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All Journal KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI EXPLORER BEES: Bulletin of Electrical and Electronics Engineering
Saifullah Saifullah
STIKOM Tunas Bangsa, Pematangsiantar
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Implementasi Metode Decision Tree Pada Tingkat Prestasi Belajar Siswa di SMK Swasta Anak Bangsa Nurhayati Nurhayati; Saifullah Saifullah; Riki Winanjaya
BEES: Bulletin of Electrical and Electronics Engineering Vol 1 No 3 (2021): Maret 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

Data Mining is a series of processes to explore added value in the form of knowledge that has not been known manually from a data set. This grouping aims to determine the level of student success in the learning process that has been carried out. The approach used is quantitative. The subjects of this grouping are Class X (Ten) Academic Years 2018 to 2020. The data collection technique used is the learning outcome test. This grouping is done using Data Mining Rapid Miner 5.3 software, where the results will prove that the results of the evaluation of learning achievement are carried out by applying the C4.5 Algorithm. The results obtained are an accuracy value of 71.43%, meaning that the resulting rule is close to 100% correctness. Where the results of the Class Achieving precision label is 63.89% and the label Not Achieving is 92.31%. In accordance with these provisions, the results of manual calculations with Rapid Miner testing produce 11 models of rules or rules for Student
Sistem Pendukung Keputusan Dalam Menentukan Kelayakan Kredit Pembelian Mobil Dengan Metode TOPSIS Paulus Hendrico Silalahi; Saifullah Saifullah; Irfan Sudahri Damanik
Resolusi : Rekayasa Teknik Informatika dan Informasi Vol. 1 No. 5 (2021): RESOLUSI Mei 2021
Publisher : STMIK Budi Darma

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Abstract

Four-wheeled vehicles become the needs of society is quite important at this time, can be seen from the density of traffic every day. Type of four-wheeled vehicles are also very varied, among others, Honda, Izusu, Toyota, Mazda, and many more, therefore there are still many people who are confused when wanting to buy a car. People who want to buy a car also often ask for help or advice from others to choose what car is suitable for the buy. Many aspects must be considered in buying a car, then people who are often confused to choose because it is faced with the large selection of types of cars in the Sorum. I am here to provide a short solution how to choose a car that according to the will and our individual tastes using the method.
Sistem Pendukung Keputusan Pemilihan Promotor Vivo Terbaik (Studi Kasus : Pematangsiantar) Ali Akhbar Nasution; Saifullah Saifullah; Eka Irawan
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2729

Abstract

Abstract− ecision support system is a computer-based system that can help decisions to solve certain problems by utilizing certain data and models. Many cases can be used as research in decision support systems, one of which is determining the best Vivo promoter. In this research, a decision making system will be designed using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The TOPSIS method is a multi-criteria decision making method that uses the principle that the chosen alternative must have the shortest distance to the positive ideal solution and the farthest distance to the negative ideal solution. The steps used in the TOPSIS method are the normalization matrix calculation process, the weighted normalization matrix calculation process, the process of determining positive ideal solutions and negative ideal solutions, the process of calculating the distance of each alternative to the ideal solution, and the process of calculating the preference value of each alternative. The results obtained from this study are in the form of the best vivo Pramotor data in one month.Keywords: Decision Support System, TOPSIS, Best Vivo Pramotor
Analisis M Etode The Extended Promethee II (Exprom II) Pada Penentuan Handsanitizer Terbaik Berdasarkan Konsumen Dewinta Marthadinata Sinaga; Agus Perdana Windarto; Saifullah Saifullah; Dedy Hartama; Irfan Sudahri Damanik
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2589

Abstract

Washing hands with a handsanitizer can inhibit growth and kill bacteria, viruses and fungi. The aim of this study was to analyze the determination of the best handsanitizer based on consumer choice. The data collection method is done by interviewing consumers who use handsanitizer in Pematangsiantar city. Based on these results, it can be obtained the assessment criteria for Material Content (C1), Type (C2), Aroma (C3), Availability of Goods (C4). The alternatives used are 4, namely: A1 = Dettol, A2 = Antis, A3 = Nuvo, A4 = Lifebouy. The results of research using the EXPROM II method show that the Dettol (A1) handsanitizer product with a value of 1.1594 is recommended to be the best handsanitizer product based on consumers. It is hoped that the research results can provide information to consumers in determining the best handsanitizer based on predetermined criteria and alternatives.Keywords: Decision Support System, EXPROM II, Handsanitizer
Penerapan Algoritma Backpropagation dalam Memprediksi Kebutuhan Blangko Sertipikat Tanah pada Kantor BPN Kota Pematangsiantar Astri Veranita Sinaga; Saifullah Saifullah; Jaya Tata Hardinata
TIN: Terapan Informatika Nusantara Vol 1 No 11 (2021): April 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

Pematangsiantar City is one of the regions in Indonesia which has a land area of ​​79.97 KM2. the office of the National Land Affairs Agency is a non-ministerial government institution in Indonesia that has the task of carrying out government duties in the land sector in the city of Pematangsiantar and providing land certificates for the community. A system for predicting the need for blank soil certificates using the Artificial Neural Network method is a method that is able to perform a mathematical process to predict the need for blank certificates for soil certificates using the backpropagation algorithm for data processing implemented with matlab. Data were collected through direct observation and grouped based on the annual need for blango factors. The results obtained from the test are the performance and epoch values ​​where each architecture is not the same. The test results are displayed in the form of a graph comparing the target value with the training and testing process.
Penerapan Algoritma Backprogation Untuk Memprediksi Tingkat Kerawanan Banjir di Wilayah Kabupaten Mandailing Natal Putriyani Matondang; Saifullah Saifullah; Jaya Tata Hardinata
TIN: Terapan Informatika Nusantara Vol 1 No 11 (2021): April 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

The purpose of this study was to determine the flood vulnerability in the Mandailing Natal Regency. In this study, researchers used the Artificial Neural Network method with the Backprogation algorithm. Artificial neural network method is a method that is able to perform mathematical processes to predict flood-prone areas with backprogation algorithms for data management that is applied by matlap. The data source used is direct observation in the area of Mandailing Natal Regency. The data will be managed based on flood disasters that occur every year. The results obtained from the test are performance and epoch values where each architecture is not the same, the test results are displayed in the form of a graph comparing the target value with the training and testing process.
Penerapan Metode ORESTE untuk Pemilihan Smartphone Gaming Terbaik di Kelas Menengah Berdasarkan Konsumen Samantha Arta Sinuhaji; Sandy Hardiansyah; Candra Harapan Simanjuntak; Saifullah Saifullah
Journal of Computer System and Informatics (JoSYC) Vol 6 No 4 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.8093

Abstract

This study aims to help consumers choose the best mid-range gaming smartphone using the ORESTE (Organisation, Élimination et Choix Traduisant la Réalité) method. This method is applied to provide recommendations based on important criteria, such as processor performance, RAM capacity, screen quality, battery life, and price. The first stage of this study is problem identification that focuses on choosing the best mid-range gaming smartphone. Next, the determination of alternatives and relevant criteria is carried out, followed by data collection using a questionnaire distributed to 78 respondents. The collected data is then analyzed using the ORESTE method to calculate the global ranking of each alternative. The results of the analysis show that Samsung smartphones are in first place as the best choice based on the ranking obtained from each criterion. The use of the ORESTE method allows systematization in decision-making, by considering the weight and priority of relevant criteria. This study contributes to helping consumers make more objective and targeted decisions in choosing a gaming smartphone that suits their needs and preferences. In addition, this study is also expected to be a reference for smartphone manufacturers in understanding consumer preferences and improving the quality of their products. Thus, the results of this study are not only beneficial for consumers, but also for the smartphone industry as a whole.
Integrasi Strategi Pre-processing Data untuk Optimalisasi Akurasi Algoritma Backpropagation Widodo Saputra; Saifullah Saifullah; Eka Irawan; Anjar Wanto
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2743

Abstract

Backpropagation is one of the artificial neural network algorithms widely used in classification and prediction processes due to its ability to recognize data patterns accurately. However, the performance of this algorithm is highly influenced by the quality of the input data. Unstructured data, differences in data scales, missing values, and irrelevant features can reduce the model’s accuracy. This study aims to analyze the effect of integrating data pre-processing strategies to optimize the accuracy of the Backpropagation algorithm. The dataset used in this research was obtained from the Badan Pusat Statistik (BPS) in the form of Open Unemployment Rate data for the population aged 15 years and above in North Sumatra Province from 2019 to 2024. The applied pre-processing stages included data cleaning, normalization, missing value handling, and feature reduction. The research method was conducted by comparing the model testing results using standard pre-processing and partial pre-processing on several network architectures. The results showed that the implementation of pre-processing strategies was able to improve the performance of the Backpropagation model. The highest accuracy value was obtained in the 3-56-1 architecture with an increase from 80.00% to 85.88%. In addition to improving accuracy, the model training process became more stable and the error convergence was achieved faster. Therefore, the integration of data pre-processing strategies has proven to be effective in optimizing the accuracy of the Backpropagation algorithm for numerical data-based prediction problems
Co-Authors Agus Perdana Windarto Ali Akhbar Nasution Astri Veranita Sinaga Candra Harapan Simanjuntak Dedy Hartama Dewinta Marthadinata Sinaga Eka Irawan Irfan Sudahri Damanik Jaya Tata Hardinata Nurhayati Nurhayati Paulus Hendrico Silalahi Putriyani Matondang Samantha Arta Sinuhaji Sandy Hardiansyah Wanto, Anjar Widodo Saputra Winanjaya, Riki