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Agus Perdana Windarto
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
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Application of the ANN Algorithm to Predict Access to Drinkable Water in North Sumatra Regency/City Muhammad Alfahrizi Lubis; Deza Geraldin Salsabilah Saragih; Indah Dea Anastasia; Agus Perdana Windarto; Putrama Alkhairi
International Journal of Informatics and Data Science Vol. 1 No. 1 (2023): December 2023
Publisher : ADA Research Center

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

Abstract

The increase in population has an impact on increasing the need for drinking water, but this is not in line with the fact that not 100% of the people in Indonesia physically receive or consume safe drinking water. This analysis is based on data from the Central Statistics Agency to look at the social, economic and demographic factors of households regarding the availability of adequate physical quality drinking water. This research aims to predict the percentage of households that have access to adequate drinking water using the Artificial Neural Network (ANN) method. The technique used is Backpropogation. Backrpopagation is a supervised neural network training method, it evaluates the error contribution of each neuron after a set of data has been processed. The goal of backpropagataion is to modify weights to train a neural network to map arbitrary inputs to outputs correctly. Therefore, looking at the above problems, this research aims to determine access to adequate drinking water sources by predicting which households have adequate drinking water so that there is no lack of adequate drinking water sources in the City Regency area. Methods and basic data are needed to make predictions. In this research, data was obtained from BPS which used data from 2014 - 2021, with training data from 2014 - 2020 and testing data from 2015 - 2021. Based on the best architecture produced in this research, namely the 6-17-1 architecture with an accretion of 90%. Thus it can be concluded that the Backpropagation Neural Network can provide good accuracy in carrying out the prediction process.
Sistem Pendukung Keputusan Pemilihan Mahasiswa Penerima Bantuan Uang Kuliah Tunggal Menggunakan Metode Simple Additive Weighting (SAW) Nur Wulandari; Nurul Izzah Hadiana; Mesran Mesran; Rohmat Indra Borman; Agus Perdana Windarto
Journal of Decision Support System Research Vol. 1 No. 1 (2023): September 2023
Publisher : ADA Research Center

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Abstract

Single Tuition Assistance (UKT) is a government program to help students who are academically capable and economically disadvantaged. The form of UKT assistance provided by the government to recipient students is tuition assistance which is transferred directly to the university's account. This assistance is intended for underprivileged students. In order for the provision of UKT assistance to be right on target, the selection in receiving aid must be better and objective in accordance with predetermined criteria. However, universities often face several problems in the selection process, which is carried out in stages so that it takes quite a long time. Therefore, a decision support system is needed as a solution to these problems by applying the Simple Additive Weighting (SAW) method. The test results obtained that the best alternative that was considered worthy as a student receiving UKT assistance was in alternative A4 on behalf of Khairunnisa which resulted in the best preference value of 0.8865 as the first rank.
ENHANCING HERBAL PLANT LEAF IMAGE DETECTION ACCURACY THROUGH MOBILENET ARCHITECTURE OPTIMIZATION IN CNN Anan Wibowo; Rahmat Zulpani; Agus Perdana Windarto; Anjar Wanto; Sundari Retno Andani
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.v10i4.6498

Abstract

Herbal plants have various health benefits, but their type identification remains challenging for the general public. This study aims to improve the accuracy of herbal plant leaf classification using Convolutional Neural Network (CNN) based on MobileNetV2 architecture. To enhance model performance, various optimization techniques including fine-tuning, batch normalization, dropout, and learning rate scheduling were implemented. The experimental results showed that the proposed optimized model achieved an accuracy of 100%, significantly outperforming previous studies that used standard MobileNet with an accuracy of 86.7%. While these perfect results warrant additional validation with more diverse datasets to confirm generalizability, this study contributes to the development of a more accurate herbal plant classification system that is readily accessible to the general public. Future work should explore model performance under varying environmental conditions and with expanded plant species datasets.
OPTIMIZATION OF THE INCEPTIONV3 ARCHITECTURE FOR POTATO LEAF DISEASE CLASSIFICATION Khairun Nisa Arifin Nur; Nazlina Izmi Addyna; Agus Perdana Windarto; Anjar Wanto; Poningsih Poningsih
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.v10i4.6554

Abstract

Potato leaf diseases can cause significant yield losses, making early detection crucial to prevent major damages. This study aims to optimize the Inception V3 architecture in a Convolutional Neural Network (CNN) for potato leaf disease classification by applying Fine Tuning Pre-Trained. This method leverages weights from a pre-trained model on a large-scale dataset, enhancing accuracy while reducing the risk of overfitting. The training process involves adjusting several final layers of Inception V3 to better adapt to specific features of potato leaf diseases. The results show that this approach improves classification performance, achieving an accuracy of 97.78%, precision of 98%, recall of 98%, and an F1-score of 98%. With better computational efficiency compared to previous architectures, this model is expected to be widely applicable in plant disease detection systems, particularly for farmers or institutions with limited resources.
Model Deep Learning Berbasis Inception V3 untuk Klasifikasi Penyakit Daun Apel Menggunakan Citra Digital Arifin Nur, Khairun Nisa; Wanto, Anjar; Windarto, Agus Perdana; Solikhun, Solikhun
Journal of Computer System and Informatics (JoSYC) Vol 6 No 3 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i3.7003

Abstract

Apple plants have high economic value, but their productivity is often disrupted by leaf diseases that can reduce quality and yield. Apple leaf disease identification is still largely performed manually, which is prone to errors and requires specialized expertise. Therefore, a method is needed to improve the accuracy and efficiency of apple leaf disease classification. This study aims to enhance the accuracy of apple leaf disease classification by implementing the Convolutional Neural Network (CNN) architecture, specifically Inception V3. The method involves collecting images of infected apple leaves, data preprocessing, and model training and evaluation. The results show that the Inception V3 model achieved an accuracy of 96%, which is higher than previous methods. The main advantage of this architecture lies in its ability to capture features at multiple scales simultaneously, improving the model’s ability to recognize disease patterns more accurately. With these findings, this study contributes to the development of AI-based plant disease detection technology and provides a practical solution for farmers to enhance apple farming productivity.
Penerapan Teknik Neural Network dalam memprediksi Perkembangan Impor Kelompok Industri Tekstil dengan Metode Backpropagation Ranjani; Suci Cahya Mita; Agus Perdana Windarto
Journal of Informatics Management and Information Technology Vol. 3 No. 1 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v3i1.252

Abstract

The aim of this research is to analyze the development of the textile industry group in Indonesia using Artificial Intelligence. The analysis is conducted through a predictive model that will be used to predict the import development of the textile industry group. The dataset is sourced from the Indonesian Central Bureau of Statistics through the website https://www.bps.go.id/. The technique used is neural network with backpropagation method, and the analysis is conducted using Matlab. Backpropagation is a training method that has a target to be sought. This method is also a multilayer method, which has input, hidden, and output layers. The research process consists of two stages, namely the training stage and the testing stage. Out of several architecture models tested (3-10-1, 3-25-1, 3-50-1, 3-80-1, and 3-100-1), the best architecture model obtained is 3-100-1 with an MSE of 0.000999996 and an accuracy value of 100 percent.
Investigating the Impact of ReLU and Sigmoid Activation Functions on Animal Classification Using CNN Models M Mesran; Sitti Rachmawati Yahya; Fifto Nugroho; Agus Perdana Windarto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5367

Abstract

VGG16 is a convolutional neural network model used for image recognition. It is unique in that it only has 16 weighted layers, rather than relying on a large number of hyperparameters. It is considered one of the best vision model architectures. However, several things need to be improved to increase the accuracy of image recognition. In this context, this work proposes and investigates two ensemble CNNs using transfer learning and compares them with state-of-the-art CNN architectures. This study compares the performance of (rectified linear unit) ReLU and sigmoid activation functions on CNN models for animal classification. To choose which model to use, we tested two state-of-the-art CNN architectures: the default VGG16 with the proposed method VGG16. A dataset consisting of 2,000 images of five different animals was used. The results show that ReLU achieves a higher classification accuracy than sigmoid. The model with ReLU in fully connected and convolutional layers achieved the highest precision of 97.56% in the test dataset. The research aims to find better activation functions and identify factors that influence model performance. The dataset consists of animal images collected from Kaggle, including cats, cows, elephants, horses, and sheep. It is divided into training sets and test sets (ratio 80:20). The CNN model has two convolution layers and two fully connected layers. ReLU and sigmoid activation functions with different learning rates are used. Evaluation metrics include accuracy, precision, recall, F1 score, and test cost. ReLU outperforms sigmoid in accuracy, precision, recall, and F1 score. This study emphasizes the importance of choosing the right activation function for better classification accuracy. ReLU is identified as effective in solving the vanish-gradient problem. These findings can guide future research to improve CNN models in animal classification.
Comparative Analysis of Gradient Descent Learning Algorithms in Artificial Neural Networks for Forecasting Indonesian Rice Prices Rica Ramadana; Agus Perdana Windarto; Dedi Suhendro
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5822

Abstract

Artificial Neural Networks (ANN) are a field of computer science that mimics the way the human brain processes data. ANNs can be used to classify, estimate, predict, or simulate new data from similar sources. The commonly used algorithm for prediction in ANN is Backpropagation, which yields high accuracy but tends to be slow during the training process and is prone to local minima. To address these issues, appropriate parameters are needed in the Backpropagation training process, such as an optimal learning function. The aim of this study is to evaluate and compare various learning functions within the Backpropagation algorithm to determine the best one for prediction cases. The learning functions evaluated include Gradient Descent Backpropagation (traingd), Gradient Descent with Adaptive Learning Rate (traingda), and Gradient Descent with Momentum and Adaptive Learning Rate (traingdx). The dataset used is the average wholesale rice price in Indonesia, obtained from the Central Statistics Agency (BPS) website. The evaluation results show that the traingdx learning function with a 5-5-1 architecture model achieves the highest accuracy of 83.33%, representing an 8.3% improvement over the traingd and traingda learning functions, which both achieved a maximum accuracy of 75%. Based on this study, it can be concluded that using various learning functions in Backpropagation yields better accuracy compared to standard Backpropagation.
Enhancing Premier League Match Outcome Prediction Using Support Vector Machine with Ensemble Techniques: A Comparative Study on Bagging and Boosting Agus Perdana Windarto; Putrama Alkhairi; Johan Muslim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6173

Abstract

Predicting football match outcomes is a significant challenge in sports analytics, requiring models that are both accurate and resilient. This study evaluates the effectiveness of ensemble techniques, specifically Bagging and Boosting, in enhancing the performance of Support Vector Machine (SVM) models for predicting match outcomes in the English Premier League. The dataset comprises detailed match statistics from 1,520 matches across multiple seasons, including features such as team performance, player statistics, and match outcomes. Four models were examined: baseline SVM, SVM with Bagging, SVM with Boosting, and a combined SVM + Bagging + Boosting approach. Evaluation metrics include accuracy, recall, precision, F1 score, and ROC-AUC, providing a comprehensive assessment of each model's performance. Experimental results indicate that ensemble methods substantially improve model accuracy and stability, with the SVM + Bagging + Boosting combination achieving perfect scores in accuracy, recall, precision, and F1 score, alongside an ROC-AUC value of 0.88. However, this model's slightly reduced ROC-AUC compared to others and its high computational cost highlight potential risks of overfitting and the need for significant resources. These findings underscore the practical potential of combining Bagging and Boosting with SVM for robust and accurate predictions. Limitations include the dataset's focus on a single league and the high resource requirements for ensemble methods. Future research could expand this approach to other sports and leagues, improve computational efficiency, and explore real-time predictive applications
Peningkatan Literasi Digital Bagi Masyarakat Desa Melalui Pelatihan Keamanan Siber Dasar Berbasis Komunitas Putrama Alkhairi; Agus Perdana Windarto; Solikhun; Anjar Wanto
JPM: Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v6i1.2529

Abstract

Low digital literacy in rural communities is a big gap for increasing cases of digital crime such as online fraud, phishing, and the spread of hoaxes. This community service activity aims to increase the understanding and awareness of the community of LK.I Sukamulia Village, Sinaksak Village, Tapian Dolok District, Simalungun Regency regarding the importance of cybersecurity through community-based basic training. The activity was carried out for five days, attended by 50 participants with various professional backgrounds such as farmers, traders, laborers, motorcycle taxi drivers, teachers, and online entrepreneurs. The method of implementing the activity consisted of the initial survey stage, module preparation, interactive training, direct practice, and evaluation of results. The material presented included an introduction to digital threats, how to create a secure password, the use of two-factor authentication, and the practice of using security applications. Evaluation was carried out through pre-tests and post-tests as well as observations during the activity. The results showed an average increase in participant understanding of 83% after participating in the training. In addition, the Village Digital Community was also formed as a sustainable step for local digital security education. This activity proves that a practical and contextual educational approach can increase community resilience to digital risks and can be replicated in other villages to expand the impact of community service.
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 Batubara, Egi 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 Fildzah Nadya Arieni 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 Jeperson Hutahaean 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 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 Putrama Alkhairi Rafiqotul Husna Raharjo, Mokhamad Ramdhani Rahmat Widia Sembiring - 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 Roznim Roznim 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 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 Sussolaikah, Kelik 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