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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Jurnal Magister Manajemen Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Kitektro Journal of Aceh Physics Society Bulletin of Electrical Engineering and Informatics Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro Jurnal Penelitian Pendidikan IPA (JPPIPA) Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Inotera Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Vokasi Journal of Applied Engineering and Technological Science (JAETS) JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Jurnal Teknik Informatika (JUTIF) International Journal of Engineering, Science and Information Technology AJAD : Jurnal Pengabdian kepada Masyarakat J-Innovation Aceh International Journal of Science and Technology Jurnal Teknologi Informasi Jurnal Teknik Indonesia Jurnal Rekayasa elektrika Enrichment: Journal of Multidisciplinary Research and Development Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) kawanad Jurnal Teknik Sipil PESARE: Science and Engineering Service Journal Jurnal Polimesin Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Mathematics Education Journal Jurnal Pengabdian Rekayasa dan Wirausaha Nawadeepa: Jurnal Pengabdian Masyarakat Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Rekayasa elektrika
Yuwaldi Away
Universitas Syiah Kuala
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Public Health Social Sciences Transportation Other

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Peningkatkan Keamanan ElGamal Menggunakan CNN dan Rolling Hash untuk Generasi Kunci dalam Enkripsi Gambar Fauzi, Achmad; Arif, Teuku Yuliar; Away, Yuwaldi; Roslidar, Roslidar
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.4484

Abstract

The large scale exchange of digital images requires security mechanisms that are robust not only at the cryptographic algorithm level but also in the key generation process, which is often the weakest component of the system. In conventional ElGamal schemes, security may degrade due to static entropy sources and predictable key patterns. This study proposes an ElGamal key generation model based on a pipeline of Convolutional Neural Networks (CNNs) and a rolling hash function, utilizing visual image content as an adaptive entropy source. The CNN extracts latent features through a fully connected layer, while the rolling hash enhances diffusion and key sensitivity to minor image variations. The model was evaluated using the CIFAR-10 dataset in PNG, WEBP, and JPG formats. Experimental results show stable key generation times ranging from 0.426 to 0.444 ms, with high entropy values between 7.98 and 7.99 bits, indicating strong randomness and resistance to prediction. Strong diffusion characteristics were also observed (PSNR 5.94 dB, SSIM −0.24, MAE 0.43). During encryption, WEBP achieved the fastest processing time (0.48 ms), followed by PNG (1.01 ms) and JPG (15.39 ms), while PNG demonstrated the highest size efficiency with a reduction of up to 70.6%. Decryption remained highly reliable, with success rates exceeding 97% across all formats. Overall, the results confirm that integrating CNNs and rolling hash significantly enhances ElGamal key generation security without compromising decryption reliability or image quality.
Pelatihan Internet of Things Berbasis Embedded System untuk Meningkatkan Kompetensi Penelitian Mahasiswa Teknologi Informasi Yuwaldi Away; Andri Novandri; Isyatur Raziah
Kawanad : Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 1 (2026): March
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/kjpkm.v5i1.390

Abstract

The rapid advancement of Internet of Things (IoT) technology requires Information Technology students to go beyond theoretical understanding and develop hands-on technical skills in building functional real-world systems. This community service activity aims to strengthen students' research competence through integrated training on Embedded Systems and IoT. The implementation approach encompasses theoretical instruction on foundational concepts, hardware familiarization, and direct practice in programming and system integration. Training materials cover the use of the ESP8266 microcontroller, temperature and humidity sensors (DHT22), voltage and current sensors (INA219), the Node-RED platform, and Cloud Server-based databases. Data transmission was carried out using an internet-based communication protocol, while Node-RED served as the primary platform for data flow management and real-time dashboard visualization. The outcomes demonstrate measurable improvement in participants' ability to design remote monitoring systems, perform real-time data visualization, and apply datalogging techniques. These competencies are expected to support the quality of undergraduate final research projects and technology-based innovation initiatives relevant to current societal needs.  
Temporal Deep Learning with Multiscale Principal Component Features for Autism Classification from Electroencephalographic Signals Muliyadi Muliyadi; Melinda; Yuwaldi Away; Syahrul Gazali; Aufa Rafiki; W.K Wong
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1735

Abstract

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition whose early identification remains challenging because clinical assessment still relies heavily on behavioral observation and expert judgment. Electroencephalography (EEG) offers a noninvasive approach for capturing neural dynamics. Still, EEG-based ASD classification remains difficult because of signal nonstationarity, limited sample sizes, and the risk of subject-identity leakage. This study proposes a comparative temporal deep learning framework for EEG-based ASD classification by evaluating principal component analysis (PCA) and multiscale principal component analysis (MS-PCA) as feature representations combined with a recurrent neural network with bidirectional long short-term memory (RNN-BiLSTM) and a temporal convolutional network with self-attention (TCN-SA). Resting-state eyes-open EEG signals were acquired from only 10 participants, consisting of 5 individuals with ASD and 5 typically developing controls, using a 16-channel acquisition system. The signals were filtered using a fourth-order Butterworth band-pass filter, transformed into PCA or MS-PCA representations, segmented into 4 s windows with 50% overlap, and evaluated using subject-wise 5-fold cross-validation to reduce subject-identity leakage. The results showed that MS-PCA produced higher descriptive performance than PCA in both temporal architectures, with the strongest descriptive result obtained by the MS-PCA + TCN-SA scheme, which achieved a mean accuracy of 97.96 ± 2.37% and balanced precision, recall, F1-score, and specificity. However, the inferential comparison between PCA and MS-PCA did not reach statistical significance at the 0.05 level, and the cohort size was limited to 10 participants. Therefore, these findings should be interpreted as preliminary descriptive evidence within the present cohort rather than evidence of diagnostic readiness or robust clinical applicability. Larger, independent, and demographically diverse EEG datasets with richer clinical characterization are required to confirm the observed trend and evaluate the generalizability of the proposed framework.
Pemodelan Daya Photovoltaic Berdasarkan Distribusi Termal Menggunakan Algoritma Support Vector Regression Isyatur Raziah; Andri Novandri; Cut Mutia; Yuwaldi Away
Jurnal Teknologi Informasi Vol 5, No 1 (2026): Mei
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/jti.v5i1.14747

Abstract

Kinerja photovoltaic (PV) sangat dipengaruhi oleh karakteristik termal, terutama temperatur yang berdampak langsung terhadap daya keluaran. Pada kondisi nyata, distribusi temperatur pada permukaan panel tidak selalu seragam, sehingga pemodelan berbasis temperatur rata-rata sering kali kurang akurat. Penelitian ini bertujuan untuk memodelkan daya keluaran PV berdasarkan distribusi temperatur menggunakan algoritma Support Vector Regression (SVR). Variabel input yang digunakan meliputi temperatur atas dan bawah panel, irradiance matahari serta kelembapan udara, sementara daya keluaran PV dijadikan sebagai variabel target. Model SVR diterapkan dengan fungsi kernel Radial Basis Function (RBF) untuk menangkap hubungan nonlinier antara variabel input dan output. Hasil pengujian menunjukkan bahwa akurasi model meningkat seiring dengan bertambahnya jumlah dan variasi dataset, dengan performa terbaik diperoleh pada dataset 10 hari yang menghasilkan nilai error rendah serta nilai  dan   yang tinggi. Temuan ini menunjukkan bahwa SVR efektif dan andal dalam memprediksi daya keluaran PV berbasis distribusi temperatur panel.
Risk Mitigation of Planning for Procurement of Material Repair and Operation with the Integration of House of Risk and Criticality Ranking Assessment methods based on Failure mode effect Analysis (Case study: PT. Pupuk Iskandar Muda) Firdaus Firdaus; Yuwaldi Away; Syifaul Huzni
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 4 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i4.419

Abstract

To maintain factory reliability, one of the essential factors is the availability of spare parts. Therefore, the spare parts procurement process implemented by the company must be effective and efficient. The complexity of supply chain activities in the spare parts procurement process involving multiple parties and uncertainties that occur in rapid dynamic changes may lead to the emergence of risk events with negative impacts in both short and long terms. The focus of this research is to identify operational risks in the procurement of MRO spare parts and identify alternative efforts to handle the risk of spare parts unavailability. The method used in this research is House of Risk (HOR) to identify risk events, risk agents, and design mitigation action formulations for priority risk agents based on the Aggregate Risk Potential (ARP) value that can suppress the emergence of risk agents. The research results obtained 26 risk events and 25 risk agents. Priority risk agents were obtained based on the highest ARP value, using Pareto analysis approach resulted in 14 risk agents that contributed to 81.32% of the emergence of risk events. There are 9 mitigation actions that can minimize the emergence of risk agents in the supply chain process of MRO spare parts procurement.
Analysis of Factory Operational Scheme Using Decision Tree Method with C4.5 Algorithm Hendra Muzawwir; Yuwaldi Away; Irwansyah Irwansyah
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 4 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i4.420

Abstract

Optimizing the utilization of idle industrial assets is a crucial issue in asset-based management strategies, especially in the high-risk and capital-intensive petrochemical sector. PT Pupuk Iskandar Muda (PT PIM) as part of the development of the Arun Lhokseumawe Special Economic Zone (SEZ), is faced with the urgency to determine the most rational and value-added operational scheme in reactivating the ex-PT AAF Hydrogen Peroxide (H?O?) Factory. There are three alternative schemes analysed in this context: lease, operating cooperation (KSO) and self-management, each of which has different financial characteristics and risk profiles. The complexity of such decision-making requires a data-driven approach that is able to integrate economic and risk aspects holistically. This study aims to determine the most feasible operational scheme based on economic indicators such as Net Present Value (NPV), Internal Rate of Return (IRR) and Payback Period (PP), as well as two main risk dimensions, namely operational risk and market risk. This study also aims to build a decision tree-based decision-making model using the C4.5 algorithm to classify the feasibility of the scheme in a systematic and transparent manner. The research method uses a descriptive-analytical quantitative approach. The data used are secondary data from the project feasibility study and PT PIM's internal documents. Modeling was carried out by processing six main variables in categorical form to form a decision tree structure through the C4.5 algorithm. The process starts from calculating the entropy value, gain, to determining the root node and branch of the decision. The classification results show that market risk is the most decisive attribute in the decision-making process, followed by NPV as the main separating indicator between the "Feasible" and "Very Feasible" feasibility levels. The main findings of this study are that the self-management scheme is the most feasible option with the best financial performance: NPV of ±Rp 38.77 billion, IRR of 14.48%, and Payback Period of 5.58 years. The structure of the established decision tree is able to explicitly describe how the combination of risk and financial indicators can lead to different final decisions. This model serves not only as a classification tool, but also as an analytical instrument in understanding the relationships between variables and their implications for investment feasibility. This research makes a methodological contribution to the application of the C4.5 algorithm for data-driven strategic decision-making. This approach has been proven to improve accuracy, objectivity, and transparency in assessing the operational feasibility of industrial assets, and can be replicated for similar cases in other sectors.
Energy-Proportional Modelling of a Dual-Axis Sun Tracker Controller Based on ANFIS Rauzatul Jannah; Yuwaldi Away; Roslidar Roslidar
Jurnal Rekayasa Elektrika Vol. 22 No. 2 (2026): Vol. 22, No. 2, June 2026
Publisher : Universitas Syiah Kuala

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

Abstract

The Sun tracker consists of two energy types: proportional energy and operational energy. Proportional energy refers to the clean energy stored in the battery, while operational energy is used to support the mechanical performance of the sun tracking system. One of the main challenges in such systems is the high operational energy consumption, which can reduce the overall system efficiency. This study aims to develop a model to predict the proportional energy resulting from a dual-axis sun-tracker controller by implementing the Adaptive Neuro-Fuzzy Inference System (ANFIS). The method comprises analyzing the performance of the dual-axis sun tracker system and modeling the ANFIS. The performance of the dual-axis was investigated by observing the limitation of servo motor movement based on the difference in LDR sensor readings using threshold values of 50, 100, and 150. The ANFIS modeling was conducted by testing 24 configurations of membership functions to determine the most optimal structure. The results of the threshold value analysis show that a threshold value of 100 provides the best efficiency in generating proportional movement and energy. While modeling the ANFIS, the highest proportional energy was obtained using the Generalized Bell (belief) membership function type with a 9×9 configuration, yielding the lowest error value of 0.011692. Model validation using external test data showed an RMSE of 0.1128 and an MSE of 0.0127, indicating high predictive accuracy and good generalization capability. Implementing ANFIS control on the analyzed dual-axis PV system demonstrated an average increase in the proportional energy efficiency of 3%, from 90% to 93%. The findings indicate the effectiveness of ANFIS in enhancing the performance of the sun tracking system by adaptively adjusting to variations in light intensity.
Co-Authors - Firmansyah . Melinda . Roslidar . Zulfan Abdul Karim Abdullah Abdullah Abdullah Abdullah Abdullah Abdullah Achmad Fauzi Achmi Yuliani Adriman, Ramzi Ahmadiar, Ahmadiar Al Bahri Alfina Alfina Alfisyahrin Alfisyahrin, Alfisyahrin Andri Novandri ANISAH Arfah Salwa Ariandi, Teuku Asfianda, Muhammad Aufa Rafiki Aufa Rafiki Aulia Rahman Azizah, Nadiatul Bahri, Al Bakhtiar, Dandi Basir, Nurlida Basyir, M. Budi Arianto Chairullah, Chairullah Chakrit Chotamongsak Cholis Cholis Cut Ita Erliana Cut Mutia Dedy Ardana Deny Novrizal Devi Sara, Ira Dinda Damayanty Dirhamsyah, Dirhamsyah Dirhamsyah, Muhammad Doni Gunawan Edwar Iswardy Edy Fradinata Erdiwansyah Erdiwansyah Fachrurrazi Fachrurrazi Fachrurrazi Fachrurrazi Fardian Fardian Fathurrahman Fathurrahman Feri susilawati Firdaus Firdaus Fitri Arnia Fitri Arnia Fitriyani Fitriyani Fitriyani, Martunis Gunawan, Doni Gustin Yulian Nova Hari Anna Lastya Hasan, Hafidh Hasan, Vania Pratama Hasanuddin, Iskandar Hendra Muzawwir Hendri Farliza Herawati, Rama Heri Arya Supriyatna Husni Husni Ikram Muddin Ikramullah, Ikramullah Intan Permata Sari Irfan Mulia Irwandi Irwansyah Irwansyah Irwansyah Irwansyah Iskandar Hasanuddin Iskandar Hasanuddin Isyatur Raziah Jalil, Asri Muhammad Jamil, M. Jeperson Hutahaean Jeperson Hutahaean Kadarsah Kahlil Muchtar Kahlil, Kahlil Khadafi, M Khairul Munadi Khairul Munadi lestari, mulia Lubis, Rakhmad Syaputra Lulusi Lulusi M Ikhsan M. Dirhamsyah M. Dirhamsyah M. Dirhamsyah M. Ikhsan M. Jamil M. Khadafi Mahdi Syukri Mahmuddin Mahmuddin Mailizar Maimun Masri Ibrahim Masri Ibrahim Masri Masri Maulana, Edi Mirza Rahmat, Muhammad Mirza Tabrani Misbah Sulaiman Nura Moulina, Aisyah Rayhan Muhammad Aden Fahadi Muhammad Asfianda Muhammad Haries Muhammad Ikhsan Muhammad Ikhsan Muhammad Irhamsyah Muhammad Isya Muhammad Nur Hasan Muhammad Rizal Fachri Muhammad Thalhah Muliyadi Muliyadi Muliyadi Muliyadi Munawir Munawir Muslimsyah Muslimsyah Muslimsyah Muslimsyah Muslimsyah, Muslimsyah Nargaza, Juanda Nasaruddin Nasaruddin Nasaruddin Nasaruddin Nazilla, Izza Nikmal Maula Mirda Noprida Sari Novandri, Andri Nurhanif Nurhanif Nurmalia Zakaria Nuzula, Mukhsin Oktiana, Maulisa Oktiana, Maulisa Putra, T. Edisah Raden Mohamad Herdian Bhakti Rafiqa Shahnaz Noor Rahmah Johar Rahmat Sufri Raihan, Siti Raja Ariffin Raja Ghazilla Rakhmad Syafutra Lubis Ramdhan Halid Siregar Ramdhana, Rizka Rauzatul Jannah Raziah, Isyatur Rizal Munadi Rizal Munadi Roslidar RR. Ella Evrita Hestiandari Rudiansyah Putra Saddam Azmi Saddami, Khairun Safrizal Razali Safrizal Razali Safrizal Safrizal Safrizal Z.A Safwan Saifuddin Muhammad Jalil Saiful Husin Samsuddin Samsuddin Setiawati, Cut lilis Siti Rusdiana Sofyan, Sarwo Edhy Suhaeri Suhaeri, Suhaeri Surbakti, Muhammad Syukri Suriadi Suriadi Suriadi Suriadi Suriadi Suriadi Surya Surya Syahrial Syahrial, Syahrial Syahriza, Syahriza Syahrizal Syahrizal Syahrul Gazali Syahrul Gazali Syifaul Huzni Syukriyadin Syukriyadin T. Edisah Putra Tamlicha, Akram Tarmizi Tarmizi Tarmizi Tarmizi Taufik A. Gani Taufik A. Gani2 Taufiq A. Gani Taufiq A. Gani Taufiq A.Gani Taufiq Abdul Gani Teuku Budi Aulia Teuku Budi Aulia Teuku Yuliar Arif Teuku Yuliar Arif Udink Aulia Ulya Zikra W.K Wong W.K Wong Wahyu Sukmananda Wardana, Surya William Ramdhan Wong, W. K Yessica Siagian Yessica Siagian Yopi Ilhamsyah Yudha Nurdin Yunidar Yunidar Yunidar Yusria Darma Zakiah Zakiah Zichri, Zichri Zulfikar Zulfikar zulhelmi zulhelmi