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Agus Perdana Windarto
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
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Bone fracture classification using convolutional neural network architecture for high-accuracy image classification Solikhun, Solikhun; Windarto, Agus Perdana; Alkhairi, Putrama
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6466-6477

Abstract

This research introduces an innovative method for fracture classification using convolutional neural networks (CNN) for high-accuracy image classification. The study addresses the need to improve the subjectivity and limited accuracy of traditional methods. By harnessing the capability of CNNs to autonomously extract hierarchical features from medical images, this research surpasses the limitations of manual interpretation and existing automated systems. The goal is to create a robust CNN-based methodology for precise and reliable fracture classification, potentially revolutionizing current diagnostic practices. The dataset for this research is sourced from Kaggle's public medical image repository, ensuring a diverse range of fracture images. This study highlights CNNs' potential to significantly enhance diagnostic precision, leading to more effective treatments and improved patient care in orthopedics. The novelty lies in the unique application of CNN architecture for fracture classification, an area not extensively explored before. Testing results show a significant improvement in classification accuracy, with the proposed model achieving an accuracy rate of 0.9922 compared to ResNet50's 0.9844. The research suggests that adopting CNN-based systems in medical practice can enhance diagnostic accuracy, optimize treatment plans, and improve patient outcomes.
Sectoral vulnerabilities and adaptations to climate change: insights from a systematic literature review Prihandoko, Prihandoko; Windarto, Agus Perdana; Yanto, Musli; Yuhandri, Muhammad Habib
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6944-6957

Abstract

Climate change is an urgent global issue impacting various life sectors, including health, agriculture, and infrastructure. This systematic literature review (SLR) aims to provide a comprehensive synthesis of research on sectoral vulnerabilities and adaptation strategies to climate change. Utilizing bibliometric analysis, the review identifies key themes and research gaps, highlighting the successes and challenges in implementing adaptation strategies. Key findings reveal that topics such as climate change, adaptive management, agriculture, public health, and food security are central to the research discourse. However, areas like health equity, sanitation, and agricultural worker adaptation remain under-researched. The analysis underscores the necessity for holistic, context-specific, and innovative approaches to policy-making, Scopus integrating sustainable development and public health to enhance resilience and adaptive capacity in vulnerable regions. This review offers valuable insights for researchers and policymakers aiming to develop effective adaptation strategies and address the multifaceted challenges of climate change.
Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization Defit, Sarjon; Windarto, Agus Perdana; Alkhairi, Putrama
Telematika Vol 17, No 1: February (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i1.2824

Abstract

Optimizing classification methods (forward selection, backward elimination, and optimized selection) and ensemble techniques (AdaBoost and Bagging) are essential for accurate sentiment analysis, particularly in political contexts on social media. This research compares advanced classification models with standard ones (Decision Tree, Random Tree, Naive Bayes, Random Forest, K-NN, Neural Network, and Generalized Linear Model), analyzing 1,200 tweets from December 10-11, 2023, focusing on "Indonesia" and "capres." It encompasses 490 positive, 355 negative, and 353 neutral sentiments, reflecting diverse opinions on presidential candidates and political issues. The enhanced model achieves 96.37% accuracy, with the backward selection model reaching 100% accuracy for negative sentiments. The study suggests further exploration of hybrid feature selection and improved classifiers for high-stakes sentiment analysis. With forward feature selection and ensemble method, Naive Bayes stands out for classifying negative sentiments while maintaining high overall accuracy (96.37%).
METODE JARINGAN SARAF TIRUAN DALAM MEMPREDIKSI JUMLAH POPULASI ITIK MANILA BERDASARKAN PROVINSI DI INDONESA Della Puspita; Agus Perdana Windarto; Hendry Qurniawan
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 2 No. 3 (2022): November : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v2i3.328

Abstract

Peternakan merupakan salah satu subsektor pertanian yang sangat diperlukan untuk dapat memenuhi kebutuhan pangan masyarakat, terutama kebutuhan gizi protein hewani. Komoditas terbesar di peternakan saat ini berasal pada sektor per-unggas-an, hampir 70% di Sektor peternakan dan didominasi per-unggas-an. Berdasarkan data populasi itik manila di Indonesia dari beberapa provinsi menampilkan jumlah populasi pertahun nya yang memiliki nilai yang tidak stabil. Pada sampel data yang digunakan dalam penelitian ini diambil berdasarkan lima tahun terakhir yaitu 2017 – 2021, dilihat bahwa pada tahun 2017 – 2020 jumlah populasi mengalami penurunan namun pada tahun 2020 – 2021 jumlah populasi mengalami kenaikan. Pada penelitian ini menggunakan metode back-propagation. Penelitian ini memberikan kontribusi dalam pemahaman peramalan atau prediksi pada jumlah populasi itik manila di masa yang akan datang dan juga penelitian ini memperkenalkan implementasi algoritma back-propagation untuk memprediksi jumlah populasi itik manila. Hasil penelitian dengan percobaan yang dilakukan arsitektur terbaik yaitu 3 – 15 – 1 untuk memprediksi jumlah populasi itik manila pada tahun 2022 dengan menunjukan hasil akurasi sebesar 85,3%. Mse Testing sebesar 0,0010. Dari model ini maka dapat dihasilkan prediksi jumlah populasi itik manila berdasarkan provinsi dari masing – masing provinsi yang ada di Indonesia.
Analisis Penerapan Jaringan Saraf Tiruan Backpropagation dalam Memprediksi Penjualan Produk Es Kristal Anjani, Dila Dwi; Prakasiwi, Cindy; Windarto, Agus Perdana
Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 1 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v3i1.1610

Abstract

Ice Crystal Inti’s factory is the only producer of the ice crystals in Pematangsiantar. Untill now, the factory uses a simple system for recording sales, which creates difficulties in predicting sales. Preeiction calculation manually has a fairly high level of risk and hampers the sales performance process. For this reason, the factory need a system that can calculate sales predicting for ice crystal products and reduce that risk of lost. This study aims to make predictions using Artificial Intelligence with the Backpropagation algirithm. The data used is sourced from the Ice Crystal Inti’s Factory in Pematangsiantar for the 2020-2021 period. The process is done by dividing the training data and testing data to obtain the best architectural model. The training architecture model used to predict sales of ice crystal products is : 11-2-1; 11-25-1; 11-50-1; 11-50-75-1; dan 11-100-1. From a series of trials, the best pattern of the backpropagation architecture is 11-2-1 with a Means Square Error of 0.0009997950, an epoch of 414557, and an accuracy of 75% which will then be used to make predictions.
Model Arsitektur Backpropagation Dalam Meramalkan Jumlah Tindak Pidana Menurut Kepolisian Daerah Sumatera Utara Annisa, Liza; Isnaini, Alvina; Windarto, Agus Perdana
Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 1 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v3i1.1611

Abstract

A criminal act is a violation that can involve communities and is related to the law. The purpose of this study is to create the best architectural model using the backpropagation method where the best model can be used to predict the number of criminal acts according to the regional police in North Sumatra. The dataset used is sourced from the Central Statistics Agency of North Sumatra on the number of criminal acts in 2001-2020. The method used is the backpropagation method. The analysis process uses the help of Matlab 6.1 software. From the trials conducted using several architectural models 9-4-1, 9-8-1, 9-12-1, 9-16-1, and 9-20-1, the best architectural model is the 9-8 model. -1 with 90% correctness accuracy and MSE 0.0009992573.
Analisis Penerapan Data Mining Terhadap Kasus Positif Covid-19 Menggunakan Metode K-Means Clustering Azhari, Ridhan; Hartama, Dedy; Lubis, Muhammad Ridwan; Nasution, Della Fatricia; Windarto, Agus Perdana
Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 2 (2023): Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v3i2.1760

Abstract

This study has problems such as the absence of the use of the K-means clustering algorithm for data on positive COVID-19 cases in the Indonesian province. The purpose of this study is to apply the K-means clustering method in finding the closest distance to produce the lowest and highest clusters of data on positive COVID-19 cases in the Indonesian province. K-means is one of the algorithms in the non-hierarchical Clustering technique that tries to partition the existing data in the form of one or more clusters. The results obtained from the k-means clustering method produced 2 clusters, namely the lowest cluster C1 = 30 items while the highest cluster C2 = 4 items. This research can be used as a reference and can be further developed with other clustering methods or algorithms such as k-medoid in order to get a comparison of results and steps to use algorithms related to clustering.
Penerapan Artificial Neural Network dengan Metode Backpropagation Dalam Memprediksi Harga Saham (Kasus: PT. Bank BCA, Tbk) Ridho, Ihda Innar; Ramadhani, Cerah Fitri; Windarto, Agus Perdana
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 1 (2023): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i1.612

Abstract

The Indonesia Stock Exchange (IDX) is a marketplace where individuals and investors can purchase or invest their capital in stocks for potential profits. There are currently 800 listed companies on the IDX, and one of them is PT Bank BCA Tbk, the largest private bank in Indonesia with a capital of Rp 42.93 trillion. Stocks serve as securities that demonstrate an investor's ownership in a company. In order to predict the future stock prices of companies, especially PT Bank BCA, and to increase the chances of profit for investors, an analysis is necessary. The purpose of this study is to create a forecast model using the Artificial Neural Network (ANN) method to predict the stock price of Bank BCA. Historical stock price data from Yahoo Finance (finance.yahoo.com) from 2016 to 2022 was used as the dataset. The goal of this research is to examine the effectiveness of the Backpropagation method in predicting Bank BCA's stock price. This research provides valuable information and considerations for investors when deciding whether to buy, hold, or sell their stocks. The accuracy rate of this research is 91.66666667%, with a testing MSE of 0.0010000650, and a total of 7695 epochs.
Deep Learning to Extract Animal Images With the U-Net Model on the Use of Pet Images Windarto, Agus Perdana; Rahadjeng, Indra Riyana; Siregar, Muhammad Noor Hasan; Alkhairi, Putrama
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7199

Abstract

This article explores the innovative application of deep learning techniques, specifically the U-Net model, in the realm of computer vision, focusing on the extraction of animal images from diverse pet datasets. As the digital landscape becomes increasingly saturated with pet imagery, the need for precise and efficient image extraction methods becomes paramount. The study delves into the challenges posed by varying animal poses and backgrounds, presenting a comprehensive analysis of the U-Net model's adaptability in handling these complexities. Through rigorous experimentation, this research refines existing methodologies, enhancing the accuracy of animal image extraction. The findings not only contribute to advancing the field of computer vision but also hold significant implications for wildlife monitoring, veterinary diagnostics, and the broader domain of image processing.
Optimization of the Activation Function for Predicting Inflation Levels to Increase Accuracy Values Windarto, Agus Perdana; Rahadjeng, Indra Riyana; Siregar, Muhammad Noor Hasan; Yuhandri, Muhammad Habib
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7776

Abstract

This study aims to optimize the backpropagation algorithm by evaluating various activation functions to improve the accuracy of inflation rate predictions. Utilizing historical inflation data, neural network models were constructed and trained with Sigmoid, ReLU, and TanH activation functions. Evaluation using the Mean Squared Error (MSE) metric revealed that the ReLU function provided the most significant performance improvement. The findings indicate that the choice of activation function and neural network architecture significantly influences the model's ability to predict inflation rates. In the 5-7-1 architecture, the Logsig and ReLU activation functions demonstrated the best performance, with Logsig achieving the lowest MSE (0.00923089) and the highest accuracy (75%) on the test data. These results underscore the importance of selecting appropriate activation functions to enhance prediction accuracy, with ReLU outperforming the other functions in the context of the dataset used. This research concludes that optimizing activation functions in backpropagation is a crucial step in developing more accurate inflation prediction models, contributing significantly to neural network literature and practical economic applications.
Co-Authors Abdul Karim Abdullah Ahmad Acai Sudirman Ade Dwi Amanda Adinda Putri Azhari Afrialita Widiastari Afrina Wati Alkhairi, Putrama Alkhairi, Putrama Alrizca Trydillah Alrizca Trydillah M Amanda, Ade Dwi Ambariyanto Ambariyanto Amri Amri Anan Wibowo Anandi Ayu Anggi Trifani Anjani, Dila Dwi Annisa, Liza Aprilia Syahputri Arfandi Arfandi Ariana, Anak Agung Gede Bagus Arieni, Fildzah Nadya Arifah Hanum Arifin Nur, Khairun Nisa Aulanda, Lulu Aulia Sugarda Aulia Sugarda Ayu Wulandari Ayu, Nur Zannah sekar Azhari, Ridhan Azzahra, Fahrija B. Herawan Hayadi Badawi, Masrof Beauti, Intan Bintang Aufa Sultan Butarbutar, Marisi Chairul Fadlan Chairul Fadlan Chintya Irwana Cici Astria Cici Astria Cici Astria Dedi Suhendro Dedi Suhendro Dedi Suhendro Dedi Suhendro Dedi Suhendro Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Defit, Sarjon Della Puspita Deri Setiawan Desi Asima Silitonga Desi Asima Silitonga Desi Ratna Sari Devi Syahfitri Dewi Fortuna Efendi Dewinta Marthadinata Sinaga Deza Geraldin Salsabilah Saragih Dicky Wahyudi Manurung Dinda Nabila Batubara Dinda Nabila Batubara Dinda Nabila Batubara Dini Rizky Sitorus P Dio Hutabarat Disty Wahyuli Dwi Findi Auliasari Dwi Findi Auliasari Dwira Azi Pragana Dwira Azi Pragana Dwita Elisa Sinaga Edi Suharto Edy Satria Efendi, Muhamad Masjun Ega Widya Sari Eka Desriani Aritonang Eka Irawan Eka Irawan Eka Irawan Erbin Chandra Erlin Windia Ambarsari Evani Sitohang Fachri, Barany Fadhillah Azmi Tanjung Fadilla Anissa Fadillah Alwi Pambudi Fadlan, Chairul Fahrija Azzahra Fahry Husaini Fahry Husaini Fajar Syahputra Fania, Fira Fanny Adelia Fatmawati, Kiki Febiola, Adinda Fica Oktavia Lusiana Fifto Nugroho Fira Fania Fira Fania Fitri Rizki Frskila Parhusip Gita Febrianti Gita Febrianti Gumilar Ramadhan Pangaribuan Handrizal Handrizal Handrizal Handrizal Hanifah Urbach Sari Hanifah Urbach Sari Harahap, Zaki Faizin Hartama, Dedy Hartama, Dedy Hasudungan Siahaan Hendry Qurniawan Hendry Qurniawan Hendry Qurniawan Hersatoto Listiyono Heru Satria Tambunan Ht. Barat, Ade Ismiaty Ramadhona I Gede Iwan Sudipa Ida Mayanju Pandiangan Ihsan Maulana Muhamad Ihsan Syajidan Iin Indriani Iin Parlina Iin Parlina Iin Parlina Iis Warlinda Ikhwan Lubis Ilham Syahputra Saragih Ima Kurniawan Indah Dea Anastasia Indah Pratiwi M.S Indah Syahputri Indra Riyana Rahadjeng Indri Fatma Irfan Sudahri Damanik Irnanda, Khairunnissa Fanny Irwana, Chintya Isnaini, Alvina Ivo Yohana Manurung Iwan Purnama Jahril Jalaluddin Jalaluddin Jaya Tata Hardinata Johan Muslim Jufriadif Na`am, Jufriadif Khairun Nisa Arifin Nur Khairunnissa Fanny Irnanda Khairunnissa Fanny Irnanda Kiki Apni Puspita Sari Kiki Fatmawati Kurniawan Kurniawan Kusuma, Rizky Tri Leza Khairani Linda Sari Dewi Listy Oktaviani Lubis, Ikhwan M Fauzan M Fauzan M Fauzan M Fauzan M Fauzan M Fauzan M FAUZAN M Mesran M Mesran M. Fauzan M.Ridwan Lubis Manurung, Dicky Wahyudi Maria Etty Simbolon Marini Marini Masitha Masitha Masitha, Masitha Maulidya Rahma Siregar Mawaddah Anjelita Mawaddah Anjelita Mesran Mesran Mesran, Mesran Mhd Gading Sadewo Mhd Gading Sadewo Mhd Gading Sadewo Mhd Ridhon Ritonga Millah Sari Miralda, Viya Mita Yustika Mokhamad Ramdhani Raharjo Mokhamad Ramdhani Raharjo Mora Malemta Sitomorang Muhamad Muhamad Muhammad Alfahrizi Lubis Muhammad Aliyul Amri Muhammad Dwi Chandra Muhammad Fachrur Rozi Muhammad Fauzan Muhammad Kurniawansyah Muhammad Mahendra Muhammad Noor Hasan Siregar Muhammad Ridwan Lubis Muhammad Ridwan Lubis Muhammad Yasin Simargolang muhammad yuda rizki Muhammad Yuda Rizki Muliadi Musiafa, Zayid Mustika Azzahra N Nurhayati N Nurhayati Nasution, Della Fatricia Nasution, Irmanita Nasution, Rizki Alfadillah Nazlina Izmi Addyna Nelson Butarbutar Nila Soraya Damanik Ninaria Purba Ningsih, Selfia Novika, Tri Nur Wulandari Nurul Atina Nurul Izzah Hadiana Nurul Rofiqo Nurwijayanti Ogi Wahyudi Okprana, Harly Oktaviani, Selli Onita Sari Sinaga P, Dini Rizky Sitorus P.P.P.A.N.W Fikrul Ilmi R.H.Zer Parinduri, Ikhsan Parlina, Iin Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih, Poningsih Prakasiwi, Cindy Pramesti, Adinda Frizy Prihandoko Prihandoko Putrama Alkhairi Putrama Alkhairi Putrama Alkhairi Rafiqotul Husna Raharjo, Mokhamad Ramdhani Rahmat Zulpani Raichan Septiono Ramadana, Rica Ramadani, Sri Ramadhani, Cerah Fitri Ranjani Rapianto Sinaga Ratih Ramadhanti Ratika Rizka Lubis Razalfa Aindi Siregar Rica Ramadana Ridho, Ihda Innar Rika Nur Adiha Rika Setiana Rika Setiana Rika Setiana Riski Yanti Rizal Efendi Rizki, Muhammad Yuda Rofiqo, Nurul Rohmat Indra Borman Rohmat Indra Borman Ronal Watrianthos Roni Kurniawan Rosanti, Yerika Puspa Rotua Sihombing Hutasoit Roy Chandra Telaumbanua Rozy, Muhammad Fachrur S Solikhun S Solikhun Sadewo, Mhd Gading Sahendra Fahreza Saidah, Fatiyah Saifullah Saifullah Saifullah Saifullah Salis, Rahmi Samosir, Rafiah Aini Sandy Erlangga Sari, Hanifah Urbach Sari, Riyani Wulan Sari, Riyani Wulan Sarjon Defit Sekar Rizkya Rani Selfia Ningsih Sembiring, Rahmat Widia Setiawan, Yudika Dwi Setiawansyah Setiawansyah Sigit Anugerah Wardana Sinaga, Dolli Sari Sinaga, Waris Pardingatan Sinta Maulina Dewi Sinta Maulina Dewi Sintya Sintya Siregar, Razalfa Aindi Siregar, Sandy Putra Siti Hajar Siti Hawani Siti Maysaroh Siti Sundari Sitompul, Wati Rizky Pebrianti Sitti Rachmawati Yahya Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun, Solikhun Sri Rahayu Ningsih Sri Ramadani Suci Cahya Mita Suhada Suhada Suhendro, Dedi Sundari Retno Andani Sundari Retno Andani Susi Susilowati, Susi Syahfitri, Retno Ayu Syahputra, Fajar Syahputra, Muhammad Tania Dian Tri Utami Tanjung, Fadhillah Azmi Tanjung, Fatimah Dwi Puspa Tia Imanda Sari Tia Imandasari Tia Imandasari Tira Sifrah Saragih Manihuruk Tri Ayu Lestari Tri Novika Tri Novika Tri Welanda Trydillah, Alrizca Ulfah Indriani Viya Miralda Waldi Setiawan Wanto, Anjar Warlinda, Iis Wendi Robiansyah Wendi Robiansyah Wida Prima Mustika Widiastari, Afrialita Widodo Saputra Widya Try Taradipa Winanjaya, Riki Winda Lidyasari Winda Permata Sari Wiranto Hernandesz Sirait Yanto, Musli Yuegilion Pranavarna Purba Yuegilion Pranayama Purba Yuhandri Yuhandri, Yuhandri Yuhandri, Muhammad Habib Yuli Sartika Nasution Yulia Andini Yuni Sara Luvia Zahra Nur Atthiyah Zahra Syahara Zaki Faizin Harahap Zer, P. P.P.A.N.W.Fikrul Ilmi R.H. Zulfia Darma Zuly Budiarso