Article Per Year (5 Year)
p-Index From 2021 - 2026
11.142
P-Index
This Author published in this journals
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

Published : 87 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 87 Documents
Search

 5   6   7   8   9 
ANALYSIS OF INFORMATION TECHNOLOGY INNOVATION GOVERNANCE USING COBIT: SYSTEMATIC LITERATURE REVIEW Melinne Maldini Rosady; Alva Hendi Muhammad; Asro Nasiri
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5961

Abstract

Information Technology (IT) has become a critical element in all aspects of business, especially in the current digital era. To ensure continuity and operational effectiveness, companies must maintain their IT systems' security, availability and integrity. Success in managing IT innovation impacts operational efficiency and directly influences the company's overall performance and sustainability. Therefore, implementing effective IT innovation governance is crucial to reducing risks and maximizing benefits from IT investments. This research focuses on using the COBIT framework as the primary tool in managing IT innovation. COBIT, especially the COBIT 2019 version, is the dominant choice in innovation governance practices in the industrial and digital sectors. Through the Systematic Literature Review (SLR) method, this research identified and analyzed 30 related articles that provide in-depth insight into the application of COBIT in various industrial contexts. The research results highlight several challenges faced in implementing COBIT, including a need for a more in-depth understanding of this framework, a lack of proper documentation of work processes, and the need for clear operational standards to manage IT innovation effectively. Thus, this research not only provides practical guidance for practitioners in the field but also contributes a deeper understanding of the importance of integrated IT innovation governance with broader business strategy.
IT GOVERNANCE DESIGN IN IMPROVING THE QUALITY OF DATA AND INFORMATION SECURITY USING COBIT 2019 Bagus Setya; Alva Hendi Muhammad; Mei Parwanto Kurniawan
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6509

Abstract

This study aims to enhance the quality of data and information security at PT XYZ by developing Information Technology (I.T.) governance based on the C.O.B.I.T. 2019 framework. Data and information security are of utmost importance for Business Continuity in the contemporary digital age, particularly for companies operating in the property development industry, such as PT XYZ. The selection of C.O.B.I.T. 2019 as the framework is based on its comprehensive approach to I.T. governance, encompassing areas such as planning and organizing, acquisition and implementation, delivery and support, and monitoring and evaluation. The objective of this research is to pinpoint deficiencies in existing I.T. governance procedures and provide suggestions for improved measures that may be implemented to strengthen data and information security. The study entails qualitative analysis conducted through comprehensive interviews with essential stakeholders and examination of pertinent documents. An action plan is established based on the findings, which entails deploying an I.T. governance team and formulating more stringent security standards. By implementing the I.T. governance plan derived from C.O.B.I.T. 2019, PT XYZ aims to enhance the quality of data and information security, thus bolstering the company’s long-term viability and expansion
Implementasi Transfer Learning ResNet-50 dalam Klasifikasi Penyakit Daun Tomat Berbasis CNN Christin Soyan Dengen; Alva Hendi Muhammad
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.7191

Abstract

Tomat merupakan produk pertanian penting di banyak negara, termasuk Indonesia. Namun, penyakit daun tomat dapat berdampak signifikan pada hasil panen dan kualitas tanaman. Oleh karena itu, deteksi dini penyakit sangat penting untuk meningkatkan hasil panen. Dalam penelitian ini, kami menerapkan transfer learning  menggunakan arsitektur ResNet-50 untuk klasifikasi penyakit daun tomat berbasis Convolutional Neural Network (CNN). Dataset yang digunakan berisi 2902 gambar daun tomat yang mencakup 10 kategori termasuk daun sehat dan sembilan jenis penyakit. Proses penelitian meliputi akuisisi data, preprocessing citra dengan augmentasi untuk meningkatkan keragaman dataset, dan pengembangan model menggunakan ResNet-50 untuk ekstraksi fitur. Hasil evaluasi model menunjukkan akurasi keseluruhan sebesar 99%, dengan rata-rata presisi dan perolehan lebih besar dari 0,97 untuk sebagian besar kategori penyakit. Kategori Two-Spotted Spider Mite menunjukkan performa terbaik dengan nilai presisi, recall, dan skor F1 sebesar 1,00. Meskipun terdapat sedikit kesalahan klasifikasi pada beberapa kategori seperti Tomato Yellow Leaf Curl Virus, model tersebut tetap menunjukkan kinerja yang baik dalam mendeteksi keriting daun tomat. Penelitian ini diharapkan dapat memberikan kontribusi terhadap pengembangan sistem deteksi penyakit tanaman berbasis teknologi pengolahan citra yang lebih efisien dan akurat.
DETEKSI SERANGAN SIBER PADA PERANGKAT KESEHATAN BERBASIS WIFI DAN MQTT DENGAN MACHINE LEARNING Roymond Chandra Pradana; Alva Hendi Muhammad
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.7489

Abstract

Perangkat kesehatan yang tergabung dalam Internet of Medical Things (IoMT) rentan terhadap serangan siber, terutama saat menggunakan protokol komunikasi seperti WiFi dan MQTT. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis serangan pada perangkat IoMT serta mengembangkan model deteksi yang efektif berbasis machine learning. Metode yang digunakan meliputi pengumpulan data dari dataset terbuka, preprocessing data, dan penerapan berbagai algoritma machine learning seperti Random Forest, SVM, KNN, LightGBM, SGD Classifier, CatBoost, dan XGBoost. Hasil pengujian menunjukkan model yang dikembangkan memiliki tingkat akurasi tinggi, yakni 99,5% untuk deteksi dua kategori serangan, 91,5% untuk enam kategori, dan 86,9% untuk sembilan belas kategori. Temuan ini membuktikan bahwa machine learning dapat meningkatkan deteksi serangan siber pada perangkat medis secara signifikan. Penelitian ini memberikan kontribusi penting bagi keamanan IoMT dengan menerapkan teknik machine learning yang canggih. Selain itu, studi ini menekankan pentingnya inovasi dalam mendeteksi serangan siber serta memberikan rekomendasi untuk pengembangan algoritma yang lebih efisien di masa depan.
Quality Evaluation of Ticketing Management System Using ISO/IEC25010:2023 Standards and AHP Method Puji Ariningsih; Alva Hendi Muhammad
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1870

Abstract

Purpose: Information Technology plays a crucial role in supporting education service systems. When system-related issues arise, a Ticket Management System (TMS) becomes essential to address various software and hardware problems. Evaluating the performance and quality of TMS applications is necessary to ensure their effectiveness. This study assesses the quality of a TMS application developed by University of Amikom Yogyakarta, using ISO/IEC 25010:2023. Methods/Study design/approach: The Analytic Hierarchy Process method is employed to prioritize three key ISO/IEC 25010 characteristics by engaging TMS application users. Following the ranking, the study conducts quality measurements using questionnaires and black box testing. The questionnaire results are assessed using a Likert scale to determine scores for the TMS application based on the sub-characteristics of the three selected ISO/IEC 25010:2023 characteristics and the AHP-derived rankings. Result/Findings: The findings indicate that the TMS application achieved a quality score of 4.354. This shows that the TMS application is in the good category. Novelty/Originality/Value: The study highlights the need for performance efficiency improvements, specifically in the Time Behavior sub-characteristic, to enhance the overall quality of the TMS application.
COMPARISON OF BAYESIAN MARKOV CHAIN MONTE CARLO AND MACHINE LEARNING ALGORITHMS FOR STUDENT CUMULATIVE GRADE POINT AVERAGE PREDICTION WITH FEATURE ENGINEERING Husni Hidayat Malik; Indra Surya Permana; Alva Hendi Muhammad; Kusnawi Kusnawi
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/vfk6qw80

Abstract

Prediksi Indeks Prestasi Kumulatif (IPK) merupakan salah satu tantangan penting dalam manajemen akademik perguruan tinggi. Penelitian ini mengusulkan penerapan algoritma Markov Chain Monte Carlo (MCMC) berbasis inferensi Bayesian untuk memprediksi IPK mahasiswa berdasarkan Indeks Prestasi Semester (IPS) 1-8, dilengkapi dengan tiga fitur rekayasa: rata-rata IPS, variabilitas IPS (standar deviasi), dan tren IPS. Keunggulan utama pendekatan Bayesian adalah kemampuannya menghasilkan kuantifikasi ketidakpastian (uncertainty quantification) berupa interval kepercayaan 95% untuk setiap prediksi, yang tidak dapat dilakukan metode machine learning konvensional. Model MCMC dibandingkan secara komprehensif dengan tujuh algoritma machine learning: Random Forest, XGBoost, Gradient Boosting, SVM, KNN, AdaBoost, dan Bagging, menggunakan dataset 543 mahasiswa dari Institut Teknologi dan Kesehatan Mahardika periode 2017-2021. Evaluasi dilakukan melalui train-test split (80:20) dan 5-fold cross-validation menggunakan metrik RMSE, MAE, MAPE, dan R². Hasil pada data testing menunjukkan MCMC Bayesian memperoleh RMSE 0.0792 dan R² 0.867, berada pada peringkat kedua setelah Gradient Boosting. Namun pada evaluasi cross-validation yang lebih robust, MCMC Bayesian unggul dengan RMSE terendah 0.0832±0.0162 dan R² tertinggi 0.848. Temuan ini menunjukkan MCMC Bayesian tidak hanya kompetitif dari sisi akurasi, tetapi juga memberikan nilai tambah unik berupa estimasi ketidakpastian prediksi yang sangat berguna untuk sistem peringatan dini akademik.   Predicting Cumulative Achievement Index (GPA) is a key challenge in higher education academic management. This study proposes the application of Markov Chain Monte Carlo (MCMC) Bayesian inference for predicting student GPA based on Semester Achievement Index (IPS) from semesters 1-8, enriched with three engineered features: IPS mean, IPS variability (standard deviation), and IPS trend. The primary advantage of the Bayesian approach is its ability to produce uncertainty quantification in the form of 95% credible intervals for each prediction, which conventional machine learning methods cannot provide. The MCMC model was comprehensively compared against seven machine learning algorithms: Random Forest, XGBoost, Gradient Boosting, SVM, KNN, AdaBoost, and Bagging, using a dataset of 543 students from Institut Teknologi dan Kesehatan Mahardika for the period 2017-2021. Evaluation was conducted through an 80:20 train-test split and 5-fold cross-validation using RMSE, MAE, MAPE, and R² metrics. Test set results show MCMC Bayesian achieved RMSE 0.0792 and R² 0.867, ranking second after Gradient Boosting. However, in the more robust cross-validation evaluation, MCMC Bayesian outperformed all competitors with the lowest RMSE of 0.0832±0.0162 and highest R² of 0.848. These findings demonstrate that MCMC Bayesian is not only competitive in accuracy but also provides the unique value of prediction uncertainty estimation, which is highly useful for early academic warning systems
MODEL LAYERED MAPPING EVALUASI KEAMANAN SISTEM INFORMASI BERBASIS NIST CSF 2.0, COBIT 2019, DAN NIST SP 800-53 Fendi Setiabudi; Alva Hendi Muhammad; Sri Ngudi Wahyuni
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 3 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/0g2gqn02

Abstract

Cybersecurity evaluation is necessary to identify the security condition and gaps within an information system. The Forest Product Administration Information System (SIPUHH) is an information system used to support electronic recording and reporting in forest product administration processes. This study aims to develop a layered mapping approach for evaluating SIPUHH cybersecurity in a structured and traceable manner. The frameworks used are NIST CSF 2.0 as the cybersecurity outcome layer, COBIT 2019 APO13 – Managed Security as the management layer, and NIST SP 800-53 as the security control layer. This study employs a qualitative approach using an evaluative research design with a descriptive-analytical orientation. Data were collected through interviews with SIPUHH developers and responsible personnel, as well as through verification of technical and documentary evidence. The results of the layered mapping were operationalized into 27 indicators to establish the Current Profile, formulate the Target Profile, and identify SIPUHH cybersecurity gaps. The evaluation results show that 10 indicators are classified as High priority, 15 indicators as Medium priority, and 2 indicators as Maintenance. The findings indicate that SIPUHH has established several technical and operational security capabilities; however, these capabilities are not yet fully supported by formal and structured security governance, policies, procedures, documentation, and evaluation processes. The analysis of interrelationships among the findings resulted in six security improvement programs covering governance and risk management, third parties and interconnections, protection controls, monitoring and event analysis, incident response, and service resilience and recovery.
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'ah Zitnaa Dhiaaul KWA Zubaedi, Umam Faqih