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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Semesta Teknika Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Electrical Technology UMY Indonesian Journal of Information System Jurnal Surya Masyarakat Emerging Information Science and Technology Prosiding Seminar Nasional Program Pengabdian Masyarakat Scientific Journal of Informatics

Slamet Riyadi

Universitas Muhammadiyah Yogyakarta

Author-ID : 2393377

Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Medicine & Pharmacology Public Health Social Sciences

Published : 26 Documents Claim Missing Document

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Classification of Mangosteen Surface Quality Using Principal Component Analysis Slamet Riyadi; Amelia Mutiara Ayu Ratiwi; Cahya Damarjati; Tony K. Hariadi; Indira Prabasari; Nafi Ananda Utama
Emerging Information Science and Technology Vol 1, No 1: February 2020
Publisher : Universitas Muhammadiyah Yogyakarta

Mangosteen (Garcinia mangostana L) is one of the primary contributor for Indonesia export. For export commodity, the fruit should comply the quality requirement including its surface. Presently, the surface is evaluated by human visual to classify between defect and non- defect surface. This conventional method is less accurate and takes time, especially in high volume harvest. In order to overcome this problem, this research proposed images processing based classification method using principal component analysis (PCA). The method involved pre-processing task, PCA decomposition, and statistical features extraction and classification task using linear discriminant analysis. The method has been tested on 120 images by applying 4-fold cross validation method and achieve classification accuracy of 96.67%, 90.00%, 90.00% and 100.00% for fold-1, fold-2, fold-3 and fold-4, respectively. In conclusion, the proposed method succeeded to classify between defect and non-defect mangosteen surface with 94.16% accuracy.

Implementation of Multiclass Support Vector Machine for Classification of New Students Receiving Achievement Scholarships at Universitas Muhammadiyah Yogyakarta Nurfahmi Nurfahmi; Slamet Riyadi; Asroni Asroni
Emerging Information Science and Technology Vol 1, No 3: August 2020
Publisher : Universitas Muhammadiyah Yogyakarta

The selection process for scholarship grantees at Universitas Muhammadiyah Yogyakarta (UMY) still utilizes the conventional method, namely Microsoft Excel. It is conducted by inputting all student data, then sorted from the highest to the lowest. Scholarships for prospective new students must be right on the target, meaning that they meet the criteria for students eligible for scholarships. It is intended not to harm other prospective students who should get scholarships. In previous studies, the classification process used many algorithms such as Naive Bayes, C.45, Decision Tree, k-Nearest Neighbor, and Support Vector Machine. The use of the Support Vector Machine algorithm employed a two-class classification. Support Vector Machine and Decision Tree algorithms are two classification methods that can obtain precise and accurate results. This study aims to use the Multiclass Support Vector Machine (LibSVM) algorithm to classify the achievement scholarship rankings for new students. The minimum amount of data used affected the classification results. From the 2015 to 2019 data, the highest amount of data was 2015, obtaining the highest accuracy result of 84.34% using the sigmoid kernel type and the k-fold value of 3. The classification was based on the entry system stages. PMDK stage 2 obtained an accuracy of 81.38%, with the most data amounting to 268 from stage 3.

Designing a Payroll System Database for Staff of the Informatics Engineering Department of Universitas Muhammadiyah Yogyakarta Nisrina Akbar Rizky Putri; Slamet Riyadi; Aprilia Kurnianti
Emerging Information Science and Technology Vol 1, No 3: August 2020
Publisher : Universitas Muhammadiyah Yogyakarta

The development of a staff payroll system aims to create a system that can help an administrator recapitulate attendance and payroll data of Informatics Engineering (IE) Department staff quickly and accurately. Such a development requires a database. The database design is divided into four stages: requirement collection and analysis, conceptual database design, logical database design, and physical database design. Design testing was performed on the database by testing the access policies, anomaly check, and view check. The results reveal that the proposed system worked well did not encounter anomalies.

Applying the Naive Bayes Algorithm to Predict the Student Final Grade Ronald Adrian; Muhammad Aldi Joko Satria Perdana; Asroni Asroni; Slamet Riyadi
Emerging Information Science and Technology Vol 1, No 2: May 2020
Publisher : Universitas Muhammadiyah Yogyakarta

The teaching and learning process of the Faculty of Engineering of Universitas Muhammadiyah Yogyakarta has used e-learning intensively. One of the benchmarks in determining students’ final grade is to take the values in e-learning. This study aims to predict students’ final grades by utilizing the data mining process and the Naive Bayes algorithm. This study provides students and lecturers information to enhance the teaching and learning process to improve students’ final grades and maintain satisfactory final grades until the lecture is complete. The research began with the literature study, data collection, data selection, data cleaning, data transformation and implementation with rapidminer and conclusion drawing. Based on the prediction of students’ final grades, one course obtained many unsatisfactory grades with an accuracy rate of 93.75%. Thus, the higher the accuracy value, the closer the predicted final value to the actual value.

Scholarship Acceptance Selection Using Neural Network Method Rammadhany Rammadhany; Slamet Riyadi; Asroni Asroni
Emerging Information Science and Technology Vol 1, No 4: November 2020
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v1i4.16595

Muhammadiyah University of Yogyakarta is one of the private tertiary institutions that provides scholarship programs. UMY provides scholarship programs for outstanding students and middle-lower class students. In the process of receiving a scholarship, UMY has two stages, namely registration and selection. In order to obtain a scholarship, students who register must be declared to pass administrative selection and meet the assessment component as a condition for students to be eligible for scholarships. In order to know the scholarship information received by students, data processing is needed, data processing is often referred to as data mining. This writing is done to predict prospective scholarship recipients using the Neural network algorithm. The methodology at this writing begins with searching for literature studies, choosing data mining methods, data collection, data processing, application and testing of models, results and conclusions. The data used at this writing are 2019 general scholarship data, GPA data, and organizational active information data. The attributes used are parental income, GPA, scholarships, qualification and non-qualification, and active organizational information. The caption attribute qualification and non-qualification is used as a label. At this writing the authors get an accuracy of 72.62%..

Prediction of Student Study Period Based on Admission Pathways Using Support Vector Machine Algorithm Cut Maya Putri Audilla; Slamet Riyadi; Asroni Asroni
Emerging Information Science and Technology Vol 1, No 4: November 2020
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v1i4.16598

In Indonesia, the quality of a university is measured based on the accreditation by BAN-PT (National Accreditation Board for Higher Education). BAN-PT possesses several main standards in measuring the quality of a university, one of which is students and graduates. The accuracy of the student study period is a crucial issue because it is the basis for the effectiveness of a university. Prediction is a process of systematically estimating something most likely to happen in the future based on past and present information to minimize the error (difference between something that happens and the forecast results). One technique used to make predictions is data mining. Universitas Muhammadiyah Yogyakarta (UMY), as one of the best private universities in Indonesia, must maintain the quality of its students. Student admission at UMY is an internal selection carried out through several methods: student achievement and academic ability tests. The Support Vector Machine (SVM) method is part of the prediction method. Analysis of the SVM prediction utilized the historical data from alumni of the Faculty of Law of UMY in the graduation year of 2015-2019. The application of SVM has provided better accuracy, precision, and recall results. The best kernel accuracy level was the SVM RBF kernel with an optimum C value of 10 and a gamma value of 0.4 with an accuracy of 96.00%.

Prediction of New Student Registration at Universitas Muhammadiyah Yogyakarta using the Naïve Bayes Classification Algorithm Lusiana Ning Saputri; Asroni Asroni; Slamet Riyadi
Emerging Information Science and Technology Vol 2, No 1: May 2021
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v2i1.16869

All public and private universities perform new student admission each year, with the number of applicants reaching the thousands. Prospective new students who intend to continue and meet the higher education criteria must re-register according to the university’s timetable after enrolling through their selected pathway and being pronounced passed. Due to the movement of prospective students to different departments or universities, the number of applications typically does not match the number of individuals who have re-registered. If the probability of a new student candidate departing can be discovered early, higher education management can take the necessary steps to retain the prospective student. Data mining and the Naïve Bayes algorithm were employed to analyze the data. The information was extracted from Universitas Muhammadiyah Yogyakarta’s database of Information Technology freshmen applicants for 2016-2017. Microsoft Excel, RapidMiner, and SQL Server 2014 Management Studio were utilized.

Prediction of Student Decisions in Choosing the Type of Bank Using Support Vector Machine (SVM) Muhammad Habil; Slamet Riyadi
Emerging Information Science and Technology Vol 3, No 1 (2022): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v3i1.16889

A bank is an intermediate financial institution authorized to take deposits, lend money, and issue promissory notes or banknotes. In the present day, every adult must have at least one bank account. Additionally, bank services range from regular and hajj savings to large-scale loans. Students, one of the bank’s customers, usually utilize services confined to savings to preserve pocket money received from their parents and ordinary transactions like transfers and payments. Several factors, including the atmosphere, administrative fees, and the accessibility of ATMs and bank branch offices, impact students’ decisions about where to save money. It prevents the bank from predicting which services must be enhanced to encourage customers, particularly students, to select the bank. Therefore, prediction is required to ascertain the students’ choice of bank. This study employed data mining and the Support Vector Machine (LibSVM) algorithm. The quantity of data impacted the outcomes of the SVM classification. In addition, kernel types, k-fold values, and sampling techniques also influenced classification accuracy. LibSVM with a kernel type of RBF, a k-fold of 8, and shuffled sampling classified 200 data with an accuracy of 68.40%.

Web-Based Electrical Customer Service System Muhammad Rizki Frebian; Slamet Riyadi; Dwijoko Purbohadi
Emerging Information Science and Technology Vol 3, No 2 (2022): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v3i2.16894

The Electricity Customer Service Information System is a website for making new electricity requests and changing electrical power. Along with the development and advancement of technology, industrial technology is closely related to electric power, one of the most significant aspects that vigorously supports the development, particularly in the Information Technology sector in the urban world. Electric power is a fundamental element for enhancing the welfare of society. Therefore, electrical energy is a measure of societal development. Thus, a website-based information system was developed employing an observational data-gathering technique. This study utilized System Development Live Cycle (SDLC) as the system development approach, Unified Modeling Language (UML) for system analysis and design, PHP as the programming language, and MYSQL as the database.

Classification of Student Understanding on Covid-19 Booster Vaccine Using Machine Learning Cahya Damarjati; Slamet Riyadi; Ricki Irawan
Emerging Information Science and Technology Vol 3, No 2 (2022): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v3i2.18680

The outbreak of COVID-19 has been declared a global pandemic by the World Health Organization (WHO). Developing a vaccine is one of the best ways to reduce the virus's impact. Nevertheless, the development of virus mutations produces new variants that diminish the efficacy of the previous vaccine. Booster doses of the Covid-19 vaccine is still a matter of debate among the public, particularly among students, as evidenced by the low rate of booster vaccinations in the community, which is a result of a lack of knowledge about booster vaccines. The purpose of this study is to assess the level of understanding among Universitas Muhammadiyah Yogyakarta (UMY) students regarding booster vaccinations, with the results subsequently serving as a factor or strategy for future government booster vaccination policy decisions. ANN and SVM algorithms could be used to predict the level of understanding of booster vaccinations among UMY students. However, the maximum level of precision in classifying the level of comprehension is not yet known. To determine which of the two methods, kernel and k-fold, provided the maximum level of accuracy, a comparative study was conducted between them. The research was conducted by disseminating questionnaires containing assessments of booster vaccinations to a total of 2095 respondents. Using randomized sampling type, this study yielded an accuracy of 88.45% for the ANN method and 89.93% for the SVM method in each scenario. In addition, the authors conduct feature efficiency, which aims to reduce the time and cost associated with data computation.

Co-Authors Abdul Eriawan Nahar Abyiyansyah Meidy Laksono Agus Jamal Amelia Mutiara Ayu Ratiwi Anna NN Chamim Aprilia Kurnianti Arif Ahmad Fadlil Arif Bintoro Johan Asnor Juraiza Ishak Asroni Asroni Badrahini Masajeng Respati Cahya Damarjati Cut Maya Putri Audilla Dwijoko Purbohadi Dyah Mutiarin Fadhan Anwarodin Fauri Hakim Haris Setyawan Indira Prabasari Kamarul Hawari Ghazali Kurnianti, Apriliya Laila Ma’rifatul Azizah Lusiana Ning Saputri Milou Derks Muhammad Aldi Joko Satria Perdana Muhammad Habil Muhammad Rizki Frebian Nadiyah Maharty Ali Nafi A Utama Nafi Ananda Utama Nasrul Saefullah Naufal Gita Mahardika Nisrina Akbar Rizky Putri Nurfahmi Nurfahmi Rammadhany Rammadhany Ricki Irawan Ridho Novradinata Rohmana Zulfa Bakhtiar Ronald Adrian Ronald Adrian Sakir Ridho Wijaya Sartika Puspita Siti Khotimah Sofiani, Erma Sudiwijaya, Erwan Suparman Suwadi Suwadi Suzaimah Ramli Tony K Hariadi Yunita Lestari Zulfan Fadholi

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