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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) 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 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 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 International Research on Big-data and Computer Technology (IRobot) Bulletin of Computer Science Research 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 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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QUALITY MANAGEMENT OF INFORMATION TECHNOLOGY GOVERNANCE COBIT 2019 FRAMEWORK EDUCATION FACTORS IN INDONESIA: A REVIEW Prasetya, Bismar Rifki wahyu; Muhammad, Alva Hendi
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 1 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i1.9498

Abstract

This study examines information technology (IT) governance in Indonesia's education sector using the COBIT 2019 framework through a systematic literature review (SLR) approach. COBIT 2019 is a globally recognized framework designed to help organizations manage IT effectively by integrating quality management principles to achieve strategic objectives. In the education sector, implementing robust IT governance is crucial to supporting ongoing digital transformation efforts. The SLR process involved identifying, selecting, and analyzing relevant literature to assess the implementation of COBIT 2019 in the Indonesian education sector. The findings indicate that this framework can enhance IT governance quality, particularly in risk management, resource efficiency, and operational sustainability. However, challenges persist, including limited managerial understanding, shortages of skilled human resources, and inadequate infrastructure support. To address these challenges, collaboration among the government, educational institutions, and the private sector is essential. Additionally, continuous training programs are necessary to enhance the competencies of management and IT personnel in effectively implementing COBIT 2019. The study underscores the importance of integrating technological and educational aspects to improve service quality in the education sector. Furthermore, the COBIT 2019 framework is recognized as a valuable tool for fostering collaboration among stakeholders to achieve sustainable education development in Indonesia.
PEMANFAATAN TEKNOLOGI LEARNING ANALYTICS DALAM MENDETEKSI POLA BELAJAR UNTUK MENINGKATKAN KUALITAS PEMBELAJARAN DI SMK NEGERI 1 KABUPATEN SORONG Novel Adil Dwijaksana; Alva Hendi Muhammad
Jurnal Pendidikan UNIGA Vol 19 No 1 (2025): Jurnal Pendidikan UNIGA
Publisher : Fakultas Pendidikan Islam dan Keguruan Universitas Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52434/jpu.v19i1.42480

Abstract

Penelitian ini bertujuan untuk memanfaatkan teknologi Learning Analytics dalam mendeteksi pola belajar siswa guna meningkatkan kualitas pembelajaran di SMK Negeri 1 Kabupaten Sorong. Tiga pendekatan analisis digunakan: K-Means Clustering untuk mengelompokkan siswa berdasarkan performa akademik, Decision Tree untuk mengidentifikasi faktor-faktor kunci yang memengaruhi hasil belajar, serta Multiple Linear Regression untuk memprediksi capaian akademik siswa. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis penelitian eksploratif Data yang dianalisis mencakup kehadiran, nilai tugas, dan partisipasi dalam kelas pada pembelajaran luring. Hasil clustering menunjukkan adanya tiga klaster siswa: performa tinggi, sedang, dan rendah. Decision Tree mengungkap bahwa kehadiran dan partisipasi aktif merupakan indikator utama dalam menentukan performa akademik. Sementara itu, model regresi menghasilkan nilai R-squared sebesar 0,76, yang menunjukkan bahwa variabel kehadiran dan nilai tugas memiliki pengaruh signifikan terhadap capaian belajar. Temuan ini memperkuat bukti bahwa pendekatan Learning Analytics mampu memberikan rekomendasi pembelajaran yang lebih cepat, akurat, dan personal. Penelitian ini diharapkan dapat menjadi referensi dalam pengambilan keputusan pembelajaran berbasis data dan mendukung guru dalam merancang strategi pengajaran yang adaptif dan efektif. Kata-kata Kunci: Learning Analytics, K-Means Clustering, Decision Tree, Multiple Linear Regression, Pembelajaran Luring
Deep Learning Deteksi Dan Klasifikasi Penyakit Daun Tomat Menggunakan ResNet-50 Raynold, Raynold; Alva Hendi Muhammad
Computer Science and Information Technology Vol 6 No 1 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i1.8501

Abstract

Tomatoes are a popular food around the world, especially in Indonesia. Many tomato farmers experience crop failure due to lack of understanding and delays in recognizing diseases that attack their plants. The purpose of this study is to identify and assess the types of diseases on tomato leaves based on trends, data sources, methodologies, and characteristics used in detecting diseases on tomato leaves. The dataset used is sourced from kaggle consisting of 10 classes and contains a total of 11,000 images. The data division used consists of 90% training data and 10% test data. The augmentation and fine-tuning process is carried out to reduce over fitting. This research uses the ResNet-50 algorithm to detect and classify diseases on tomato leaves. ResNet will compare leaf images to classify them with 10 disease classes in the dataset. From the ResNet method, the average accuracy value is 93%. This shows that the ResNet-50 method for image classification can produce accurate accuracy in solving real-world problems
Analisis Perbandingan Algoritma SVM dan CNN dalam Mendeteksi Website Judi Online Berdasarkan Konten Teks Simanjuntak, Nurcahaya; Muhammad, Alva Hendi
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.586

Abstract

This study aims to compare the effectiveness of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms in detecting Indonesian-language online gambling websites. With the increasing number of online gambling players in Indonesia, it is essential to develop effective methods for identifying gambling content. The dataset used consists of 34,336 gambling websites and 36,529 non-gambling websites, collected through web scraping. The SVM model demonstrated an accuracy of 99%, with evaluation metrics including a precision of 1.00, recall of 0.99, and F1-score of 0.99. In contrast, the CNN model achieved perfect accuracy of 100%, with precision, recall, and F1-score all at 1.00. However, it is important to note that this perfect accuracy was achieved under certain conditions, including a relatively clean dataset and optimal training processes. Evaluation results using cross-validation techniques indicated that SVM maintained a consistent accuracy of approximately 99%, while CNN exhibited an average accuracy of 99.61% with a very low standard deviation. This research emphasizes the importance of data pre-processing in enhancing model accuracy and highlights the advantages of CNN in capturing complex patterns within text. These findings contribute significantly to the development of detection methods for online gambling websites in Indonesia and open avenues for further research in this field.
Conversion Prediction in Google Search Ads Keyword Selection Using the K-Nearest Neighbor and C4.5 Algorithms Harahap, Muhammad Sya'ban; Muhammad, Alva Hendi
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5174

Abstract

This study was conducted to analyze and compare the effectiveness of two algorithms—K-Nearest Neighbor (K-NN) and C4.5—in predicting keyword conversion on the Google Ads platform. With the rapid growth of digital marketing, selecting the right keywords has become crucial for improving conversion rates. The research utilized a dataset of 673 entries with 12 relevant attributes, collected from historical ads and the Google Ads Keyword Planner. A comparative experimental approach was employed, with the data split into training (80%) and testing (20%) sets. The analysis revealed that the C4.5 algorithm achieved higher accuracy (85.41%) compared to K-NN (74.86%). Evaluation was based on metrics such as accuracy, precision, recall, and F1-score, which indicated that C4.5 was more effective in predicting conversions using the given dataset. These findings offer valuable insights for advertisers aiming to optimize their ad campaigns by selecting more effective keywords. However, the study also acknowledges limitations and recommends further research using larger and more diverse datasets to enhance model accuracy.
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.
Co-Authors Abdul latif Adhien Kenya Estetikha Aditama, Galih Agung Harimurti, Agung Agus Purwanto Ahmad Yusuf Alif Syaiful Huda Ananda Fikri Akbar Andi Sunyoto Anggit Dwi Hartanto Anggrainy, Shynta Eza Annisa Hestiningtyas Apriadi, Frans Nilwan Arief Rahman Hakim Arif Baktiar Ariningsih, Puji Arsad Arta Perdana, Bagus Gede Asro Nasiri Asro Nasiri A’yuni, Ashlih Qurota Baiq Yulia Fitriyani Bambang Soedijono Bambang Soedijono W.A Bambang Soedijono W.A Bambang Soedijono, Bambang Bernadhed, Bernadhed Chaedar Fatach, Muhamad Reza Danu Prawira Utama DHANI ARIATMANTO Dhani Ariatmanto Diamanta, David Eka Sakti, Putra Utama Eko Pramono Ema Utami Fauzi, Moch Farid Fitriyani, Baiq Yulia Hanafi Hanafi Hanafi Hanafi Harahap, Muhammad Sya'ban Haris, Ruby Hasan, Nurul Rahmawati Hasibuan, M. Rivai Hery Priandoko Hewen, Maria Beliti Ilham Setya Budi Irawan, Hafizhan Irawan, Ridwan Dwi Irwan Oyong Jangkung Tri Nygroho Jeki Kuswanto Joko Dwi Santoso Juslan, Wulandari kurniawan, Ade Kurniawan Kusnawi Kusnawi Kusrini Kusrini Kusrini Kusrini Kusrini, K Kusrini, Kusrini Leo, Donatus Lubna Lubna Malik, Husni Hidayat Maradona, Maradona Muh Adha Muhamad Rodi Muhammad Husein Budiraharjo Muhammad Imam Munandar 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 Raynold, Raynold Razaq, Thata Authar Richki Hardi Rifqi Anugrah Robert Marco, Robert Rosady, Melinne Maldini Saputra, Mahmuda Setiajid, Bayu Simanjuntak, Nurcahaya Sofian Dwi Hadiwinata Suparyati Suparyati Suseno, Hari Budhi Taryoko, Taryoko TONNY HIDAYAT Ula, M. Izul Verawati, Ike Wahyunia Ningsih Syam Widodo, Cynthia Wiwi Widayani, Wiwi Yana Hendriana Yossy Ariyanto Zakiri, Hasani Zitnaa Dhiaaul Kusnaa Washilatul Arba'ah Zitnaa Dhiaaul KWA Zubaedi, Umam Faqih