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All Journal JIK Jurnal Ilmu Komputer Jurnal Simetris Majalah Geografi Indonesia JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Pengabdian Masyarakat AbdiMas Aptisi Transactions on Technopreneurship (ATT) Jurnal Mantik Jurnal Abdimas Madani dan Lestari (JAMALI) JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Journal Sensi: Strategic of Education in Information System Jurnal Algoritma, Logika dan Komputasi Jurnal Ilmu Komputer dan Informatika Journal of Social Research Ilmu Komputer untuk Masyarakat Jurnal Locus Penelitian dan Pengabdian Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Indonesia Sosial Teknologi Jurnal Indonesia Sosial Sains Journal of Accounting Research, Organization and Economics (JAROE) Eduvest - Journal of Universal Studies Jurnal Ekonomi, Teknologi dan Bisnis Media Abdimas Jurnal Pengabdian Masyarakat Bangsa Infact: Jurnal Sains dan Komputer Asian Journal of Social and Humanities INOVTEK Polbeng - Seri Informatika
Widodo, Agung Mulyo
Universitas Esa Unggul
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Analysis of The Maturity Level of Cyber Security in The Context of Personal Data Protection for MSMEs in Depok City Sulistyo, Catur Agus; Firmansyah, Gerry; Tjahjono, Budi; Widodo, Agung Mulyo
Eduvest - Journal of Universal Studies Vol. 5 No. 2 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i2.50822

Abstract

This research explores the cybersecurity maturity level in the context of personal data protection for Micro, Small, and Medium Enterprises (MSMEs) in Depok City, Indonesia. The increased use of digital technology by MSMEs has raised concerns about personal data security and the vulnerability to cyberattacks. This study aims to develop an assessment tool that MSMEs can use to evaluate their compliance with the Personal Data Protection (PDP) Law and measure their readiness to face cybersecurity challenges. Through a combination of qualitative and quantitative methods, the study analyzes MSMEs' preparedness for cybersecurity and compliance with the PDP Law. The results reveal that while 60.2% of MSMEs manage personal data, a significant 93.5% have not complied with the PDP Law, exposing them to potential financial losses and cyber risks. The research emphasizes the need for MSMEs to adopt a simple yet effective cybersecurity framework to ensure data protection and compliance.
Implementation of YOLOv5 Algorithm for Exam Cheating Movement detection Suardana, Made Aka; Akbar, Habibullah; Saputra, Martin; Widodo, Agung Mulyo; Tjahjono, Budi
Eduvest - Journal of Universal Studies Vol. 5 No. 6 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i6.51480

Abstract

The decline in academic integrity due to cheating during exams has become increasingly relevant, particularly following the shift to online learning systems. The absence of direct supervision in online exams creates opportunities for cheating practices that evade detection by the naked eye. This study addresses this challenge by developing an object detection model for cheating behavior using a deep learning approach based on the YOLOv5 algorithm. The dataset comprised 60 ten-second videos, extracted into 1,200 images representing four suspicious head movement patterns. Each image was manually annotated before training five YOLOv5 variants. Models were evaluated using object detection metrics (precision, recall, and mAP at IoU thresholds 0.5–0.95) and analyzed via confusion matrices. Results indicate that the YOLOv5x variant achieved peak performance, with mAP@0.5:0.95 of 83.06% and perfect classification accuracy across all classes. This demonstrates that an object detection–based approach provides a reliable preliminary solution for monitoring cheating during online exams.
Prediksi Peringkat Akreditasi BAN PT Program Studi Sarjana Rumpun Ilmu Komputer Menggunakan Klasifikasi Machine Learning Aribowo, Budi; Tjahjono, Budi; Firmansyah, Gerry; Widodo, Agung Mulyo
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.3089

Abstract

Accreditation ranking is one of the causes and indicators chosen by prospective students when choosing a study program in higher education. From the data collected, only 5% of study programs in the Computer Science group have a Superior accreditation rating and an A accreditation rating in LLDikti Region III Jakarta. So it is necessary to know the factors that influence the accreditation ranking. The machine learning methodology used in this approach is K-Nearest Neighbors (KNN) and from the data obtained there are 6 factors that can be strongly suspected to influence the study program accreditation value. The four machine learning models, namely KNN, Gaussian Naïve Bayes Decision Tree and Logistic Regression, it was found that the KNN machine learning model with 2 input variables had the highest AUC value, namely 84.38%. Meanwhile, from the model simulation run by KNN machine learning, 2 input variables can produce relatively accurate prediction results. And the results of cross validation with 10 folds support the selected machine learning with an accuracy level of 80%. In general, the KNN machine learning model with 2 input variables was able to predict the accreditation rating of Study Programs, especially from the Computer Science Cluster.Keywords – Accreditation, Area Under Curve (AUC), Department of School, Kfold Cross Validation, Machine Learning.
Peningkatan Pengetahuan Kader Posyandu tentang Perawatan Kehamilan dan Gizi-Hidrasi melalui Pelatihan dan Pemanfaatan Media Digital Kesehatan: indonesia Mulyani, Erry Yudhya; Nurhayati, Ety; Widodo, Agung Mulyo
Jurnal Abdimas Madani dan Lestari (JAMALI) Volume 07, Issue 02, September 2025
Publisher : UII

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jamali.vol7.iss2.art20

Abstract

The prevalence of maternal mortality and KEK (Energy-Protein Deficiency) in South Tangerang in 2022 were 27.34% and 3.51%, respectively. These figures provide an illustration that although it decreased in 2022 and reached the target, it still requires attention considering the impact of mothers experiencing high-risk KEK on their fetuses. One effort that can be made is to provide pregnancy care training and nutrition-hydration education. This activity involved cadres of the RT002 Posyandu and PKK Serua Ciputat Tangerang Selatan mothers totaling 10 people. This activity was carried out for 3 months (November - January 2025). The form of activity was in the form of socialization, discussion group forums, and pregnancy care training including weight and height checks, blood pressure, and talk shows (T-1, T-2, T-10). Online socialization via Zoom for 120 minutes (45-minute lecture), Q&A discussion (60 minutes), discussion group forum (90 minutes) and training (90 minutes). The average age of cadres is 51-60 years (60.0%), D3/D4 education (60.0%), and works as a housewife (50.0%). This activity shows an increase in subject knowledge related to water needs, the role of vitamin D, understanding dehydration, T2 activities, T4 measurements, and the benefits of Fe tablets where previously less than 90.0%, to more than 90.0%. Therefore, it is necessary to carry out continuous training and practice to improve cadre skills in delivering health materials in the community.
Enhanced Dermatological Diagnosis: Autoimmune and Non-Autoimmune Skin Disease Classification Using MobileNet and ResNet Tyara Regina Nadya Putri; Widodo, Agung Mulyo
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.711

Abstract

Autoimmune diseases arise when the immune system mistakenly attacks the body's healthy cells, causing a range of symptoms that can greatly affect a patient's quality of life. In Indonesia, these conditions present a significant public health concern. According to research by Ministry of Health Republic Indonesia in 2024, autoimmune lupus affects approximately 0.5% of the population, impacting over 1.3 million individuals. This study proposes a classification and detection model utilizing Convolutional Neural Networks (CNN) with transfer learning, incorporating MobileNetV2, MobileNetV3Small, MobileNetV3Large, ResNet50, ResNet101, and ResNet152 architectures. The model's performance is assessed using a confusion matrix, evaluating precision, recall, and F1-score, while computational efficiency is analyzed using a GPU T4. Experimental results demonstrate that ResNet152 achieved the highest accuracy at 92%. These findings emphasize the crucial role of selecting an optimal CNN architecture to enhance the accuracy of autoimmune and non-autoimmune skin disease classification and detection.
Audit Tata Kelola Teknologi Informasi Menggunakan Framework COBIT 2019 Pada Rumah Sakit Medika Dramaga Andriana, Dian; Firmansyah, Gerry; Tjahjono, Budi; Widodo, Agung Mulyo; Akbar , Habibullah
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 8 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i8.4176

Abstract

Penelitian ini bertujuan untuk mengevaluasi tata kelola teknologi informasi (TI) di Rumah Sakit Medika Dramaga menggunakan kerangka kerja COBIT 2019. Hasil audit menunjukkan bahwa meskipun beberapa aspek tata kelola TI telah mencapai tingkat tertentu, masih terdapat kesenjangan antara kondisi saat ini dan harapan yang diinginkan. Pada domain APO12 (Pengelolaan Risiko), tingkat kemampuan mencapai 87% pada level 2 dan 73% pada level 3, namun tidak ada pencapaian pada level 4 dan 5. Sementara itu, pada domain APO13 (Pengelolaan Keamanan Informasi), tingkat kemampuan hanya mencapai 82% pada level 2 tanpa pencapaian pada level yang lebih tinggi. Kesenjangan ini menunjukkan bahwa pengelolaan risiko dan keamanan informasi masih memerlukan peningkatan signifikan untuk mencapai standar yang diharapkan. Berdasarkan temuan tersebut, penelitian ini memberikan beberapa rekomendasi, termasuk evaluasi kebijakan manajemen risiko, implementasi teknologi pendukung, pelatihan SDM, dan pengembangan strategi jangka panjang untuk meningkatkan tata kelola TI. Dengan menerapkan rekomendasi ini, diharapkan Rumah Sakit Medika Dramaga dapat meningkatkan keamanan dan keandalan sistem informasi serta meminimalkan risiko kebocoran data.
Evaluasi dan Optimasi Kinerja MySQL Master-Slave dengan Metode Kuantitatif pada Database Pemohon Tes Psikologi SIM PT XYZ pada POLDA METRO JAYA Haryoto, Iin Sahuri; Firmansyah, Gerry; Tjahjono, Budi; Widodo, Agung Mulyo; Akbar, Habibullah; Fatonah, Nenden Siti
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 9 (2025): : JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i9.4685

Abstract

The development of information technology and cloud computing has enabled organizations to manage large-scale data efficiently. PT XYZ, which is engaged in psychological testing for Driving Licenses (SIM), uses a web-based system with a MySQL database that has implemented master-slave replication. However, as the data volume increases to 4,000-5,000 entries per day, the system experiences performance constraints, especially in the speed of read and write queries. This study aims to optimize the performance of the MySQL database by adjusting the server configuration and specifications to improve system efficiency. The test results show that server specification settings, including processor speed, memory size, and replication configuration, play an important role in improving system performance. By adjusting the master and slave server configurations, the system shows a significant increase in database response time and operational efficiency. This optimization is expected to be a reference in the implementation and management of large-scale databases using MySQL replication.
Risk Management in Mobile JKN Application at Depok Private Hospitals with FMEA Method Dewi, Riris Septiana Sita; Firmansyah, Gerry; Widodo, Agung Mulyo; Tjahjono, Budi
Eduvest - Journal of Universal Studies Vol. 5 No. 9 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i9.51219

Abstract

The Mobile JKN application that has begun to be implemented in every hospital is the main factor in the success of hospital services. Quite complete features are presented in one application. The variables measured in this study were occurrence (frequency of occurrence), severity (impact), and detectability (monitoring). The research data was obtained based on the results of direct observation at Bhakti Yudha Hospital. Risk management in the use of Mobile JKN is the main topic in this study. This study aims to assess information security risks using the FMEA method. The FMEA method is an error analysis method that arises from the design process of a design work. The results of this study are in the form of Risk Priority Number (RPN) values based on: severity (S = Severity), occurrence (O = Occurrence), and level of detectability (D = Detectability), and a report on risk management results which contains a list of risk analysis priorities accompanied by root causes of problems and risk control measures.
Evaluation of Transfer Learning-Based Convolutional Neural Networks (InceptionV3 and MobileNetV2) for Facial Skin-Type Classification Muttaqin, Naufal Hafizh; Widodo, Agung Mulyo
Jurnal Ilmu Komputer dan Informatika Vol 5 No 1 (2025): JIKI - Juni 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jiki.264

Abstract

Manual classification of facial skin types often suffers from subjectivity and inconsistency due to reliance on human expertise. Accurate identification of skin types is crucial for selecting appropriate skincare solutions. This study evaluates the performance of two transfer-learning-based Convolutional Neural Networks (CNNs), InceptionV3 and MobileNetV2, for classifying facial skin types into four categories: normal, oily, dry, and acne-prone. A total of 1,733 facial images were collected from Kaggle and Roboflow and split into training, validation, and testing sets with a 70:20:10 ratio. Preprocessing involved normalization, augmentation, and resizing based on each model’s input size. Both models were fine-tuned and evaluated using accuracy, precision, recall, and F1-score metrics. InceptionV3 achieved the highest accuracy of 90.12% and a macro F1-score of 89.47%, particularly excelling in identifying normal and acne-prone skin. MobileNetV2 reached 81.15% accuracy and performed well on dry skin types. Confusion matrices and evaluation on new, unseen data confirmed the models’ generalization capabilities, though misclassifications still occurred among visually similar classes. These findings suggest that CNNs with transfer learning provide a robust foundation for developing AI-assisted facial skin-type classification systems, offering potential integration into dermatological applications.
Utilization of Query Expansion Using Data Mining Method In Analyzing Documents on The Irama Nusantara Website Aulia, Rizky; Widodo, Agung Mulyo
Jurnal Ekonomi Teknologi dan Bisnis (JETBIS) Vol. 3 No. 11 (2024): JETBIS : Journal Of Economich, Technology and Business
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/jetbis.v3i11.156

Abstract

In Indonesia, many local websites, such as Irama Nusantara, hold valuable information related to music and culture. Although rich in data, the utilization of this information is still limited. This research aims to utilize query expansion techniques through data mining methods in analyzing data from the Irama Nusantara website. Data was collected from the Irama Nusantara website through a crawling process, resulting in 5404 entries covering audio, images and text. The analysis was conducted using Natural Language Processing (NLP) techniques starting with the preprocessing stage. Next, the K-Means algorithm was applied for clustering, and the Term Frequency-Inverse Document Frequency (TF-IDF) method was used for term weighting. Classification models were built using Support Vector Machine (SVM) and Naive Bayes for comparison. The analysis shows that the use of query expansion significantly improves the accuracy of information retrieval on the Irama Nusantara website. The method evaluation showed that SVM gave better results in terms of accuracy and precision compared to Naive Bayes. In addition, Principal Component Analysis (PCA) shows that 70-95% of the variance in the data can be explained by the resulting principal components, which signifies the efficiency of the applied method. This research not only provides a deeper insight into the patterns and trends in the analyzed data, but also contributes to the development of information technology in the field of culture in Indonesia. This research successfully developed an effective analysis model to utilize data from the Irama Nusantara website.
Co-Authors Achmad Fansuri Achmad Randhy Hans Adhi Fernandes Gamaliel Adhikara, M. F. Arrozi Adilah Widiasti Ahmad Musnansyah Ahmad Mutedi Akbar , Habibullah Akbar, Habibullah Alivia Yufitri Andriana, Dian Annazma Ghazalba Ari Widatama, Yohanes Bagas Arif Pami Setiaji Bambang Irawan Bambang Irawan Bambang Irawan Bayu Sulistiyanto Ipung Sutejo Binastya Anggara Sekti Budi Aribowo Budi Tjahjono Budi Tjahjono BUDI TJAHJONO Budi Tjahyono Budi Tjahyono Budi Tjahyono Cahya Darmarjati Deni Iskandar Deni Iskandar Dewi, Riris Septiana Sita Doni Antoro Dulbahri Dulbahri Dwiaji, Lingga Eko Prasetyo Endang Ruswanti Endang Ruswanti Erry Yudhya Mulyani Erry Yudhya Mulyani Erry Yudhya Mulyani Euis Heryati Fadlilatunnisa, Fanny Fatonah, Nenden Siti Fikri Saefullah Firmansyah, Gerry Gerry Firmansyah Gerry Firmansyah Gerry Firmasyah Gusti Fachman Pramudi Habibullah Akbar Hadi, Muhammad Abdullah Hadjarati, Panji Ramadhan Yudha Putra Hani Dewi Ariessanti Hartono Hartono Haryoto, Iin Sahuri Hendaryatna Hendaryatna Heri Wijayanto I Gede Pasek Suta Wijaya Ichwani, Arief Ichwani, arief Ipung Sutejo, Bayu Sulistiyanto Ismiyati Meiharsiwi Iwan Setiawan Izhar Rahim Joniwan Joniwan Karisma Trinanda Putra Kartini Kartini Krisogonus Wiero Baba Kaju Kundang Karsono Juman Kundang Karsono Juman Kundang Karsono Juman Kus Hendrawan Muiz Lingga Dwiaji Lisdiana Lisdiana Martin Saputra, Martin Massie, Julius Ivander Maulana, Syaban Meria, Lista Muhamad Bahrul Ulum Muhamad Bahrul Ulum Muhammad Azzam Robbani Muhammad Fajrul Aslim Muhammad Hadi Arfian Muttaqin, Naufal Hafizh Nainggolan, Restamauli br Nina Nurhasanah Nindyo Artha Dewantara Wardhana Nixon Erzed Nixon Erzed Nizirwan Anwar Nizirwan Anwar Nurfilael, Gagas Nurfilae Nurhayati, Ety Pratama, Fajar Prayitno Purwano SK Rahaman, Mosiur Rian Adi Pamungkas Rifqi Khairurrahman RILLA GANTINO Rizki Faro Khatiningsih Rizky Aulia Roesfiansjah Rasjidin Sholeh Gunawan Simorangkir, Holder Suardana, Made Aka Sularso Budilaksono Sulistyo, Catur Agus Sunardi, Sunardi Tardiana, Arisandi Langgeng Tartila, Gilang Romadhanu Tjahjono, Budi Tyara Regina Nadya Putri Ulum, Muhamad Bahrul Ummanah Ummanah, Ummanah Vitri Tundjungsari Wahid Abdul Azis Wardhana, Nindyo Artha Dewantara Widiasti, Adilah William Nugraha Wisnujati, Andika Yanathifal Salsabila Anggraeni Yessy Oktafriani Yulhendri Yulhendri