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Analisis Backpropagation Dalam Menentukan Jumlah Perusahaan Industri Besar Dan Sedang (IBS) Di Indonesia Pramesti, Adinda Frizy; Ramadana, Rica; Beauti, Intan; Febiola, Adinda; Windarto, Agus Perdana
Journal of Informatics Management and Information Technology Vol. 4 No. 3 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v4i4.414

Abstract

Companies are economic actors whose main function is to produce goods and services needed by the community. However, in 2020, the COVID-19 pandemic had an impact on various economic activities, causing many workers to lose their jobs and the unavailability of new jobs, which led to an increase in unemployment in Indonesia. Therefore, strategic steps are needed to prevent an increase in the number of unemployed. One of them is to forecast the number of IBS companies for the next few years. Implementing early prevention as a step to identify new job opportunities in the industry. The forecast data is the number of IBS companies (large and medium industrial companies) collected by BPS for the period 2015-2022. The algorithm used for prediction is a backpropagation artificial neural network. This algorithm is able to remember what existed before and make generalizations from it. This backpropagation algorithm uses five architectural models including 6-10-1, 6-20-1, 6-35-1, 6-45-1, and 6-60-1. Of the five architectural models used, the best architecture was chosen, namely 6-35-1 which has an accuracy of 88%, MSE of 0.003821515 and the error rate used is 0.001-0.07. So this architectural model is good enough to predict the number of IBS companies.
Pelatihan Guru-Guru PAUD “MELEK” Teknologi Kabupaten Simalungun Windarto, Agus Perdana; Parlina, Iin; Wanto, Anjar
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 (322.946 KB) | DOI: 10.30645/jtunas.v1i1.1

Abstract

This community service aims to improve the knowledge and skills of PAUD teachers specifically software-based and android interactive learning in Simalungun District. This dedication was held during July 2019. This activity was carried out in two days, starting at 08.00- 11.30 WIB (first session), 14.00- 16.30 WIB (second session), on Saturdays, 20 and 27 July 2019, the location of the service was held in kindergarten Sandy Putra Pematangsiantar. The target of this activity is PAUD teachers in Simalungun District. The reason for the selection of service locations was deliberations from representatives of PAUD partner management in Simalungun District. For teachers, PAUD provides transportation to be taken to the location of the service given that this activity has a responsibility for PAUD teachers in educating the children of the nation in the industrial revolution era 4.0. The method used in the implementation is with presentations / lectures, discussions, questions and answers, and simulations / exercises. The results of this community service activity showed that of the 40 participants, PAUD teachers were able to answer all questions with a number of questions 45% (18 participants) after conducting training which previously were only able to answer 9 questions correctly as many as 13% (5 participants) before being asked for training. Therefore, in the future it is expected that further training can be carried out by PAUD teachers so that they can utilize knowledge more optimally and implement it more easily by their students.
Prediksi Perhitungan Jumlah Produksi Tahu Mahanda dengan Teknik Fuzzy Sugeno Hajar, Siti; Badawi, Masrof; Setiawan, Yudika Dwi; Siregar, Muhammad Noor Hasan; Windarto, Agus Perdana
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 1 (2020): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (619.631 KB) | DOI: 10.30645/j-sakti.v4i1.200

Abstract

"Mahanda" tofu industry is a home industry managed by family members located in the city of Pematangsiantar. The purpose of this research is to analyze the amount of "Mahanda" tofu production using fuzzy logic. Sources of data obtained by conducting interviews and direct observation. Fuzzy logic used is the Sugeno method. The variables used are demand variables, inventory variables, and production variables. Each variable has 3 fuzzy sets, the request variable consists of {down, medium, up}. Inventory variables consist of {few, medium, many}. And the production variable consists of {reduced, tolerable and increased}. The test data results there is a difference of error of 0.19% so that this method can be applied to the "Mahanda" tofu factory in the estimated tofu production for the next period.
Implementasi JST Dalam Menentukan Kelayakan Nasabah Pinjaman KUR Pada Bank Mandiri Mikro Serbelawan Dengan Metode Backpropogation Windarto, Agus Perdana
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.v1i1.25

Abstract

The purpose of this study was to develop a decision support system that can facilitate in determining the eligibility of borrowers KUR (Kredit Usaha Rakyat) through predictive use based on existing data and presents various alternative solutions in the selection of a feasibility customers in KUR loan. This study uses Artificial Neural Network applications using Backpropogation method. Criteria used as an assessment in this study is Collateral, Capacity, Loan Application Form, Income and Establishment Business License (Business License). The decision making process consists of two (2) phases where the first phase and pattern recognition, the second phase is forecast feasibility KUR customers. pattern recognition and predictive feasibility KUR customers using different data with the same process using training and testing. The conclusion by the two architectural models 5-2-1 and 5-3-1, obtained 93% accuracy with 0.0009995807 MSE is the 5-2-1 model architecture. This model is used to predict the feasibility of KUR customers with accuracy> 90% and MSE truth 0.0009566280.
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 | 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.
Implementasi Algoritma Backpropagation Untuk Prediksi Jumlah Siswa SMA Salis, Rahmi; Windarto, Agus Perdana; Suhendro, Dedi
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.v8i3.7774

Abstract

Senior High School (SMA) is one form of formal education unit that organizes general education at the secondary education level as a continuation of Junior High School (SMP). The number of high school students in Pematangsiantar City has decreased and increased from year to year. The factors causing the decrease and increase in the number of students are economic factors, population growth rate, distance from home, age, low quality of schools, lack of teachers and teaching media. This is because the number of students is very influential in determining when additional teachers, classrooms, textbooks and teaching media are needed to support the learning process. This study aims to predict the number of high school students in Pematangsiantar City. The dataset used is a dataset of the number of high school students in Pematangsiantar City in 2019-2023 obtained from the Ministry of Education, Culture, Research and Technology (Dapodik) website https://dapo.kemdikbud.go.id/pd/2/076300. The dataset is then divided into 2 parts, namely training and testing datasets. The algorithm used in the research is the Backpropagation algorithm with 6 architectural models, namely 4-15-1, 4-25-1, 4-45-1, 4-55-1, 4-75-1, and 4-85-1. The results of this study obtained the best architectural model, namely 4-25-1 with an accuracy level of 87.5%, Epoch 65, MSE Training 0.000967055, and MSE Testing 0.001440343. Based on this best architecture model will be used to predict the number of high school students in Pematangsiantar City for 2024.
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.
Analisis Metode Analytic Network Process pada Pemilihan Faktor Dominan Siswa Berprestasi di MTS Pembina Warlinda, Iis; Windarto, Agus Perdana; Fauzan, M
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 6, No 1 (2021): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v6i1.276

Abstract

School is a place for students to gain knowledge. Every school has a goal which is to improve the quality of the education world, as well as the MTS Pembina Maligas Bayu school. To realize this goal there must be improvements in service, teaching and assessment in order to make a quality school. In this case the homeroom teacher is faced with a problem that is the selection of high achieving students who fit the criteria desired by the school. The purpose of this study is to analyze which factors are the most dominant in determining student achievement. The selection of high achieving students has many factors and has different values, so we need an Analytic Network Process (ANP) method to overcome them. Analytic Network Process (ANP) methods including decision support system techniques, Analytic Network Process (ANP) is a mathematical theory that allows dealing with interrelated factors and feedback in a structured manner. The data of this study came from student questionnaires which had a rating of 1-10. With alternative morals (A1), Grades (A2), Discipline (A3), Absence (A4), The role of the teacher (A5). Whereas A1 0.16%, A2 0.02%, A3 0.02%, A4 0.07% and A5 0.06%. It is hoped that this research can provide input to the MTS Pembina schools to focus on the dominant factor in the selection of outstanding students so as to increase the number of outstanding students.
Analisis Metode ANP pada Hubungan Kerja di PT. Pp. London Sumatera Indonesia, Tbk Trydillah, Alrizca; Windarto, Agus Perdana; Fauzan, M
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 6, No 1 (2021): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v6i1.269

Abstract

Termination of Employment (PHK) for employees is a thing that is very avoided for every employee, it stops there based on their own request, but there is also a reason for regulations that no longer allow the employee to continue his work. Cases of termination of employment (layoffs) against employees in a company often occur. In PT. PP London Sumatera Indonesia, Tbk precisely in the office of the Bah Lias Research Station (BLRS) there were 76 employees laid off. And in the Bah Lias Estate (BLE) office there were 21-22 employees who were laid off. The company will make efficiency because of the excess ratio. Standardization ratio set by the company is 0.16. But the company has exceeded the existing ratio. The service period in the company is 55 years old. After 55 years the company will retire the worker according to the applicable law (Law 13 of 2000). For employees affected by the R program from the company, their rights will be paid according to applicable laws. In this efficiency, there are 4 R program criteria, namely: lose day is workers who are often absent while working or are absent, unproductive are workers who do not have work or workers who are often sick, nearing retirement. The service period in the company is 55 years old, so it is required to retire if the work period has reached 55 years. And the last criterion is undisciplined is workers who are late for work hours and are absent from work.
Penerapan Metode K-Means Pada Pengelompokkan Pengangguran Di Indonesia Tanjung, Fadhillah Azmi; Windarto, Agus Perdana; Fauzan, M
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 6, No 1 (2021): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v6i1.271

Abstract

Unemployment is a group of labor force who has not done an activity that generates money. Someone who is said to be unemployed can also be categorized as people who have not worked, people who are looking for work, or people who have worked but have not gotten productive results. The purpose of this study is to analyze the unemployment stay by province in Indonesia. This research data is sourced from the Central Statistics Agency in 2014 - 2019. This study uses data mining techniques, namely the K-means algorithm, the K-means method is a clustering method that functions to break the dataset into groups. The K-means method can be used for percentage unemployment data by province. Data will be divided or grouped into 2 Clusters, where Cluster 1 is the group of provinces with the highest potential for unemployment with the results of 13 provinces and Cluster 2 is the province with the lowest potential unemployment results which is 21 provinces. The results of this study are as a way to assist the government in expanding employment to develop and improve the economy in each province in Indonesia. It is hoped that this research can provide input to the government. In particular, the provinces with minimal employment opportunities in Indonesia have an impact on unemployment
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