Article Per Year (5 Year)
p-Index From 2021 - 2026
4.08
P-Index
This Author published in this journals
All Journal Riau Journal of Computer Science InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Informatika Upgris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Komputer AMIK BSI JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) IT JOURNAL RESEARCH AND DEVELOPMENT Jurnal Sisfokom (Sistem Informasi dan Komputer) Minda Baharu Building of Informatics, Technology and Science Jurnal Mantik Informatika Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Bulletin of Information Technology (BIT) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Jurnal Pengabdian Masyarakat Gemilang (JPMG) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Zadama: Jurnal Pengabdian Masyarakat Jurnal Arsitektur Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Ilmu Komputer Ruru
Iwan Purnama
Universitas Labuhanbatu, Labuhanbatu, Sumatera Utara, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 43 Documents
Search

 1   2   3   4   5 
Artificial Intelligence as a Science Teacher Assistant: An Analysis of Machine Learning Utilization in Diagnosing Student Misconceptions: A Review Iwan Purnama; Rian Farta Wijaya; Aziddin Harahap; Firman Edi
Jurnal Penelitian Pendidikan IPA Vol 11 No 12 (2025): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i12.13089

Abstract

Diagnosing these misconceptions in a crowded classroom context is very difficult, time-consuming, and subjective when using conventional methods, which often leads to ineffective teaching interventions. To address the urgent need for accurate and objective diagnosis, this article proposes and analyzes the role of Artificial Intelligence (AI), specifically Machine Learning (ML) technologies such as natural language processing (NLP). ML models can analyze student response data (essays) quickly and consistently, acting as science teacher assistants to strengthen diagnostic capabilities. This study uses a systematic literature review method to analyze and synthesize existing research findings regarding Artificial Intelligence as a Science Teacher's Assistant: An Analysis of the Utilization of Machine Learning in Diagnosing Student Misconceptions. This research aims to analyze and explain Artificial Intelligence as a Science Teacher's Assistant: An Analysis of the Utilization of Machine Learning in Diagnosing Student Misconceptions. The brief objectives of this study are as follows: to analyze the utilization of Machine Learning (ML) models in objectively diagnosing, categorizing, and predicting students' misconceptions in science. The findings of this review study indicate that student misconceptions are a persistent barrier to learning, and conventional (manual, paper-based) diagnostic methods have proven inefficient and subjective for crowded classrooms. This validates the urgent need for technological solutions.
Komparasi Perbandingan Algoritma C4.5, Naive Bayes, K-Nearest Neighbor, Random Forest Untuk Prediksi Faktor Penyebab Penyakit Diabetes Muhammad Bagus Fadli; Iwan Purnama; Rohani Rohani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8683

Abstract

Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels and can cause various serious complications and contribute to high mortality rates worldwide. The main problem in managing diabetes is the need for accurate patient status classification based on laboratory test data so that appropriate treatment can be carried out. This study aims to compare the performance of the C4.5 algorithm, Naive Bayes, K-Nearest Neighbor (KNN), and Random Forest in classifying diabetes patient data. The dataset used was sourced from Electronic Health Records (EHRs) with research subjects from Rantauprapat Regional General Hospital, totaling 10,000 data consisting of eight attributes and one class attribute, with 859 diabetes patient data and 9,141 non-diabetes patient data. The research method was carried out by dividing the data into training data and testing data using a ratio of 90:10, 80:20, and 70:30. Evaluation of model performance used accuracy parameters and Receiver Operating Characteristic (ROC) with Area Under Curve (AUC) values. The results showed that the C4.5 and Random Forest algorithms produced higher accuracy values ​​than Naive Bayes and KNN, especially at training data ratios of 90%:10% and 70%:30%. Based on the ROC evaluation, the Random Forest algorithm obtained the highest AUC values ​​at the 70%:30% ratio of 0.972 and 80%:20% of 0.970. Based on these test results, it can be concluded that the C4.5 and Random Forest algorithms have relatively better performance and are almost equivalent in classifying diabetes based on accuracy and AUC values.
Optimizing K-Means Algorithm With Elbow And Silhouette Methods For National Exam Score Data Clustering Ramzi Saputra; Iwan Purnama
Jurnal Ilmu Komputer Ruru Vol. 1 No. 1 (2024): Edisi Januari
Publisher : Yayasan Grace Berkat Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jikr.v1i1.1

Abstract

The national examination is an evaluation system for basic education standarts that supports student graduation. In accordance with the regulations of the Government of the Republic Indonesia, the evaluation of learning outcomes aims to evaluate the achievement of national graduate students. As the data obtained by the author, namely the National Vocational Exam Value Data for the Vocational High School in Central Java Province for the class of 2019. But the data displayed is still random and less information. Then data mining techniques are needed to classify which schools is carried out using the k-means clustering method and using elbow and silhouette optimization, with optimum k obtained K=3 and K=2 with calculations using RStudio tools. It is expected to produce the best cluster for the grouping
Co-Authors Abdul Karim Agustina Sidabutar Al Amri Ali Akbar Ritonga Ali Akbar Ritonga Ambiyar, Ambiyar Andre Ardian Arie Pramana Asyahri Hadi Nasyuha Aysyah Rengganis Aziddin Harahap Bangun, Budianto D.Leo Surya Duha Dahrul Aman Harahap Danil Polanda Deby Lorensyah Rambe Dudes Manalu Elysa Rohayani Hasibuan Elysa Rohayani Hasibuan Fahmi Rizal Fauzan Azim Firman Edi Fitri Indina Dongoran Gomal Juni Yarnis Ibnu Rasyid Munthe ibnu Rasyid Munthe Ibnu Rasyid Munthe Ibnu Rasyid Munthe Ibu Rasyid Munthe Ira Purnama Sari Jufri Khairul Khairul Masrizal Masrizal Menrisal Mesran, Mesran Mohamad Fakih Firdaus Muhammad Bagus Fadli Muhammad Halmi Dar Muslim Nelly Monica Nurhayati Nuridin Widya Pranoto Nurintan Asyiah Siregar Nurwijayanti Nur’ainun Gulo Pane, Rahmadani Pasaribu, Rohani Puput Indrayani Rahel Marianti Ritonga Raja Inganta Sinulingga Ramzi Saputra Reni Kartikaningsih Rian Farta Wijaya Rio Septian Hardinata Risnawati Agustin Rizki Hariandi Rohani Rohani Ronal Watrianthos Rosnah Ritonga Ruth Rize Paas Megahati S Ruth Rize Paas Megahati.S Ryan Utama Tambunan Safaridah Rambe Saputra, Haris Tri Saragih, Reagan Surbakti Siregar, Marlina Sulis Sumitro Sarkum Sumitro Sarkum Sumitro Sarkum Surya Ramadhan Suryadi, Sudi Sutrino Dwi Raharjo Sutrisno Dwi Raharjo Suwarti Syaiful Zuhri Harahap Syaiful Zuhri Harapan Unung Verawardina Volante, Kevin Volvo Sihombing Wijaya, Rian Farta Windo Yandri Lesmana Yandri Lesmana Zulfahmi Syahputra Zulkifli Zulkifli Zulkifli