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All Journal Jurnal Pengajaran MIPA Jurnal Manajemen Bisnis TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnas Nasional Teknologi dan Sistem Informasi ELINVO (Electronics, Informatics, and Vocational Education) Information System for Educators and Professionals : Journal of Information System ILKOM Jurnal Ilmiah Jurnal ULTIMA InfoSys JURNAL PENDIDIKAN TAMBUSAI EDUKATIF : JURNAL ILMU PENDIDIKAN Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Jurnal Litbang Edusaintech International Journal of Regional Innovation Brilliance: Research of Artificial Intelligence Jurnal Pendidikan Vokasi Raflesia Jurnal Desain Komunikasi Visual Nirmana Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Digital Transformation Technology (Digitech) Jurnal Sains dan Ilmu Terapan JATIKOM: Jurnal Aplikasi dan Teori Ilmu Komputer Jurnal Guru Komputer Scientific Journal of Informatics Jurnal PETISI (Pendidikan Teknologi Informasi) Dedicated: Journal of Community Services (Pengabdian kepada Masyarakat)
Rasim, Rasim
Universitas Pendidikan Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Project-Based Learning dalam Pembelajaran Proyek IoT untuk Meningkatkan Computational Thinking dan Kolaborasi Siswa: Tinjauan Literatur Sistematis Rosa, Elisa; Nursalman, Muhammad; Rasim, Rasim
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 6 No. 2 (2025): Jurnal PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v6i2.1990

Abstract

This study is a systematic literature review aimed at examining how the Project-Based Learning (PjBL) approach is applied in Internet of Things (IoT) project-based learning and its impact on the development of students' computational thinking (CT) and collaboration skills. Although few studies explicitly connect these three elements simultaneously, several studies indicate that Arduino-based projects, Scratch, and engineering principles within the PjBL context can support the enhancement of CT and collaboration, especially through interdisciplinary STEM-based approaches. The review findings suggest that IoT project-based learning with a PjBL framework holds strong potential to foster 21st-century skills. However, there remains a gap in developing integrated learning systems that combine PjBL, IoT, CT, and collaboration in a cohesive manner. Therefore, further empirical research is needed to design innovative learning models that are effective within the context of Informatics education in the digital era.
Development of Computer Hardware Learning Media Using Augmented Reality: A Study on Student Engagement and Understanding Ully, Rona; Nurdin, Enjang Ali; Rasim, Rasim
Jurnal Kependidikan : Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran, dan Pembelajaran Vol. 11 No. 3 (2025): September
Publisher : LPPM Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v11i3.17029

Abstract

This study aims to develop Augmented Reality-based learning media on computer hardware materials and investigate its impact on student understanding and engagement. The research employs the Research and Development (R&D) method with the ADDIE model. The study involved two lecturers as media experts, one informatics teacher as a material expert, and students in grades VIII-3 (control class) and VIII-4 (experimental class). Data collection techniques included interviews and questionnaires. Data analysis was conducted descriptively to assess expert validation and student involvement, as well as quantitatively through normality, homogeneity, t-test, and N-Gain calculation. The validation results showed that the media was highly feasible, with average scores of 97.5 from media experts and 62 from material experts. The N-Gain test revealed a greater increase in understanding in the experimental class (0.71) compared to the control class (0.41). Additionally, the average engagement score of students in the experimental class increased from 3.31 to 4.35, categorized as high, while the control class increased from 3.33 to 4.03, also categorized as high. The findings indicate that Augmented Reality-based learning media is feasible and effective in enhancing students' understanding and engagement with computer hardware materials.
How Microlearning Can Benefit Education: A Study of Factors and Trends in the Use of Microlearning Ranggana, Alfaza; Rasim, Rasim; Megasari, Rani
EDUKATIF : JURNAL ILMU PENDIDIKAN Vol 7, No 5 (2025): Oktober
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/edukatif.v7i5.8558

Abstract

Microlearning has become one of the approaches that has become increasingly in demand in the last two decades along with the increasing need for flexible and digital technology-based learning. This research aims to define the concept of microlearning and identify research trends and factors that drive its implementation. The method used is a bibliometric analysis of microlearning-themed publications obtained from the ScienceDirect database from the initial appearance of the term until 2024. The analysis was carried out based on three main aspects, namely the frequency of publications, the number of citations, and the network of co-emergence and co-authorship. The results of the study show a significant increase in the number of microlearning-related publications over the past two decades. The publications with the highest citations were mostly from the pre-COVID-19 pandemic period, while 99% of documents did not show a strong pattern of authorship collaboration. In addition, microlearning is defined through three main factors, namely mobile device use, social connectedness, and time constraints. These findings confirm that research on microlearning remains relevant and has the potential to be an important foundation for the development of digital learning strategies in the future
Sentiment Analysis of Indonesian Presidential Candidate Before and After the Election Sofyan, Muhammad Hilmy Rasyad; Zulkifli, Akmal; Rasim, Rasim
ULTIMA InfoSys Vol 15 No 2 (2024): Ultima Infosys: Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v15i2.3689

Abstract

As one of the world's democratic countries, Indonesia has just held a general election to choose its next president. The development of the times encourages presidential candidates to make a new breakthrough, such as the use of social media as campaign media. Currently, X is a popular social media used as a campaign medium. On X, users are given the freedom to share their opinions. Various opinions related to one of the presidential candidates were used by the researchers to collect data using the data crawling method. The results of the data crawling are first processed with different methods in the pre-process to make the data ready for use. Some of the steps that need to be taken in the pre-process are such as cleaning, normalisation, stopword, tokenisation, stemming and translation processes. All the processes carried out in the pre-process stage will produce mature or usable data. The mature data is then classified into positive, negative and neutral using the Naí¯ve Bayes classification method. Once the classification is complete, the results are evaluated in terms of sentiment towards one of the presidential candidates. The results of a total of 2117 data collected from 01 February 2024 to 20 May 2024, there are 390 data used for the pre-presidential election sentiment analysis and 1618 data used for the post-presidential election sentiment analysis. Both before and after the presidential election was held, this presidential candidate had more positive sentiments than the negative and neutral sentiments he received from the public.
Energy Management of a Low-Cost Power Meter using ESP8266 and PZEM-016 Syafri Syamsudin, Muhammad; Septem Riza, Lala; Rasim, Rasim
International Journal of Regional Innovation Vol. 4 No. 1 (2024): International Journal of Regional Innovation
Publisher : Inovbook Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52000/ijori.v4i1.97

Abstract

The burgeoning significance of the Internet of Things (IoT) lies in its capacity to configure interconnected environments and facilitate human-object interactions through collaborative services. This study proposes an efficient energy management approach leveraging cost-effective technologies like the ESP8266 microcontroller and the PZEM-016 Modbus RTU energy monitoring module. Tailored towards wireless connectivity, this solution is purposefully crafted for diverse sectors operating within constrained budgets, obviating the need for intricate infrastructure. A systematic deployment of the forward engineering research methodology is undertaken to discern the requisites and hurdles inherent in energy management. The amalgamation of ESP8266, PZEM-016, and the MQTT protocol, with RabbitMQ serving as a message broker, forges an efficacious framework for inter-device information exchange. The solution's instantiation entails the interconnection of power meter devices using the MQTT protocol, transmitting data in JSON format. The PZEM-016 sensor constitutes the crux, adeptly measuring voltage, current, frequency, and power with precision. Furthermore, the solution encompasses a prototype Smart Meter fortified with Wi-Fi connectivity to the internet, thus extending network coverage ubiquitously. Economic scrutiny reveals that the resultant power meter device costs less than 100 USD, competitively positioning it against analogous market offerings. This economically optimized design advocates for widespread adoption across multifarious sectors constrained by budgetary limitations, assuaging the complexities inherent in energy management through a trifecta of efficiency, reliability, and affordability.
Analisis Perubahan Sentimen Publik di Media Sosial X terhadap Konflik Palestina-Israel Menggunakan Model IndoBERT Al-Kadzim, Muhammad Ghiyats; Rasim, Rasim; Herbert, Herbert
Digital Transformation Technology Vol. 4 No. 2 (2024): Periode September 2024
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v4i2.5312

Abstract

Media sosial, khususnya X (sebelumnya Twitter), telah menjadi platform utama bagi masyarakat Indonesia untuk mengekspresikan pendapat mereka mengenai berbagai isu global, termasuk konflik Palestina-Israel yang kembali memanas. Tantangan yang dihadapi adalah melihat perubahan sentimen yang terjadi pada masyarakat Indonesia dan penyebab perubahan sentimen itu bisa terjadi. Meningkatnya perdebatan yang terjadi di X telah menggerakkan masyarakat Indonesia untuk terlibat dalam diskusi-diskusi mengenai konflik ini. Untuk mengatasi tantangan ini, penelitian ini mengadopsi pendekatan analisis sentimen menggunakan Natural Language Processing (NLP) dengan memanfaatkan model pre-trained IndoBERT, yang telah disesuaikan untuk memahami bahasa Indonesia.  Model IndoBERT yang telah di pre-train akan masuk ke dalam fase fine-tuning. Model ini digunakan untuk mengklasifikasikan sentimen ke dalam kategori positif, negatif, atau netral. Model ini menghasilkan nilai terbaik weighted avg pada batch size 16 dan epoch 5 dengan nilai accuracy 0.73, precision 0.73, recall 0.73, dan f1-score 0.73. Model yang telah di fine-tune digunakan untuk prediksi tweet yang belum dilabeli. Hasil prediksi divisualisasikan untuk melihat perubahan sentimen yang terjadi di setiap bulannya dan di analisis sehingga mendapatkan kesimpulan bahwa lonjakan dan fluktuasi sentimen publik terhadap konflik Palestina dan Israel sangat terkait dengan intensitas kekerasan seperti penyerangan dan banyaknya korban jiwa serta keputusan politik yang menjadi perhatian bagi masyarakat Indonesia.
Artificial Intelligence-Based Leveling System for Determining Severity Level of Autism Spectrum Disorder Rasim, R; Munir, M; Wihardi, Yaya; Ningrayati Amali, Lanto
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.14440

Abstract

Purpose: The aim of this research is to analyze the use of an artificial intelligence (AI)-based leveling system to determine the severity of autism spectrum disorders (ASD). Methods: The research method is a systematic literature review. This study addresses three key questions: (i) What factors are used to determine ASD severity? (ii) What algorithms or AI models are used in classifying ASD severity? (iii) What are the results of this AI-based leveling system in terms of severity levels or categories? Results: The study results identified several key factors that influence ASD severity, including age, IQ, genetic and neurological factors, co-occurring mental health conditions, and sociodemographic variables. Various AI algorithms, including machine learning and deep learning techniques, are used to classify the severity of ASD. The results of this study highlight the effectiveness of AI in providing objective, consistent, and measurable assessments of ASD severity, although challenges such as data quality and ethical considerations remain. AI-based leveling systems show significant potential in improving assessment and intervention processes for ASD. Novelty: This research systematically synthesizes studies on AI-driven ASD severity assessment, providing insights into crucial variables for AI-based evaluation tools. By analyzing the factors influencing severity and the effectiveness of AI models, this study identifies promising approaches for classification. The findings offer valuable contributions to the development of AI-based tools in clinical and educational applications. Further research is necessary to improve AI reliability, address biases, and maximize its potential in ASD assessment and intervention.
S-Know Microlearning: Integral Part of Knowledge Management for Employee Training in the Indonesian Banking Industry Indah Resti Fauzi; Herbert Siregar; Yudi Ahmad Hambali; Samialloi Nusratullo; Rasim Rasim
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 2 (2025): Agustus 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i2.2025.118-127

Abstract

The lack of integrated and easily accessible knowledge-sharing platforms within organizations, especially in the banking industry, has led to challenges in preserving institutional expertise and supporting effective employee training. S-Know (Smart Knowledge) is a web-based knowledge management system developed to address this issue by facilitating the storage, management, and distribution of organizational information. It offers features such as learning paths, learning modules, quizzes, and discussion forums to promote structured collaboration and self-directed learning among employees. The content is designed using a microlearning approach to ensure better comprehension and relevance to new staff training. The development of S-Know followed the Knowledge Management System Life Cycle (KMSLC), encompassing stages of knowledge capture, system design, implementation, and evaluation, each tailored to align with real organizational needs. Technically, S-Know leverages the Laravel framework for scalability and flexibility, with black box testing used to evaluate system functionality against user requirements. Test results and user interviews confirmed that all core features performed effectively and supported the intended goals. Overall, S-Know shows strong potential as a strategic and adaptive platform for knowledge management that can enhance human resource development and support sustainable organizational knowledge. To further support its growth and long-term value, future development may focus on encouraging greater user participation in knowledge sharing and improving accessibility, especially through integration with mobile platforms.
Klasifikasi Perilaku Konsumen Pasca Boikot Produk Israel Menggunakan Naive Bayes dan SVM Darojat, Wildan Mauli; Siregar, Herbert; Rasim, Rasim; Munir, Munir
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3429

Abstract

The ongoing Israel–Palestine conflict has contributed to the rise of consumer boycott movements directed at products associated with Israel. These reactions are prominently articulated on social media platforms and indicate changing patterns in consumer attitudes and behavior. This research seeks to analyze and classify public sentiment in Indonesia regarding the boycott issue by employing Natural Language Processing (NLP) techniques in combination with Machine Learning methods, specifically Naïve Bayes and Support Vector Machine (SVM). The dataset comprises user-generated comments obtained from TikTok and Instagram between October 2023 and September 2024 through web scraping procedures. The data were subsequently subjected to manual annotation, text preprocessing, and feature extraction using the TF-IDF weighting scheme. The dataset was partitioned into 80% training data and 20% testing data, and model performance was assessed using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the SVM model outperformed Naïve Bayes on the training set, achieving an accuracy of 81% and demonstrating stronger generalization in detecting positive sentiment. In contrast, the Naïve Bayes classifier attained an accuracy of 78%, showing consistent performance and superior capability in identifying negative sentiment. These results underscore the significance of selecting classification algorithms that are well suited to the distributional characteristics of sentiment data derived from social media.Keywords: Text Classification; Support Vector Machine; Naïve Bayes; Boycott; Consumer BehaviorAbstrakKonflik Israel–Palestina yang terus berlangsung telah mendorong munculnya gerakan boikot konsumen terhadap produk-produk yang memiliki keterkaitan dengan Israel. Respons tersebut banyak diekspresikan melalui platform media sosial dan mencerminkan perubahan pola sikap serta perilaku konsumen. Penelitian ini bertujuan untuk menganalisis dan mengklasifikasikan sentimen masyarakat Indonesia terhadap isu boikot tersebut dengan menerapkan teknik Natural Language Processing (NLP) yang dikombinasikan dengan metode Machine Learning (ML), yaitu Naïve Bayes dan Support Vector Machine (SVM). Dataset penelitian terdiri atas komentar pengguna yang dikumpulkan dari platform TikTok dan Instagram selama periode Oktober 2023 hingga September 2024 melalui teknik web scraping. Data selanjutnya melalui proses anotasi manual, praproses teks, serta ekstraksi fitur menggunakan skema pembobotan TF-IDF. Dataset dibagi menjadi 80% data latih dan 20% data uji, dengan kinerja model dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa model SVM menghasilkan performa yang lebih unggul pada data latih dengan tingkat akurasi sebesar 81% serta memiliki kemampuan generalisasi yang lebih baik dalam mendeteksi sentimen positif. Sementara itu, algoritma Naïve Bayes mencapai akurasi sebesar 78% dan menunjukkan kinerja yang konsisten serta lebih efektif dalam mengidentifikasi sentimen negatif. Temuan ini menegaskan pentingnya pemilihan algoritma klasifikasi yang sesuai dengan karakteristik distribusi data sentimen yang bersumber dari media sosial. 
A Dental Chatbot Based on IndoBERT with Next Sentence Prediction and Intent Classification Nadhief Athallah Isya; Rasim Rasim; Ani Anisyah
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.v5i2.6620

Abstract

Low public awareness regarding the importance of dental health remains a significant issue in Indonesia. This situation is exacerbated by limited access to consultation services that are easy, fast, affordable, and available at any time. As a result, many dental diseases go undetected at an early stage. Additionally, the tendency to delay dental check-ups is often caused by time constraints and the distance to healthcare facilities, leading many people to avoid consulting with dentists. To address this problem, this research developed a dental health chatbot based on Natural Language Processing (NLP) using IndoBERT. The model was pretrained with the Masked Language Model (MLM) approach and fine-tuned using Next Sentence Prediction (NSP) and intent classification tasks. The dataset was compiled from Indonesian-language dental health articles, symptom–disease sentence pairs, and follow-up questions, all validated by certified dentists. The system was implemented as a web application using React JS for the frontend, Express JS and MySQL for the backend, and integrated with the NLP model through a Flask API. Evaluation results show that the chatbot can provide relevant dental health information, including lightweight consultations to assist in early symptom detection, answer follow-up questions, and generate digital medical records. Expert validation produced an average score of “Good” across the aspects of clarity, relevance, medical accuracy, and completeness, with Likert scale scores ranging from 3.53 to 3.67. This research is expected to contribute as an accessible 24-hour online dental health information service aimed at increasing public knowledge and awareness.
Co-Authors Agim Agustiar Priyadi Ahmad Julkipli Akmal Zulkifli Al-Kadzim, Muhammad Ghiyats Aldi Zainafif Alif Ismail Salem Andy Agustian Ani Anisyah Anisyah, Ani Armein Z.R. Langi Basthatan Ilham Alhamid Bayu Wicaksono Darojat, Wildan Mauli Dedi Rohendi Dicky Kurniawan Dita Fauzia Dwi Novia Al Husaeni Dwinanto, Arif Eka Fitrajaya Eka Fitrajaya Rahman Elisa Rosa Enjang Ali Nurdin Enjun Junaeti Erlangga Erlangga Farhan Nurzaman Fatimah, Siti Fina Royana Fitra Bona Sari Herbert Siregar Indah Resti Fauzi Ira Fazira Jajang Kusnendar Judie, Rizky Rachman Julianti, Sukma Lala Septem Riza Lala Septem Riza Lanto Ningrayati Amali Lavianti, Desi M Munir Mahphoth, Mohd Halim bin Mochamad Khaairi Muhamad Firdaus Muhammad Fahru Rozi Muhammad Nursalman Munir Munir Munir Munir Munir Munir, Munir Nadhief Athallah Isya Nursalman, Muhammad Piantari, Erna Puput Yuniar Maulida Puspo Dewi Dirgantari Putro, Budi Laksono Rangga Destian Ranggana, Alfaza Rani Megasari Rani Megasari Rosa Salma Febriliani Rosa, Elisa Rully Nurul Hasanah Samialloi Nusratullo Saniyah Supwatunnida Seni Siti Inayah Septem Riza, Lala Shafarin, Anti Nuraulia Shah Nazir Siregar, Herbert Sofyan, Muhammad Hilmy Rasyad Sondari Setia Rahayu Subarna, Bambang Sumitra, Desfasa Ilham Syafri Syamsudin, Muhammad Tio Ardiyanto Ully, Rona W. Wahyudin Wahyudin Wahyudin Wawan Setiawan Wihardi, Yaya Yandi, Zidan Fitri Yaya Wihardi Yaya Wihardi Yessi Nurul Fadziah Yudi Ahmad Hambali Yusep Rosmansyah Zulkifli, Akmal