Article Per Year (5 Year)
p-Index From 2021 - 2026
2.642
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA KLIK: Kajian Ilmiah Informatika dan Komputer
Iwan Iskandar
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
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

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : building of informatics technology and science

 1 
Perbandingan Teknik Prediksi Pemakaian Obat Menggunakan Algoritma Simple Linear Regression dan Support Vector Regression Sephia Pratista; Alwis Nazir; Iwan Iskandar; Elvia Budianita; Iis Afrianty
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4260

Abstract

Public Health Centers (Puskesmas) had a crucial role in furnishing society essential healthcare services and medication management. To preempt errors in stock management, a predictive approach is employed. This prediction methodology involves comparing Data Mining techniques utilizing the Simple Linear Regression algorithm and Machine Learning methodologies harnessing the Support Vector Regression algorithm. This research uses Paracetamol 500 mg and Cetirizine drug data from January 2020 to June 2023. The selection of these algorithms is motivated by the continuous nature of the data variables and their temporal span, spanning 42 months (period). The core aim of this study is to evaluate the magnitude of predictive errors using the Mean Absolute Percentage Error (MAPE) methodology. Implementing these methods was effectuated through the programming language Python with an 80%:20% partitioning of training and testing data. Drawing from experimental endeavors conducted concerning Paracetamol 500 mg, the utilization of the Simple Linear Regression algorithm, yields a MAPE score of 20.85%, categorized as 'Moderate,' whereas the application of the Support Vector Regression algorithm generates a MAPE of 18.39%, classified as 'Good.' Otherwise, experimentation on Cetirizine employing the Simple Linear Regression algorithm, employing an identical division of training and testing data, results in a MAPE of 18.39%, also classified as 'Good.' Meanwhile, resorting to the Support Vector Regression algorithm leads to a MAPE of 17.14%, falling under the 'Good' category. Based on the MAPE obtained, the Support Vector Regression algorithm has better prediction results than the Simple Linear Regression algorithm
Comparative Study of Agglomerative Hierarchical Clustering and K-Means for Student Academic Stress Grouping Irfan Arifin; Iwan Iskandar; Elvia Budianita; Novi Yanti; Fitri Insani
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10265

Abstract

Academic stress is a common problem experienced by college students due to high academic demands, parental expectations, and social pressures during their college years. The high levels of academic stress experienced by students underscore the need for a data-driven approach to more accurately identify and map students’ stress levels. This research aims to compare the performance of the Agglomerative Hierarchical Clustering (AHC) and K-Means methods in clustering students’ academic stress levels and to determine which method produces the best clustering quality. Data were obtained from the distribution of the Perception of Academic Stress Scale (PAS) questionnaire, consisting of 18 statement items, with 361 valid respondents from the Informatics Engineering Program at UIN SUSKA Riau, class of 2022–2025. The selection of the best linkage method in AHC was performed using the Cophentic Correlation Coefficient (CCC), where Ward Linkage was selected with the highest CCC value of 0.8180. Comparative evaluation was conducted using the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index for variations in the number of clusters from K=2 to K=7. The test results showed that AHC Ward Linkage with K=2 was the best configuration with a Silhouette Coefficient of 0.4407 and a Davies-Bouldin Index of 0.8373, outperforming K-Means, which only excelled in the Calinski-Harabasz Index with a value of 419.7405 The clustering resulted in two clusters: High Stress with 244 students (67.6%) and Low Stress with 117 students (32.4%). The 2023 and 2024 cohorts had the highest proportions of high stress at 90.4% and 90.6%, respectively. This research contributes empirical evidence comparing hierarchy-based and partition-based clustering methods for academic stress data, while also demonstrating the use of the Cophenetic Correlation Coefficient as an objective basis for linkage method selection in AHC. It is hoped that the results of this study can serve as a basis for the institution in designing targeted mental health intervention programs for students.
Co-Authors Aditya Dyan Ramadhan Ahmad Efendi Alwis Nazir Beni Basuki Elin Haerani Elvia Budianita Fadhilah Syafria Fitri Insani Gusti, Siska Kurnia Haikal Zikri Ibrahim Armadian Pujakesuma Ihda Syurfi Iis Afrianty Irfan Arifin Jefitra Abdu Ar Razzak Lestari Handayani Lola Oktavia M. Fahrizal M. Saski M. Septrio Rafinaldo Muhammad Affandes Muhammad Rifaldo Al Magribi Muhammad Rizky Ramadhan Nazruddin Safaat H Nining Nur Habibah Novi Yanti Novriyanto Novriyanto Nur Iza Pizaini Pizaini Rahmad Kurniawan Reski Mai Candra Reski Mai Candra Said Nurfan Hidayad Tillah Sephia Pratista Syahbudin Hamwar Wahyu Cahyadi