Articles
Rancang Bangun Sistem Rem Otomatis pada Kendaraan Menggunakan Sensor Ultrasonik
Edu Wardo Saragih;
Muhammad Ridwan Lubis;
Anjar Wanto;
Solikhun Solikhun;
Jalaluddin Jalaluddin
Jurnal Penelitian Inovatif Vol 1 No 2 (2021): JUPIN Desember 2021
Publisher : CV Firmos
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DOI: 10.54082/jupin.11
Teknologi kendaraan hari ke hari semakin meningkat, namun risiko mengalami kecelakaan tetap ada dan tidak bisa dihindari. Khususnya sepeda motor yang memiliki angka kecelakaan terbesar. Indonesia sendiri merupakan negara dengan pengguna sepeda motor yang sangat tinggi, hal ini juga yang membuat Indonesia menempati peringkat ke lima dunia sebagai negara dengan tingkat kecelakaan lalu lintas tertinggi. Adapun tujuan dari penelitian ini adalah merancang sistem pengereman otomatis pada sepeda motor untuk membantu mengurangi risiko kecelakaan dan mempermudah pengendara sepeda motor menerapkan budaya berkendara yang baik dan aman. Jenis penelitian yang digunakan adalah jenis penelitian kualitafif dengan metode eksperimental. Hasil dari penelitian ini adalah alat yang diintegrasikan pada sepeda motor untuk menjalankan sistem pengereman otomatis. Kesimpulan dari penelitian ini adalah dari hasil pengujian yang didapatkan, sensor juga dapat mendeteksi jarak hingga 300 cm dengan output berupa sistem pengereman otomatis yang ditarik oleh motor servo.
Decision Support System for Determination of Village Fund Allocation Using AHP Method
Vasma Vitriani Sianipar;
Anjar Wanto;
M Safii
The IJICS (International Journal of Informatics and Computer Science) Vol 4, No 1 (2020): March 2020
Publisher : STMIK Budi Darma
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DOI: 10.30865/ijics.v4i1.2101
This study aims to describe the management of the allocation of village funds in physical development in the village of Siborna, Panei Subdistrict, Simalungun Regency and to identify inhibiting factors and supporting factors in managing village fund allocation. The focus of this research is the management of village fund allocations which include: planning, implementation. The determination of the allocation of village funds will later use the Analytical Hierarchy Process (AHP) method with 4 criteria including: villagers, rural poor, village area and Village Geographical Difficulty Index. Alternative Samples in this study were 4 villages, including the villages of Sosor Hamlet, Simpang Bahbirong Hamlet, Hutabagasan Hamlet and Kebun Sayur Hamlet. The results of this study using the AHP method obtained by the Village of Vegetable Farm Village is an alternative with the highest value that is eligible to get a village fund allocation from the government with a value of 3,0000
Analysis of the Resilient Method in Training and Accuracy in the Backpropagation Method
Widodo Saputra;
Agus Perdana Windarto;
Anjar Wanto
The IJICS (International Journal of Informatics and Computer Science) Vol 5, No 1 (2021): March 2021
Publisher : STMIK Budi Darma
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DOI: 10.30865/ijics.v5i1.2922
Artificial Neural Network (ANN) is one of the clusters of computer science that leads to artificial intelligence, there are several methods in ANN, one of which is the backpropagation method. This method is used in the prediction process. In some cases the backpropagation method can help in problems solving, especially predictions. However, the backpropagation method has weaknesses. The results of the backpropagation method are very influenced by the determination of the parameters so that the convergence becomes very slow. So needed an optimization method to optimize the performance of the bakpropagation method. The resilient backpropgation method is one solution, this method can change the weight and bias of the network with a direct adaptation process of weighting based on local gradient information from learning iterations so that it can provide optimal results. The data used is the Higher Education Gross Enrollment Rate in Indonesia from 2015-2020 by province. The results were obtained from several data testing with architectural experiments 3-5-1, 3-20-1, 3-37-1, 3-19-1, 3-26-4 and 3-4-1 from backpropagation and resilient testing, shows that the data training process can be optimized significantly, but the accuracy is not evenly optimal
Analisis Pengambilan Keputusan Dalam Menentukan Mahasiswa PKL Menggunakan Metode PROMETHEE
Tia Imandasari;
Anjar Wanto;
Agus Perdana Windarto
JURIKOM (Jurnal Riset Komputer) Vol 5, No 3 (2018): Juni 2018
Publisher : STMIK Budi Darma
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DOI: 10.30865/jurikom.v5i3.677
Every educational institution whether it school or university which is vocational based definitely requires student university to conduct field practice, not excepted to STIKOM Tunas Bangsa Pematangsiantar. LPPM STIKOM Tunas Bangsa is one institution which manage activity in research and dedication to the society and become a place for the lectures of STIKOM Tunas Bangsa to develops the knowledge that they have based on the each of the knowledge discipline through research field. Recrurtment activities of PKL Students in LPPM STIKOM Tunas Bangsa STILL DONE MANUALLY, SO IT COULD HAPPEN TO BE A MISTAKE (human error) in choosing the student of PKL. The method which is used in the research is Promethee. Based on calculation that is done, there are two student who is deserve to be conduct PKL in LPPM, They are student 2 and 4 Which is based on the highest net flow value using promethee. The purpose of this research is to assist the LPPM in determining the students who will be street vendors in LPPM. This research is expected to assist LPPM in recruiting student PKL.
Prediksi Jumlah Penjualan Produk di PT Ramayana Pematangsiantar Menggunakan Metode JST Backpropagation
Muhammad Syafiq;
Dedy Hartama;
Ika Okta Kirana;
Indra Gunawan;
Anjar Wanto
JURIKOM (Jurnal Riset Komputer) Vol 7, No 1 (2020): Februari 2020
Publisher : STMIK Budi Darma
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DOI: 10.30865/jurikom.v7i1.1963
Product is the one of thing which more important in the business especially for the retail industry. Ramayana is the one exact place for selling retail products such as clothing, shoes, or slipper. On 2012-2018, the number of sales of products in Ramayana experience curve up and down. That thing can cause profit and lose for Ramayana, to avoid that thing need to be held a prediction for the next months so that Ramayana side can know what will happen in the next months in selling it’s product and can take a step for more effective in selling it’s products. The data which used in this research is the data report monthly product sales of shoes & sandal sourced from Ramayana from 2012 until 2018. This research uses the Backpropagation neural network method using 5 architectures namely 3-26-1, 3-31-1, 3-35-1, 3-39-1 and 3-40-1. The best architecture is 3-35-1 with an accuracy rate of 92%. The results obtained are the results of the prediction of the number of sales for 2019, 2020, 2021 and 2022
Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density
Anjar Wanto;
Agus Perdana Windarto;
Dedy Hartama;
Iin Parlina
IJISTECH (International Journal of Information System and Technology) Vol 1, No 1 (2017): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v1i1.6
Artificial Neural Network (ANN) is often used to solve forecasting cases. As in this study. The artificial neural network used is with backpropagation algorithm. The study focused on cases concerning overcrowding forecasting based District in Simalungun in Indonesia in 2010-2015. The data source comes from the Central Bureau of Statistics of Simalungun Regency. The population density forecasting its future will be processed using backpropagation algorithm focused on binary sigmoid function (logsig) and a linear function of identity (purelin) with 5 network architecture model used the 3-5-1, 3-10-1, 3-5 -10-1, 3-5-15-1 and 3-10-15-1. Results from 5 to architectural models using Neural Networks Backpropagation with binary sigmoid function and identity functions vary greatly, but the best is 3-5-1 models with an accuracy of 94%, MSE, and the epoch 0.0025448 6843 iterations. Thus, the use of binary sigmoid activation function (logsig) and the identity function (purelin) on Backpropagation Neural Networks for forecasting the population density is very good, as evidenced by the high accuracy results achieved.
Implementation of ANN for Prediction of Unemployment Rate Based on Urban Village in 3 Sub-Districts of Pematangsiantar
Nuraysah Zamil Purba;
Anjar Wanto;
Ika Okta Kirana
IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v3i1.40
Unemployment is a serious social and economic problem faced by the Pematangsiantar City government, high unemployment is also caused by the low education and skills of the workforce. To be able to reduce the number of unemployed, especially in the city of Pematangsiantar, it is necessary to predict the unemployment rate based on urban villages in the three sub-districts of the city of Pematangsiantar, so that the government has a policy so that it can tackle the number of unemployed. The data used in this study are unemployment data based on 19 urban areas from 2013-2017 in 3 districts in Pematangsiantar City. Data sources were obtained from the Pematangsiantar 03 / SS Koramil Office. The research method used is Backpropagation Artificial Neural Network. Data analysis was performed with backpropagation algorithm using Matlab. There are 5 network architecture used, namely 2-35-1, 2-38-1, 2-41-1, 2-43-1, 2-46-1 with the best model is 2-38-1 which produces accuracy by 79%. Thus this model is good enough to be used to predict the unemployment rate based on wards in 3 sub-districts in the city of Pematangsiantar.
Implementation of Data Mining Algorithms for Grouping Poverty Lines by District/City in North Sumatra
Mhd Ali Hanafiah;
Anjar Wanto
IJISTECH (International Journal of Information System and Technology) Vol 3, No 2 (2020): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v3i2.66
The poverty line is useful as an economic tool that can be used to measure the poor and consider socio-economic reforms, such as welfare programs and unemployment insurance to reduce poverty. Therefore, this study aims to classify poverty lines according to regencies/cities in North Sumatra Province, so that it is known which districts/cities have high or low poverty lines. The grouping algorithm used is K-Means data mining. By using this algorithm, the data will be grouped into several parts, where the process of implementing K-Means data mining uses Rapid Miner. The data used is the poverty line data by district/city (rupiah/capita/month) in the province of North Sumatra in 2017-2019. Data sourced from the North Sumatra Central Statistics Agency. The grouping is divided into 3 clusters: high category poverty line, medium category poverty line, and the low category poverty line. The results for the high category consisted of 5 districts/cities, the medium category consisted of 18 districts/cities and the medium category consisted of 10 districts/cities. This can provide input and information for the North Sumatra government to further maximize efforts to overcome the poverty line in the area.
Evacuation Planning for Disaster Management by Using The Relaxation Based Algorithm and Route Choice Model
Dedy Hartama;
Agus Perdana Windarto;
Anjar Wanto
IJISTECH (International Journal of Information System and Technology) Vol 2, No 1 (2018): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v2i1.14
Research in the field of disaster management is done by utilizing information and communication technology. Where disaster management is discussed is about evacuation planning issues. The evacuation stage is a very crucial stage in the disaster evacuation process. There have been many methods and algorithms submitted for the evacuation planning process, but no one has directly addressed evacuation planning on dynamic issues concerning time-varying and volume-dependent. This research will use the Relaxation-Based Algorithm combined with the Route Choice Model to produce evacuation models that can be applied to dynamic issues related to time-varying and volume-dependent because some types of disaster will result in damage as time and evacuation paths are volume-dependent so as to adjust to the change in the number of people evacuated. Disaster data that will be used in this research is sourced from Disaster Information Management System sourced from DesInventar. The results of this study are expected to produce an evacuation planning model that can be applied to dynamic problems that take into account the time-varying and volume-dependent aspects.
GRDP Growth Rate Clustering in Surabaya City uses the K-Means Algorithm
Nur Ahlina Febriyati;
Achmad Daengs GS;
Anjar Wanto
IJISTECH (International Journal of Information System and Technology) Vol 3, No 2 (2020): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa
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DOI: 10.30645/ijistech.v3i2.60
Gross Regional Domestic Product (GRDP) is an indicator used to measure economic performance in a period. GRDP is the amount of added value generated by all business units in a particular area. It can also be said to be the sum of the value of the final goods and services produced by all economic units. Therefore, this study aims to cluster the GRDP Growth Rate according to business fields in the city of Surabaya, so that it is known which sectors have high or low growth. The clustering algorithm used is K-Means. By using this method, the data will b,e grouped into several clusters, where the implementation of the K-Means Clustering process uses the Rapid Miner tools. The data used is the GRDP Growth Rate in Surabaya City by Business Field, 2010-2019 (Percent). The data is divided into 3 clusters: high, medium, and low. The results obtained are nine categories/sectors with high clusters, 5 categories / sectors with medium clusters, and three categories,s / sectors with low clusters. This can be input and information for the Surabaya City government to further maximize efforts to increase the GRDP Growth Rate in the area.