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All Journal International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology Jurnal Informatika Upgris JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika Technomedia Journal Jurnal Tekno Insentif Jurnal Telematika Jurnal Teknologi Terapan Mobile and Forensics Jurnal Teknik Industri Terintegrasi (JUTIN) Journal of Applied Data Sciences Journal of Dinda : Data Science, Information Technology, and Data Analytics Nuansa Informatika
Syafrial Fachri Pane, Syafrial Fachri
Politeknik Pos Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Neuroscience

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Security Analysis of Two-Factor Authentication Applications: Vulnerabilities in Data Storage and Management Pane, Syafrial Fachri; Haq, Dzikri Izzatul; Siregar, M. Amran Hakim
Mobile and Forensics Vol. 7 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i2.13312

Abstract

In the digital era, two-factor authentication (2FA) is used as an additional security measure to protect user access to digital services. However, the storage methods of authentication data in 2FA applications have potential security vulnerabilities that can be exploited. This study analyzes five popular 2FA applications, namely Google Authenticator, 2FAS, Aegis Authenticator, Okta Verify, and TOTP Authenticator, focusing on how secret keys are stored and the potential exploitation risks. The experiment was conducted in a virtual Android environment using rooted BlueStacks 5. Data acquisition was performed using Media Manager and X-Plore File Manager, followed by data analysis with SQLite Browser and bypass with PyOTP. The results indicate that some applications store secret keys in plaintext or weak encryption, making authentication bypass possible by manually generating OTP codes. This study concludes that strengthening data storage security in 2FA applications is crucial to prevent unauthorized exploitation by malicious actors.
Menentukan Faktor-Faktor Akademik yang Mempengaruhi Hasil Belajar Online Selama Pandemi COVID-19 Pane, Syafrial Fachri; Fajri, Ravi Rahmatul
Eksplora Informatika Vol 12 No 1 (2022): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v12i1.789

Abstract

Pandemi COVID19 adalah krisis kesehatan global. Dalam bidang pendidikan, pembelajaran online dengan sistem e-learning merupakan kebutuhan yang tidak tergantikan. Banyak yang berpendapat bahwa pembelajaran online adalah krisis pendidikan saat ini. Namun, sebagian besar siswa tidak tertarik untuk belajar online karena mengandalkan kualitas Internet, yang membatasi interaksi mereka dan membuat kualitas suara dan gambar tidak stabil. Tentu tidak mudah untuk mengetahui faktor akademik yang mempengaruhi hasil belajar online selama pandemi COVID-19. Oleh karena itu, penelitian ini bertujuan untuk menentukan faktor-faktor akademik yang mempengaruhi hasil belajar online selama pandemi COVID-19. Menggunakan data lokal Politeknik di Pulau Jawa. Penelitian ini menggunakan analisis Cronbach-Alpha, Bayesian Exploration, EFA-tradisional dan Analisis Regresi Multivariat (OLS). Hasil evaluasi skala penelitian menunjukkan bahwa 28 variabel diamati. Hasil uji hipotesis menunjukkan bahwa hasil belajar online dipengaruhi oleh enam faktor. Desain kursus, kegunaan yang dirasakan, kemudahan penggunaan, Karakteristik pembelajaran, Kapasitas fakultas, Konten kursus. Regresi multivariat berdasarkan metode kuadrat minimum (OLS) untuk mengevaluasi faktor-faktor spesifik yang mempengaruhi pembelajaran online dan menguji hipotesis. Tingkat akurasi model OLS sebesar 45,8%.
Hybrid Multi-Objective Metaheuristic Machine Learning for Optimizing Pandemic Growth Prediction Adiwijaya, Adiwijaya; Pane, Syafrial Fachri; Sulistiyo, Mahmud Dwi; Gozali, Alfian Akbar
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.981

Abstract

Pandemic and epidemic events underscore the challenges of balancing health protection, economic resilience, and mobility sustainability. Addressing these multidimensional trade-offs requires adaptive and data-driven decision-support tools. This study proposes a hybrid framework that integrates machine learning with multi-objective optimization to support evidence-based policymaking in outbreak scenarios. Six key indicators—confirmed cases, disease-related mortality, recovery count, exchange rate, stock index, and workplace mobility—were predicted using eight regression models. Among these, the XGBoost Regressor consistently achieved the highest predictive accuracy, outperforming other approaches in capturing complex temporal and socioeconomic dynamics. To enhance interpretability, we developed SHAPPI, a novel method that combines Shapley Additive Explanations (SHAP) with Permutation Importance (PI). SHAPPI generates stable and meaningful feature rankings, with immunization coverage and transit station activity identified as the most influential factors in all domains. These importance scores were subsequently embedded into the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to construct Pareto-optimal solutions. The optimization results demonstrate transparent trade-offs among health outcomes, economic fluctuations, and mobility changes, allowing policymakers to systematically evaluate competing priorities and design balanced intervention strategies. The findings confirm that the proposed framework successfully balances predictive performance, interpretability, and optimization, while providing a practical decision-support tool for epidemic management. Its generalizable design allows adaptation to diverse geographic and epidemiological contexts. In general, this research highlights the potential of hybrid machine learning and metaheuristic approaches to improve preparedness and policymaking in future health and socioeconomic crises.
Komparasi Model Klasifikasi Naïve Bayes Dan C4.5 Pada Data Prestasi Kerja PNS Vegita, Yola; Prianto, Cahyo; Pane, Syafrial Fachri
Jurnal Informatika UPGRIS Vol 8, No 2: Desember 2022
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v8i2.13205

Abstract

— Salah satu bagian yang terpenting untuk mencapai keberhasilan dalam kemajuan suatu organisasi adalah SDM atau sumber daya manusia. Pegawai yang tidak menuntaskan pekerjaannya, maka target organisasi tidak akan tercapai. Berdasarkan hal tersebut, apabila seorang pegawai tidak maksimal dan tidak dapat bekerja dengan baik, pastinya akan mempengaruhi perkembangan dan kemajuan dari perusahaan atau organisasi. Untuk melakukan evaluasi  kinerja PNS Dinas Perhubungan Provinsi Jawa Barat dengan memanfaatkan hasil penilaian prestasi kerja, yang mana data yang digunakan adalah penilaian pada tahun 2020. Banyaknya pegawai membuat penilaian Prestasi Kerja menjadi sulit dan tidak dipungkiri penilaian juga terkadang dilakukan tidak objektif. Untuk melakukan suatu penilaian kerja dapat menggunakan metode pendukung, salah satunya dengan melakukan klasifikasi data pegawai dengan data mining. Penelitian ini membandingkan algoritma performance algoritma Naïve Bayes dan C4.5 dengan mengevaluasi hasil pemodelan dengan Confusion Matrix dan Classification Report. Hasilnya, C4.5 memiliki akurasi 99.12% sedangkan Naïve Bayes hanya 83%.
Penentuan rute terpendek antara dua titik di gudang menggunakan Dijkstra’s Algorithm dan Microsoft Excel Sanggala, Ekra; Pane, Syafrial Fachri; Habibi, Roni
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 1 (2025): January
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i1.39060

Abstract

Sebuah gudang merupakan suatu faktor penting dalam logistik dan mempunyai peran vital dalam mengontrol dan mengurangi biaya logistik. Secara umum operasional pada gudang terdiri dari lima fungsi dasar, yaitu: receiving, sorting, storing, order picking dan delivering. Kecepatan pada order picking merupakan faktor penting untuk kepuasan pelanggan. Maka mempersingkat waktu order picking merupakan hal yang penting. Order Picking yang paling sederhana adalah saat produk yang dibutuhkan pelanggan hanya terletak pada satu rak saja, sehingga picker hanya perlu bergera dari titik awal menuju ke titik rak dimana produk berada. Permasalahan penentuan rute terpendek antara dua titik dapat didefinisikan sebagai Shortest Path Problem. Dijkstra’s Algorithm merupakan algoritma yang paling populer dalam menyelesaikan Shortest Path Problem. Untuk menyelesaikan Shortest Path Problem dengan Dijkstra’s Algorithm diperlukan sebuah tool yang dapat membantu menyelesaikan perhitungannya. Microsoft Excel merupakan salah satu tool yang sangat populer dan mudah digunakan untuk menyelesaikan berbagai perhitungan. Dengan mengkombinasikan berbagai formula yang terdapat pada Microsoft Excel terbukti bahwa perhitungan Dijkstra’s Algorithm untuk menyelesaikan Shortest Path Problem dapat dilakukan dengan baik.
PSO-Enhanced ensemble techniques for pandemic prediction and feature importance analysis Pane, Syafrial Fachri; Sulistiyo, Mahmud Dwi; Gozali, Alfian Akbar; Adiwijaya, Adiwijaya
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2091

Abstract

During the pandemic crisis that hit after 2020, Indonesia, like many other countries, faced tremendous challenges in areas such as health, economy, and mobility. An in-depth understanding of the dynamics and changes in these areas is essential to address the impacts of the pandemic. This research is an attempt to deeply analyze the impact of the pandemic and the most effective forecasting methods based on data and phenomena. Indonesia, with its growing economy and constantly adapting health system, faces conventional economic impacts, while its health system response tries to keep up with urgent needs driven by the spread of the virus. In the context of mobility, changes in how people move and interact significantly affect virus transmission. Modeling a pandemic event with all its complexities is not an easy task. Even more so, in finding the right method for prediction, ensemble techniques such as stacking and regression voting are emerging as promising approaches. However, deep learning and particle swarm optimization (PSO) techniques offer new innovations. The results of this study show that the ensemble vote provides the best performance in predicting confirmed positive cases and mortality based on factors of health, economic and population mobility in Indonesia. Through feature importance analysis using MDI and Tree SHAP, we conclude that factors such as active cases, the number of vaccinations, and economic indicators, such as close IDR and close IHSG, have a significant influence on the growth of confirmed positive cases. Meanwhile, recovery factors and vaccination number play an important role in the growth of the number of death cases. This study confirms that a multivariate approach that considers health, economy and mobility is the key to understanding and responding more effectively to the pandemic in Indonesia.
Co-Authors Adiwiguno, Waskitho Cito Adiwijaya Alfian Akbar Gozali Ekra Sanggala Faisal Abdullah Fajri, Ravi Rahmatul Fatonah, Nuraini Siti Grahitama, Fulandi Hudza Habibi, Roni Haq, Dzikri Izzatul Harani, Nisa Hanum Iwan Setiawan Khaera Tunnisa, Khaera Kurniawan, Alit Fajar Kurniawan, Cahya Mahmud Dwi Sulistiyo Poetra, Chandra Kirana Prianto, Cahyo Putratama, Virdiandry Ramadhan, Bachtiar Rolly Maulana Awangga, Rolly Maulana Siregar, M. Amran Hakim Vegita, Yola Zain, Rofi Nafiis