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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Tunas Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat BEES: Bulletin of Electrical and Electronics Engineering Bulletin of Data Science Journal of Artificial Intelligence and Engineering Applications (JAIEA) JOMLAI: Journal of Machine Learning and Artificial Intelligence Journal of Informatics, Electrical and Electronics Engineering Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Okprana, Harly
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
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Rekomendasi Pemilihan Peserta Lomba Kompetensi Siswa (LKS) Tingkat Kejuruan Dengan Teknik Promethee Andi Sahputra; Eka Irawan; Harly Okprana
Journal of Informatics, Electrical and Electronics Engineering Vol. 1 No. 1 (2021): September 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

Participants in the student competency competition (LKS) of the RK Bintang Timur Pematangsiantar Private Vocational School were conducted manually by collecting students, counting and comparing with supporting data such as competency report cards by productive teachers. Therefore the RK Bintang Timur Pematangsiantar Private Vocational School needs to overcome problems in selecting students as participants in the student competency competition to be more effective and on target. The Promethee algorithm is used in order of order or priority in an efficient and simple multi-criteria analysis. Based on the research data sample, the highest score was obtained in alternative A1 with a value of 0.406 with 6 criteria, namely academic value, number of absences, student council scores, attitude, craft and tidiness. Then proceed to the matrix normalization process which produces the outflow, inflow and net flow values ??used in the ranking process
Penerapan Jaringan Saraf Tiruan Backprogation Dalam Memprediksi Jumlah Pasien Rumah Sakit Dea Dwi Rizki Tampubolon; Irfan Sudahri Damanik; Harly Okprana
Journal of Informatics, Electrical and Electronics Engineering Vol. 1 No. 2 (2021): Desember 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

Artificial Neural Network is one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. Artificial Neural Network (ANN) is defined as an information processing system that has characteristics similar to human neural networks. ANN is an information processing system that has similar characteristics to a biological neural network. The hospital is an integral part of a social and health organization with the function of providing services, healing disease and preventing disease to the community. Backpropagation network is one of the algorithms that are often used in solving problems. complicated problem. This algorithm is also used in regulatory applications because the training process is based on a simple relationship. The problems that occur at the Djasemen Saragih Pematangsiantar Hospital are the lack of doctors working at the hospital so that there is a density of patients that occur every year, and the absence of patient rooms that are placed at home. ill when there was an increase that was not recognized by the hospital. With the data available every year, it is expected that the use of artificial neural networks using the backprogation method is very useful for the hospital in determining the prediction of the number of hospital patients for the next year can be used as the basic material for changes or additional patient rooms when there is an excess of predicted patients.
Optimalisasi JST dalam Memprediksi Kunjungan Wisatawan Mancanegara Untuk Perencanaan dan Pengembangan Pariwisata yang Efektif Riki Winanjaya; Harly Okprana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6739

Abstract

Foreign tourist predictions assist the government and stakeholders in planning long- and short-term tourism strategies. Accurate information on the estimated number of tourists enables appropriate infrastructure development, efficient budget allocation and setting of relevant policies. Foreign tourists discussed in this study are foreign tourists based in ASEAN countries. This research will utilize historical data on foreign tourist arrivals from the Ministry of Tourism, the Ministry of Law and Human Rights (Directorate General of Immigration) and Mobile Positioning Data. The data that has been obtained will be processed and filtered to obtain relevant and accurate data before being used as input in the creation of an Artificial Neural Network (ANN) model. The algorithm proposed in this study is the Cyclical Rule algorithm with the optimization of the Bayesian Regulation algorithm, which can be used to solve data prediction problems. This study was analyzed using 10 (ten) architectural models, including 4-4-1, 4-5-1, 4-8-1, 4-10-1, 4-12-1, 4-15-1, 4-16-1, 4-20-1, 4-24-1, and 4-25-1. Based on the analysis, the results obtained from the 4-10-1 model with the optimization of the Bayesian Regulation algorithm as the best model with the smallest testing MSE compared to the other models, equal to 0.00786961. Based on the prediction results, foreign tourist arrivals from ASEAN countries in 2023 are expected to decrease compared to 2022. Tourism actors can take advantage of the results of this prediction to improve the quality and quantity of services provided to tourists, as well as adjust the needs of tourists with the resources available at tourist destinations.
Analisis Algoritma C4.5 Terhadap Faktor Penyebab Menurunnya Potensi Belajar Siswa Pada Masa Pandemi F Fitriyani; Harly Okprana; Rizky Khairunnisa Sormin
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 3, No 1 (2022): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v3i1.96

Abstract

At the end of 2019 the world was shaken by the Covid-19 outbreak. The Ministry of Education has also implemented a policy of studying at home online or online. When using the online learning system, it doesn't always run smoothly, there must be problems that can occur, namely problems such as students not understanding the material presented by the teacher and many students who only have one Android and there are even students who don't have Android at all, their environmental factors also do not support them to learn with focus. The purpose of this study was to determine the factors causing the decline in student learning potential during the pandemic using the c4.5 data mining method. The source of the research data was obtained from distributing questionnaires to high school students of the Family College Foundation. In this study, there are five criteria that influence the factors that cause the decline in student learning potential during the pandemic, namely: How to learn, study time, understanding of the material, assignment and environment. The calculation results state that the environmental criteria are the criteria that have the most influence on the factor of decreasing student learning potential. Tests were also carried out using the Rapidminer software and obtained an accuracy of 73.33%.
Klasifikasi Calon Nasabah Baru Menggunakan C.45 Sebagai Dasar Pemberian Pertanggungan Asuransi di PT Asuransi Central Asia Pematangsiantar Syahfitri, Retno Ayu; Windarto, Agus Perdana; Okprana, Harly
Bulletin of Data Science Vol 1 No 1 (2021): October 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (616.596 KB)

Abstract

The problem that often arises in insurance problems is the number of customers who are in arrears in paying premiums, therefore a system is needed that can classify which prospective customers are in the eligible group and which customers are in the unfit group in submitting as insurance customers. so that the insurance company can solve the problem early. In this study, an information system for the classification of acceptance of prospective insurance customers was built using the Classification Tree C4.5. Classification tree C4.5 is used to generate the rules needed for the smooth classification process of customers in paying premiums and is formed from the conversion result of the construction of a classification tree (classification tree). The C4.5 algorithm is considered an algorithm that is very helpful in classifying data because the characteristics of the classified data can be obtained clearly, both (decision tree) and in the form of rules or If - then rules, making it easier for users to extract information on the data in question
Model Prediksi Jaringan Saraf Tiruan Pada Anggaran Inventaris Di Pemerintahan Kota Pematang Siantar Tatahardinata, Jaya; Okprana, Harly; Winanjaya, Riki
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.v8i1.614

Abstract

Inventory is the process of managing the procurement or inventory of goods owned by an office or company in carrying out its operational activities. Without an inventory a business activity will not be carried out, the existence of an inventory is very important. Office inventory is very important for the continuity of an agency. If one or more equipment is disturbed, it will definitely hinder the running of the company's economy which is usually in the form of irregular office inventory organization or lack of a system for inventorying office equipment. Therefore, the Neural Network is a powerful data model that is able to capture and represent complex Input-Output relationships, because of its ability to solve several problems, it is relatively easy to use, robustness of data input speed for execution, and initialization of complex systems. The method used in this research is the Backpropagation algorithm, which is a supervised method, with the help of the MATLAB application with Fletcher-reeves parameters. The research data used is Goods Identity Card data for 2018-2021. Based on this data, a network architecture model will be determined, including 1-10-1, 1-15-1, 1-20-1, and 1-30-1. From the five models, training and testing were carried out first and then obtained the results that the best architectural model was 1-10-1 with 0.01397196. So it can be concluded that the model can be used to predict inventory budget data, especially in Pematangsiantar City.
Workshop Pemanfaatan AI untuk Meningkatkan Literasi Digital Guru-Guru SMK dalam Proses Pembelajaran di Sekolah Achmad Daengs GS; Ni Luh Wiwik Sri Rahayu Ginantra; Teuku Afriliansyah; Anjar Wanto; Harly Okprana
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 4 No 1 (2024): Mei 2024
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v4i1.2838

Abstract

This activity aims to equip UISU Siantar Private Vocational School teachers with knowledge and skills in utilizing Artificial Intelligence (AI) to increase learning effectiveness. This activity was carried out over two days with various sessions, including basic AI theory, the use of AI applications in learning, and direct practice in implementing AI in the classroom. The activity focuses on implementing AI workshops to increase vocational school teachers' digital literacy, especially at UISU Siantar Private Vocational School. This program is driven by rapid technological developments and the need to improve the quality of education through the integration of advanced technology, as well as equipping vocational school teachers with knowledge and skills in utilizing AI for various aspects of learning, including curriculum design, student evaluation, and classroom management. The team delivered the activity workshop in 2 ways, face-to-face and virtual, via the Zoom application. A pre-test and post-test were carried out on participants to measure the workshop's effectiveness. The average pre-test score was 60, while the average post-test score increased to 71.9. The analysis results show a significant increase in the level of teacher understanding. This increase indicates that the workshop successfully increased the digital literacy of vocational school teachers so that they are better prepared to integrate AI technology into the learning process at school.
Analisis Algoritma JST untuk Prediksi Perkembangan PDRB Menurut Lapangan Usaha Atas Dasar Harga Berlaku Robiansyah, Wendi; Okprana, Harly
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5994

Abstract

Gross Regional Domestic Product (GRDP) data plays a vital role as a reference in regional development planning. However, the main challenge faced is the inaccuracy of GRDP growth predictions due to complex and fluctuating economic dynamics, especially in areas such as Simalungun Regency. Therefore, this study aims to analyze the development of Gross Regional Domestic Product (GRDP) by business field based on current prices in Simalungun Regency using three Artificial Neural Network (ANN) algorithms, namely Backpropagation, Bayesian Regulation, and Levenberg-Marquardt. The research data is GRDP times-series data for 2015-2023 obtained from the Central Statistics Agency of Simalungun Regency. The analysis used five models of the same architecture, namely 7-5-1, 7-10-1, and 7-15-1, with a target error of 0.01 and a maximum epoch of 1000 iterations. The results of the study indicate that the Levenberg-Marquardt algorithm with the 7-10-1 architecture model provides the best performance with an accuracy rate of 100% and the smallest Mean Squared Error (MSE) value of 0.0000214320 compared to other algorithms and architecture models. This finding indicates that the Levenberg-Marquardt algorithm is superior in predicting the development of GRDP in Simalungun Regency. The implementation of the results of this study is expected to help local governments and related agencies provide information on the development of GRDP in Simalungun Regency so that they can design more accurate and effective economic policies. In addition, this study also contributes to the development of artificial intelligence-based economic prediction methods, especially in the application of JST for the analysis of complex and dynamic regional economic data.
Analisis Laju Pembelajaran dalam Mengklasifikasi Data Wine Menggunakan Algoritma Backpropagation Hardinata, Jaya Tata; Okprana, Harly; Windarto, Agus Perdana; Saputra, Widodo
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 3, No 2 (2019): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (810.284 KB) | DOI: 10.30645/j-sakti.v3i2.161

Abstract

Backpropagation is an artificial neural network that has the architecture in conducting training and determining the right parameters to produce the correct output of similar but not the same input. One of the parameters that influences the determination of bacpropagation architecture is the rate of learning, where if the value of the learning rate is too high then the network architecture becomes unstable otherwise if the value of the learning rate is too low the network architecture converges and takes a long time in training network architecture. This research data is secondary data sourced from UCI Data Mechine Learning. The best network architecture in this study is 13-10-3, with different learning rates ranging from 0.01, 0.03, 0.06, 0.01, 0.13, 0.16, 0.2, 0.23, 0.026, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.9. From the 21 different learning rate values in the 13-10-3 network architecture, it is found that the level of learning rate is very important to get the right and fast network architecture. This can be seen in experiments with a learning rate of 0.65 can produce a better level of accuracy compared to a learning rate smaller than 0.65.
Strategi SEO Berbasis WEB untuk Pengoptimalan Pemasaran UMKM Berbasis Digital: Memanfaatkan Peluang Ekonomi Digital Okprana, Harly; Darma, Surya
TIN: Terapan Informatika Nusantara Vol 5 No 6 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i6.5999

Abstract

This research aims to optimize digital marketing for Micro, Small, and Medium Enterprises (MSMEs) by applying web-based SEO (Search Engine Optimization) methods to capitalize on digital economic opportunities. The main issue faced by MSMEs is low visibility on search engines, which impacts organic traffic and online market competitiveness. The proposed solution is an SEO strategy that includes content optimization, website structure, relevant keywords, quality backlinks, and search engine algorithm analysis. The system used involves a case study at Primecom Store in Pematangsiantar, North Sumatra, with the implementation of SEO steps such as on-page and off-page optimization, and monitoring using Google Analytics. Over six months of implementation, the research results show an increase in organic traffic from 200 to 523 visits per month, an average keyword ranking improvement of 35%, and page visits rising from 100 to 417 per month. Additionally, quality backlinks increased from 10 to 40, while page load time decreased from 4.5 seconds to 2.8 seconds. These results demonstrate that the implementation of a web-based SEO strategy can enhance MSMEs' visibility and competitiveness in the digital market. This research provides practical recommendations for MSMEs to effectively leverage digital economic opportunities through an integrated SEO strategy.
Co-Authors Agus Perdana Windarto Ambariyanto Ambariyanto Andi Sahputra Annas Prasetio Aulanda, Lulu Bahrudi Efendi Damanik Cici Astria Dea Dwi Rizki Tampubolon Dedy Hartama Eka Irawan Eka Irawan F Fitriyani GS , Achmad Daengs Hadinata, Jaya Tata Halimatusakdiah Pohan Haryoto, Parawystia Prabasini Hasudungan Siahaan Hendro Sanjaya Heru Satria Tambunan Heru Satria Tambunan Ilham Syahputra Saragih Ina Syafitri Ingkana Devi Cahaya Irfan Sudahri Damanik Jaya Tata Hardinata Millah Sari Muhammad Zarlis Nasution, Rizki Alfadillah Nasution, Selvi Salsabillah Nasution, Siti Andry Yani Ni Luh Wiwik Sri Rahayu Ginantra Poningsih Poningsih Poningsih Poningsih, Poningsih Primatua Sitompul Rizky Khairunnisa Sormin Robiansyah, Wendi Sepridho, Jaka Sonia Tarigan Surya Darma Syahfitri, Retno Ayu Syahputra, Muhammad Riza Teuku Afriliansyah Tri Ayu Lestari Tri Novika Wanto, Anjar Widodo Saputra Winanjaya, Riki Yuegilion Pranayama