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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) POSITIF Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pendidikan UNIGA Jurnal Ilmiah Universitas Batanghari Jambi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INOVTEK Polbeng - Seri Informatika IJIS - Indonesian Journal On Information System Sebatik ILKOM Jurnal Ilmiah INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) IJISTECH (International Journal Of Information System & Technology) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika METIK JURNAL Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) PRAJA: Jurnal Ilmiah Pemerintahan Indonesian Journal of Electrical Engineering and Computer Science JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Pilar Teknologi : Jurnal Penelitian Ilmu-ilmu Teknik Jurnal Teknik Informatika (JUTIF) JiTEKH (Jurnal Ilmiah Teknologi Harapan) Journal of Electrical Engineering and Computer (JEECOM) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Buletin Poltanesa Journal of Applied Computer Science and Technology (JACOST) International Research on Big-data and Computer Technology (IRobot) Journal of Applied Sciences, Management and Engineering Technology (JASMET) Journal of Information Technology (JIfoTech) Jurnal Informatika Teknologi dan Sains (Jinteks) JAIA - Journal of Artificial Intelligence and Applications Nusantara of Engineering (NOE) Jurnal Bangkit Indonesia Jikom: Jurnal Informatika dan Komputer Bulletin of Network Engineer and Informatics (BUFNETS) Journal of Informatics, Electrical and Electronics Engineering SmartComp Jurnal Informatika Polinema (JIP) TECHNOVATAR Intechno Journal : Information Technology Journal Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Teknologi : Jurnal Ilmiah Sistem Informasi
Alva Hendi Muhammad
Universitas Amikom Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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PERBANDINGAN MODEL TRANSFORMER, DEEP LEARNING, DAN MACHINE LEARNING UNTUK DETEKSI BERITA PALSU: STUDI KASUS PADA TEKS BERBAHASA INDONESIA Arief Rahman Hakim; Alva Hendi Muhammad
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 8 No. 2 (2025): MISI Juni 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v8i2.1591

Abstract

Deteksi berita palsu dalam bahasa Indonesia masih menjadi tantangan dalam pemrosesan bahasa alami (NLP). Penelitian ini membandingkan enam metode: RoBERTa, BERT, IndoBERT, SVM, LSTM, dan CNN dalam mengidentifikasi berita palsu. Dataset yang digunakan telah melalui proses pembersihan dan tokenisasi sebelum diterapkan pada masing-masing model. Penelitian ini memberikan analisis komprehensif terhadap keunggulan model Transformer dibandingkan dengan metode klasik seperti SVM, CNN, dan LSTM. Selain itu, penelitian ini juga menegaskan bahwa model yang dilatih khusus untuk bahasa Indonesia, seperti IndoBERT, memiliki performa lebih baik dibandingkan BERT standar. Hasil evaluasi menunjukkan bahwa model berbasis Transformer memiliki performa terbaik, dengan RoBERTa sebagai model paling akurat. Temuan ini dapat menjadi referensi bagi pengembangan sistem deteksi berita palsu yang lebih akurat dan efisien dalam bahasa Indonesia. Akurasi yang diperoleh dari masing-masing model adalah sebagai berikut: RoBERTa (99,5%), IndoBERT (98,6%), BERT (98,2%), SVM (95,9%), CNN (93,9%), dan LSTM (92,3%).
Analisis Manajemen Risiko TI Berbasis COBIT 2019 Pada Lembaga Amil Zakat Nasional XYZ Razaq, Thata Authar; Muhammad, Alva Hendi
JURNAL FASILKOM Vol. 15 No. 1 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i1.9093

Abstract

Analisis pengelolaan risiko Teknologi Informasi (TI) di Lembaga Amil Zakat Nasional (LAZNAS) XYZ dilakukan dengan menggunakan framework COBIT 2019, khususnya pada domain EDM 03 (Ensure Risk Optimization), APO 12 (Manage Risk), dan APO 13 (Manage Security). Mengingat pentingnya TI dalam mendukung operasional dan pengelolaan dana Zakat, Infak, Sedekah, dan Wakaf (ZISWAF), tujuan utama adalah menilai efektivitas manajemen risiko TI yang diterapkan. Metode yang digunakan adalah pendekatan deskriptif kualitatif melalui studi kasus, dengan pengumpulan data melalui observasi, wawancara, dan kuesioner kepada responden yang terlibat dalam pengelolaan TI. Hasil penelitian menunjukkan bahwa LAZNAS XYZ telah mencapai tingkat kapabilitas yang memadai pada domain EDM 03 (Ensure Risk Optimization), APO 12 (Manage Risk), dan APO 13 (Manage Security), dengan rata-rata level 3. Namun, terdapat kesenjangan pada domain APO 12 dan APO 13, yang memerlukan peningkatan untuk mencapai level 4. Rekomendasi perbaikan meliputi penguatan pemantauan metrik risiko, perluasan cakupan pengumpulan data risiko, serta peningkatan efektivitas kebijakan keamanan melalui audit berkala dan pelatihan staf. Kesimpulan penelitian ini adalah bahwa penerapan COBIT 2019 dapat membantu LAZNAS XYZ meningkatkan tata kelola dan manajemen risiko TI, sehingga mendukung kepercayaan donatur dan kepatuhan terhadap regulasi. Penelitian ini juga membuka peluang pengembangan lebih lanjut, seperti integrasi dengan kerangka kerja lain seperti ISO 27001 atau studi komparatif dengan organisasi filantropi sejenis.
Analisis Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Untuk Klasifikasi Cyberbullying Pada Sosial Media Twitter Maradona, Maradona; Kusrini, Kusrini; Alva Hendi Muhammad
METIK JURNAL (AKREDITASI SINTA 3) Vol. 7 No. 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.591

Abstract

This research focuses on analyzing the impact of social media on society, particularly addressing the issue of cyberbullying on the Twitter platform. Based on statistics, the majority of internet users in Indonesia actively utilize social networks, with Twitter being the most dominant platform used for communication and interaction. Therefore, cyberbullying cases often occur on this social media platform. In this study, two classification methods, namely Decision Tree and K-Nearest Neighbor (KNN), were employed to classify cyberbullying-related messages on Twitter. The aim of this research is to compare the performance of these two methods and to identify early signs of cyberbullying as relevant digital evidence for legal proceedings. The dataset used in this study consists of 650 comment records from the period 2019 to 2021, with predefined labels. The analysis results indicate that K-Nearest Neighbor achieved the highest accuracy, reaching 75.99%, compared to Decision Tree with 65.00%. Hence, K-Nearest Neighbor is considered a more effective method for cyberbullying analysis on the Twitter platform. Additionally, the identification of early signs of cyberbullying in comment id 2 can serve as relevant digital evidence for legal purposes. This research provides better insights into the effectiveness of classification in addressing cyberbullying issues on the Twitter platform.
Literature Review Audit Tata Kelola Teknologi Informasi Menggunakan Kerangka Kerja COBIT 2019 A’yuni, Ashlih Qurota; Muhammad, Alva Hendi; Nasiri, Asro
Jurnal Informa : Jurnal Penelitian dan Pengabdian Masyarakat Vol 9 No 1 (2023): Juni
Publisher : Politeknik Indonusa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46808/informa.v9i1.247

Abstract

Information Technology Governance Audit is an evaluation process carried out to evaluate the level of maturity or readiness of an organization in managing information technology. Information Technology governance audit basically focuses more on IT management and its implementation to then produce evaluations and recommendations for company improvement. COBIT 2019 can be used as a framework for conducting IT governance audits. COBIT 2019 is the latest version of COBIT with various advantages, namely, flexibility and openness, novelty and relevance, has a level of adaptation to developments with the latest technology today, provides more in-depth guidance on corporate IT governance according to the needs of each company. Of the 15 articles that have been collected, there are 2 articles that really discuss the entire information technology governance audit process, and 2 articles that discuss in detail the planning of information technology governance audits. While the other 11 articles only arrive at the calculation of the level of capability, the calculation of the maturity level of information technology governance. From these findings, it is hoped that there will be better research in the future.
Enhancing vocational computer engineering education with a GPT-driven speech recognition tool Eka Sakti, Putra Utama; Muhammad, Alva Hendi; Nasiri, Asro
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp564-574

Abstract

This research investigates the effectiveness of an AI-driven speech recognition and GPT-powered learning tool in enhancing vocational students’ proficiency in computer networks. The study involved 100 students from vocational hig school, who used the prototype as part of their learning process. A pre-test/post-test design was employed to assess changes in proficiency, and students also provided feedback on the tool’s usability and impact. The results showed a consistent improvement in proficiency across all classes. A strong positive correlation was found between students’ feedback and their proficiency improvement, suggesting that students who rated the prototype as Very Helpful were more likely to see significant learning gains. However, the correlation between time spent using the tool and proficiency improvement was minimal, indicating that the quality of engagement with the tool was more important than the duration of usage. These findings highlight the prototype’s potential to improve vocational learning outcomes and underscore the importance of user satisfaction in driving success, with future refinements necessary to ensure the tool’s broader effectiveness across different learning contexts.
EVALUASI TATA KELOLA INFORMASI DAN DATA MENGGUNAKAN FRAMEWORK COBIT 2019 (DOMAIN APO14) PADA INSTANSI XYZ Suseno, Hari Budhi; Muhammad, Alva Hendi
International Research on Big-Data and Computer Technology: I-Robot Vol 9, No 1 (2025): April
Publisher : UNIVERSITAS DHARMA WACANA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53514/ir.v9i1.646

Abstract

Penelitian ini bertujuan untuk menganalisis tingkat kapabilitas tata kelola teknologi informasi (TI) dalam pengelolaan data di Instansi XYZ menggunakan framework COBIT 2019. Fokus penelitian berada pada domain APO14 (Managed Data. Permasalahan utama yang diidentifikasi meliputi ketidakkonsistenan data, proses manual, dan keterbatasan integrasi sistem. Metode yang digunakan mencakup studi kasus, kuesioner, wawancara, serta analisis kapabilitas proses berbasis skala COBIT. Hasilnya diharapkan memberikan rekomendasi arsitektur pengelolaan data yang lebih efisien, terstandarisasi, dan mampu mengurangi risiko pengelolaan data. Penelitian ini diharapkan berkontribusi terhadap peningkatan efektivitas tata kelola TI sektor publik, khususnya dalam mendukung transformasi digital Instansi XYZ.
Efektivitas Pelatihan Awal Berbasis Domain Spesifik Legal-BERT Untuk Natural Language Processing Hukum: Replikasi Dan Perluasan Studi Casehold Zakiri, Hasani; Alva Hendi Muhammad; Asro Nasiri
Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v5i1.2610

Abstract

Abstract?The emergence of domain-specific language models has demonstrated significant potential across various specialized fields. However, their effectiveness in legal natural language processing (NLP) remains underexplored, particularly given the unique challenges posed by legal text complexity and specialized terminology. Legal NLP has practical applications such as automated legal precedent search and court decision analysis that can accelerate legal research from weeks to hours. This study evaluates the CaseHOLD dataset to provide comprehensive empirical validation of domain-specific pretraining benefits for legal NLP tasks with focus on data efficiency and context complexity analysis. We conducted systematic experiments using the CaseHOLD dataset containing 53,000 legal multiple-choice questions. We compared four models: BiLSTM, BERT-base, Legal-BERT, and RoBERTa across varying data volumes (1%, 10%, 50%, 100%) and context complexity levels. Paired t-tests with 10-fold cross-validation and Bonferroni correction ensure robust methodology that guarantees finding reliability. Legal-BERT achieved the highest macro-F1 score of 69.5% (95% CI: [68.0, 71.0]), demonstrating a statistically significant improvement of 7.2 percentage points over BERT-base (62.3%, p < 0.001, Cohen's d= 1.23). RoBERTa showed competitive performance at 68.9%, nearly matching Legal-BERT. The most substantial improvements occurred under limited data conditions with 16.6% improvement at 1% training data. Context complexity analysis revealed an inverted-U pattern with optimal performance on 41-60 word texts. The introduced Domain Specificity Score (DS-score) showed strong positive correlation (r = 0.73, p < 0.001) with pretraining effectiveness, explaining 53.3% of performance improvement variance. These findings provide empirical evidence that domain-specific pretraining offers significant advantages for legal NLP tasks, particularly under data-constrained conditions and moderate-high context complexity. The key distinction of this research is the development of a predictive DS-score framework enabling benefit estimation before implementation, unlike previous studies that only evaluated post-hoc performance. The results have practical implications for developing legal NLP systems in resource-limited environments and provide optimal implementation guidance for Legal-BERT.
Analisis Dampak Karakteristik Siswa pada Masa Pandemi COVID-19 terhadap Prestasi Akademik menggunakan Analisis Diskriminan dan Regresi Multinomial Widodo, Cynthia; Muhammad, Alva Hendi; Kusnawi, Kusnawi
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 2 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i2.9070

Abstract

Berdasarkan analisis karakteristik siswa di tengah pandemi COVID-19, studi ini menggunakan analisis diskriminan dan regresi multinomial untuk mengeksplorasi dampaknya terhadap prestasi akademik. Faktor-faktor seperti usia, jenis kelamin, tingkat stres, dan transisi ke lingkungan pembelajaran virtual diperiksa untuk memahami pengaruhnya terhadap hasil pendidikan. Temuan ini menyoroti peran penting manajemen stres dan tantangan yang ditimbulkan oleh lingkungan pembelajaran virtual, serta menekankan perlunya intervensi yang ditargetkan untuk mendukung kesejahteraan siswa dan keberhasilan akademik. Analisis diskriminan mengidentifikasi faktor-faktor utama yang membedakan tingkat prestasi akademik, sementara regresi multinomial memodelkan hubungan kompleks di antara variabel-variabel yang mempengaruhi pencapaian siswa. Penelitian ini berkontribusi pada strategi pendidikan yang disesuaikan dengan kebutuhan siswa yang terus berkembang di lanskap pendidikan yang ditransformasi secara digital.
Evaluasi Kinerja Metode Peningkatan Kontras (CLAHE & HE) pada Klasifikasi Ras Kucing menggunakan VGG16 Juslan, Wulandari; Muhammad, Alva Hendi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29578

Abstract

Cat breed classification is challenging in image processing due to complex visual variations from crossbreeding, which affect care requirements. This study evaluates the effectiveness of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Histogram Equalization (HE) in cat breed classification using a VGG16-based Convolutional Neural Network (CNN). The dataset consists of 4,656 cat images from six breeds, processed with CLAHE and HE for contrast enhancement before training. It is divided into 70% for training, 15% for validation, and 15% for testing. The model is trained for 10 epochs using the Adam optimizer, a 0.0001 learning rate, and batch sizes of 16, 32, and 64. Evaluation using accuracy, precision, recall, and F1-score shows that CLAHE achieves the highest accuracy (99.39%), surpassing HE (99.17%) by 3.29%. CLAHE is more effective in preserving local details, improving precision (78.67%), recall (78.33%), and F1-score (78%). The highest performance is in the Sphinx breed (F1-score 92%), while the lowest is in American Shorthair (F1-score 72%). A high standard deviation indicates classification variations across breeds, but CLAHE consistently improves model accuracy. These findings suggest that CLAHE is more effective than HE in enhancing cat breed classification and offers a more efficient solution than adopting a complex model architecture.
User Interface Yang Adaptif Pada Kernwerk Mobile App Berbasis Ekstensi Modular UEQ+ Alif Syaiful Huda; Alva Hendi Muhammad; Tonny Hidayat
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 2 (2024): Mei : Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i2.44

Abstract

The diversity in societal exercise preferences has increased significantly, with fitness emerging as a favored modern activity, particularly in urban areas of Indonesia. Fitness is valued for its effectiveness in restoring body fitness and achieving ideal body shapes swiftly. However, in the era of Industry 4.0, technological advancements have revolutionized the approach to fitness. Smartphone fitness applications have replaced the role of personal trainers by providing tailored exercise and dietary programs. User Interface (UI) plays a pivotal role in fitness applications, influencing User Experience (UX). The challenge lies in designing UI to accommodate user heterogeneity, both internally and externally. Adaptive UI emerges as a solution, capable of altering layout and content according to user characteristics. Kernwerk® Functional Fitness exemplifies a fitness application utilizing AI to optimize fitness routines. To enhance Kernwerk's UI adaptability, UX evaluation is conducted using UEQ+ modular extension, a comprehensive instrument for effectively and efficiently measuring user experience. Through this evaluation, components of UI and UX requiring further development to enhance Kernwerk's adaptability can be identified.
Co-Authors Abdul latif Adhien Kenya Estetikha Aditama, Galih Agung Harimurti, Agung Agus Purwanto Ahmad Yusuf Alif Sahputra Alif Syaiful Huda Ananda Fikri Akbar Andi Sunyoto Anggit Dwi Hartanto Anggrainy, Shynta Eza Annisa Hestiningtyas Arief Rahman Hakim Arief Setyanto Arif Baktiar Arsad Arta Perdana, Bagus Gede Asro Nasiri Asro Nasiri A’yuni, Ashlih Qurota Bagus Setya Baiq Yulia Fitriyani Bambang Soedijono Bambang Soedijono W.A Bambang Soedijono W.A Bambang Soedijono, Bambang Bernadhed, Bernadhed Bismar Rifki wahyu Prasetya Chaedar Fatach, Muhamad Reza Christin Soyan Dengen Danu Prawira Utama David Diamanta Dhani Ariatmanto DHANI ARIATMANTO Dony Ariyus Eka Sakti, Putra Utama Eko Pramono Ema Utami Fauzi, Moch Farid Fendi Setiabudi Ferry Wahyu Wibowo Fitriyani, Baiq Yulia Hanafi Hanafi Harahap, Muhammad Sya'ban Haris, Ruby Hasan, Nurul Rahmawati Hasibuan, M. Rivai Hery Priandoko Hewen, Maria Beliti Husni Hidayat Malik I Gusti Ngurah Wikranta Arsa Arsa Ilham Setya Budi Indra Surya Permana Intan Sari Gusti Irawan, Hafizhan Irawan, Ridwan Dwi Irwan Oyong Jangkung Tri Nygroho Jeki Kuswanto Joko Dwi Santoso Juslan, Wulandari kurniawan, Ade Kurniawan Kusnawi Kusnawi Kusrini Kusrini, K Leo, Donatus Lubna Lubna M. Hanafi Malik, Husni Hidayat Maradona, Maradona MEI PARWANTO KURNIAWAN Melinne Maldini Rosady Muh Adha Muhamad Rodi Muhammad Husein Budiraharjo Muhammad Imam Munandar Muhammad Rizky Hajar Muhartini, Sitti Muktafin, Elik Hari Nadya Chitayae Nasiri, Asro Nor Riduan Novel Adil Dwijaksana Nugroho, Hanantyo Sri Nur Aini Nur Aziz Nugroho Prasetya, Bismar Rifki wahyu Prasetya, Rendra Prima Giri Pamungkas Puji Ariningsih Raynold, Raynold Razaq, Thata Authar Richki Hardi Rifqi Anugrah Robert Marco Roymond Chandra Pradana Saputra, Mahmuda Setiajid, Bayu Setyanto, Arif Sofian Dwi Hadiwinata Solehatin, Solehatin Sri Ngudi Wahyuni, Sri Ngudi Suparyati Suparyati Suseno, Hari Budhi Taryoko, Taryoko TONNY HIDAYAT Ula, M. Izul Verawati, Ike Very Kurnia Bakti, Very Kurnia Wahyunia Ningsih Syam Widodo, Cynthia Wiwi Widayani, Wiwi Yana Hendriana Yossy Ariyanto Zakiri, Hasani Zitnaa Dhiaaul Kusnaa Washilatul Arba&#039;ah Zitnaa Dhiaaul KWA Zubaedi, Umam Faqih