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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 Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control InComTech: Jurnal Telekomunikasi dan Komputer 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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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.
Efektifitas Penerapan Wibsite (Online) Pmb Dengan Menggunakan Pendekatan Technology Acceptance Model (Tam) Sekolah Tinggi Ilmu Tarbiyah Islamiyah Karya Pembangunan Paron Setiajid, Bayu; Alva Hendi Muhammad; Asro Nasiri
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 14 No 1 (2024): January
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v14i1.4473

Abstract

Pemanfaatan website pada perguruan tinggi merupakan salah satu cara penerimaan mahasiswa baru (PMB) yang dapat dilakukan secara online. Website online PMB  memungkinkan calon mahasiswa  mengakses berbagai informasi terkait  penerimaan mahasiswa baru di sekolah tinggi dan menyelesaikan proses pendaftaran  online dari lokasi manapun, selama terhubung dengan  internet, sehingga calon mahasiswa tidak harus berkunjung ke kampus untuk melakukan pendaftaran. Dengan melakukan evaluasi implementasi sistem informasi akademik yang terintegrasi menggunakan metode TAM. Metode TAM yang dijelaskan oleh Davis (1989) medefinisikan suatu metode yang memudahkan kita untuk mengakses dan mengetahui bagaimana  pengguna atau user menerima pemakaian dalam sistem informasi. Dalam metode TAM terdiri dari lima variabel yang bisa digunakan untuk mengetahui dan beberapa indicator yang menjadi faktor-faktor interaksi dengan penerimaan sistem informasi, yaitu: Perceived kegunaan, sudut pandang kemudahan penggunaan, perilaku pada penggunaan (attitude way of use), niat perilaku untuk menggunakan dan penggunaan secara aktual.
Analisis Rekomendasi untuk Meningkatkan Nilai Capability Level Domain APO 14 Pada COBIT 2019 Taryoko, Taryoko; Muhammad, Alva Hendi; Kusnawi, Kusnawi
Jurnal Ilmiah Universitas Batanghari Jambi Vol 24, No 1 (2024): Februari
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v24i1.4380

Abstract

The purpose of this study was to determine data management with consideration of the APO 14 domain at XYZ Agencies using the 2019 COBIT Framework. This research method uses a case study. The results of this study indicate that first, the capability level test value is entered at level 3, namely Establish. Second, the average value generated on the Capability level test value is 3.14 or 0.031, which means the XYZ agency So that it can be ensured that the XYZ agency has carried out the implementation process and is able to achieve process results in accordance with what is targeted in the APO domain 14. Third, the average GAP value produced is worth 3 with a difference of 1 value from the expected value in accordance with the 2019 COBIT provisions.
An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning Hanafi, Hanafi; Muhammad, Alva Hendi; Verawati, Ike; Hardi, Richki
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.990

Abstract

In the last decade, the number of attacks on the internet has grown significantly, and the types of attacks vary widely. This causes huge financial losses in various institutions such as the private and government sectors. One of the efforts to deal with this problem is by early detection of attacks, often called IDS (instruction detection system). The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It can scan the internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity under the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of Naive Bayes, KNN, Decision Tree, and SVM. The researchers evaluated the performance using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness to more than 2% in NSL-KDD Dataset, including in binary class and multi-class evaluation methods. Moreover, using SDAE also improved traditional machine learning with modern deep learning such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection
Mapping of Food Crop Commodity Production Areas in Indonesia Using The Average Linkage Method Hery Priandoko; Alva Hendi Muhammad; Anggit Dwi Hartanto
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 2 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i2.219

Abstract

Indonesia consists of several regions that have the potential to meet food needs. One of the main sectors that meet food needs is the agricultural sector. The agricultural sector is a sector that needs significant attention from the central and regional governments in meeting national food needs. Food needs are currently often scarce so people find it difficult to obtain these food needs. The problem of dependence on food needs can endanger the availability of the country's food supply. Importing food crop commodities is one solution to maintaining food availability in Indonesia. Imports of food crop commodities carried out by Indonesia show that the amount of food commodity availability cannot meet national food needs. In Indonesia, some regions have food crop commodity production so that they can help in the availability of these food needs. From the existing problems, researchers tried to conduct research by mapping the regions or areas in Indonesia to find out which regions have food crop commodity production. In this study, the mapping that will be used is using the hierarchical cluster method. The hierarchical cluster method that will be used is the agglomerative hierarchical cluster method with the average linkage method. The results of this study will be formed into 3 clusters with the following details: high cluster, medium cluster, and low cluster. The highest cluster obtained 2 members, namely the Provinces of East Java and Central Java. The Medium Cluster obtained 1 member, namely the Province of West Java. The Low Cluster obtained 31 members, namely the Provinces of Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Bangka Belitung Islands, Riau Islands, DKI Jakarta, DI. Yogyakarta, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku, North Maluku, West Papua, and Papua.
MSME AI Readiness Analysis Using The AIRI Framework: Analisis Kesiapan AI UMKM Menggunakan Kerangka Kerja AIRI Muhammad Husein Budiraharjo; Alva Hendi Muhammad; Kusnawi
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 2 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i2.307

Abstract

AI is expected to become one of the key technologies supporting the development of MSMEs, which represent a major pillar of Indonesia's economy. Successful adoption and implementation of AI require the right strategies, one of which stems from an analysis of a company’s AI readiness. In this study, an AI readiness analysis was conducted using the AIRI framework on six MSMEs from various business sectors. The results of the analysis provided the AI readiness levels of each MSME, along with comparisons to similar industries and to industries of comparable business scale (MSME). The analysis also yielded several recommendations for AI adoption and strategies to enhance the AI readiness of each MSME. All the MSMEs involved in the study positively accepted the AI readiness analysis and the adoption recommendations provided. The study did not produce any feedback for improvements to the AIRI framework itself; however, there were suggestions for further development of the AIRI application to better assist MSMEs in determining AI readiness targets and appropriate AI implementation strategies in the future..
Classification of Mental Disorders Using Modified Balanced Random Forest And Feature Selection Arsad; Alva Hendi Muhammad; Tonny Hidayat
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 2 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i2.320

Abstract

This study employs the Modified Balanced Random Forest (MBRF) algorithm and Correlation-based Feature Selector (CfsSubsetEval) for mental disorder classification. The "Mental Disorder Classification" dataset from Kaggle was used with the aim of improving accuracy, evaluating feature selection, and assessing MBRF's performance in handling data imbalance. The study compares the performance of Random Forest (RF) and MBRF, and examines the impact of feature selection using CFS on mental disorder classification. The results indicate that MBRF outperforms RF with an 8.33% improvement in accuracy, 8.61% in precision, 8.33% in recall, and 9.08% in F1-Score. Additionally, the comparison between MBRF and MBRF with CFS reveals that while accuracy and recall remain the same, MBRF achieves 0.23% higher precision and 0.81% higher F1-Score than MBRF with CFS. In conclusion, the use of MBRF proves to be superior to the standard RF in addressing data imbalance for mental disorder classification, significantly improving accuracy, precision, recall, and F1-Score. However, feature selection with CFS does not significantly enhance performance. While accuracy and recall remain unchanged, MBRF without CFS demonstrates higher precision and F1-Score, indicating that the model performs better without feature selection in maintaining the balance between precision and recall.
Music Genre Classification Based on Spectrogram Using CNN-MobileNet Leo, Donatus; Muhammad, Alva Hendi
Sebatik Vol. 29 No. 2 (2025): December 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i2.2634

Abstract

Music is a universal form of art that has a significant impact on human life. In the digital era, managing increasingly large music collections requires an effective classification system to facilitate searching and storage. One of the growing methods is music genre classification, which helps organize music based on specific characteristics. This study explores the application of Convolutional Neural Network (CNN) and the MobileNet architecture for music genre classification based on spectrogram images. Spectrogram representation is used to convert audio signals into visual form, allowing the classification problem to be approached as an image classification task. The dataset used is GTZAN, consisting of six genres: blues, classical, country, hiphop, jazz, and metal. Image augmentation is applied to increase the diversity of training data, including rotation, translation, zooming, brightness adjustment, and horizontal flipping. The evaluation results show that the CNN-MobileNet model achieves an overall accuracy of 83%, with a macro precision of 85%, macro recall of 83%, and macro F1-score of 84%. The classical genre achieved the best performance with an F1-score of 93%. This research demonstrates that spectrogram-based music genre classification using CNN-MobileNet is an effective approach for automatic music recognition tasks
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 Arief Setyanto 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 Bismar Rifki wahyu Prasetya Chaedar Fatach, Muhamad Reza Danu Prawira Utama David Diamanta Dengen, Christin Soyan DHANI ARIATMANTO Dhani Ariatmanto Eka Sakti, Putra Utama Eko Pramono Ema Utami Fauzi, Moch Farid Fitriyani, Baiq Yulia Hanafi Hanafi Harahap, Muhammad Sya'ban Haris, Ruby Hasan, Nurul Rahmawati Hasibuan, M. Rivai Hery Priandoko Hewen, Maria Beliti I Gusti Ngurah Wikranta Arsa Arsa 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, K Leo, Donatus Lubna Lubna Malik, Husni Hidayat Maradona, Maradona MEI PARWANTO KURNIAWAN 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 Pradana, Roymond Chandra Prasetya, Bismar Rifki wahyu Prasetya, Rendra Prima Giri Pamungkas Raynold, Raynold Razaq, Thata Authar Richki Hardi Rifqi Anugrah Robert Marco, Robert Rosady, Melinne Maldini Roymond Chandra Pradana Saputra, Mahmuda Setiajid, Bayu Setya, Bagus 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