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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) 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 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) Jurasik (Jurnal Riset Sistem Informasi dan Teknik 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) 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 RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI JPM: JURNAL PENGABDIAN MASYARAKAT DEVICE Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) 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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Penerapan Algoritma Decision Tree C4.5 untuk Klasifikasi Tingkat Kesejahteraan Keluarga pada Desa Tiga Dolok Susi Fitryah Damanik; Anjar Wanto; Indra Gunawan
Jurnal Krisnadana Vol 1 No 2 (2022): Jurnal Krisnadana - Januari 2022
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1051.957 KB) | DOI: 10.58982/krisnadana.v1i2.108

Abstract

Klasifikasi tingkat kesejahteraan keluarga di Desa Tiga Dolok merupakan permasalahan yang dialami Masyarakat di desa itu. Dimana klasifikasi Tingkat kesejahteraan keluarga di Desa tersebut belum sepenuhnya akurat sehinga mengakibatkan penyaluran subsidi pemerintah tidak tepat sasaran. Permasalahan klasifikasi tingkat kesejahteraan menjadi tujuan dilakukannya penelitian agar mendapatkan hasil yang akurat dalam status tingkat kesejahteraan keluarga. Untuk mengatasi masalah tersebut diusulkan model baru dengan memanfaatkan sebuah metode komputasi C4.5 agar menghasilkan klasifikasi tingkat kesejahteraan yang akurat. Pada penelitian ini algoritma yang digunakan untuk melakukan klasifikasi tingkat kesejahteraan pada Desa Tiga Dolok adalah algoritma C4.5. Algoritma ini dipilih karena proses klasifikasinya sederhana dan cepat. Data penelitian yang digunakan nantinya adalah Data Isian Dasar Keluarga Desa Tiga Dolok Tahun 2019. Sumber data diperoleh berdasarkan kuisioner yang dibagikan kepada masyarakat Tiga Dolok. Berdasarkan data ini akan dilakukan klasifikasi tingkat kesejahteraan dengan menggunakan aplikasi rapid miner. Dengan metode ini akan dibentuk pohon keputusan agar nantinya mendapatkan hasil klasifikasi yang diinginkan.
Utilization of the ELECTRE and SMART Algorithms for Determining the Head of Administration for the Gunung Maligas Sub-District Office Fajar Ramadan; Rahmat W. Sembiring; Anjar Wanto
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 1 (2023): March
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i1.1932

Abstract

This study aims to apply the ELECTRE (Elimination and Choice Expressing Reality) and SMART (Simple Multi-Attribute Rating Technique) algorithms in determining the administrative head of the Gunung Maligas sub-district office. The head of administration is an important position in a government organization responsible for managing various administrative and coordinating activities. The ELECTRE method produces an alternative ranking of administrative head candidates based on multiple relevant attributes. Work experience, communication skills, organizational knowledge, and leadership skills are considered. The SMART method assigns weights to each point and combines attribute values to produce an overall score for each candidate. The data in this study were obtained through surveys and interviews with related parties. After the data is collected, an analysis process is carried out using the ELECTRE and SMART algorithms to produce a ranking of candidates that best suit the needs of the Gunung Maligas sub-district office. The results of this study are expected to provide objective and accurate recommendations for selecting qualified administrative heads. By using the ELECTRE and SMART algorithm approaches, the process of determining the executive authority can be more efficient and effective and help improve managerial performance and coordination at the Gunung Maligas sub-district office.
Algoritma Machine Learning untuk penentuan Model Prediksi Produksi Telur Ayam Petelur di Sumatera Ihsan Maulana Muhamad; Sigit Anugerah Wardana; Anjar Wanto; Agus Perdana Windarto
Journal of Informatics, Electrical and Electronics Engineering Vol. 1 No. 4 (2022): Juni 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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

Abstract

Laying hens eggs are one of the livestock commodities that make a very large contribution to the supply of eggs as a community need. Therefore, it is necessary to predict the egg production of laying hens in the future so that in the future the need for eggs in Indonesia is stable and can meet the demands of the Indonesian people. The method used in this research is a machine learning algorithm, namely Polak-Ribiere which is one of the artificial neural network methods that is often used to predict data. This study does not discuss the prediction results, but will discuss the ability of the Machine Learning algorithm to make predictions based on the egg production dataset of laying hens obtained from the Central Statistics Agency. The research data used is data on the production of laying hens in Sumatra from 2015-2020. Based on this data, network architecture models will be determined, including 4-5-1, 4-10-1, 4-15-1, 4-20-1, and 4-25-1. Of the five models, training and testing were carried out first and then obtained the results that the best architectural model was 4-25-1 with 0.03144841, the lowest among the other 4 models. So it can be concluded that the model can be used to predict the egg production of laying hens.
JST: Prediksi Perkembangan Produksi Tanaman Sayuran Dalam Upaya Pemenuhan Gizi Masyarakat dengan Algoritma Resilient Azwar Anas Manurung; Indra Satria; Anjar Wanto
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.
Utilization of the Profile Matching Method for Recommendations for the Appointment of Honorary Teachers to Become Permanent Teachers R Tri Hadi Febriyanto; Anjar Wanto; Bahrudi Efendi Damanik
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.2357

Abstract

This study aims to utilize the Profile Matching method in the recommendation process for hiring honorary teachers to become permanent teachers at Taman Siswa Bah Jambi Private High School. Honorary teachers are an important part of the education system in Indonesia, and recommendations for their appointment as permanent teachers require the right approach to ensure fair and efficient selection. In this study, an analysis of the Profile Matching method was carried out in assessing the feasibility and integrity of honorary teachers. Data collection was carried out by collecting information on academic achievement, teaching experience, and other qualifications of honorary teachers at Taman Siswa Bah Jambi Private High School. The results of the study show that Profile Matching provides recommendations that are quite relevant and can be considered in the appointment of honorary teachers to become permanent teachers. The Profile Matching method tends to place more emphasis on suitability of teaching qualifications and experience. This research is expected to provide valuable information for related parties in choosing the appropriate method for recommendations for hiring honorary teachers to become permanent teachers at Taman Siswa Bah Jambi Private High School.
Prediction of Palm Oil Seed Stock Production Results with the Back-propagation Algorithm Tri Febri Damayanti; Anjar Wanto; Heru Satria Tambunan
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.2391

Abstract

Palm oil is the largest plantation export commodity in Indonesia because Indonesia has a soil structure that is suitable for planting oil palms. As is the case with the production of oil palm seed stock, of course, it does not always increase, and the production of oil palm seed stock will undoubtedly decrease. Therefore, an algorithm is needed to predict it so that the company can find out the future development of oil palm seed stock production using the Back-propagation algorithm. The Back-propagation Algorithm is used to predict the yield of oil palm seed stock production using data from the Marihat unit Oil Palm Research Center (PPKS) in 2019-2022. The Back-propagation Algorithm is an algorithm that reduces the error rate by adjusting the weights based on the desired output and target, as well as Testing the Back-propagation algorithm using Matlab. Based on the test results of the five architectural models used, one best architectural model was obtained, namely 2-14-1, using the Back-propagation method, which produced an MSE value of 0.0551030 with a Training time of 08:00 seconds with a test accuracy of 75%. Based on the research results obtained, it is expected to be input, suggestions, and efforts, especially for the Marihat Unit PPKS company, increase the stock of oil palm production seeds in each period to increase company profits more optimally.
JST: Prediksi Perkembangan Produksi Tanaman Sayuran Dalam Upaya Pemenuhan Gizi Masyarakat dengan Algoritma Resilient Azwar Anas Manurung; Indra Satria; Anjar Wanto
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.
Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World Sunil Setti; Anjar Wanto
JOIN (Jurnal Online Informatika) Vol 3 No 2 (2018)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v3i2.205

Abstract

The Internet today has become a primary need for its users. According to market research company e-Marketer, there are 25 countries with the largest internet users in the world. Indonesia is in the sixth position with a total of 112.6 million internet users. With the increasing number of internet users are expected to help improve the economy and also education in a country. To be able to increase the number of internet users, especially in Indonesia, it is necessary to predict for the coming years so that the government can provide adequate facilities and pre-facilities in order to balance the growth of internet users and as a precautionary step when there is a decrease in the number of internet users. The data used in this study focus on data on the number of internet users in 25 countries in 2013-2017. The algorithm used is Artificial Neural Network Backpropagation. Data analysis was processed by Artificial Neural Network using Matlab R2011b (7.13). This study uses 5 architectural models. The best network architecture generated is 3-50-1 with an accuracy of 92% and the Mean Squared Error (MSE) is 0.00151674.
A Comprehensive Bibliometric Analysis of Deep Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023) Agus Perdana Windarto; Anjar Wanto; S Solikhun; Ronal Watrianthos
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5274

Abstract

The objective of this study is to perform a comprehensive bibliometric analysis of the existing literature on breast cancer segmentation using deep learning techniques. Data for this analysis were obtained from the Web of Science Core Collection (WOS-CC) that spans from 2019 to 2023. The study is based on a comprehensive collection of 985 documents that cover a substantial body of research findings related to the application of deep learning techniques in segmenting breast cancer images. The analysis reveals an annual increase in the number of published works at a rate of 16.69%, indicating a consistent and robust increase in research efforts during the specified time frame. Examining the occurrence of keywords from 2019 to 2023, it is evident that the term "convolutional neural network" exhibited a notable frequency, reaching its peak in 2021. However, the term "machine learning" demonstrated the highest overall frequency, peaking around 2021 as well. This emphasizes the importance of machine learning in the advancement of image segmentation algorithms and convolutional neural networks, which have shown exceptional effectiveness in image analysis tasks. Furthermore, the utilization of latent Dirichlet Allocation (LDA) to identify topics resulted in a relatively uniform distribution, with each topic having an equivalent number of abstracts. This indicates that the data set encompasses a diverse range of topics within the field of deep learning as it relates to breast cancer image segmentation. However, it should be noted that topic 4 has the highest level of significance, suggesting that the application of deep learning for diagnosis was extensively explored in this study.
Model Prediksi Algoritma ANN Pada Jumlah Ekspor Barang Perhiasan Dan Berharga Menurut Negara Tujuan Arifah Hanum; Tri Welanda; Anjar Wanto; Agus Perdana Windarto
TIN: Terapan Informatika Nusantara Vol 3 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v3i1.1773

Abstract

Currently Indonesia is one of the exporting countries to industrialized and developing countries. The methods carried out in the research of the prediction of the export of jewelry and valuables from this main destination country use the ANN (Artificial Neural Network) method. The research data used comes from the official website of the government, the Indonesian Central Statistics Agency. In this study, the data used is data from 2013 to 2020 consisting of 8 destination countries, namely Switzerland, Singapore, Hong Kong, United Arab Emirates, South Africa, Taiwan, the United States, and India. Based on this data can be determined network architecture model, namely 3 - 4 - 1, 3 - 8 - 1, 3 - 12 - 1, 3 - 16 - 1 and 3 - 20 - 1. After training and testing of the 5 models, it can be obtained that the best architectural model is on the 3-12-1 model with an MSE value of 0.033777975 on the ANN method
Co-Authors Abdi Rahim Damanik Agung Pratama Agung Wibowo Agung Yusuf Pratama Agus Perdana Windarto Agus Perdana Windarto Andi Sanggam Sidabutar Arifah Hanum Asro Pradipta Ayu Artika Fardhani Azwar Anas Manurung Azwar Anas Manurung Bahrudi Efendi Damanik Bil Klinton Sihotang Cici Astria 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 Fikri Yatussa’ada Fitri Anggraini Flora Sabarina Napitupulu GS , Achmad Daengs Gumilar Ramadhan Pangaribuan Hartama, Dedy Hartama, Dedy Herlan F Silaban Heru Satria Tambunan Heru Satria Tambunan 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 Gunawan Indra Gunawan Indra Satria Indra Satria Indri Sriwahyuni Purba Irawan Irawan Irfan Christian Saragih Irfan Sudahri Damani, Irfan Sudahri Jalaluddin Jalaluddin Jalaluddin Jalaluddin Jaya T Hardinata Jaya Tata Hardinata Jonas Rayandi Saragih Jonas Rayandi Saragih Joni Wilson Sitopu Jufriadif Na`am, Jufriadif Juli Wahyuni Khairunnissa Fanny Irnanda Kirana, Ika Okta M Mesran M Safii M.Ridwan Lubis 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 Nasution, Rizki Alfadillah Nasution, Zulaini Masruro Ni Luh Wiwik Sri Rahayu Ginantra Nur Ahlina Febriyati Nur Arminarahmah Nur Arminarahmah Nuraysah Zamil Purba Nurhayati Nurhayati Okprana, Harly Okta Andrica Putra Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih Poningsih R Tri Hadi Febriyanto Rahmat W Sembiring Rahmat W. Sembiring 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 Safruddin Safruddin Saifullah Saifullah Samuel Palentino Sinaga Samuel Palentino Sinaga Sandy Putra Siregar Saragih, Irfan Christian Saragih, Jonas Rayandi Saragih, Mhd. Billy Sandi Sari, Riyani Wulan Sari, Riyani Wulan Sarjon Defit Sigit Anugerah Wardana Silfia Andini Silitonga, Hotmalina Silitonga, Hotmalina Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun, Solikhun Suhada Suhada Suhada Suhada Sumarno Sumarno Sumarno Sumarno Sumarno Sumarno Sundari Retno Andani Sunil Setti Surya Hendraputra Susi Fitryah Damanik Syafri Maradu Manurung Syafrika Deni Rizki Syahri Ramadhan Tia Imandasari Titin Handayani Sinaga Tri Febri Damayanti Tri Welanda Vasma Vitriani Sianipar 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