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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Mantik Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika
Wildanil Ghozi
Universitas Dian Nuswantoro
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences

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Design of a Telegram Chatbot to Control Internet Connection on Computer Laboratory Wildanil Ghozi; Elkaf Rahmawan P.; Oki Setiono
Jurnal Mantik Vol. 6 No. 3 (2022): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i3.3156

Abstract

Colleges with majors in computer science have a large number of computer labs to support practicum. Each laboratory room has several practicum classes per day. Moreover, each practicum requires different software and internet connection. Software can be prepared at pre-lecture. But the internet needs to be configured every class starts. A staff is stationed in each room to serve the needs of the practicum. However the staff does not have access rights to the router to configure the internet. To maintain network security, routers can only be managed by the network administrator. This requires staff to ask the network administrator to turn the internet on or off and to increase bandwidth limit. This interaction takes time so that the performance of the laboratory is poor. To solve this problem, we designed an internet controller bot as a bridging system between laboratory staff and routers. This chatbot provides limited router features needed by staff. So that the router's vital features cannot be accessed by staff. This system has been successfully developed so that staff can fulfill requests from lecturers to turn on/off the internet, check bandwidth consumption and increase bandwidth limit quickly.
Redesign and Simulation of Computer Networks in IoT-Based Medical Records Laboratory Oki Setiono; Retno Astuti Setijaningsih; Wildanil Ghozi
Jurnal Mantik Vol. 6 No. 3 (2022): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i3.3281

Abstract

The medical record laboratory has a very important function, one of which is in serving patients using electronic medical records or often referred to as hospital management information systems. A computer network in a medical record laboratory in an educational institution is a very important infrastructure in carrying out learning activities, without exception on the computer network in the medical records laboratory at Dian Nuswantoro University. The current condition of the computer network still has shortcomings such as inappropriate network management, network devices that are not configured and features on the server that are less than optimal. The purpose of this research is to redesign and simulate computer networks with simulators to design a more optimal network and add network features that do not yet exist including the application of the internet of things (IoT) for maximum medical record laboratory services. The expected results of the redesign and simulation are that in order to run properly, there are changes to the design and needs of network devices that can be adjusted to the conditions they should be. Network devices that have been configured have DHCP server, NTP, FTP and Web server facilities. The addition of a motion sensor to turn on the laboratory room lights before use can also work well.
Rekomendasi Paket Mata Pelajaran Pilihan (MPP) pada SMA Negeri 1 Kebumen Menggunakan Algoritma K-means Gustina Alfa Trisnapradika; Wildanil Ghozi; Yuminah Yuminah
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 2 (2023): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i2.8514

Abstract

Curriculum changes are needed to adapt education to the times. Since the covid-19 pandemic, face-to-face learning has been suspended. Online learning is an alternative used during a pandemic. This has an impact on learning loss so that the quality of learning decreases. Recovery of learning during the pandemic and post-pandemic Covid-19 is important to reduce the impact of learning loss on students. After the pandemic, the independent curriculum was launched which was a refinement of the 2013 curriculum which had only been implemented in several schools. The subject structure of the Merdeka curriculum for SMA level in Fese E or grade 10, all students get the same subjects. While in Phase F (grades 11 and 12), the subject structure is divided into 2 main groups, namely general subjects and elective subjects. Based on the provisions of the SMKA 2021-2022 curriculum structure, SMA Negeri 1 Kebumen prepares elective subjects (MPP) which are made up of 7 MPP packages. This study uses a clustering technique of student scores using the K-Means algorithm to obtain MPP package recommendations that suit student abilities. For each MPP package, clustering is carried out into 2 clusters with features in the form of predetermined subject scores. The result of this clustering is that each student gets a "yes" or "no" recommendation for each MPP package.
Implementasi Website BumDes Manggala Karsa Desa Karangsari, Kec. Pejawaran, Kab. Banjarnegara Oki Setiono; Abdus Salam; Wildanil Ghozi; L. Budi Handoko
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 4 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Cv. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Desa Karangsari, Pejawaran, Banjarnegara belum memiliki website untuk BUMDes yang digunakan sebagai sarana informasi kegiatan dan promosi hasil produk UMKM desa. Kegiatan BUMDes belum terdokumentasi dengan baik, layanan kepada masyarakat untuk kegiatan BUMDes belum menggunakan teknologi informasi serta informasi produk dan jasa yang dikelola BUMDes belum tersebar dengan maksimal.Tujuan yang hendak dicapai adalah pendampingan pengembangan website BUMDes desa Karangsari, Kec. Pejawaran, Kab. Banjarnegara untuk meningkatkan kinerja dan layanan BUMDes kepada masyarakat. Hasil yang dicapai berupa website BUMDes untuk layanan publik dan mengenalkan unit usaha serta penjualan produk UMKM desa
Pengenalan Financial Technology Kepada Petani Bawang Desa Selo Boyolali dengan Media Film Himawan, Heribertus; Ghozi, Wildanil
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 6, No 4 (2023): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v6i4.5062

Abstract

Teknologi finansial (fintech) berkembang dengan sangat pesat memberikan dampak positif dan negatif bagi masyarakat. Terlebih pada masa pandemic covid19 pada tahun 2020-2022 yang mengharuskan masyarakat untuk menerapkan sistem cashless sebagai metode pembayaran. Pada wilayah perkotaan, masyarakat dapat dengan baik beradaptasi menerima dan memanfaatkan fintech sebagai kebiasaan baru dalam bertransaksi. Namun pada masyarakat pedesaan yang memiliki akses informasi dan sumber daya yang terbatas mengalami kesulitan dalam penggunaan fintech. Pengetahuan yang terbatas justru dapat menimbulkan dampak-dampak negatif karena salah dalam memanfaatkan fintech. Untuk meningkatkan pemahaman dan pemanfaatan fintech dalam transaksi keuangan pada masyarakat pedesaan, perlu edukasi yang mendalam kepada semua lapisan masyarakat. Dalam meningkatkan efektifitas penyampaian materi, perlu penggunaan media pengajaran yang mampu membangkitkan motivasi dan memudahkan pemahaman. Salah satu media yang dapat digunakan dalam pembelajaran adalah media film. Media film memberikan gambaran nyata dan suara akan menjadi daya tarik dan sekaligus pembelajaran yang menghibur. Kegiatan sosialisasi teknologi finansial ini mampu memberikan pemahaman yang lebih baik bagi petani bawang di wilayah desa Selo Kabupaten Boyolali. Masyarakat memahami pemanfaatan fintech yang baik dan terhindar dari risiko penyalahgunaan fintech.
Pelatihan Basic Cyber Security untuk Siswa SMA/Sederajat di Kabupaten Batang Rafrastara, Fauzi Adi; Ghozi, Wildanil; Sani, Ramadhan Rakhmat
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 3 (2024): SEPTEMBER 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i3.2470

Abstract

Teknologi informasi telah menjadi bagian dari kehidupan masyarakat modern dengan pertumbuhan pengguna yang pesat. Setiap orang menggunakan gadget untuk berkomunikasi dan mengakses berbagai informasi setiap hari. Kejahatan siber merupakan salah satu dampak negatif yang paling berbahaya yang menargetkan para pengguna teknologi informasi dan dapat menargetkan individu menjadi korban. Pemerintah memiliki tanggung jawab untuk melindungi masyarakat terhadap kejahatan siber yang menargetkan individu. Oleh karena itu, pemerintah perlu memahami berbagai skema serangan dan trend perkembangan kejahatan siber. Dengan pengetahuan skema-skema serangan yang mungkin digunakan pada kejahatan siber, maka pemerintah dapat memberikan edukasi yang tepat bagi masyarakat. Balai Pengembangan Sumber Daya Manusia dan Penelitian (BPSDMP) Komunikasi dan Informatika Yogyakarta merupakan salah satu lembaga pemerintah yang bertanggung jawab dalam meningkatkan kemampuan masyarakat dalam pemanfaatan teknologi di wilayah Jawa Tengah dan Yogyakarta termasuk di Kabupaten Batang. Universitas Dian Nuswantoro sebagai institusi pendidikan yang unggul dalam bidang teknologi informasi dan komunikasi, berkolaborasi dengan BPSDMP KOMINFO Yogyakarta untuk memberikan pelatihan basic cyber security untuk siswa SMA/sederajat di Kabupaten Batang. Pelatihan berupa penjelasan materi teori dan praktik implementasi keamanan siber. Pada akhir sesi pelatihan, sebanyak 37 dari 39 siswa peserta pelatihan dinyatakan berhasil.
Random Under Sampling for Performance Improvement in Attack Detection on Internet of Vehicles Using Machine Learning Anargya, Muhammad Alden Nayef; Ghozi, Wildanil; Rafrastara, Fauzi Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.8034

Abstract

The Internet of Vehicles (IoV) technology is one of the advancements derived from the Internet of Things (IoT) in the transportation sector, benefiting its users. However, the development of this technology cannot be separated from various security threats, particularly Denial of Service (DoS) and spoofing attacks. Given these threats, it is crucial to continuously develop methods used for detecting attacks on IoV systems. Several researchers have conducted research related to attacks and threats on IoV systems, and one such study resulted in a dataset called CICIoV2024. This dataset has an imbalanced class distribution. This study aims to examine the implementation of Random Under-Sampling to improve the performance of classification algorithms in detecting attacks on IoV systems. The algorithms used in this study include Decision Tree, K-Nearest Neighbors (KNN), and Random Forest. The test results show that the Random Forest algorithm achieved the best results with an accuracy of 98.5% and an F1-Score of 98.5%.
Enhancing XGBoost Performance in Malware Detection through Chi-Squared Feature Selection Rosyada, Salma; Rafrastara, Fauzi Adi; Ramadhani, Arsabilla; Ghozi, Wildanil; Yassin, Warusia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2293

Abstract

The increasing prevalence of malware poses significant risks, including data loss and unauthorized access. These threats manifest in various forms, such as viruses, Trojans, worms, and ransomware. Each continually evolves to exploit system vulnerabilities. Ransomware has seen a particularly rapid increase, as evidenced by the devastating WannaCry attack of 2017 which crippled critical infrastructure and caused immense economic damage. Due to their heavy reliance on signature-based techniques, traditional anti-malware solutions struggle to keep pace with malware's evolving nature. However, these techniques face limitations, as even slight code modifications can allow malware to evade detection. Consequently, this highlights weaknesses in current cybersecurity defenses and underscores the need for more sophisticated detection methods. To address these challenges, this study proposes an enhanced malware detection approach utilizing Extreme Gradient Boosting (XGBoost) in conjunction with Chi-Squared Feature Selection. The research applied XGBoost to a malware dataset and implemented preprocessing steps such as class balancing and feature scaling. Furthermore, the incorporation of Chi-Squared Feature Selection improved the model's accuracy from 99.1% to 99.2% and reduced testing time by 89.28%, demonstrating its efficacy and efficiency. These results confirm that prioritizing relevant features enhances both the accuracy and computational speed of the model. Ultimately, combining feature selection with machine learning techniques proves effective in addressing modern malware detection challenges, not only enhancing accuracy but also expediting processing times.             
Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset Latifah, Ines Aulia; Rafrastara, Fauzi Adi; Bintoro, Jevan; Ghozi, Wildanil; Osman, Waleed Mahgoub
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2294

Abstract

Malware, or malicious software, continues to evolve alongside increasing cyberattacks targeting individual devices and critical infrastructure. Traditional detection methods, such as signature-based detection, are often ineffective against new or polymorphic malware. Therefore, advanced malware detection methods are increasingly needed to counter these evolving threats. This study aims to compare the performance of various feature selection methods combined with the XGBoost algorithm for malware detection using the Drebin dataset, and to identify the best feature selection method to enhance accuracy and efficiency. The experimental results show that XGBoost with the Information Gain method achieves the highest accuracy of 98.7%, with faster training times than other methods like Chi-Squared and ANOVA, which each achieved an accuracy of 98.3%. Information Gain yielded the best performance in accuracy and training time efficiency, while Chi-Squared and ANOVA offered competitive but slightly lower results. This study highlights that appropriate feature selection within machine learning algorithms can significantly improve malware detection accuracy, potentially aiding in real-world cybersecurity applications to prevent harmful cyberattacks.
Deteksi Serangan Denial of Service (DoS) dan Spoofing pada Internet of Vehicles menggunakan Algoritma k-Nearest Neighbor (kNN) Ghozi, Wildanil; Rafrastara, Fauzi Adi; Sani, Ramadhan Rakhmat; Abdussalam, Abdussalam
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i2.11309

Abstract

Implementasi teknologi Internet of Things pada kendaraan bermotor mengalami peningkatan dari waktu ke waktu dan dikenal dengan istilah Internet of Vehicle (IoV). IoV semakin dibutuhkan masyarakat karena dapat menghadirkan kenyamanan, keamanan, dan efisiensi dalam berkendara. Sayangnya, penggunaan teknologi internet pada IoV justru memunculkan potensi serangan siber, seperti Denial of Service (DoS) dan Spoofing. Intrusion Detection System pada IoV belum sepenuhnya berjalan dengan baik mengingat teknologi ini juga tergolong baru. Oleh karena itu, dengan adanya potensi ancaman sekaligus dampak yang dihasilkan menjadikan penelitian tentang hal ini menjadi urgent untuk dilakukan. Penelitian ini bertujuan untuk mengevaluasi performa algoritma machine learning k-Nearest Neighbor (kNN) dalam mendeteksi serangan siber pada IoV. Kelas yang diprediksi pada penelitian ini berjumlah enam, yaitu: Benign, DoS, Gas-Spoofing, Steering Wheel-Spoofing, Speed-Spoofing, dan RPMSpoofing. Dua jenis serangan pada IoV tersebut (DoS dan Spoofing) beresiko menghadirkan gangguan operasional pada kendaraan yang dapat membahayakan pengemudi dan pengguna jalan lainnya. Dataset yang digunakan adalah dataset publik bernama CIC IoV2024. Performa algoritma kNN tersebut juga dibandingkan dengan tiga algoritma lain sebagai state-of-the-arts, seperti Naïve Bayes, Deep Neural Network, dan Random Forest. Hasilnya, k-Nearest Neighbor (kNN) mendapatkan performa terbaik dengan skor 98.7% untuk metrik akurasi maupun F1- Score. kNN mengungguli Naïve Bayes yang berada di urutan ke-dua, dengan skor 98.1% untuk akurasi dan 98.0% untuk F1-Score. Selanjutnya, algoritma kNN dapat direkomendasikan sebagai classifier dalam pengembangan intrusion detection system pada IoV.
Co-Authors Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Anargya, Muhammad Alden Nayef Bintoro, Jevan Dwi Puji Prabowo, Dwi Puji Elkaf Rahmawan Pramudya Fauzi Adi Rafrastara Gustina Alfa Trisnapradika Heribertus Himawan Heru Lestiawan Hussein, Jassim Nadheer Ilma, Zidni L. Budi Handoko Latifah, Ines Aulia Oki Setiono Osman, Waleed Mahgoub Paramita, Cinantya Putra Hartanto, Octaviano Ryan Eka Ramadhan Rakhmat Sani Ramadhani, Arsabilla Retno Astuti Setijaningsih Ricardus Anggi Pramunendar Rosyada, Salma Saputra, Ardi Yassin, Warusia Yuminah Yuminah