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Macine Learning Approach in Evaluating News Labels Based on Titles: Online Media Case Study Yuranda, Rezky; Sutabri, Tata; Wahyuningsih, Delpiah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1808

In the current digital era, information availability is abundant, and news serves as a primary source of up-to-date and reliable information for the public. However, with the increasing volume of information, a robust evaluation method is necessary to ensure accurate and dependable news labeling. This research employs a machine learning approach, utilizing three common classification algorithms: Naive Bayes, SVM, and Random Forest, to evaluate news labels based on their titles. The dataset utilized in this study is obtained from Jakarta AI Research and consists of 10,000 samples covering various news topics. Evaluation is conducted using accuracy, precision, recall, and F1-Score metrics to gain a comprehensive understanding of the classification algorithm's performance. The results of this research demonstrate that the SVM algorithm exhibits the best performance, achieving an accuracy rate of 92.92%. Random Forest follows with an accuracy rate of 91.21%, and Naive Bayes with an accuracy rate of 89.61%. These findings provide deep insights into the effectiveness of the machine learning approach in evaluating news labels based on their titles. Furthermore, the study highlights the importance of considering other evaluation metrics such as precision, recall, and F1-Score to obtain a more holistic understanding of the algorithm's performance. Further research is encouraged to involve additional classification algorithms and more diverse and extensive datasets to enhance the comprehension of news label evaluation comprehensively. Such endeavors can significantly contribute to the development of automated systems for classifying news with higher accuracy and reliability in the future

Application of Deep Learning Algorithm for Web Shell Detection in Web Application Security System Yuranda, Rezky; Negara, Edi Surya
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.v13i3.2234

A web shell is a script executed on a web server, often used by hackers to gain control over an infected server. Detecting web shells is challenging due to their complex behavior patterns. This research focuses on using a deep learning approach to detect web shells on the ISB Atma Luhur web server, aiming to develop a model capable of precise detection. By training the model with labeled PHP files, malicious web shells are distinguished from benign files. The study is crucial for enhancing the server's security, preventing hacker attacks, and safeguarding sensitive data. Through preprocessing techniques such as opcode extraction and feature selection, useful pattern recognition for web shell detection is achieved. Training deep learning models like CNN and RNN with LSTM on processed data leads to accuracy evaluation using classification metrics. The CNN model demonstrates superior performance in detection, emphasizing the effectiveness of deep learning for web shell detection. The research contributes to enhancing security in web-based applications, protecting against cyber threats like web shells.

PENINGKATAN SKILL JURUSAN REKAYASA PERANGKAT LUNAK DALAM PEMBELAJARAN WEB PROGRAMMING PADA SMK NEGERI 1 SIMPANG KATIS Wahyuningsih, Delpiah; Yuranda, Rezky; Irawan, Devi; Kirana, Chandra; Anisah
Jurnal Pengabdian Masyarakat Berbasis Teknologi Vol 6 No 01 (2025): Volume 6, Nomor 1, Mei 2025
Publisher : ISB Atma Luhur

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

SMK Negeri 1 Simpang Katis offers a range of vocational programs, including the Software Engineering department. This community service program (PkM) was designed to enhance students’ competencies in web programming, particularly targeting final-year students. The program was implemented through a hands-on, practice-based approach using predefined case studies to guide participants through the learning process. The primary objective was to equip students with practical programming skills that would be beneficial either for entering the workforce or continuing to higher education upon graduation. As the main output of the program, participants developed a simple web-based calculator application and delivered presentations demonstrating their work. These presentations served as an evaluation tool to assess the participants’ comprehension and application of the material covered during the training.

Application of Deep Learning Algorithm for Web Shell Detection in Web Application Security System Yuranda, Rezky; Negara, Edi Surya
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.v13i3.2234

A web shell is a script executed on a web server, often used by hackers to gain control over an infected server. Detecting web shells is challenging due to their complex behavior patterns. This research focuses on using a deep learning approach to detect web shells on the ISB Atma Luhur web server, aiming to develop a model capable of precise detection. By training the model with labeled PHP files, malicious web shells are distinguished from benign files. The study is crucial for enhancing the server's security, preventing hacker attacks, and safeguarding sensitive data. Through preprocessing techniques such as opcode extraction and feature selection, useful pattern recognition for web shell detection is achieved. Training deep learning models like CNN and RNN with LSTM on processed data leads to accuracy evaluation using classification metrics. The CNN model demonstrates superior performance in detection, emphasizing the effectiveness of deep learning for web shell detection. The research contributes to enhancing security in web-based applications, protecting against cyber threats like web shells.

Pengukuran Keefektifian Aplikasi PMB di ISB Atma Luhur Menggunakan Metode Usabillity Testing Yuranda, Rezky; Sutabri, Tata
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 1 No. 3 (2023)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijmst.v1i3.166

Aplikasi Peneriamaan Mahasiswa Baru (PMB) merupakan sebuah kompenen penting dalam sebuah sistem informasi di Institut Sains dan Bisnis (ISB) Atmaluhur. Keefektifan aplikasi PMB dapat menjadi faktor krusial dalam memastikan pengalaman pengguna yang baik dan kelancaran proses penerimaan mahasiswa baru. Penelitian ini bertujuan untuk mengukur keefektifan aplikasi PMB di ISB Atmaluhur dengan menggunakan metode usabillity testing. Metode ini melibatkan pengujian aplikasi oleh pengguna dengan tujuan untuk mengidentifikasi masalah keusabilityan yang mungkin muncul dan mengusulkan perbaikan yang diperlukan. Penelitian ini melibatkan sejumlah responden yang mewakili calon mahasiswa baru di ISB Atmaluhur. Usabillity testing dilakukan dengan mengamati interaksi pengguna dengan aplikasi yang akan di uji dan mengumpulkan umpan balik mengenai fitur dan fungsionalitas yang disediakan. Data yang terkumpul akan dianalis secara kualitatif dan kuantitatif untuk mengevaluasi keefektifan aplikasi PMB. Hasil penelitian menunjukan beberapa masalah keusabilityan yang ditemukan dalam penggunaan aplikasi PMB di ISB Atmaluhur. Beberapa masalah yang teridentifikasi meliputi antarmuka pengguna yang kurang intuitif, kesulitan navigasi dan kesalahan dalam pengisian formulir. Berdasarkan temuan tersebut, beberapa rekomendasi perbaikan diajukan, seperti penyederhanaan antaramua pengguna, panduan navigasi yang jelas dan perbaikan validasi dalam pengisian formulir. Penelitian ini diharapkan dapat meberikan masukan berharga untuk pengembangan dan perbaikan aplikasi PMB di ISB Atmaluhur. Pengukuran keefektifan aplikasi melalui metode usabillity testing ini akan membantu meningkatkan pengalaman penggua serta memastikan proses penerimaan mahasiswa baru yang efisien dan lancar

Macine Learning Approach in Evaluating News Labels Based on Titles: Online Media Case Study Yuranda, Rezky; Sutabri, Tata; Wahyuningsih, Delpiah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1808

In the current digital era, information availability is abundant, and news serves as a primary source of up-to-date and reliable information for the public. However, with the increasing volume of information, a robust evaluation method is necessary to ensure accurate and dependable news labeling. This research employs a machine learning approach, utilizing three common classification algorithms: Naive Bayes, SVM, and Random Forest, to evaluate news labels based on their titles. The dataset utilized in this study is obtained from Jakarta AI Research and consists of 10,000 samples covering various news topics. Evaluation is conducted using accuracy, precision, recall, and F1-Score metrics to gain a comprehensive understanding of the classification algorithm's performance. The results of this research demonstrate that the SVM algorithm exhibits the best performance, achieving an accuracy rate of 92.92%. Random Forest follows with an accuracy rate of 91.21%, and Naive Bayes with an accuracy rate of 89.61%. These findings provide deep insights into the effectiveness of the machine learning approach in evaluating news labels based on their titles. Furthermore, the study highlights the importance of considering other evaluation metrics such as precision, recall, and F1-Score to obtain a more holistic understanding of the algorithm's performance. Further research is encouraged to involve additional classification algorithms and more diverse and extensive datasets to enhance the comprehension of news label evaluation comprehensively. Such endeavors can significantly contribute to the development of automated systems for classifying news with higher accuracy and reliability in the future

PELATIHAN PENGUATAN SKILL PENGGUNAAN APLIKASI PERKANTORAN UNTUK SISWA XII MAN 1 BANGKA Wahyuningsih, Delpiah; anisah; Kiswanto; Yuranda, Rezky; Irawan, Devi; Kirana, Chandra; Yanuarti, Elly
Jurnal Pengabdian Masyarakat Berbasis Teknologi Vol 6 No 02 (2025): Volume 6, Nomor 2, Oktober 2025
Publisher : ISB Atma Luhur

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

Siswa MAN Bangka hampir tidak pernah melakukan praktikum mata pelajaran maupun ektrakurikuler terkait penggunaan komputer. Kemajuan teknologi saat ini siswa siswi seharusnya mampu dalam mengoperasikan aplikasi perkantoran dengan baik. Dengan adanya pelatihan aplikasi perkantoran bertujuan untuk siswa XII MAN Bangka memiliki kemampuan dapat mengoperasikan aplikasi perkantoran dengan baik. Pelatihan ini langsung praktikum penggunaan komputer yang dilaksanakan 4 kali pertemuan di laboratorium komputer 1 dan laboratorium komputer 2 MAN Bangka. Pelatihan penguatan skill ini dapat menambah kemampuan siswa XII dalam mengoperasikan Office baik microsoft word, microsoft power point dan microsoft excel. siswa dapat menjadikan bekal mereka kedepannya dalam memasuki dunia kerja ataupun melanjutkan ke jenjang Pendidikan. Selama 4 hari kegiatan ini dilaksanakan siswa-siswi XII MAN Bangka bersemangat mengikuti kegiatan dengan bukti mereka selalu hadir tepat waktu, mengikuti kegiatan hingga selesai. Pelatihan ini diberikan latihan di tempat untuk mengukuran keberhasilan pelatihan dan melihat kemampuan mereka setelah mengikuti pelatihan ini. Kata kunci: Aplikasi Perkantoran, MAN Bangka, Penguatan Skill

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