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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pengajaran MIPA TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer (JIK) Indonesian Journal of Disability Studies Journal of Engineering and Technological Sciences ELINVO (Electronics, Informatics, and Vocational Education) Jurnal Penelitian dan Pembelajaran IPA Indonesian Journal of Science and Technology QUANTUM: Jurnal Inovasi Pendidikan Sains JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan Knowledge Engineering and Data Science Jurnal Penelitian Pendidikan IPA (JPPIPA) Momentum: Physics Education Journal MUST: Journal of Mathematics Education, Science and Technology Journal of Natural Science and Integration JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL PENDIDIKAN TAMBUSAI Journal of Education Technology Jurnal Tekno Insentif Jurnal Sains Dirgantara Education and Human Development Journal Jurnal Paedagogy Cendikia : Media Jurnal Ilmiah Pendidikan Journal Evaluation in Education (JEE) Brilliance: Research of Artificial Intelligence Jurnal Pengabdian Masyarakat untuk Negeri (UN-PENMAS) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Digital Transformation Technology (Digitech) Journal of Coaching and Sports Science IJOEM: Indonesian Journal of Elearning and Multimedia Finger : Jurnal Ilmiah Teknologi Pendidikan Bulletin of Social Informatics Theory and Application Jurnal Guru Komputer Jurnal Pendidikan Teknologi Informasi dan Komunikasi Jurnal Ilmiah Sistem Informasi Journal of Computers for Society Cadika Journal.
Lala Septem Riza
Universitas Pendidikan Indonesia, Bandung, Indonesia
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 Physics Social Sciences Other

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Natural Language Processing and Levenshtein Distance for Generating Error Identification Typed Questions on TOEFL Lala Septem Riza; Faisal Syaiful Anwar; Eka Fitrajaya Rahman; Cep Ubad Abdullah; Shah Nazir
Journal of Computers for Society Vol 1, No 1 (2020): JCS: June 2020
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jcs.v1i1.24940

Abstract

Test of English as a Foreign Language (TOEFL) is one of the evaluations requiring good quality of the questions so that they can reflect the English abilities of the test takers. However, it cannot be denied that making such questions with good quality is time consuming. In fact, the use of computer technology is able to reduce the time spent in making such questions. This study, therefore, develops a model to generate error identification typed questions automatically from news articles. Questions from the sentences on news sites are created by utilizing Natural Language Processing, Levenshtein Distance, and Heuristics. This model consists of several stages: (1) data collection; (2) preprocessing; (3) part of speech (POS) tagging; (4) POS similarity; (5) choosing question candidates based on ranking; (6) determining underline and heuristics; (7) determining a distractor. Testing ten different news articles from various websites, the system has produced some error identification typed questions. The main contributions of this study are that (i) it can be used as an alternative tool for generating error identification typed questions on TOEFL from news articles; (ii) it can generate many questions easily and automatically; and (iii) the question quality are maintained as historical questions of TOEFL.
Correlation Analysis of Open Street Map, Demography, and Vaccination on the Number of Covid-19 Cases Using Multiple Linear Regression and Pearson Correlation Product Moment Aqhbar Habib; Erna Piantari; Lala Septem Riza
Journal of Computers for Society Vol 5, No 2 (2024): JCS: September 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jcs.v5i2.70798

Abstract

At the beginning of 2020, the world was shocked by the spread of Coronavirus Disease 2019 (Covid-19). The resulting losses cover various areas. This research aims to analyze the correlation between spatial data, demographic data, and vaccination data on the spread of Covid-19 in Bandung City using Multiple Linear Regression (MLR) and Pearson Correlation Product Moment (Pearson's r). The results show that there are only 3 variables that are significantly correlated with Covid-19 cases. The lowest variables are Residential, Population Density, and Healthy Homes. Has a significant simultaneous correlation with Covid-19 cases with a coefficient of determination (R^2) of 0.55404. The model built also passed the 3 Classical Assumptions test so that the results can be trusted for their level of truth and feasibility. The results of experiments using the Pearson's r model involving 5 vaccination periods show that out of 30 sub-districts in Bandung City, there are 20 sub-districts that have a significant correlation between vaccination and the addition of Covid-19 cases and have a negative correlation direction of 80.54%. The results of the Pearson's r model experiment involving 6 vaccination periods show that there are 9 sub-districts that have a relationship. With a negative correlation direction of 72.93%.
Apache Spark Implementation on Algorithms Boyer-Moore Horspool for Case Studies Internal Transcribed Spacer and Restriction Enzyme Fidela Zhafirah; Topik Hidayat; Lala Septem Riza
Journal of Computers for Society Vol 5, No 1 (2024): JCS: June 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jcs.v5i1.70790

Abstract

The huge increase in the amount of data is a problem today. The increase in large amounts of data makes storage very large and processing data becomes very long. Meanwhile, the speed of the process is very necessary to streamline time. This research is dedicated to solving storage and process problems as a big data processing solution by creating a string matching computational model using the Boyer-Moore Horspool algorithm using the Big Data platform, Apache Spark where the Hadoop Distributed File System as data storage on the cluster. In this study, a comparison of string matching process time between stand-alone, the use of Apache Spark single nodes, the use of Apache Spark 3 nodes, 5 nodes, 11 nodes and 16 nodes using Hadoop Distributed File System storage on clusters on Google Cloud Platform. The case study used is bioinformatics by solving two problems in the field of biology, namely the search for motives related to determining the group of flowering plants with other plant groups and the search for motives as detection of begomovirous symptoms as the cause of curly leaf disease. In the results of the study, insignificant time was obtained because the data used could still be processed by classical programs so that the execution time was not much different. The accuracy of the program run on Apache Spark is 83.5%.
Analysis Of The Validity And Reliability Of A Critical Thinking Skills Instrument On The Topic Of Wave-Particle Duality Using Rasch Model Juandi, Tarpin; Kaniawati, Ida; Samsudin, Achmad; Riza, Lala Septem; Susilawati, Susilawati; Sapiruddin, Sapiruddin
QUANTUM: Jurnal Inovasi Pendidikan Sains Vol 15, No 2 (2024): Oktober 2024
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/quantum.v15i2.19996

Abstract

This study aims to analyse the validity and reliability of a critical thinking skills instrument on the topic of wave-particle dualism using the Rasch model. Data collection was carried out by administering a critical thinking skills test to students enrolled in a modern physics course. A total of 36 students from a university in West Nusa Tenggara participated. Data analysis was performed using the Rasch model through the Winsteps 4.6.1 software. The results indicated that the instrument is valid and reliable. The instrument's validity was tested by examining the data's fit to the Rasch model through infit and outfit MNSQ values and ZSTD, all of which were within the expected acceptance range. Construct validity was analysed through standardized residual variance, showing that the Rasch model can explain most of the variance in the data. Similarly, the instrument's reliability showed that the item reliability was in the very good category (0.93) and the person reliability was in the moderate category (0.68), with a Cronbach's alpha value of 0.86, indicating very good internal reliability. These findings confirm that the Rasch model is effective in assessing and improving the quality of critical thinking skills evaluation instruments in the context of modern physics education.
Utilizing Learning Media In Biology: A Step Towards Interactive Media Development Putri, Iffa Ichwani; Rahmat, Adi; Riandi, Riandi; Riza, Lala Septem
Journal of Natural Science and Integration Vol 7, No 2 (2024): Journal of Natural Science and Integration
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jnsi.v7i2.29493

Abstract

The study addresses the complexities inherent in teaching and learning biology, a subject that is often challenging for students due to its intricate concepts. Focusing on the intersection of pedagogy and technology, this research evaluates the perceptions of high school teachers and students towards the integration of contemporary learning media in biology instruction. Utilizing a descriptive and quantitative approach, the research engaged a purposively sampled cohort of educators and learners to explore their views. this study involved 56 teachers and 133 students spread across several schools. Data were gleaned through a combination of structured questionnaires and interviews, subsequently subjected to quantitative analysis. The findings reveal a consensus on the critical role of media in facilitating biology education. Teachers and students agree that technology-based media can improve understanding of biological concepts and strengthen student engagement. While there is a significant reliance on media to convey biological concepts, the findings indicate that the potential of technology-based media. However, the study uncovers a lag in the effective deployment of technological resources, with interactive multimedia being underutilized by a notable fraction of educators. This gap underscores the need for enhanced strategies to foster the adoption of technology-enhanced learning tools in the biological sciences.Keywords: interactive, multimedia, science, technology
Comparison of Machine Learning Algorithms for Species Family Classification using DNA Barcode Riza, Lala Septem; Rahman, M Ammar Fadhlur; Prasetyo, Yudi; Zain, Muhammad Iqbal; Siregar, Herbert; Hidayat, Topik; Samah, Khyrina Airin Fariza Abu; Rosyda, Miftahurrahma
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p231-248

Abstract

Classifying plant species within the Liliaceae and Amaryllidaceae families presents inherent challenges due to the complex genetic diversity and overlapping morphological traits among species. This study explores the difficulties in accurate classification by comparing 11 supervised learning algorithms applied to DNA barcode data, aiming to enhance the precision of species family classification in these taxonomically intricate plant families. The ribulose-1,5-bisphosphate carboxylase-oxygenase large sub-unit (rbcL) gene, selected as a DNA barcode locus for plants, is used to represent species within the Amaryllidaceae and Liliaceae families. The experimental results demonstrate that nearly all tested models achieve accurate species classification into the appropriate families, with an accuracy rate exceeding 97%, except for the Naïve Bayes model. Regarding computational time, the Random Forest model requires significantly more time for training than other models. Regarding memory usage, the Least Squares Support Vector Machine with a polynomial kernel, and Regularized Logistic Regression consume more memory than other models. These machine learning models exhibit strong concordance with NCBI's classifications when predicting families using the test dataset, effectively categorizing species into the Amaryllidaceae and Liliaceae families.
The Implementation of Project-Based Learning (PBL) with ADDIE Model to Improve Students' Creative Thinking Ability Wahyudin, Wahyudin; Qobus, Muhammad Shofwan; Fatimah, Nusuki Syariati; Riza, Lala Septem; Adedokun-Shittu, Nafisat Afolake
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 2 (2024): November 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i2.77240

Abstract

Creative thinking ability is one of the thinking concepts used to find ideas that people are starting to be interested in. Creative thinking can be used as a relevant tool in building innovation and as a method for building innovation models, one of which is a learning model. The project-based learning model is a solution that influences students' activeness and creativity in learning. The purpose of this research is to apply a project-based learning model that is expected to improve students' creative thinking abilities on creative product and entrepreneurship subjects on the Internet of Things material. The development model used in this research is ADDIE (Analyze, Design, Development, Implementation, Evaluation) with a One Group Pretest-Posttest research design. Based on the research results, there are several conclusions, including the following: 1) Students' creative thinking abilities by implementing the project-based learning model can be seen from the average pretest score of 38.24 and the average posttest score of 70.15. 2) The normalized gain test results obtained a mean of 0.517 with the "Medium" criteria, which means there is a difference in creative thinking abilities after the treatment process. There are four aspects given when giving the TAM questionnaire to students, namely the user's perception of usefulness with a percentage of 86.67%, the user's perception of ease of use with a percentage of 84.71%, attitude towards use with a percentage of 83.53 and attention. With a percentage of 86.27%, and the average obtained for the four aspects was 85.29% in the "Very Good" category.
Prediction of House Price using the Multivariate Adaptive Regression Spline Method ISKANDAR, AYSHA ALIA; Al Husaeni, Dwi Fitria; Sahidin, M. Zaenal Iskandar; Riza, Lala Septem; Wahyudin, W.
Jurnal Tekno Insentif Vol 19 No 1 (2025): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v19i1.1858

Abstract

Kekritisan lingkungan adalah kondisi suatu wilayah yang menunjukkan tingkat kerusakan atau kekritisan yang dapat memengaruhi kemampuan lingkungan dalam mendukung kehidupan manusia dan ekosistem. Faktor penyebabnya meliputi rendahnya vegetasi (NDVI), meningkatnya lahan terbangun (NDBI), suhu permukaan tinggi (LST), dan kepadatan penduduk. Tujuan penelitian ini menganalisis tingkat kekritisan lingkungan di Kota Bandar Lampung serta hubungan antara parameter tersebut terhadap kekritisan lingkungan. Metode yang digunakan adalah algoritma Environmental Critical Index (ECI) yaitu pendekatan integratif yang menggabungkan beberapa parameter lingkungan untuk menilai tingkat kekritisan wilayah secara spasial dan kuantitatif dengan data citra Landsat 8 (2014) dan Landsat 9 (2023). Hasil penelitian menunjukkan adanya peningkatan kekritisan cukup signifikasan sebesar 50% terjadi pada area sangat kritis seluas 1.447,74 ha. Sebaliknya terjadi penurunan pada area tidak kritis sebesar 35% atau seluas 1.029,16 ha dan pada area kritis sebesar 15% atau seluas 418,58 ha. Faktor yang paling berpengaruh terhadap kekritisan lingkungan adalah NDBI.
Machine Learning-Based Clustering for Program Learning Outcomes in Higher Education: A Systematic Review Wahyudin, W.; Riza, Lala Septem; Erlangga, E.; Al Husaeni, Dwi Novia
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5953

Abstract

This study aims to systematically review the application of machine learning-based clustering algorithms in the evaluation of Graduate Learning Outcomes (CPL) in higher education. The review was conducted using the PRISMA approach on articles published in the Scopus database during the period 2020–2025. A total of 52 articles were analyzed to identify trends in the algorithms used, implementation challenges, and their contributions to curriculum development. The findings show that algorithms such as K-Means, Hierarchical Clustering, and Fuzzy C-Means are frequently used in mapping student competencies. However, their implementation in practice remains limited due to insufficient model validation, lack of justification for algorithm selection, and a disconnect between analytical results and academic decision-making. This situation reflects a broader issue in the integration of machine learning into educational contexts, where the technical potential of algorithms has not yet been fully translated into meaningful pedagogical impact. As a conceptual contribution, this study develops a machine learning-based computational model that includes the stages of CPL data collection, preprocessing, cluster modeling, result evaluation, and integration into curriculum policy. The proposed model is designed to enhance transparency, adaptability, and evidence-based decision-making in curriculum management systems. This study also highlights the need for the development of soft clustering techniques, integration with digital learning systems, and attention to the ethics and transparency of algorithms in data-based evaluation. Thus, this study emphasizes the importance of bridging the gap between algorithmic analysis and applicable educational strategies within higher education institutions.
Predicting Student Depression Using the Naive Bayes Model on the Student Depression Dataset from Kaggle Sonjaya, Rebina Putri; Gintara, Andre Rangga; Riza, Lala Septem; Nursalman, Muhammad; Nugraha, Eki; Wahyudin, Didin
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 1 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i1.448

Abstract

Background of Study: The increasing prevalence of depression among college students highlights the urgent need for effective early detection strategies to promote mental well-being within higher education environments. Aims and Scope of Paper: This study aims to develop a predictive model for student depression using the Naive Bayes classification algorithm, with a focus on identifying key contributing factors from student-related data. Methods: The research utilizes the Student Depression dataset from Kaggle, containing structured survey data on academic stress, sleep duration, financial stress, GPA, and family mental health history. Data preprocessing included feature selection, handling of missing values, and normalization. The dataset was split into training and testing sets at a 75:25 ratio. Model training was conducted using the R programming language with the application of Laplace smoothing. Result: The Naive Bayes model achieved an accuracy of 77.66%, a specificity of 84.21%, and a sensitivity of 68.42%, indicating strong predictive performance, particularly in identifying depressive cases. Financial and academic stress were identified as the most influential factors. Conclusion: Despite its simplicity, the Naive Bayes algorithm proves to be an effective tool for initial screening of students at risk of depression, offering valuable support for educational institutions in delivering timely mental health interventions.
Co-Authors Abdullah, Cep Ubad Abu Samah, Khyrina Airin Fariza Achmad Samsudin Ade Gafar Abdullah, Ade Gafar Ade Rohayati Ade Sobandi, Ade Adedokun-Shittu, Nafisat Afolake Adi Rahmat Ahmad Zainal Abidin Akbar, Anthonio Al Husaeni, Dwi Fitria Al Husaeni, Dwi Novia Aldi Zainafif Alejandro Rosales Pérez Alejandro Rosales-Pérez Alfitri, Latifahny Aridia Aliyya, Farrel Rahma Amay Suherman Amirah Misdan, Nur Farhanah Anisyah, Ani anne Hafina, anne Aqhbar Habib Arianti, Andini Setya Asep Bayu Dani Nandiyanto Asep Wahyudin Asep Wahyudin Asri, Novri Atqiya, Muhammad Azka AZ Pranata Basallamah, Muhammad Alam Budiana, Dian Budiman Budiman Cep Ubad Abdullah Dadang Lukman Hakim Destian, Rangga Dewini Dewini Didin Wahyudin, Didin Edy Soewono Edy Soewono Eka Fitrajaya Rahman Eki Nugraha Eliyawati Eliyawati Enjang Ali Nurdin Enjun Junaeti Erlangga, E. Erna Piantari Erry Fuadillah Faisal Syaiful Anwar Farhan Dhiyaa Pratama Farizi, Syahandhika Naufal Fathimah, Nusuki Syari'ati Fatimah, Nusuki Syariati Ferry Mukharradi Simatupang Fidela Zhafirah Firdaus, Pipin Gerraldi, Alief Gintara, Andre Rangga Gunarso Hamzah, Raseeda Hasanah , Lilik Nur Hasrol Jono, Mohd Nor Hajar Hayati , Nurlaila Herbert Siregar Homdijah, Oom Siti Huda, Kirana Syafa Husni Firmansyah Ida Kaniawati ISKANDAR, AYSHA ALIA Isma Widiaty Jaja Kustija Judhistira Aria Utama Kafilli, Muhammad Fikri Kenny David Kenny David Kesuma, Muhammad Salman Khyrina Airin Fariza Abu Samah Kuntjoro Adji Sidarto Kuntjoro Adji Sidarto Liliasari M. FURQON Mahmoud Fahsi Masnur Ali Mediayani, Melani Mohd Nor Hajar Hasrol Jono Muhammad Afif Auliya Muhammad Aziz Muhammad Bahrul Ulum Muhammad Hazmi Zuhdi Muhammad Irfan Firmansyah Muhammad Ramdan Pamungkas Muhammad Syafri Syamsudin Mumu Komaro Munir Munir Munir Munir, Munir N. Nurjanah Nanang Dwi Ardi Naufal Rabah Wahidin Nazir, Shah Nor Aiza Moketar Novi Sofia Fitriasari Novitasari , Eka Fitri Nur Maisarah Nor Azharludin Nuraulia, Anti Nurhayati, Ai Siti Nurqueen Sayang Dinnie Wirakarnain Nursalman, Muhammad Nusratullo, Samialloi Olyan, Warzuqni Parlindungan Sinaga Pérez, Alejandro Rosales Pertiwi, Anita Dyah Piantari, Erna Prabawa, Harsa Wara prasetyaningsih prasetyaningsih Prasetyaningsih Prasetyaningsih, Prasetyaningsih Prasetyaningsih, Prasetyaningsih Pudjo Sukarno Pudjo Sukarno Putri , Ananda Hafizhah Putri , Liandha Arieska Putri Amelia Solihah Putri, Iffa Ichwani Qobus, Muhammad Shofwan Rahman, M Ammar Fadhlur Rambari Apandi, Anjar Rani Megasari Raseeda Hamzah Rasim, Rasim Rena Zaen Rendi Adistya Rosdiyana Riandi Riandi Riezqa Andika Rika Rafikah Agustin Rizky Rachman Judie Rooseno Rahman Dewanto Rosa, Elisa Rosi Oktiani Rosyda, Miftahurrahma Safitri, Fibriyana Sahidin, M. Zaenal Iskandar Samah, Khyrina Airin Fariza Abu Sapiruddin, Sapiruddin Selvi Marcellia Shah Nazir Shah Nazir Sigit Nugroho Siregar, Herbert Siregar, Nofi Marlina Solihat, Syifa Sonjaya, Rebina Putri Sugeng Rifqi Mubaroq Suratno Susilawati, Susilawati Tarpin Juandi Taufiq Hidayat Topik Hidayat Tutuka Ariadji Tyas Farrah Dhiba Wahyudin Wahyudin - Wahyudin Wahyudin Sanusi Rosada Wahyudin Wahyudin Wahyudin, W. Wawan Setiawan Wibisono, Yudi Wihardi, Yaya Yudi Prasetyo Zain, Muhammad Iqbal Zainab Othman Zsalzsa Puspa Alivia