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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Inovasi Teknologi Pendidikan Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Repositor Community Development Journal: Jurnal Pengabdian Masyarakat Syntax: Journal of Software Engineering, Computer Science and Information Technology
Akbi, Denar Regata
Universitas Muhammadiyah Malang
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Public Health

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PENINGKATAN KAPASITAS APARATUR DESA MELALUI PELATIHAN MICROSOFT OFFICE Evi Dwi Wahyuni; Vinna Rahmayanti Setyaning N.; Denar Regata Akbi
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2023): Volume 4 Nomor 1 Tahun 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v4i1.12213

Abstract

Pemerintahan desa sebagai pemerintahan terkecil di masyarakat, dituntut untuk memberikan pelayanan yang prima kepada masyarakat. Aparat desa diharapkan mampu bekerja dengan cepat, efektif dan efisien agar masyarakat dan terlayani dengan baik. Hal ini perlu ditunjang dengan kemampuan sumber daya manusia agar mampu memanfaatkan teknologi informasi untuk mempercepat dan mempermudah pekerjaannya. Kegiatan pelatihan dan pendampingan ini dilaksanakan di Desa Junrejo Kecamatan Junrejo Kota Batu. Jenis kegiatan berupa pelatihan Microsoft Office bagi aparat desa. Pelaksana kegiatan adalah Aparatur Desa dan Tim Pengabdian. Dari hasil kegiatan, diperoleh manfaat berupa peningkatan kemampuan administrasi aparatur desa yang dapat membantu dalam penyelesaian tugas-tugas administrasi di desa. Kata kunci: Pelatihan, Microsoft Office, Administrasi, Aparatur, Kapasitas, Desa
Detect Malware in Portable Document Format Files (PDF) Using Support Vector Machine and Random Decision Forest Abdachul Charim; Setio Basuki; Denar Regata Akbi
JOIN (Jurnal Online Informatika) Vol 3 No 2 (2018)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v3i2.196

Abstract

Portable Document Format is a very powerful type of file to spread malware because it is needed by many people, this makes PDF malware not to be taken lightly. PDF files that have been embedded with malware can be Javascript, URL access, media that has been infected with malware, etc. With a variety of preventive measures can help to spread, for example in this study using the classification method between dangerous files or not. Two classification methods that have the highest accuracy value based on previous research are Support Vector Machine and Random Forest. There are 500 datasets consisting of 2 classes, namely malicious and not malicius and 21 malicius PDF features as material for the classification process. Based on the calculation of Confusion Matrix as a comparison of the results of the classification of the two methods, the results show that the Random Forest method has better results than Support Vector Machine even though its value is still not perfect.
COMPARISON OF MACHINE LEARNING TECHNIQUES FOR CLASSIFICATION OF DISTRIBUTED DENIAL OF SERVICE ATTACKS BASED ON FEATURE ENGINEERING IN SDN-BASED NETWORKS Rizaldi, Muhammad Ikhwananda; Chandranegara, Didih Rizki; Akbi, Denar Regata
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 3 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i3.5262

Abstract

Distributed Denial-of-Service (DDoS) attacks present a noteworthy cybersecurity hazard to software-defined networks (SDNs). This investigation presents an approach that depends on feature engineering and machine learning to discern DDoS attacks in SDNs. Initially, the dataset acquired from Kaggle goes through cleansing and normalization procedures, and the optimal subset of features is determined by employing the Correlation-based Feature Selection (CFS) algorithm. Subsequently, the optimal subset of features is trained and evaluated utilizing diverse Machine Learning algorithms, specifically Random Forest (RF), Decision Tree, Adaptive Boosting (AdaBoost), K-Nearest Neighbor (k-NN), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). The outcomes demonstrate that XGBoost outperforms the other algorithms in various performance metrics (e.g., accuracy, precision, recall, F1, and AUC values). Furthermore, a comparative analysis was carried out among various models and algorithms, revealing that the technique proposed by the researchers yielded the most favourable outcomes and effectively detected and identified DDoS attacks in SDN. Consequently, this investigation provides a novel perspective and resolution for SDN security.
Analisis Klasterisasi Malware: Evaluasi Data Training Dalam Proses Klasifikasi Malware Akbi, Denar Regata; Rosyadi, Arini R
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 2 No. 2 (2018)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v2i2.88

Abstract

Data latih merupakan salah satu bagian penting pada proses klasifikasi. Terutama jika data tersebut digunakan untuk membuat sistem pendeteksi malware. Penelitian ini melakukan perbandingan data latih yang dihasilkan dari dua penelitian yang telah dilakukan sebelumnya, data yang digunakan pada kedua penelitian tersebut merupakan data malware android berdasarkan frekuensi system call sejumlah 600 data. Penelitian pertama melakukan klasifikasi dan menghasilkan 4 jenis malware, sedangkan penelitian kedua melakukan klastering dan menghasilkan 8 klaster. Dari kedua penelitian tersebut, peneliti melakukan evaluasi data latih dari masing - masing penelitian untuk mendapatkan hasil data latih yang lebih akurat, dengan menggunakan data uji sejumlah 50, peneliti melakukan evaluasi dan uji coba dengan menggunakan algoritme kNN. Hasil yang didapatkan, penggunaan data latih berdasarkan hasil klastering pada proses klasifikasi lebih direkomendasikan, hasil Error Prediction penelitian pertama: 0,995 sedangkan pada penelitian kedua: 0,998. Hasil Recall dan akurasi menggunakan metode cross validation, penelitian pertama, Recall: 0,665 akurasi: 0,66, penelitian kedua, Recall: 0,893 akurasi: 0,89, sedangkan Hasil Recall dan akurasi menggunakan metode precentage split, penelitian pertama, Recall: 0,657 akurasi: 0,65, penelitian kedua, Recall: 0,798 akurasi: 0,79. Berdasarkan hasil pengujian, proses klastering yang menggunakan data frekuensi system call malware menghasilkan data latih yang lebih akurat dibandingkan dengan data latih yang dihasilkan dengan menggunakan suatu situs penamaan malware.
Performance Comparison of GLCM Features and Preprocessing Effect on Batik Image Retrieval Azhar, Yufis; Akbi, Denar Regata
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2179

Abstract

The use of the Grey-Level Co-occurrence Matrix (GLCM) for feature extraction in image retrieval with complex motifs, such as batik images, has been widely used. Some features often extracted include energy, entropy, correlation, and contrast. Other than these four features, the addition of dissimilarity and homogeneity features to the GLCM method is proposed in this study. Preprocessing methods such as Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are also used to see whether the two methods can increase the precision value of the retrieval results. This study used the Batik 300 dataset, which consists of 50 classes. Batik was chosen because this type of image has complex patterns and motifs so that it will maximize the role of the GLCM method itself. In addition, Batik is also a world heritage art, so its sustainability needs to be maintained. The test results show that adding dissimilarity and homogeneity features and using the CLAHE method in the preprocessing step can improve model performance. Combining these two methods has produced higher precision values than not using either. Batik, a globally recognized art form, holds the status of a world heritage, necessitating the preservation of its sustainability. Test results have demonstrated that incorporating dissimilarity and homogeneity features, alongside using the CLAHE method during the preprocessing stage, leads to enhanced model performance. The amalgamation of these two methods has yielded precision values that surpass those achieved when either method is used in isolation.
PENERAPAN TOOLS JADX PADA SERANGAN MALWARE BERBASIS ANDROID MENGGUNAKAN METODE NIST : STUDI KASUS UNDANGAN.APK Muthohirin, Bashor Fauzan; Nasuhah, Alfin Zahrotun; Akbi, Denar Regata
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 5, No 2 (2024): Desember 2024
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v5i2.5489

Abstract

Perkembangan teknologi smartphone dengan sistem operasi Android yang pesat telah membuat pengguna menghabiskan rata-rata 5,3 jam per hari. Hal tersebut menjadikan smartphone dengan sistem operasi Android rentan terhadap ancaman malware, termasuk serangan melalui aplikasi berbahaya seperti Undang.apk yang didistribusikan melalui pesan WhatsApp. Serangan ini menggunakan teknik sosial engineering untuk melakukan menipuan, mencuri data pribadi, dan menyebabkan kerugian material kepada korban. Penelitian ini bertujuan untuk menganalisis malware Undang.apk menggunakan metode forensik NIST, Proses awal investigasi dilakukan dengan melakukan akuisisi barang bukti digital menggunakan MOBILedit Forensic Tool, identifikasi malware melalui VirusTotal, serta analisis struktur kode APK menggunakan JADX GUI. Hasil penelitian menunjukkan bahwa malware tersebut memanfaatkan izin berbahaya untuk membaca dan mengirim pesan korban ke bot Telegram milik pelaku. Laporan akhir menyajikan temuan penting yang tidak hanya dapat digunakan sebagai bukti dalam proses hukum, tetapi juga sebagai panduan mitigasi untuk mencegah ancaman serupa di masa mendatang. Kata Kunci: Malware, Android, JADX, Forensics, NIST.  ABSTRACT The rapid development of smartphone technology with the Android operating system has made users spend an average of 5.3 hours per day. This makes smartphones with the Android operating system vulnerable to malware threats, including attacks through malicious applications such as Undang.apk which are distributed via WhatsApp messages. This attack uses social engineering techniques to commit fraud, steal personal data, and cause material losses to victims. This study aims to analyze the Undang.apk malware using the NIST forensic method. The initial investigation process was carried out by acquiring digital evidence using the MOBILedit Forensic Tool, identifying malware through VirusTotal, and analyzing the APK code structure using JADX GUI. The results of the study showed that the malware utilized dangerous permissions to read and send victim messages to the perpetrator's Telegram bot. The final report presents important findings that can not only be used as evidence in legal proceedings, but also as a mitigation guide to prevent similar threats in the future. Keywords: Malware, Android, JADX, Forensics, NIST.
Leveraging ESRGAN for High-Quality Retrieval of Low-Resolution Batik Pattern Datasets Azhar, Yufis; Marthasari, Gita Indah; Regata Akbi, Denar; Minarno, Agus Eko; Haqim, Gilang Nuril
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3202

Abstract

As one of the world's cultural heritages in Indonesia, batik is one of the quite interesting research subjects, including in the realm of image retrieval. One of the inhibiting factors in searching for batik images relevant to the query image input by the user is the low resolution of the batik images in the dataset. This can affect the dataset's quality, which automatically also impacts the model's performance in recognizing batik motifs with complex details and textures. To address this problem, this study proposes using the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method to increase the resolution of batik images. By increasing the resolution, it is hoped that ESRGAN can clarify the details and textures of the initial low-resolution image so that these features can be extracted better. This study proves that ESRGAN can produce high-resolution batik images while maintaining the details of the batik motif itself. The resulting image's high PSNR and low MSE values confirm this. The implementation of ESRGAN has also been proven to improve the performance of the image retrieval system with an increase in precision and average precision values between 1-5% compared to other methods that do not implement it.
UMM metaverse batik as a learning media to introduce nitik batik motifs in the Sonobudoyo Museum Minarno, Agus Eko; Faiz, Ahmad; Wibowo, Hardianto; Akbi, Denar Regata; Munarko, Yuda
Jurnal Inovasi Teknologi Pendidikan Vol. 12 No. 1 (2025): March
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jitp.v12i1.81821

Abstract

The exposure of Yogyakarta's Nitik Batik motifs is one of the important efforts to maintain and introduce Indonesia's cultural heritage to the younger generation. In this context, metaverse-based learning media is used as an innovative solution. This research discusses the implementation of metaverse-based learning media with an Extended Reality (XR) approach to introduce the Yogyakarta Nitik Batik motif. This research uses the Game Development Life Cycle (GDLC) development method to design a VR-based Batik museum virtual space, with black box testing and refinement testing to assess functionality and fun aspects. Involving 33 participants from visitors to the Sono Budoyo Batik exhibition in Yogyakarta, this study analyzed the data descriptive quantitative to develop recommendations for improving user experience and introducing Yogyakarta Nitik Batik culture through the metaverse. The test results showed that the virtual space of the Batik Museum passed the functional test without failure and had a feasibility rate of 86.1% in the category of "Excellent." These findings indicate that VR technology effectively introduces and preserves Batik culture, especially as an educational material in virtual media. This metaverse based learning media is anticipated to be an innovative step in introducing Yogyakarta's dotted Batik while offering a valuable immersive experience for users. Future research can be done by adding gamification to increase visitor involvement and optimizing multimedia aspects that have not been the main focus.
Performance Evaluation of Outgoing Interface Selection Method on Fortigate SD-WAN for Network Optimization Kholil Romadhoni, Mufti; Kenanga, Larynt Sawfa; Akbi, Denar Regata; Risqiwati, Diah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2120

Abstract

Reliable and high-performance network services are essential to facilitate communication between parent companies and subsidiaries as well as among the subsidiaries themselves. Challenges arise in managing and optimizing outgoing interface selection in an effective and reliable Software-Defined Wide Area Network (SD-WAN) environment. This research evaluates four outgoing interface selection methods, namely Manual, Best Quality, Lowest Cost, and Maximize Bandwidth (SLA), using a tree-based network topology simulated in GNS3 with FortiGate devices as part of the simulation. The results show that under simulated disturbances, such as limiting a single connection line to 10 kbps, the Manual, Best Quality, and Lowest Cost methods perform worse than the Maximize Bandwidth method. In contrast, the Maximize Bandwidth method outperformed the others, achieving only 0% packet loss, 22.275 ms one-way delay, and 7.03 ms jitter, while maintaining the ITU-T G.1010 standard at the preferred level. These findings highlight the reliability and effectiveness of the Maximize Bandwidth method in ensuring consistent data transmission even under fault conditions, while providing practical guidance for network engineers in configuring SD-WAN for uninterrupted high-quality network services in complex business environments.
Detect Malware in Portable Document Format Files (PDF) Using Support Vector Machine and Random Decision Forest Charim, Abdachul; Basuki, Setio; Akbi, Denar Regata
JOIN (Jurnal Online Informatika) Vol 3 No 2 (2018)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v3i2.196

Abstract

Portable Document Format is a very powerful type of file to spread malware because it is needed by many people, this makes PDF malware not to be taken lightly. PDF files that have been embedded with malware can be Javascript, URL access, media that has been infected with malware, etc. With a variety of preventive measures can help to spread, for example in this study using the classification method between dangerous files or not. Two classification methods that have the highest accuracy value based on previous research are Support Vector Machine and Random Forest. There are 500 datasets consisting of 2 classes, namely malicious and not malicius and 21 malicius PDF features as material for the classification process. Based on the calculation of Confusion Matrix as a comparison of the results of the classification of the two methods, the results show that the Random Forest method has better results than Support Vector Machine even though its value is still not perfect.
Co-Authors Abdachul Charim Abdul Rauf Achmad Rizal Yogaswara Adam Tedy Agustin Agus Eko Minarno Ahmad Faiz, Ahmad ahmad gholib tammami Arini Rahmawati Rosyadi AZMI, AZMI Bagus Prasetyo Wahyudi Bashor Fauzan Muthohirin Basuki, Setio Bayu Adhitia Wardana Bayu Satria Kusuma Chandranegara, Didih Rizki Charim, Abdachul Desy Mahmuda Diah Risqiwati Dwi Nurmasari Pratiwi Evi Dwi Wahyuni Gita Indah Marthasari Haqim, Gilang Nuril Hardianto Wibowo Hawwin Purnama Akbar Heru Setiawan Ibnu Haris Fawaid Ilfan Arif Romadhan Kenanga, Larynt Sawfa Kholil Romadhoni, Mufti Kusuma, Bayu Satria Lathifi, Andi Syahrial Mahar Faiqurahman Muhammad Fuad Muzaki Muhammad Malik Madani Muhammad Malik Madani Muhammad Qaidin Syahputra Naser Jawas, Naser Nasuhah, Alfin Zahrotun Nur Ihsan, Abdul Jabbar Pratama, Ramadan Ramadan Pratama Rizaldi, Muhammad Ikhwananda Rosyadi, Arini R Rosyadi, Arini R Rosyadi, Arini Rahmawati S, Vinna Rahmayanti Sari, Zamah Septian Dwi Kusuma Sudirman Sudirman Sumadi, Fauzi Dwi Setiawan Syaifuddin Syaifuddin Syaifudin Syaifudin Vinna Rahmayanti Setyaning N. Wahyu Andhyka Kusuma Waskitho Wibisono Wildan Suharso Yuda Munarko Yufis Azhar Yusril Izza Rizqony Yusuf, Elsa Annas Sonia Zamah Sari