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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 THABIEA (JOURNAL OF NATURAL SCIENCE TEACHING) 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) Digital Transformation Technology (Digitech) 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 Journal of Computers for Society
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 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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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.
A Content Analysis of Digital Edutainment for Disaster Literacy in Higher Education Prasetyaningsih, Prasetyaningsih; Kaniawati, Ida; Riza, Lala Septem; Utama, Judhistira Aria
Jurnal Penelitian dan Pembelajaran IPA Vol 11, No 1 (2025): Available Online in May 2025 (Web of Science Indexed)
Publisher : Department of Science Education, Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30870/jppi.v11i1.29857

Abstract

Disaster literacy is a crucial competency for communities at risk of natural hazards, especially for higher education students who will significantly contribute to future preparedness efforts.  This research aimed to examine the efficacy of digital edutainment, including films and interactive content on platforms such as YouTube, TikTok, Instagram, and Coursera, in improving disaster literacy.  We conducted a qualitative content analysis of materials concerning disaster knowledge, attitudes, and practical skills pertinent to mitigation and emergency response. Results indicate that edutainment effectively enhances foundational knowledge and promotes proactive attitudes; however, practical skills, including evacuation procedures and preparedness drills, are inadequately represented.  It is advisable to collaborate with scientific agencies such as BMKG and BNPB to ensure the accuracy and relevance of content.  This research emphasises the importance of incorporating edutainment into higher education curricula as an effective combination of learning and entertainment, supporting a comprehensive approach that integrates knowledge, attitudes, and skills to enhance societal preparedness for future disasters.
ANALISIS PENERIMAAN PENGGUNA TERHADAP PEMBELAJARAN MULTIMEDIA PEMROGRAMAN BERORIENTASI OBJEK BERBASIS QR CODE PADA PENDIDIKAN VOKASI DENGAN PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL (TAM) Al Husaeni, Dwi Fitria; Komaro, Mumu; Riza, Lala Septem; Rahman, Eka Fitrajaya; Piantari, Erna; Suherman, Amay
MUST: Journal of Mathematics Education, Science and Technology Vol 10 No 1 (2025)
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/must.v10i1.25503

Abstract

The research objective is to analyze the Technology Acceptance Model (TAM) in measuring QR Code-based object-oriented programming learning multimedia in supporting vocational school students' learning on problem-based object-oriented programming (PBO) material. The TAM model variables used consist of Perceived Usefulness (PU), Perceived Ease of User (PEU), and User Acceptance of IT (UA-IT). The research respondents consisted of 35 students of SMK Negeri 1 Cimahi. The research stages consisted of familiarizing respondents with the use of multimedia, distributing TAM questionnaires, and testing the TAM model using SmartPLS software. The research results show that the average value of the user response questionnaire for QR Code-based PBO learning multimedia is 84.95% in the "Very Good" category. Based on the analysis of the relationship between TAM variables using PLS, it is known that the perceived ease of user variable has a positive influence on the acceptance of QR Code-based PBO learning multimedia, the perceived usage variable has a positive influence on the acceptance of QR Code-based PBO learning multimedia, and the variable perceived ease of use and perceived use. together they have a positive influence on the acceptance of QR Code-based PBO learning multimedia. This research is expected to provide an explanation regarding the use of TAM analysis in learning multimedia
Stock Price Prediction Using the ETSFormer Model Case Study: PTBA Atqiya, Muhammad Azka; Riza, Lala Septem; Anisyah, Ani
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6729

Abstract

The capital market in Indonesia is currently experiencing very rapid development. This growth is significantly evidenced by the increasing number of investors, especially from the millennial and Gen Z demographics. However, this growing investor base also faces a major challenge: high stock price volatility. These fluctuations are triggered by various factors, ranging from domestic economic policies and global geopolitical conditions to rapidly changing market sentiment. This research aims to build a stock price prediction model for PT Bukit Asam Tbk (PTBA) using the ETSFormer architecture, a modern Transformer-based method designed for time-series data. The historical stock price data used in this study covers a five-year period from 2020 to 2025. To ensure optimal model performance, the best model was identified using the Grid Search technique to find the most effective combination of hyperparameters. The results of this study determined that the best model was achieved with the hyperparameters model dimension = 16, batch size = 16, and a learning rate = 0.01, which yielded a validation loss of 0.0074. In the evaluation phase, this model demonstrated solid performance with a MAPE score of 3.28%, an MAE of 86.76, and an RMSE of 117.2. Although the resulting model is quite good at reading long-term trend directions, observations indicate limitations in capturing short-term price volatility. This implies that the model is more suitable for strategic trend analysis than for predicting daily fluctuations.
Design and Development of Virtual Reality Media on Computer System Learning to Enhance Students' Cognitive Abilities Wahyudin; Akbar, Anthonio; Nugraha, Eki; Riza, Lala Septem; Nazir, Shah
Journal of Education Technology Vol. 9 No. 2 (2025): May
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jet.v9i2.94553

Abstract

The quality of education in Indonesia remains a significant concern, as reflected in the PISA survey, which ranks Indonesia 72nd out of 77 participating countries. One contributing factor is the limited development of Higher Order Thinking Skills (HOTS) among students, particularly in cognitive, psychomotor, and affective domains. This study aims to design and develop Virtual Reality (VR) media integrated with a Self-Directed Learning (SDL) model to enhance students' cognitive abilities in computer system learning. Employing a Research and Development (R&D) approach with the ADDIE model, this experimental research involved 33 students and applied a One Group Pre-test Post-test design. Data were collected through cognitive tests and student response questionnaires, and analyzed using paired sample t-tests and N-Gain calculations. The results indicated a significant improvement in students' cognitive abilities, with overall conceptual gains categorized as moderate and positive student responses toward the VR media. These findings suggest that SDL-based VR media can effectively foster students’ cognitive development, encourage active, independent learning, and serve as an innovative instructional solution to address educational quality challenges. The study implies that immersive technology integration, when paired with appropriate learning models, holds substantial potential in enhancing students' higher-order thinking skills in the digital era.
Deteksi Aksi Kekerasan pada Video CCTV Berbasis Skeleton dan Frame Grouping Menggunakan ConvLSTM Kafilli, Muhammad Fikri; Riza, Lala Septem; Wihardi, Yaya
JATISI Vol 12 No 3 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i3.12873

Abstract

Manual monitoring of surveillance video (CCTV) is inefficient and prone to human oversight. This drives the need for an automated violence detection system that is fast and accurate. Existing deep learning models are often too computationally heavy for real-time implementation, creating a dilemma between accuracy and efficiency. This research proposes a lightweight two-stream ConvLSTM architecture to address this dilemma. The method efficiently models spatio-temporal relationships by combining skeleton representation and change detection, which is then packaged through a frame grouping technique. The ConvLSTM layer serves as the main temporal model, supported by a SeparableConv2D backbone for efficient feature extraction. The model is trained on the RWF-2000 dataset and evaluated using cross-dataset validation on the Surveillance Camera Fight Dataset to test its generalization capability. The results show that the proposed model achieves superior performance with an accuracy and F1-Score of 74.00%, and is highly efficient with an inference speed of 518.45 FPS. This research demonstrates that the two-stream architecture combining skeleton representation, frame grouping, and ConvLSTM modeling successfully creates a robust, fast violence detection system, offering a practical solution for real-world monitoring applications.
Development of a Drill-and-Practice Chatbot for Enhancing English Pronunciation through Interactive Dialogue Exercises Iskandar, Aysha Alia; Megasari, Rani; Nazir, Shah; Riza, Lala Septem
Jurnal Paedagogy Vol. 12 No. 4 (2025): October
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jp.v12i4.17760

Abstract

This study aims to implement a drill and practice-based chatbot to improve English speaking skills, particularly in the aspect of pronunciation. The research employed a mixed-methods approach by combining the Research and Development (ADDIE) model with a quasi-experimental design using a pretest-posttest control group pattern. The participants consisted of 76 eighth-grade students from SMPN 5 Cirebon, divided into experimental and control groups. The instruments used included a pronunciation assessment rubric based on the Cambridge English Linguaskill Speaking Global Assessment Criteria, observation sheets, and student perception questionnaires. Data analysis was conducted through normality tests, the Wilcoxon Signed Rank Test, the Mann-Whitney U Test, and N-Gain calculation, complemented by qualitative analysis from observations and questionnaires. The findings revealed that the use of a drill and practice-based chatbot had a positive impact on improving students' pronunciation skills, although the improvement achieved remained merely in the low category, with an N-Gain score of 0.25. The chatbot was proven to provide broader, more flexible, and personalized practice opportunities for students, as well as facilitate instant feedback that is difficult to obtain in conventional learning. These results indicate that chatbots can serve as an effective supplementary medium in English language learning, particularly for practicing pronunciation both independently and in integration with classroom learning, suggesting the potential for further development and integration of chatbot technology in language education.
Learning Evaluation Using Block Programming on Object-Oriented Programming Materials to Improve Cognitive Skills Huda, Kirana Syafa; Alfitri, Latifahny Aridia; Hamzah, Raseeda; Riza, Lala Septem
IJOEM Indonesian Journal of E-learning and Multimedia Vol. 4 No. 3 (2025): Indonesian Journal of E-learning and Multimedia (October 2025)
Publisher : CV. Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijoem.v4i3.473

Abstract

Background: Vocational students in Indonesia face low cognitive performance due to curricula that emphasize memorization and shallow understanding. In programming subjects such as Object-Oriented Programming (OOP), students often manage to write syntactically correct code but struggle with conceptual mastery. This limits their ability to develop higher-order thinking skills such as analysis, evaluation, and creation.Aims: This study aims to evaluate the use of block programming in OOP materials and its impact on improving students’ cognitive abilities in class X PPLG 3 at SMKN 4 Bandung.Methods: A quantitative approach was applied using a one-group pretest–posttest experimental design. Research instruments included expert validation sheets, cognitive evaluation tests, and student response questionnaires. Data were collected from 34 students to measure learning improvementResults: The findings revealed a significant increase in student performance, with average scores rising from 27.03 (pretest) to 85.47 (posttest). The N-Gain score reached 0.80 (80.29%), categorized as “high.” Student responses toward block programming media reached 93.61%, showing strong engagement. The integration of block programming with Problem-Based Learning (PBL) provided a contextualized and intuitive approach, transforming abstract OOP concepts into more tangible visual representations.Conclusion: Block programming is effective as a learning evaluation medium in OOP. It supports cognitive development, enhances student engagement, and simplifies complex concepts. This study recommends the broader use of block programming in evaluating OOP learning to create interactive and measurable experiences.
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 Alivia, Zsalzsa Puspa Amay Suherman Amirah Misdan, Nur Farhanah Anisyah, Ani anne Hafina, anne Aqhbar Habib Arianti, Andini Setya Asep Bayu Dani Nandiyanto Asep Wahyudin Atqiya, Muhammad Azka AZ Pranata 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 Erlangga, E. Erna Piantari Faisal Syaiful Anwar Farhan Dhiyaa Pratama Fathimah, Nusuki Syari'ati Fatimah, Nusuki Syariati Ferry Mukharradi Simatupang Fidela Zhafirah 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, Jaja Judhistira Aria Utama Junaeti, Enjun Kafilli, Muhammad Fikri Kenny David Kenny David Khyrina Airin Fariza Abu Samah Kuntjoro Adji Sidarto Kuntjoro Adji Sidarto Liliasari M. FURQON Mahmoud Fahsi 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 N. Nurjanah Nanang Dwi Ardi Naufal Rabah Wahidin Nazir, Shah Nor Aiza Moketar Novi Sofia Fitriasari 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 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 Siregar, Herbert 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