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Bulletin of Informatics and Data Science
Published by Asosiasi Peneliti Data Science Indonesia
ISSN : -     EISSN : 25808389     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Bulletin of Informatics and Data Science journal discusses studies in the fields of Informatics, DSS, AI, and ES, as a forum for expressing research results both conceptually and technically related to Data Science
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 8 Documents
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Search results for , issue "Vol 1, No 2 (2022): November 2022" : 8 Documents clear
Implementasi Metode Weighted Aggregated Sum Product Assesment (WASPAS) dalam Pemilihan Oli Mesin Sepeda Motor 150 CC Juniar Hutagalung; Ahmad Fitri Boy; M Arief Yahdie
Bulletin of Informatics and Data Science Vol 1, No 2 (2022): November 2022
Publisher : PDSI

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Abstract

There are many types and types of engine lubricating oil, so you must be selective in choosing the right lubricant (oil) to preserve the life time of the engine. With the oil, the friction between the components in the engine becomes smoother and makes it easier for the engine to reach the ideal temperature. Oil is often used to reduce friction (friction), if two surfaces that are attached to each other move, friction will arise. If the engine heat is not absorbed, the wear and tear of engine components will accelerate. The purpose of this study is to implement a decision support system using the Weighted Aggregated Sum Product Assessment (WASPAS) method in selecting the best motorcycle engine oil so that it can assist motorcycle riders in choosing the best engine oil. Motorcycle owners do not experience difficulties and do not need a long time in the selection of engine oil for their motorcycles. The Waspas method can be applied in solving the problem of determining the best lubricant (oil) for 150cc sport motorbikes. The best lubricant (oil) based on the calculation results of 5 alternatives with a value above 0.60, namely deltalube daily with the highest score of 0.6906, repsol mx25 with a value of 0.6902, ahm oil mpx 2 with a value of 0.6644, federal ultratec with a value of 0.6238 and shell advance ax5 with a value of 0.6097. With the application of the desktop-based Waspas method in decision making, it can make it easier for motorcycle owners to select the best lubricant (oil) for 150cc sport motorbikes.
Prediksi Jumlah Sampel Tes PCR Covid-19 Menggunakan Metode Single Moving Average Nilam Fadhilah; Rachman Arief
Bulletin of Informatics and Data Science Vol 1, No 2 (2022): November 2022
Publisher : PDSI

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Abstract

Covid-19 has spread all over the world. This virus was first discovered in the city of Wuhan, China. To avoid this virus, the community has implemented several strategies such as wearing masks, washing hands and conducting swab tests to determine whether the results are contaminated with the Covid-19 virus or not. In Indonesia, swab tests have been provided, such as the Antigen swab test and the PCR swab test. However, WHO recommends a PCR swab test because the results are more accurate. Until now, Indonesia has not been able to meet the minimum number of PCR test samples according to the WHO (World Health Organization) version, which is 1 per 1,000 people every week. Therefore, the purpose of this researcher is to create a system to predict the rise and fall of the number of daily PCR test samples at laboratories in West Java in the future. This research was conducted in West Java because the capacity of the number of PCR test samples was not optimal, only relying on manuals with limited human resources. Therefore, the authors are able to predict the number of PCR test samples in the future using the Single Moving Average method. This method takes a group of observations and looks for the average value of the daily development of the number of PCR test samples in West Java, as a forecast in the future. The number of Covid-19 PCR test samples from the MSE (Mean Square Error) calculation is 689,873. With the SMA method, it makes it easier for admins to see predictions for the number of PCR samples for the future.
Penerapan Metode Multi-Objective Optimization on the Basic of Ratio Analysis (MOORA) dalam Keputusan Penerimaan Siswa Baru Mesran Mesran, M.Kom; Juanda Hakim Lubis; Iwan Fitrianto Rahmad
Bulletin of Informatics and Data Science Vol 1, No 2 (2022): November 2022
Publisher : PDSI

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Abstract

This research was made so that the school can easily select new students who are running as students in a school. This selection is done based on the criteria that have been determined by the school. To solve this problem, a decision support system is needed using the Multi-Objective Optimization Method on The Basic of Ratio Analysis (MOORA). this method is also quite easy to use and the results are good by obtaining the best value of 0.2296 with alternative A1 and followed by alternative A5 with a preference value of 0.1383. So that the decision support system by applying the MOORA method can solve problems in the selection of new students who are accepted or declared graduated with a systematic and precise selection. The MOORA method can also be used in people who really need it
Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine Suryani Suryani; Mustakim Mustakim
Bulletin of Informatics and Data Science Vol 1, No 2 (2022): November 2022
Publisher : PDSI

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Abstract

SMK Negeri 5 Pekanbaru aims to prepare competent graduates who can compete in the global market. The realization of these goals is influenced by student achievement at school. Student achievements determine the ability of students to work in certain fields. Based on observations, it is known that student achievement at SMK Negeri 5 Pekanbaru tend to be low. This is also shown by the data that has been collected through the Curriculum section. Based on the data, there can be extraction using the supervised learning method to make a classification model of student achievements. The supervised learning algorithm used in this research is a Support Vector Machine (SVM). The data used in this study are student's data grade X SMK Negeri 5 Pekanbaru in 2020 totaling 160 data. The classification process is carried out by applying the GridSearch method to find the best kernel to be implemented. Based on the implementation of GridSearch, the kernel to be used is Radial Basis Function (RBF) with Cost (C) and Gamma (?)  parameters. Based on 16 experiments with different parameter values, the best classification results are obtained using the value of  Cost (C) = 0.1 and the value of Gamma (?)  = 0.01, with accuracy values of 94%.
UMKM Class Determination Support System Using Profile Matching Setiawansyah Setiawansyah; Adhie Thyo Priandika; Bustanul Ulum; Ade Dwi Putra; Dyah Ayu Megawaty
Bulletin of Informatics and Data Science Vol 1, No 2 (2022): November 2022
Publisher : PDSI

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Abstract

UMKM are businesses run by individuals, households, or small business entities. The classification of MSMEs is usually carried out with limits on turnover per year, the amount of wealth or assets, and the number of employees. Determining the UMKM class using the profile matching method which can make effective decisions on existing problems. With the decision support system to determine the class of UMKM in determining the class of UMKM by looking at the highest value of the ranking results based on several aspects of the assessment including turnover, assets, human resources, marketing, and permits. The results of the calculation for the assessment of the Micro UMKM class for PT Hasta Karya Nugraha are 1.5275 for the Micro UMKM Class Appropriate because it passes the minimum standard value for Micro MSMEs, the Small MSME Class for PT Hasta Karya Nugraha is 2.2975 Appropriate because it passes the minimum standard value for Small UMKM, and The Small UMKM class for PT Hasta Karya Nugraha is 2.3 Not suitable because it does not pass the minimum standard value for Medium UMKM. As for the results of the calculation of the assessment of the Micro UMKM class for CV Permata Jaya, namely 0.54 the Micro MSME Class is Appropriate because it passes the minimum standard value for Micro MSMEs, the Small UMKM Class for CV Permata Jaya is Appropriate because it passes the Small MSME minimum standard value, and the Small UMKM Class for CV Permata Jaya. CV Permata Jaya which is 1.62 Appropriate because it passes the minimum standard value of Medium UMKM. The conclusion of the assessment results of the application of the profile matching method to determine UMKM to move up the class for PT Hasta Karya Nugraha is in the Micro class, and CV Permata Jaya is in the Middle class.
Penentuan Penerima Bantuan Pangan Non Tunai (BPNT) Menerapkan Metode Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) Ahmad Yanda; Mesran Mesran
Bulletin of Informatics and Data Science Vol 1, No 2 (2022): November 2022
Publisher : PDSI

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Abstract

Non-Cash Food Assistance (BPNT) is something that is made as assistance in the form of food, to help the community, especially in social services that are given to the community in a non-cash way or called electronic money whose source is the government and is given to underprivileged families or beneficiary families. (KPM). This assistance is distributed every month using a card and exchanged at e-Warong. The result of the exchange is food or in the form of rice, milk, instant noodles etc. In the process of selecting candidates for BPNT assistance, it will be very difficult if it is decided manually. Therefore, a system called DSS is needed. Decision Support System (DSS) is a stage in finding a solution to a problem where the process is carried out using a solution according to how the computer works. DSS can work optimally if using the method. In this study, the authors chose the Multi-Objective Optimization Method on The Basic of Ratio Analysis (MOORA) which is a method that can be used in the completion and completion process of the DSS. This method has a working procedure that uses mathematical calculations. The results of this study were obtained as the best candidate for the recipient of BPNT funds, alternative A4 on behalf of Jefri with a value of 0.2362 as the first rank
Analisis Usability Testing pada SITIDES Menggunakan System Usability Scale dan PIECES Framework Valian Yoga Pudya Ardhana
Bulletin of Informatics and Data Science Vol 1, No 2 (2022): November 2022
Publisher : PDSI

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Abstract

SITIDES Bengkaung Village is a website-based integrated system owned by Bengkaung Village located in West Lombok Regency, West Nusa Tenggara (NTB) Province under the management of village officials. All information related to Bengkaung Village is displayed on SITIDES Bengkaung Village. However, since the system was officially used, there has never been an evaluation and testing of the level of user satisfaction, in this case the manager of SITIDES Bengkaung Village, so a usability analysis is needed which in this case uses two methods, namely the PIECES Framework and the System Usability Scale. The purpose of this evaluation and testing is to measure the level of satisfaction of SITIDES users in Bengkaung Village, in this case the managers of which there are 9 people who have different access rights. Based on the research that has been done, the results of the analysis using the Pieces Framework method are a value of 4.36 obtained from the average value of 6 domains. This value is in the very satisfied category. While the results of the user satisfaction level of SITIDES Bengkaung Village using the System Usability Scale method are obtained a value of 77.78 where the value for the acceptability range version is Acceptable, while the grade scale results from the level of user acceptance are included in class B. Based on the measurement results using 2 The method obtained almost the same results, which showed that the users were very satisfied with the SITIDES system in Bengkaung Village so that the system was very feasible to use.
Perbandingan Metode Random Forest Classifier dan SVM Pada Klasifikasi Kemampuan Level Beradaptasi Pembelajaran Jarak Jauh Siswa Ilham Adriansyah; Muhammad Diemas Mahendra; Errissya Rasywir; Yovi Pratama
Bulletin of Informatics and Data Science Vol 1, No 2 (2022): November 2022
Publisher : PDSI

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Abstract

WHO has declared that COVID-19 or SARS-CoV-2 has been a global pandemic since March 2020. Distance learning as we often hear is learning that prioritizes independence. Teachers can deliver teaching materials to students without having to meet face to face in the same room. This kind of learning can be done at the same time or at different times. This study aims to compare the results of the classification of students' distance learning adaptability levels with the random forest classifier and SVM methods. Obtaining the evaluation results of each algorithm used. Precision, recall, f1-score, and accuracy are evaluation indicators. The results of the classification of each adaptivity class got 73.1% for Moderate, 74.7% for Low and 66.1% for High. With the total accuracy of the SVM algorithm on the tested data of 73.36%. The results of the classification of each adaptivity class got 92.1% for Moderate, 92% for Low and 86% for High. With the total accuracy of the Random Forest Classifier algorithm on the tested data, it is 91.5%. From 1205 test data contents for each model, it was found that the Random Forest model has a higher accuracy but has an incorrect classification value of 321 data, and the accuracy of the Support Vector Machine model is lower but has an incorrect classification value of as much as 101 data

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