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All Journal JOIN (Jurnal Online Informatika) TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer International Journal of Engineering Continuity
Azzery, Yasep
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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ANALISA PREDIKSI PERTUMBUHAN START-UP DI ERA INDUSTRI 4.0 MENGGUNAKAN METODE MARKOV CHAIN Hidayat, Taufik; Sari, Dewi Yunita; Azzery, Yasep
TEKNOKOM Vol. 3 No. 2 (2020): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (422.025 KB) | DOI: 10.31943/teknokom.v3i2.45

Abstract

Bisnis Start-up semakin tumbuh dan berkembang di berbagai daerah di Indonesia, tidak terkecuali di Jabodetabek. Dari bisnis Start-up dapat menghasilkan berbagai platform atau aplikasi yang diminati konsumen, seperti marketplace pada E-commerce. Jabodetabek merupakan daerah dengan tingkat pertumbuhan Start-up tertinggi sebanyak 522 Start-up, yang mayoritas berasal dari kota Jakarta. Penggunaan metode Markov chain untuk memprediksi pertumbuhan dengan melakukan perhitungan matriks transisi probabilitas, serta menghitung probabilitas keadaan sampai diperoleh keadaan Steady state yang merupakan data acuan untuk hasil prediksi pertumbuhan Start-up di Jabodetabek di 5 tahun berikutnya.
ANALISIS STATISIK PERBANDINGAN MANIPULASI SUARA DAN SUARA ASLI MENGGUNAKAN TEKNIK AUDIO FORENSIK Azzery, Yasep
TEKNOKOM Vol. 3 No. 1 (2020): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (220.445 KB) | DOI: 10.31943/teknokom.v3i1.50

Abstract

Kejahatan digital yang semakin beragam menuntut Tim ahli forensik untuk meningkatkan pengetahuan dalam pengungkapan kasus didalam dunia digital. Teknik audio forensik merupakan bagian dari ilmu digital forensik, yang lebih fokus pada analisa suara serta berbagai manipulasi didalamnya yang harus dapat dibuktikan di persidangan. Salah satu tantangan yang dihadapi dalam mengungkap kejahatan melalui audio yaitu adanya manipulasi suara yang berbeda dengan suara sumber atau pelaku. Analisa yang dilakukan menggunakan rekaman suara laki-laki yang terdiri dari 20 kata dan dilakukan manipulasi dengan menaikkan speed audio sebesar 20%. Metode yang digunakan yaitu dengan membandingkan hasil analisa Picth, Formant, dan Spectogram dari suara asli dan suara yang dimanipulasi. Hasil analisis perbandingan satistik Pitch, formant, spectogram menunjukkan bahwa terdapat perbedaan nilai dan range dari suara barang bukti dan suara subjek. Analisa statisik dilakukan dengan teknik One Way Annova menyatakan bahwa kedua suara rekaman tersebut Tidak Identik. Makalah ini diharapkan dapat menambah wawasan bagi Tim forensik untuk melakukan analisa lebih lanjut terhadap barang bukti yang sudah dimanipulasi.
MEMORY FORENSIC DEVELOPMENT AND CHALLENGES IN IDENTIFYING DIGITAL CRIME : A REVIEW Azzery, Yasep; Dwi Mulyanto, Nur; Hidayat, Taufik
TEKNOKOM Vol. 5 No. 1 (2022): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (365.764 KB) | DOI: 10.31943/teknokom.v5i1.73

Abstract

Digital forensic technology is currently advancing along with the demands to uncover various crimes using technology. Memory Forensic is one of the investigative fields in digital forensics. We use the Systematic Literature Review method to identify the developments and challenges of Forensic Memory in identifying digital crimes, analyzed from various reference papers according to the Include and Exclude Criteria and based on the specified Research Question. Authors chose from 30 reference journals from 3 online journal databases namely IEEE Explore, Sciencedirect, and Springer with themes related to forensic memory based on certain criteria for further review to determine the development of digital crime. The results of the SLR that we convey are the result of a study related to the use of Memory Forensic in identifying various digital attacks and challenges faced in the future.
Load Balancing Network by using Round Robin Algorithm: A Systematic Literature Review Hidayat, Taufik; Azzery, Yasep; Mahardiko, Rahutomo
JOIN (Jurnal Online Informatika) Vol 4 No 2 (2019)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v4i2.446

Abstract

The use of load balance on a network will be very much needed if the network is an active network and is widely accessed by users. A reason is that it allows network imbalances to occur. Round Robin (RR) algorithm can be applied for network load balancing because it is a simple algorithm to schedule processes so that it can provide work process efficiency. Authors use the Systematic Literature Review (SLR) method in which it can be applied for criteria selection during papers search to match the title being raised. SLR is divided into five stages, namely formalization of questions, criteria selection, selection of sources, selection of search results, and quality assessment. By using SLR, it is expected that papers according to criteria and quality can be found.
Integration of BERT and LSTM for Predicting Cybersecurity Service Trends Based on LinkedIn Data Firdaus, Mohamad; Azzery, Yasep; Prasetio, Dimaz Arno
International Journal of Engineering Continuity Vol. 4 No. 2 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i2.424

Abstract

The analysis and prediction of evolving cybersecurity service demands are constrained by existing methodologies, which are either semantically shallow (keyword-based TF-IDF) or contextually limited (standalone LSTM time-series models that overlook textual meaning). To bridge this scientific gap, this study develops and validates an integrated artificial intelligence framework combining Bidirectional Encoder Representations from Transformers (BERT) for deep semantic analysis and Long Short-Term Memory (LSTM) for sequential trend prediction. This pipeline is applied to a large-scale corpus of cybersecurity job descriptions collected from LinkedIn, serving as a proxy for real-world market intelligence. The methodology utilizes BERT embeddings (768-dimensional) for nuanced feature extraction, which are then combined with pseudo-temporal segmented data (proxy timeline) to enable sequential forecasting via the LSTM component. Experimental results confirm the model's robustness, the BERT component achieved 89% classification accuracy (87% precision, 88% recall) in service categorization, significantly outperforming baseline methods such as TF-IDF (which typically achieve below 75% accuracy). The LSTM component demonstrated strong predictive capability for trend forecasting, achieving a Root Mean Squared Error (RMSE) of 0.12. These findings validate the technical viability of the unified BERT-LSTM architecture for capturing both contextual and sequential patterns in professional data. The output provides organizations with objective, data-driven insights for strategic planning, thereby enhancing organizational resilience and market competitiveness in dynamic environments, particularly relevant for the Indonesian cybersecurity market.
Co-Authors Dewi Yunita Sari Dwi Mulyanto, Nur Mahardiko, Rahutomo Mohamad Firdaus, Mohamad Prasetio, Dimaz Arno Taufik Hidayat Taufik Hidayat