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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JURNAL SISTEM INFORMASI BISNIS Jurnal Pendidikan Teknologi dan Kejuruan Techno.Com: Jurnal Teknologi Informasi Jurnas Nasional Teknologi dan Sistem Informasi CESS (Journal of Computer Engineering, System and Science) Register: Jurnal Ilmiah Teknologi Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Informatika Upgris E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JIKO (Jurnal Informatika dan Komputer) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL ILMIAH INFORMATIKA SINTECH (Science and Information Technology) Journal Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) JURTEKSI Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Tunas Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Revolusi Indonesia JiTEKH (Jurnal Ilmiah Teknologi Harapan) IJISTECH Journal of Applied Data Sciences RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI JPM: JURNAL PENGABDIAN MASYARAKAT DEVICE Bulletin of Computer Science Research Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat Jurnal Penelitian Inovatif BEES: Bulletin of Electrical and Electronics Engineering JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal Krisnadana STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Jurnal Krisnadana Journal of Informatics, Electrical and Electronics Engineering
Wanto, Anjar
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia.
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PKM Pelatihan Penulisan Karya Ilmiah Bagi Pelajar SMK Di Kabupaten Simalungun Dan Kota Pematangsiantar Wanto, Anjar; Lubis, Muhammad Ridwan; Parlina, Iin
Jurnal TUNAS Vol 1, No 1 (2019): Edisi November
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (206.335 KB) | DOI: 10.30645/jtunas.v1i1.2

Abstract

This training aims to foster and increase interest in writing, especially writing scientific papers for vocational students in Simalungun Regency and Pematangsiantar City. This dedication is carried out in 2 different Vocational Schools which are Partners of the dedication team, Partner 1 is the National Children's Vocational School located in Simalungun district and an exemplary Private Vocational School located in Pematangsiantar City. The activity was held for 4 days during July 2019, at the National Private Vocational School. The activity was carried out on Saturday and Sunday 6-7 July 2019, starting at 08.00-17.00 WIB with 52 students participating, while the Exemplary Private Vocational School was held on the 13th -14 July 2019 at the same time, with 25 students. The targets of this activity are vocational students in Simalungun District (SMK Anak Bangsa) and Pematangsiantar City (Exemplary SMK). The reason for the selection of service locations is because of the lack of student interest in these schools in terms of writing scientific papers, as well as the lack of scientific papers produced. This activity uses the method of presentation / lecture, discussion, question and answer, and practice / practice. The results of this community service activity show that out of 52 student participants in the Vocational School Private Nation, 25 students were able to answer the questions (Post Test) correctly as many as 10 questions (48%) where previously only able to answer the questions (Pre Test) correctly 9 questions as many as 8 participants (15%). While from 25 student participants in Exemplary Private Vocational Schools, 12 students were able to answer questions (Post Test) correctly as many as 10 questions (48%) where previously only able to answer questions (Pre Test) correctly 9 questions as many as 2 participants (8%).
Analisis Dan Pemodelan Posisi Access Point Pada Jaringan Wi-Fi Menggunakan Metode Simulate Annealing Wanto, Anjar; Hardinata, Jaya T; Silaban, Herlan F; Saputra, Widodo
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v1i1.35

Abstract

Laying the position of the access point on the Wi-Fi network in a room is needed to optimize the signal strength received from the transmitter to the receiver. The parameters that determine the performance of the access point is the value of the signal strength. Strong or weak a signal access point will be affected by distance and barriers that exist between the access point and a client that accesses the access point. This study has been performed several simulations in multiple rooms are placed the access point to the receiver. The parameters used to measure the signal strength using inSSIDer applications that generate value RSSI (Received Signal Strength Indication) of a transmitter to the receiver and barriers (barriers) that may influence the strength of the signal. From this research strength of the signal received by the receiver not only in pengaruhui by the distance between accespoint to the recipient, but rather influenced by barriers (barriers) which is in a room. From the results of the research are expected to be able to obtain appropriate modeling to optimize access point placement position using the Simulate annealing method.
JST: Prediksi Perkembangan Produksi Tanaman Sayuran Dalam Upaya Pemenuhan Gizi Masyarakat dengan Algoritma Resilient Manurung, Azwar Anas; Satria, Indra; Wanto, Anjar
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.658

Abstract

Vegetable plants are very important in human life because they have a significant role as a source of nutrition and fulfillment of community nutrition. Therefore it is important to predict the production of vegetable crops. This study will use the Resilient algorithm which is one of the algorithms from Artificial Neural Networks (ANN) which is commonly used to predict data. This study uses times series data on vegetable crop production in North Sumatra Province from 2013 to 2022, obtained from the Indonesian Central Statistics Agency (BPS) website. The research topic will be analyzed using 5 ANN models, including: 8-8-1, 8-16-1, 8-24-1, 8-32-1 and 8-40-1. Based on the analysis results, model 8-32-1 was chosen as the best model, because it has an accuracy rate of 89% (the highest compared to other models). The results showed that the Resilient algorithm was able to predict vegetable crop production well. This research has important implications in supporting the sustainability of agricultural and food systems by providing information on developments in vegetable crop production to help farmers, producers and governments plan agricultural activities more effectively.
DEEP GATED RECURRENT UNITS PARAMETER TRANSFORMATION FOR OPTIMIZING ELECTRIC VEHICLE POPULATION PREDICTION ACCURACY Jeni Sugiandi; Solikhun Solikhun; Anjar Wanto
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.v10i4.6429

Abstract

The development of electric vehicles is an important innovation in reducing greenhouse gas emissions while reducing dependence on fossil fuels. The main problem in developing electric vehicles is the lack of adequate infrastructure. Inaccurate predictions regarding the number of electric vehicles hinder adequate infrastructure planning and development. This research proposes the use of the Gated Recurrent Units (GRU) algorithm to improve the accuracy of electric vehicle population predictions by carrying out GRU parameter transformations. This parameter transformation involves searching and adjusting the parameters of the GRU model in more depth to increase its ability to handle uncertainty in electric vehicle population data. After carrying out the training and testing process, the result was that the hyperparameter model using RandomizedSearchCV was the best model because it had the highest accuracy compared to other models tested with a combination of GRU_unit 64 and 128, dropout 0.5 and 0.6, batch size 64 and the number of epochs was 100 which had MAE results: 257.94, MSE: 66655.087, RMSE: 258.176, and Accuracy of 100%.
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.
Bird and Drone Image Classification Using ResNet CNN: A Deep Learning Approach for Aerial Surveillance Ahmad, Abdullah; Anjar Wanto; Adnan, Syed Muhammad
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.545

Abstract

Accurate classification of bird and drone images is crucial in supporting aerial surveillance and security systems, particularly to distinguish between natural objects such as birds and man-made objects such as drones. Manual classification methods have limitations in terms of speed and accuracy, thus necessitating a more efficient and reliable technology-based approach. This study aims to implement a ResNet-50 based Convolutional Neural Network (CNN) architecture to automatically classify bird and drone images. The dataset used was obtained from the Kaggle platform and consists of two classes: Bird and Drone, with a total of 22,407 images. The data was split into training (17,323 images), testing (844 images), and validation (1,740 images). All images underwent preprocessing and augmentation steps to enhance data quality and model training performance. The model was developed using the ResNet-50 architecture, which is well-regarded for handling complex image classification tasks. Evaluation results show that the model achieved an accuracy of 92%. For the Bird class, a precision of 0.83 and a recall of 0.99 were obtained, while for the Drone class, precision reached 0.99 and recall was 0.86. The average F1-score of 0.92 indicates that the model delivers balanced and reliable performance in the binary image classification task.
Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques Siregar, Sandy Putra; Akbari, Imam; Poningsih, Poningsih; Wanto, Anjar; Solikhun, Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6410

Abstract

Identification of tomato leaf disease remains difficult because standard approaches are frequently incorrect in identifying distinct signs. Convolutional Neural Networks (CNNs) perform well in image classification and pattern identification, although they are prone to overfitting. Thus, max pooling was employed to reduce dimensionality while retaining crucial information. This paper offers an improved CNN through hyperparameter tuning and compares it to Transfer Learning models such as InceptionV3, NASNetMobile, and VGG16, which were chosen for their efficiency and accuracy. The dataset comprises 7,178 photos classified as Healthy, Leaf Late Blight, Septoria Leaf Spot, and Yellow Leaf Curl Virus, collected from Kaggle.. The dataset is separated into three sections: training, validation, and testing, with a ratio of 70:15:15. The results of this study revealed that the proposed method achieved the highest accuracy of 98.24%. In the application of transfer learning, the inceptionV3 model achieved an accuracy of 96.94%, whereas NASNetMobile obtained 97.50%, and VGG16 showed an accuracy of 96.76%. The evaluation is based on accuracy, precision, recall, F1-score and Inference time to determine the optimum model for accuracy and computing efficiency. This project uses the proposed method and Transfer Learning Techniques to categorize illness images on tomato leaves. These findings will drive further research to improve tehe performance of the proposed method for foliar disease classification and comparable 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): Juli 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 Abdi Rahim Damanik Abdullah Ahmad Achmad Noerkhaerin Putra Adnan, Syed Muhammad Agung Pratama Agung Wibowo Agung Yusuf Pratama Agus Perdana Windarto Akbari, Imam Anan Wibowo Andi Sanggam Sidabutar Arifah Hanum Arifin Nur, Khairun Nisa Asro Pradipta Astuti, Wiwik Sri Ayu Artika Fardhani Azwar Anas Manurung Azwar Anas Manurung Bil Klinton Sihotang Cici Astria Damanik, Bahrudi Efendi Damayanti, Tri Febri Daniel Sitorus Dedi Kusbiantoro Dedi Suhendro Dedi Suhendro Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Dedy Hartama Deri Setiawan Desi Insani Natalia Simanjuntak Dewi, Rafiqa Dinda Nabila Batubara Edu Wardo Saragih eko hartato Eko Hartato Eko Kurniawan Eko Purwanto Elfin Efendi Eva Desiana Fajar Ramadan Fazira, Rizky Nazwa Febriyanto, R Tri Hadi Fikri Yatussa’ada Fitri Anggraini GS , Achmad Daengs Gumilar Ramadhan Pangaribuan Hardinata, Jaya T Harly Okprana Hartama, Dedy Hartama, Dedy Heru Satria Tambunan Heru Satria Tambunan, Heru Satria Ht. Barat, Ade Ismiaty Ramadhona Hutasoit, Rahel Adelina Hutasoit, Rahel Adelina Ihsan Maulana Muhamad Iin Parlina Iin Parlina Iin Parlina Iin Parlina Iin Parlina Iin Parlina Ika Okta Kirana Ika Okta Kirana Ika Okta Kirana Ika Okta Kirana Ika Okta Kirana Ika Purnama Sari Ilham Syahputra Saragih Imelda Asih Rohani Simbolon Indra Gunawan Indra Gunawan Indra Satria Indra Satria Indra Satria Indri Sriwahyuni Purba Irawan Irawan Irfan Sudahri Damanik Jalaluddin Jalaluddin Jalaluddin Jalaluddin Jaya Tata Hardinata Jeni Sugiandi Jonas Rayandi Saragih Jonas Rayandi Saragih Joni Wilson Sitopu Jufriadif Na`am, Jufriadif Juli Wahyuni Khairun Nisa Arifin Nur Khairunnissa Fanny Irnanda Kirana, Ika Okta M Mesran M Safii M. Safii M.Ridwan Lubis Manurung, Azwar Anas MARIA BINTANG Marseba Situmorang Martina Silaban Mesran, Mesran Meychael Adi Putra Hutabarat Mhd Ali Hanafiah Mhd Gading Sadewo Mhd. Billy Sandi Saragih Mhd.Buhari Sibuea Mora Malemta Sitomorang Muhammad Aliyul Amri Muhammad Aliyul Amri Muhammad Julham Muhammad Julham Muhammad Mahendra Muhammad Ridwan Lubis Muhammad Ridwan Lubis Muhammad Ridwan Lubis Muhammad Ridwan Lubis Muhammad Syafiq Muhammad Wijaya Napitupulu, Flora Sabarina Nasution, Rizki Alfadillah Nasution, Zulaini Masruro Nazlina Izmi Addyna Ni Luh Wiwik Sri Rahayu Ginantra Nur Ahlina Febriyati Nur Arminarahmah Nur Arminarahmah Nur, Khairun Nisa Arifin Nuraysah Zamil Purba Nurhayati Nurhayati Okprana, Harly Okta Andrica Putra Parlina, Iin Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih, Poningsih Putrama Alkhairi Rahmat W Sembiring Rahmat W. Sembiring Rahmat Zulpani Ramadani, Saputra Rapianto Sinaga Ratih Puspadini Reza Pratama Rita Mawarni Rizky Khairunnisa Sormin Ronal Watrianthos Roulina Simarmata Roy Chandra Telaumbanua Ruri Eka Pranata S Solikhun S Solikhun S Sumarno Sadewo, Mhd Gading Safii, M. Safruddin Safruddin Saifullah Saifullah Samuel Palentino Sinaga Samuel Palentino Sinaga Sandy Putra Siregar Saputra Ramadani Saragih, Irfan Christian Saragih, Jonas Rayandi Saragih, Mhd. Billy Sandi Sari, Riyani Wulan Sari, Riyani Wulan Sarjon Defit Setti, Sunil Sigit Anugerah Wardana Silaban, Herlan F Silfia Andini, Silfia Silitonga, Hotmalina Silitonga, Hotmalina Siregar, Sandy Putra Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun, Solikhun Suhada Suhada Suhada Suhada Sumarno Sumarno Sumarno Sumarno Sumarno Sumarno Sundari Retno Andani Sundari Retno Andani Sunil Setti Surya Hendraputra Susi Fitryah Damanik Syafri Maradu Manurung Syafrika Deni Rizki Syahri Ramadhan Teuku Afriliansyah Tia Imandasari Titin Handayani Sinaga Tri Welanda Vasma Vitriani Sianipar Veithzal Rivai Zainal Venny Vidya utari Vitri Roma Sari Wida Prima Mustika Widodo Saputra Widya Tri Charisma Gultom Widyasuti, Meilin Widyasuti, Meilin Winanjaya, Riki Yuhandri Yuhandri, Yuhandri Yuli Andriani Yuri Widya Paranthy Zulaini Masruro Nasution Zulaini Masruro Nasution Zulaini Masruro Nasution Zulaini Masruro Nasution Zulia Almaida Siregar