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FTK Image For Forensic Data Processing In Forensic Tools Rachmat Suryadithia; Witriana Endah Pangesti; Muhammad Faisal; Aji Nurrohman; W Wibisono; Arman Syah Putra
IJISTECH (International Journal of Information System and Technology) Vol 5, No 6 (2022): April
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i6.199

The background of this research is how the use of a software can help find forensic data, which is needed so that the tool used is the right tool in helping forensic problems. The method used in this study is the NIJ method using 5 stages in a process of determining the answer. The first stage is preparation, the second stage in collecting data, the third is testing and the fourth stage is analyzing and the last is the reporting stage with the five stages. The direction of the research will be clearer. The problem raised in this research is how to find evidence using FTK images software. Using this software, you can search for the desired forensic data so that it can be proven that there is forensic evidence. The purpose of this study is how to prove data, especially photo data, can be used as forensic data that can be used as evidence, by using the right tools, namely the existing FTK images software, with the software, it can help parties in proving, especially in terms of forensics

FTK Image For Forensic Data Processing In Forensic Tools Rachmat Suryadithia; Witriana Endah Pangesti; Muhammad Faisal; Aji Nurrohman; W Wibisono; Arman Syah Putra
IJISTECH (International Journal of Information System and Technology) Vol 5, No 6 (2022): April
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

The background of this research is how the use of a software can help find forensic data, which is needed so that the tool used is the right tool in helping forensic problems. The method used in this study is the NIJ method using 5 stages in a process of determining the answer. The first stage is preparation, the second stage in collecting data, the third is testing and the fourth stage is analyzing and the last is the reporting stage with the five stages. The direction of the research will be clearer. The problem raised in this research is how to find evidence using FTK images software. Using this software, you can search for the desired forensic data so that it can be proven that there is forensic evidence. The purpose of this study is how to prove data, especially photo data, can be used as forensic data that can be used as evidence, by using the right tools, namely the existing FTK images software, with the software, it can help parties in proving, especially in terms of forensics

COMPARATIVE ANALYSIS OF AUTOMATION FUNCTIONAL TESTING TOOLS PERFORMANCE FOR PLAYSTORE APPS WITH DIA METHOD Faizal Riza; Berliyanto Berliyanto; Aji Nurrohman; Rachmat Setiabudi
Jurnal Techno Nusa Mandiri Vol 21 No 1 (2024): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v21i1.5363

The complexity of smartphone applications presents challenges for developers, who must ensure flawless functionality despite limitations such as budget and time constraints. Manual testing is time-consuming, prompting a shift towards automated testing methods to ensure efficiency and reliability. In this context, researchers are evaluating the efficacy of three leading test automation frameworks—Robot Framework, Katalon Studio, and UI Path—against key performance parameters. Using the Distance to the Ideal Alternative (DIA) method on playstore apps. The main performance parameters used as a reference are automated testing progress and tools usability. Katalon Studio emerges as the top performer, securing the top rank with a remarkably close to the alternative ideal positive distance (Ri) value of 0.00001. UI Path occupies the second position with a Ri value of 0.00135, while Robot Framework trails behind with a Ri value of 0.00295. This research contributes to the understanding of the performance of different automation frameworks in the context of functional testing, providing valuable insights for developers and organizations seeking to optimize their testing processes. The findings underscore the significance of Katalon Studio's exceptional performance and highlight opportunities for improvement in UI Path and Robot Framework. Additionally, implementing a robust monitoring and evaluation framework is crucial for tracking the ongoing performance and optimizing the efficiency of these automation frameworks.

KOMPARASI DAN IMPLEMENTASI ALGORITMA MACHINE LEARNING UNTUK KLASIFIKASI KREDIT BERMASALAH PADA PT BPR NUSUMMA KLATEN Teguh Muryanto; Aji Nurrohman; Rachmat Setiabudi; Wibisono Wibisono; Berliyanto Berliyanto
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/r5rncz02

Tingkat kredit bermasalah yang tinggi dapat mengganggu stabilitas keuangan lembaga perbankan, sehingga diperlukan sistem klasifikasi yang akurat untuk mendeteksi potensi gagal bayar sejak dini. Penelitian ini bertujuan untuk membangun dan membandingkan model klasifikasi risiko kredit menggunakan algoritma machine learning, yaitu Random Forest, XGBoost, dan Support Vector Machine (SVM). Permasalahan yang diangkat dalam penelitian ini meliputi ketidakakuratan dalam klasifikasi nasabah, kurangnya pemanfaatan data historis, serta belum diterapkannya metode analitik berbasis algoritma cerdas. Metode penelitian mengikuti pendekatan Cross-Industry Standard Process for Data Mining (CRISP-DM) yang mencakup pemahaman bisnis, eksplorasi data, praproses data, pemodelan, evaluasi model, hingga tahap implementasi. Dataset yang digunakan berasal dari laporan historis nasabah kredit di PT BPR Nusumma Klaten. Evaluasi dilakukan dengan mengukur akurasi, precision, recall, F1-score, dan AUC. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki kinerja terbaik dengan nilai evaluasi yang lebih stabil dibandingkan XGBoost dan SVM. Temuan ini diharapkan dapat membantu lembaga keuangan dalam meningkatkan efisiensi proses analisis risiko kredit dan pengambilan keputusan berbasis data. Kata Kunci: Kredit Bermasalah, Random Forest, XGBoost, SVM, Klasifikasi

EVALUASI STRATEGI MOVING AVERAGE, RELATIVE STRENGTH INDEX, DAN PARABOLIC SAR TERHADAP PERGERAKAN HARGA EUR/USD PADA PT ROYAL TRUST FUTURES Abdurrahman Abdurrahman; Sigit Wibisono; Bagus Prabowo; Aji Nurrohman; Irlon Irlon
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/nmen2h79

Perdagangan valuta asing (forex) merupakan salah satu instrumen investasi yang memiliki risiko tinggi dan memerlukan analisis yang tepat dalam pengambilan keputusan. Salah satu pendekatan yang banyak digunakan adalah analisis teknikal dengan bantuan indikator teknikal. Penelitian ini bertujuan untuk mengevaluasi kinerja tiga indikator teknikal, yaitu Moving Average periode 5 (MA5), Relative Strength Index (RSI), dan Parabolic SAR dalam memberikan sinyal beli dan jual terhadap pasangan mata uang EUR/USD. Permasalahan dalam penelitian ini adalah untuk mengetahui sejauh mana efektivitas masing-masing indikator dalam membaca pergerakan harga dan menghasilkan profit yang optimal. Data yang digunakan adalah data historis EUR/USD periode 2018–2025 yang diperoleh dari platform MetaTrader 4, dengan pendekatan metode CRISP-DM dan pengolahan data menggunakan bahasa pemrograman Python. Hasil evaluasi menunjukkan bahwa Parabolic SAR merupakan indikator paling unggul dengan win rate 76.84%, net return sebesar 65.43%, dan CAGR sebesar 7.46%. MA5 menunjukkan hasil moderat dengan win rate 36.55% dan net return 6.15%, sedangkan RSI menunjukkan performa terendah dengan hasil negatif. Penelitian ini memberikan gambaran mengenai efektivitas masing-masing indikator teknikal dan dapat menjadi referensi untuk pengambilan keputusan trading yang lebih tepat. Kata Kunci: Evaluasi indikator teknikal, MA5, RSI, Parabolic SAR, pergerakan harga EUR/USD

A System For Monitoring Indoor Air Quality Based On The Internet Of Things Aji Nurrohman; Berliyanto Berliyanto; Sigit Wibisono; Surya Darma; Wibisono Wibisono; Leni Devera Asrar; Triyono Budi Santoso
Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika Vol. 7 No. 1 (2024)
Publisher : Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/inovatif.v7i1.925

In this era of rapidly evolving information technology, the need to monitor and manage indoor air quality is becoming increasingly important. Poor air quality can have a negative impact on human health and productivity. Therefore, this research aims to design and implement an Internet of Things (IoT)-based Indoor Air Quality Monitoring System. The proposed system uses sensors connected to the IoT network to measure air quality parameters, such as temperature, humidity, particle content, and certain gases. The data collected by these sensors will be sent in real-time to a server via an internet connection. The server will process the data and present it in a form that can be accessed through a web interface. The implementation of IoT allows for more efficient and accurate monitoring, and provides easy access to data remotely. Users can monitor indoor air quality through their mobile devices or personal computers. The use of IoT technology in this monitoring system is expected to increase responsibility and awareness of indoor air quality. The results of this research are expected to contribute to the development of innovative solutions to improve the health and comfort of indoor environments. The conclusion of this research includes evaluation of system performance, analysis of the data generated, as well as potential development and improvement for further research.

Bridging the Language of Cybersecurity: A Semantic Comparison Between NIST and ISO/IEC 27000 Terminologies Eka Julianti; Dewi Andriyanti; Aji Nurrohman; Rudolf Sinaga
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 2 (2026): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i2.203

Abstract: Background: Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; NIST; ISO/IEC 27000 Terminology

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