideal ratio of 1 teacher for 20 students, an additional 785 educators are needed in sub-districts that experience shortages. This prediction system is developed in the form of a web-based application using PHP and MySQL to support the automatic simulation process. The implementation of this system is expected to help the Education Office in planning educators' needs efficiently and data-based. Keyword: Prediction; Monte Carlo; Educators; Students. Abstrak: Penelitian ini bertujuan untuk memprediksi kebutuhan tenaga pendidik tingkat SMP di Kabupaten Asahan berdasarkan pertumbuhan jumlah siswa menggunakan metode Monte Carlo. Proses ini dilakukan dengan membangkitkan angka acak melalui metode Linear Congruential Method (LCM) dan memetakannya ke dalam distribusi probabilitas jumlah siswa berdasarkan data historis. Hasil prediksi menunjukkan jumlah siswa SMP pada tahun 2026 mencapai 41.161 orang. Berdasarkan rasio ideal 1 guru untuk 20 siswa, dibutuhkan tambahan sebanyak 785 tenaga pendidik di kecamatan yang mengalami kekurangan. Sistem prediksi ini dikembangkan dalam bentuk aplikasi berbasis web menggunakan PHP dan MySQL untuk mendukung proses simulasi secara otomatis. Implementasi sistem ini diharapkan dapat membantu Dinas Pendidikan dalam merencanakan kebutuhan tenaga pendidik secara efisien dan berbasis data. Kata kunci: Prediksi; Monte Carlo; Tenaga Pendidik; Siswa.   
Pengabdian Masyarakat dalam Pengenalan Dunia Cyber untuk Membangun Kesadaran Literasi Digital Bagi Siswa SMA N 1 Ujung Padang: Pengabdian Wanayumini; Muhammad Azwar Al Ayyub; Dini Farhatun; Triana Puspa handayani; M yoggi saputra; Mhd Fauzan Yafi
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 2 (October 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i2.3054

Abstract

Kegiatan pengabdian masyarakat ini diselenggarakan di SMA Negeri 1 Ujung Padang dengan tujuan untuk meningkatkan pengetahuan dan kesadaran siswa mengenai pentingnya literasi digital dan pengenalan keamanan cyber. Meskipun perkembangan teknologi digital yang pesat menawarkan banyak manfaat, namun juga menimbulkan sejumlah risiko, termasuk maraknya penyebaran hoaks, pencurian data, dan kejahatan cyber. Oleh karena itu, siswa perlu diajarkan cara menggunakan teknologi secara cerdas, aman, dan bertanggung jawab. Metode yang digunakan dalam kegiatan ini adalah koordinasi dengan pihak sekolah, penyampaian materi dalam bentuk presentasi, diskusi, serta sesi tanya jawab. Kegiatan ini diikuti oleh siswa/i kelas XII IPA dan IPS. Dapat disimpulkan bahwa kegiatan sosialisasi telah sukses dan berhasil dilaksanakan dengan baik. Dimana dalam kegiatan para siswa sangat antusias dan aktif dalam diskusi dan tanya jawab. Siswa memperoleh pemahaman yang lebih baik tentang risiko di dunia digital serta strategi menjaga keamanan data pribadi. Kegiatan ini juga menumbuhkan kesadaran untuk memanfaatkan teknologi tidak hanya untuk hiburan, tetapi juga sebagai sarana pembelajaran dan pengembangan diri.
Co-Authors Ade Clinton Sitepu Ade Clinton Sitepu Adelina, Mimi Chintya Al Ayyub, Muhammad Azwar Alfitra, Andra Amanda, Windi Winona Ammar Yasir Nasution Andi Zulherry Annas Prasetio Annas Prasetio Ardana, Abdul Aziz Arjuna Ginting ayadi, B. Herawan H B. Herawan Hayadi Darma, Ali Dedy Hartama Desi Irfan Desi Irfan Devy Pratiwi Dini Farhatun Doughlas Pardede Elisabeth S, Noprita Erica Rian Safitri Erlina Erlina Fajar Hardiansyah 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 Lili Tanti, Lili Lubis, Cindy Paramitha lvindra, Farhan A M yoggi saputra M. Ari Iskandar Maharani, Puan Margolang, Khairul Fadhli Masri Wahyuni Mhd Fauzan Yafi Mhd Zahir Az Zikri Miftahul Jannah Muhammad Fachrurrozi Nasution Muhammad Nasri Gea Muhammad Sadikin Muhammad Sayid Amir Ali Lubis Muhammad Zarlis Mutiara S. Simanjuntak Nasir Fadillah Marpaung Novendra Adisaputra Sinaga NURLIANA NURLIANA Nurul Akmal Jodhy P.P.P.A.N.W. Fikrul Ilmi R.H. Zer Prasetya, Hardi Putri, Nazifa Rahma, Intan Dwi Rahmat 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 Safitri, Erica Rian Sartika Mandasari Selase, Septinur Sihombing, Rotua Simangunsong, Dame Lasmaria Siti Gkhonia Sri Ayu Rosiva Srg Sugeng Riyadi Sugeng Riyadi Sultan Nico Nur'Arsy Sumantri, Ekoliyono Wahyu Syahrizal Syahrizal 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 Utami Wardah Hafiz Vicky Rolanda Vivin Wulandari Wardana, Revo Wulandari, Wulandari Yuni Franciska Br Tarigan Yunita Sari Zakarias Situmorang Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.