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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) AKSIOMA: Jurnal Program Studi Pendidikan Matematika Jurnal CoreIT Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNOLOGI DAN OPEN SOURCE Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Pengabdian Masyarakat Multidisiplin JPMB: Jurnal Pemberdayaan Masyarakat Berkarakter IJISTECH Jutsi: Jurnal Teknologi dan Sistem Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat J-COSCIS : Journal of Computer Science Community Service Edumatica: Jurnal Pendidikan Matematika Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat SATIN - Sains dan Teknologi Informasi JURNAL KARYA ILMIAH MULTIDISIPLIN INOVTEK Polbeng - Seri Informatika Edu Komputika Journal
Ahmad Zamsuri, Ahmad
Universitas Lancang Kuning
Aerospace Engineering Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analysis of the Use of the Merdeka Mengajar Platform Using the Hedonic Motivation System Adoption Model Djusar, Syahtriatna; Abini, Eka Yestira Nita; Asril, Elvira; Zamsuri, Ahmad
Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i1.10741

Abstract

The success of the implementation of the Merdeka Mengajar Platform (PMM) is measured by its acceptance and sustained use by teachers. This study will measure the factors influencing the acceptance and continued use of PMM by 53 teachers as respondents, from four high schools in the Rumbai District of Pekanbaru City, Riau Province, each of whom was given 21 questions. To accomplish this, the Hedonic Motivation System Adoption Model was used, which includes the following variables: Perceived Ease of Use (X1), Perceived Usefulness (X2), Curiosity (X3), Joy (X4), Control (X5), Behavioral Intention to Use (Y1), and Focused Immersion (Y2). Data analysis was conducted using Validity Tests, Reliability Tests, Normality Tests, Linearity Tests, Correlation Tests, Partial Tests, Simultaneous Tests, and Hypothesis Tests. The aspects derived from the use of PMM indicate that the hedonic factors for ease of use (X1) and curiosity (X3) in relation to intention (Y1) have correlation values of 0.560 and 0.661, respectively. Ease of use (X1) has full control over joy (X4) in relation to intention (Y1), with correlation values of 0.560 and 0.657. The factors influencing the success of PMM acceptance are curiosity (X3) and joy (X4). The higher the ease of use combined with curiosity and joy, the stronger the intention to use the application.
Optimizing brain tumor MRI classification using advanced preprocessing techniques and ensemble learning methods Pardede, Akim Manaor Hara; Zamsuri, Ahmad; Nuroini, Indi; Alkhairi, Putrama
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5106-5119

Abstract

Brain tumor classification is a critical task in medical imaging that directly impacts the accuracy of diagnosis and treatment planning. However, the complexity and variability of magnetic resonance imaging (MRI) images pose significant challenges, often resulting in reduced model reliability and generalization. This study addresses these limitations by proposing a novel ResNet+Bagging model, leveraging the strengths of residual networks and ensemble learning to enhance classification performance. Using publicly available brain tumor MRI datasets, including images labeled as benign, malignant, and normal, the study employs advanced preprocessing techniques such as normalization, data augmentation, and noise reduction to ensure high-quality inputs. The proposed model demonstrated significant improvements, achieving the highest testing accuracy of 72%, outperforming other tested models such as LeNet, standard ResNet, GoogleNet, and VGGNet. Precision (0.6010), recall (0.6000), and F1-score (0.5990) metrics further highlight its superior balance in detecting positive and negative classes. The novelty of this research lies in the application of Bagging to ResNet, which effectively mitigates overfitting and enhances predictive stability in complex medical datasets. These findings underscore the proposed model's potential as a robust solution for brain tumor classification, contributing to more accurate and reliable diagnostics.
Synthetic Minority Oversampling Technique for Efforts to Improve Imbalanced Data in Classification of Lettuce Plant Diseases Nasution, Nurliana; Feldiansyah, Feldiansyah; Zamsuri, Ahmad; Hasan, Mhd Arief
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 6 No. 1 (2023): Jurnal Teknologi dan Open Source, June 2023
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v6i1.2883

Abstract

In this study we classified lettuce plant diseases. These plant diseases are available in the form of images that have been converted in .csv format to be classified. These plant diseases are available in the form of images that have been converted in .csv format to be classified. Image These plant diseases have been divided into several classes or categories. Then we determine the features of each row and column of the dataset. Each line in the CSV file represents one image, and each column represents one feature Each line in the CSV file represents one image, and each column represents one feature. Then a label is made for each line in the CSV file, namely the class or category where the images are grouped. Thus, so that we get datasets that are ready to be processed with machine learning. However, in processing the dataset, we get imbalanced data. So we added the Synthetic Minority Over-sampling Technique (SMOTE) method to overcome the imbalance that occurs. So that the data can be classified using several algorithms to find the best accuracy.
PRO DAN KONTRA PENGGUNAAN AI PADA DUNIA PENDIDIKAN Zamsuri, Ahmad; Syafitri, Wenni; Guntoro, Guntoro; Waldelmi, Idel; Bimby, Novia Putri
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol 5, No 2 (2025): Desember 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v5i2.5414

Abstract

Abstract: The Community Service (PkM) activity aims to enhance the knowledge of teachers at MI AL FATTAAH regarding the utilization of Generative Artificial Intelligence (Gen-AI) in education. Currently, student assessment is still conducted conventionally, meaning the utilization of Gen-AI technology is not yet optimal. Non-involvement in this technological development could negatively impact the quality of education in the future. Through socialization and education activities, this PkM introduced the concepts, usage, and result analysis of Gen-AI in the educational context, highlighting the pros and cons of its implementation. Effectiveness assessment was conducted using pre-tests and post-tests with the Coefficient of Reproducibility (CR) and Coefficient of Scalability (CS). CR and CS results of 1 indicate that the knowledge transfer was effective and the activity was executed well. This PkM not only improved the teachers' understanding of AI but also has the potential to become a learning model for similar educational institutions.            Keywords: Gen-AI, Socialization, Utilization, Education  Abstrak: Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan meningkatkan pengetahuan guru MI AL FATTAAH mengenai pemanfaatan Generative Artificial Intelligence (Gen-AI) dalam pendidikan. Selama ini, penilaian murid masih dilakukan secara konvensional, sehingga pemanfaatan teknologi Gen-AI belum optimal. Ketidakterlibatan dalam perkembangan teknologi ini dapat berdampak negatif terhadap kualitas pendidikan di masa depan. Melalui kegiatan sosialisasi dan edukasi, PkM ini memperkenalkan konsep, penggunaan, serta analisis hasil Gen-AI dalam konteks pendidikan, dengan menyoroti aspek pro dan kontra penerapannya. Penilaian efektivitas dilakukan melalui pre-test dan post-test menggunakan koefisien Reprodusibilitas (CR) dan Skalabilitas (CS). Hasil CR dan CS sebesar 1 menunjukkan bahwa transfer pengetahuan berlangsung efektif dan kegiatan terlaksana dengan baik. PkM ini tidak hanya meningkatkan pemahaman guru terhadap AI, tetapi juga berpotensi menjadi model pembelajaran bagi lembaga pendidikan sejenis. Kata kunci: Gen-AI, Sosialisasi, Pemanfaatan, Edukasi
TRANSFORMASI PEMBELAJARAN STATISTIKA MELALUI PENGEMBANGAN E-MODULE INTERAKTIF UNTUK PENINGKATAN LITERASI STATISTIS MAHASISWA Herlina, Sari; Kusumah, Yaya S; Juandi, Dadang; Zamsuri, Ahmad; Julianti, Dola
AKSIOMA: Jurnal Program Studi Pendidikan Matematika Vol 14, No 4 (2025)
Publisher : UNIVERSITAS MUHAMMADIYAH METRO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ajpm.v14i4.13308

Abstract

Di era digital, kemampuan untuk memahami dan menganalisis data statistik semakin penting. Kemahiran dalam statistik atau literasi statistis memungkinkan individu untuk memahami data yang digambarkan melalui grafik, tabel, dan ilustrasi, membantu mereka untuk membuat keputusan yang tepat berdasarkan data. Penelitian ini bertujuan untuk mengembangkan e-module menggunakan  Kvisoft Flipbook Maker untuk meningkatkan Literasi Statistis calon guru matematika. Dalam penelitian ini didesain bahan ajar berupa E-Module yang didesain menggunakan Kvisoft Flipbook Maker digunakan dalam pembelajaran Statistika Pendidikan. E-Module ini dikembangkan dengan menggunakan software Kvisoft Flipbook Maker yang menyajikan materi Statistika Pendidikan dengan visualisasi yang menarik serta kaya dengan fitur multimedia. Metode penelitian ini merupakan penelitian pengembangan dengan mengadaptasi model ADDIE. Subjek penelitiannya adalah mahasiswa calon guru matematika di Program Studi Pendidikan Matematika, Universitas Islam Riau. Jumlah subjek penelitian sebanyak 42 orang mahasiswa. Instrumen yang digunakan terdiri dari lembar validasi, tes literasi statistis, lembar observasi dan lembar wawancara. Data dikumpulkan melalui tes literasi statistis, observasi, dan respons mahasiswa melalui wawancara. Hasil penelitian menunjukkan bahwa: 1) Hasil desain E-Module menggunakan  Kvisoft Flipbook Maker telah diuji kelayakannya. Dari segi validitas, kelayakan desain ini berada dalam kategori valid, sedangkan kepraktisannya berada pada kategori praktis. Desainnya juga efektif, karena ada peningkatan literasi statistik yang mencapai 76,19%. Dengan demikian, penelitian ini menghasilkan desain E-Module digital menggunakan  Kvisoft Flipbook Maker yang efektif dan layak digunakan untuk meningkatkan Literasi Statistis mahasiswa calon guru matematika. Kesimpulan penelitian ini, E-Module dapat diaplikasikan dalam matakuliah statistika, serta e-modul dapa digunakan dilingkup yang lebih luas diberbagai program studi di Indonesia.In the digital age, the ability to understand and analyze statistical data is increasingly important. Proficiency in statistics or statistical literacy allows individuals to understand the data depicted through graphs, tables, and illustrations, helping them to make informed decisions based on data. This research aims to develop an e-module using Kvisoft Flipbook Maker to improve the Statistical Literacy of prospective mathematics teachers. In this study, teaching materials in the form of E-Modules were designed using Kvisoft Flipbook Maker to be used in learning Educational Statistics. This digital module was developed using Kvisoft Flipbook Maker software which presents educational statistics material with interesting visualizations and rich multimedia features. This research method is a development research by adapting the ADDIE model. The subject of the research is a prospective mathematics teacher student at the Mathematics Education Study Program, Universitas Islam Riau. The number of research subjects was 42 students. The instruments used consisted of validation sheets, statistical literacy tests, observation sheets and interview sheets. Data were collected through statistical literacy tests, observations, and student responses through interviews. The results of research show that 1) The design results of the Digital E-Module using Kvisoft Flipbook Maker have been tested for feasibility. In terms of validity, the feasibility of this design is in the valid category, while the practicality is in the practical category. The design is also effective, because there is an increase in statistical literacy which reaches 76.19%. Thus, this study produced a digital E-Module design using Kvisoft Flipbook Maker that is effective and suitable to be used to improve the Statistical Literacy of prospective mathematics teacher students. The conclusion  of this research is that e-modules can be applied in statistics courses, and e-modules can be used in a wider scope in various study programs in Indonesia.
Uncovering the Digital Divide: Gender-based Insights into Students’ Technology and Media Literacy in Mathematics Learning Herlina, Sari; Julianti, Dola; Zamsuri, Ahmad
Edumatica : Jurnal Pendidikan Matematika Vol 15 No 3 (2025): Edumatica: Jurnal Pendidikan matematika (Desember 2025)
Publisher : Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/edumatica.v15i3.45822

Abstract

The development of digital technology is increasingly sophisticated. The need for the existence of technology is not only in everyday life but also needed in learning mathematics. This article examines how Technology Literacy and Student Media Literacy in Mathematics Learning. The urgency of this research lies in achieving equality and inclusivity in digital education. This research is survey research. The number of samples is 165 students. Male students who filled out the questionnaire were 45.7% while female students were 54.3%. The age range of those who completed the questionnaire was 17-24 years. The data collecting approach was carried out utilizing a questionnaire using Google Form to reach many research participants. Descriptive analysis was used to analyse the data. The results showed that the average student technology literacy was 61.84% in the fairly good category, while the average student media technology was 54,46% in the pretty good category. The results of the analysis based on gender, it was found that female's technology literacy and media literacy were higher than males. Further research is urgently needed to develop learning strategies that bridge the gender-based digital divide and promote equal access and technological literacy in mathematics learning.
ANALISIS SENTIMEN PERUNDUNGAN TERHADAP GURU DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN NAÏVE BAYES Zamsuri, Ahmad; Nasution, Nurliana; Susandri, Susandri; Bimby, Novia Putri
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4916

Abstract

Abstract: This study discusses sentiment analysis of bullying experienced by teachers on social media. The research employs the Support vector machine (SVM) and Naïve Bayes methods to classify sentiments into positive, negative, or neutral categories. The data were collected from various social media platforms and analyzed using text mining techniques. The results show that the SVM method achieved a higher accuracy rate compared to Naïve Bayes in detecting negative sentiments related to bullying toward teachers. These findings contribute to a better understanding of digital bullying patterns targeting educators and provide a foundation for developing more effective policies to address bullying cases in the educational environment. Keywords: Sentiment Analysis, Bullying, Teachers, Support Vector Machine, Naïve Bayes, Text Mining. Abstrak: Penelitian ini membahas analisis sentimen terhadap perundungan yang dialami oleh guru di media sosial. penelitian ini menggunakan metode support vector machine (svm) dan naïve bayes untuk mengklasifikasikan sentimen menjadi positif, negatif, atau netral. data yang digunakan berasal dari berbagai platform media sosial dan dianalisis menggunakan teknik text mining. hasil penelitian menunjukkan bahwa metode svm memiliki tingkat akurasi yang lebih tinggi dibandingkan dengan naïve bayes dalam mendeteksi sentimen negatif terkait perundungan terhadap guru. temuan ini dapat membantu dalam memahami pola perundungan digital terhadap tenaga pendidik serta memberikan dasar untuk kebijakan yang lebih efektif dalam menangani kasus perundungan di dunia pendidikan. Kata Kunci: Analisis Sentimen, Perundungan, Guru, Support Vector Machine, Naïve Bayes, Text Mining.
Co-Authors Abini, Eka Yestira Nita Alkhairi, Putrama Alwan, Hiba Basim Anam, M Khairul Anam, M. Khairul Andi Zahran Budiman Andre Armada Aprilia Milanda Putri Arini Arita Fitri, Triyani Arpan Asparizal, Asparizal Baehaqi Bayu Febriadi, Bayu Bimby, Novia Putri Dadang Juandi Dafwen Toresa Deni Iskandar Eko Sediyono Elvira Asril, Elvira Elvira Elvira Fadly Suandi Fajar, Muhammad Al Fajrizal Fajrizal Fajrizal, Fajrizal Febrizal Alfarasy Syam Febrizal Alfarasy Syam Febrizal As-Syam Feldiansyah Feldiansyah Feldiansyah, Feldiansyah Fitri Juliani Gunadi Widi Nurcahyo Guntoro, Guntoro Hamdani Hamdani Hazira, Nadila Hendrawan, Riki Hiba Basim Alwan idel waldelmi, idel Julianti, Dola Keumala Anggraini Khairani Djahara, Khairani Lisnawita Lisnawita Loneli Costaner Mariza Devega Mhd. Arief Hasan, Mhd. Arief Muhamad Sadar, Muhamad Muzdalifah, Indah Nurfika Sari Nurliana Nasution Nurliana Nasution, Nurliana Nuroini, Indi Pandu Pratama Putra, Pandu Pratama Pane, Eddissyah Putra Pardede, Akim Manaor Hara Rahmiati Rahmiati Ramadani, Indah Ramadhani, Maya Roki Hardianto Sahrul Saputra Saputra, Eko Ikhwan Sari Herlina Sari Herlina Sari Herlina Sarjon Defit Shelydia Martha Sumijan , Sumijan Susandri, Susandri Susi Handayani Sutejo Sutejo Syafitri (Scopus ID: 57200085316), Wenni Syahtriatna D Syahtriatna Djusar Syahtriatna Djusar Syam, Salmaini Safitri Tintien Koerniawati Triyani Arita Fitri Turnandes, Yogo Vebby Vebby Vebby Walhidayat Walhidayat Walhidayat Wenni Syafitri, Wenni Wirta Agustin Yaya S. Kusumah Yenni, Heda Yogi Yunefri, Yogi Yogo Turnandes Yogo Turnandes Yoyon Efendi Yoyon Efendi Yuhelmi Yuhelmi Yuvi Darmayunata Zamzami, Zamzami