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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Sains dan Teknologi Jurnal Sarjana Teknik Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal Pengabdian UntukMu NegeRI Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat Martabe : Jurnal Pengabdian Kepada Masyarakat JURTEKSI Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jambura Journal of Informatics Building of Informatics, Technology and Science Mobile and Forensics Jurnal Informatika dan Rekayasa Perangkat Lunak International Journal of Advances in Data and Information Systems Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit Humanism : Jurnal Pengabdian Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Prima Abdika: Jurnal Pengabdian Masyarakat Bulletin of Pedagogical Research Jurnal Pengabdian Pada Masyarakat Jurnal Pengabdian Informatika (JUPITA) Bulletin of Social Informatics Theory and Application Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka Mohuyula : Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics
Herman Yuliansyah, Herman
Program Studi Teknik Informatika, Fakultas Teknologi Industri, Universitas Ahmad Dahlan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Pelatihan Kreasi Konten Digital dengan Komunikasi melalui Tools Kecerdasan Artifisial Winiarti, Sri; Soyusiawaty, Dewi; Umar, Rusydi; Yuliansyah, Herman
Jurnal Pengabdian UntukMu NegeRI Vol. 9 No. 3 (2025): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v9i3.10417

Abstract

Seiring dengan adanya kebijakan Kementrian Pendidikan Dasar dan Menengah Republik Indonesia terkait penerapan Kecerdasan Artifisial (KA) dalam pembelajaran jenjang Sekolah Dasar (SD) hingga Sekolah Menengah Atas (SMA), maka semua sekolah memerlukan adanya pemahaman terhadap pelaksanaan pembelajaran KA. Perkembangan KA telah memberikan peluang baru dalam mendukung kreativitas dan komunikasi digital dalam pelaksanaan pembelajaran. Namun, banyak guru masih kesulitan berinteraksi secara efektif dengan perangkat KA untuk menghasilkan konten pembelajaran yang meraik, khususnya untuk pembelajaran dengan model unplugged. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kompetensi guru dalam berkomunikasi dengan perangkat KA melalui pelatihan bertema “Kreasi Konten Digital dengan Komunikasi melalui Tools Kecerdasan Artifisial.” Pelatihan dilaksanakan pada 9 Juli 2025 di Kabupaten Sleman dengan peserta sebanyak 24 guru Sekolah Menengah Pertama (SMP). Metode yang digunakan meliputi tranfer pengetahuan, praktik langsung Aplikasi KA, praktek mengajar dengan Aplikasi KA dengan pendekatan PjBL dan evaluasi pelaksanaan. Materi pelatihan berupa konsep KA dalam pembelajaran, cara berkomunikasi dengan perangkat KA yang efektif dengan menggunakan prompt yang efektif. Kegiatan pelatihan ini meningkatkan keterampilan komunikasi guru sebesar 17,4% (dari 69,4% menjadi 86,8%), menunjukkan efektivitas pendekatan PjBL dalam memperkuat literasi digital dan kreativitas guru.
Multi-Label Opinion Mining Based on Random Forest with SMOTE and ADASYN Ardiansyah, Ricy; Yuliansyah, Herman; Yudhana, Anton
Compiler Vol 14, No 2 (2025): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3185

Abstract

Multi-label classification is essential to categorize data into multiple labels simultaneously. However, data imbalance poses a challenge, where some labels have much less representation, thus reducing the model performance. This study aims to propose a candidate-based sentiment analysis model on the 2024 Jakarta Presidential and Gubernatorial Election review. The SMOTE and ADASYN oversampling methods are applied to handle class imbalance. Both oversampling methods are compared with the Random Forest machine learning method. The experimental results show that. The experimental results show that in the classification of Presidential candidates, Random Forest achieves an accuracy of 0.947 with SMOTE and 0.948 with ADASYN. For sentiment labels, the accuracy of Random Forest remains high with a result of 0.989 for both SMOTE and ADASYN. In the classification of Jakarta Gubernatorial candidates, Random Forest + SMOTE produces an accuracy of 0.975, while with ADASYN it decreases slightly to 0.973. For sentiment labels, both SMOTE and ADASYN have the highest accuracy of 0.993. The application of SMOTE and ADASYN helps to improve the distribution of the minority class without decreasing the overall accuracy, as well as improving the stability in recognizing various multi-label classes in a balanced manner.
Enhancing Medical Data Security Through Blockchain Smart Contract and Decentralized Application Herman, Herman; Salji, Rinday Zildjiani; Yuliansyah, Herman
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1387

Abstract

This research studies implementation of decentralized applications (DApps) that are combined with blockchain technology and IPFS for storing patient medical data. The goal of this research is to increase the security, transparency, and access control of stored medical data to make sure only legitimate users can access the data. The proposed system uses smart contracts on the Ethereum network to handle user rights of access (doctors, patients, and admins) and ensure data integrity through the blockchain immutability feature. Patient medical records are retained in IPFS and traced using the Content Identifier (CID). Implementation outcome reveals that the system can safely process medical information, keeping patients in full control of their information, and restricting data access only to scheduled time. This system also shows the potential of blockchain and IPFS technology-based applications in achieving a more efficient health ecosystem focused on safeguarding people's data.
Klasifikasi Tingkat Serangan pada Log Jaringan Siber dengan Komparasi Naive Bayes dan K-Nearest Neighbor Apriliani, Evinda; Winiarti, Sri; Riadi, Imam; Yuliansyah, Herman
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8765

Abstract

The increasing threat of cybersecurity poses a significant impact on both organizations and individuals, necessitating a system capable of accurately detecting and classifying attack levels to support prioritization of responses. This study aims to analyze and compare the performance of two machine learning algorithms, Naive Bayes and K-Nearest Neighbor (KNN), in classifying cyberattack levels, and to evaluate the effect of hyperparameter tuning on improving model accuracy. The research methods included utilizing the cybersecurity_attacks dataset, data preprocessing, model training at three data split ratios (70:30, 80:20, and 90:10), and parameter optimization using Randomized Search and Grid Search. Performance evaluation was based on accuracy, precision, recall, and F1-score values. The results showed that KNN performed best, with a peak accuracy of 0.96 at the 80:20 ratio after tuning, increased from an accuracy of 0.947 before tuning, with precision, recall, and F1-score values ​​ranging from 0.95 to 0.96. Meanwhile, Naive Bayes only achieved a peak accuracy of 0.8485 at the same ratio. Although the improvement after hyperparameter tuning was not significant, this process still resulted in a more stable and consistent model. Future research is recommended to explore ensemble methods and test them on other datasets to produce more adaptive cyberattack classification models.
Machine Learning Algorithm for Questionnaire-Based Student Learning Style Classification Yuliansyah, Herman; Lestari, Agung Tri; Yudhana, Anton
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1536

Abstract

Identifying students’ learning styles is an important factor in supporting adaptive and data-driven learning. However, conventional methods based on manual questionnaires still have limitations in terms of efficiency and accuracy for data processing. This study presents a comparative analysis of machine learning algorithms to classify student learning styles based on questionnaire data. The dataset used consists of 1,170 student data with three learning style classes, namely visual, auditory, and kinesthetic. The four supervised learning algorithms used are Naïve Bayes, Decision Tree, Random Forest, and K-Nearest Neighbors. Model performance evaluation was conducted using 5-fold (80:20) and 10-fold (90:10) cross-validation with accuracy, precision, recall, and F1-score metrics. The results of the experiment show that the Naïve Bayes algorithm has the most optimal and stable performance with the highest accuracy value of 90.60% in both validation scenarios. These findings indicate that machine learning-based classification approaches, particularly Naïve Bayes, are effective for identifying student learning styles and have the potential to support the development of adaptive and personalized learning systems.
Similarity Identification of Large-scale Biomedical Documents using Cosine Similarity and Parallel Computing Wibowo, Merlinda; Quix, Christoph; Hussien, Nur Syahela; Yuliansyah, Herman; Adhinata, Faisal Dharma
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Document similarity computation is an important research topic in information retrieval, and it is a crucial issue for automatic document categorization. The similarity value is between 0 and 1, then the closest value to 1 is represented both documents is considered more relevant, vice versa. However, the large scale of textual information has created the problem of finding the relevance level between documents. Therefore, the relevance between mesh heading text in the PubMed documents is higher than the relevance of the abstract text in the PubMed documents. Furthermore, parallel computing is implemented to speed up the large-scale documents similarity identification process that automatically calculates in the PubMed application. The execution time of mesh heading is 15.447 seconds, and the timely execution of abstract is 74.191 seconds. The execution time of mesh heading is higher than abstract because abstract contains more words than mesh heading. This study has successfully identified the similarity between large-scale biomedical documents of the PubMed documents that implemented a cosine similarity algorithm. The result has shown that the cosine similarity of the mesh heading texts is higher than the abstract text in the form of a graph and table shown in the PubMed application. The cosine similarity is useful to measure the similarity between documents based on the TF*IDF calculation result.
Co-Authors Abdul Fadlil Adhi Prahara, Adhi Agus Setiawan, Hisyam ALYA MASITHA Anton Yudhana Apriliani, Evinda Ardiansyah, Ricy Arief Ghozali, Fanani Asti Mulasari, Surahma Ayu Laksmi Pandhita, Ayu Laksmi Bambang Sudarsono Bella Okta Sari Miranda Bidinnika, Muhammad Kunta darmanto darmanto Destriana, Rachmat Dewi Soyusiawaty Eko Hari Rachmawanto Faisal Dharma Adhinata Fatwa Tentama Febiyan, Rifal Fitriani Mutmainah, Nur Fitriani, Isah Ghozali, Fanani Arief Habie, Khairul Fathan Hafin, Aqid Fahri Hazar, Siti Hidayat, Muhammad Taufiq Hildayanti, Ica Kurnia Hildayanti, Ica Kurnia Hussien, Nur Syahela Ika Arfiani Imam Riadi Jayawarsa, A.A. Ketut Jefree Fahana Jumaedi Nasir, Ardiansyah Khoirul Anam Dahlan Khoirunnisa, Itsnaini Irvina Kintung Prayitno, Kintung Lestari, Agung Tri Lifa, Lifa Lina Handayani Listyaningrum, Prabandari M. Yogi Riyantama Isjoni Mahiruna, Adiyah Muhammad Dzikrullah Suratin, Muhammad Dzikrullah Muhammad Fahmi Mubarok Nahdli Muhammad Kunta Biddinika Muhammad Ridwan Murinto Murinto Murinto Mutmainah, Nur Fitri Nafiati, Lu'lu' Nafiati, Lu’lu’ NGATIMIN, NGATIMIN Nisa Novianti, Tria Novitasari, Isda Desy Nur Rochmah Dyah Pujiastuti Pamungkas, Gilang Pamungkas, Gilang Pratama, Ridho Haikal Pratama, Wegig Putro, Aldibangun Pidekso Quix, Christoph R. Hafid Hardyanto, Settings Rachmaliany, Nur Rahmawati, Rahmawati Raihan, Habib Aulia Rajunaidi, Rajunaidi Razak, Farhan Radhiansyah Rusydi Umar Salji, Rinday Zildjiani Sri Winiarti Subardjo Subardjo, Subardjo Sukesi , Tri Wahyuni Sulistyawati , Sulistyawati Sulistyawati Sulistyawati Sunardi Sunardi Sunardi Surahma Asti Mulasari Tri Wahyuni Sukesi Ulumiyah, Iftitah Dwi Wahyuni Sukesi, Tri Wala, Jihan Wan Ali, Wan Nur Syamilah Wibowo, Merlinda Yohanni Syahra Yulianto, Dinan Yulisasih, Baiq Nikum