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All Journal DE JURE Elkom: Jurnal Elektronika dan Komputer YUDISIA : Jurnal Pemikiran Hukum dan Hukum Islam Kodifikasia: Jurnal Penelitian Islam Episteme: Jurnal Pengembangan Ilmu Keislaman An-Nisbah: Jurnal Ekonomi Syariah Asy-Syir'ah: Jurnal Ilmu Syari'ah dan Hukum JURISDICTIE Jurnal Hukum dan Syariah International Journal of Law Reconstruction Ahkam: Jurnal Hukum Islam Jurnal Ilmiah Hukum LEGALITY INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi As-Salam: Jurnal Studi Hukum Islam & Pendidikan Al-Daulah : Jurnal Hukum dan Perundangan Islam Halu Oleo Law Review Mesin Syariah: Jurnal Hukum dan Pemikiran Jurnal Jatiswara Justisia Ekonomika El-Barka: Journal of Islamic Economics and Business SERAMBI: Jurnal Ekonomi Manajemen dan Bisnis Islam Sakina: Journal of Family Studies Al-Syakhsiyyah : Journal of Law and Family Studies Al-Manhaj: Jurnal Hukum dan Pranata Sosial Islam Istinbath : Jurnal Hukum MEANS (Media Informasi Analisa dan Sistem) Prophetic Law Review Tsarwah : Jurnal Ekonomi dan Bisnis Islam Teknik: Jurnal Ilmu Teknik dan Informatika Invest Journal of Sharia & Economic Law NEGREI: Academic Journal of Law and Governance Jurnal Hukum Ekonomi Islam (JHEI) INFORMATIKA Mazahib: Jurnal Pemikiran Hukum Islam Akademika : Jurnal Pemikiran Islam Legal Protection for the Partnership Agreement Parties Al-Istinbath: Jurnal Hukum Islam Kartika: Jurnal Studi Keislaman MAHAKIM: Journal of Islamic Family Law Journal of Technology Informatics and Engineering Constitution Journal El Dusturie: Jurnal Hukum dan Perundang-undangan Jurnal Ilmiah Sistem Informasi JIMEB: Jurnal Ilmiah Manajemen, Ekonomi dan Bisnis An-Nisbah: Jurnal Ekonomi Syariah Jurnal Ilmiah Manajemen dan Akuntansi Iqtishadia: Jurnal Ekonomi dan Perbankan Syariah Abstrak: Jurnal Kajian Ilmu Seni, Media dan Desain Ignite: Journal Islamic Global Network for Information Technology and Entrepreneurship Tapis : Jurnal Penelitian Ilmiah Journal of Engineering, Electrical and Informatics Jurnal Riset dan Inovasi Manajemen
Lukman Santoso
IAIN Ponorogo
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Journal : journal of technology informatics and engineering

 1 
The Efficient Approach in Peer-to-Peer Systems to Achieve High Efficiency Lukman Santoso; Marcus Gunadi Wibawa; Muhamad Syarifudin; Priyadi Priyadi; Titi Christiana
Journal of Technology Informatics and Engineering Vol 1 No 2 (2022): August: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i2.137

Abstract

Peer-to-peer systems nowadays are widely used because of the scalability and high reliability. File replication and consistency maintenance are widely used techniques to achieve high system performance. These techniques are connected to each other. The connection of these techniques is consistency maintenance is needed in file replication to keep the consistency between a file and the replicas. Traditional file replication and consistency maintenance methods need a high cost. The usage of IRM (Integrated file Replication and Consistency Maintenance inP2P systems) which will achieve high efficiency at a significantly lower cost can be used to solve this problem. IRM reduces redundant file replicas, consistency maintenance overhead, and unnecessary file updates.
The Efficient Approach in Peer-to-Peer Systems to Achieve High Efficiency Lukman Santoso; Marcus Gunadi Wibawa; Muhamad Syarifudin; Priyadi Priyadi; Titi Christiana
Journal of Technology Informatics and Engineering Vol. 1 No. 2 (2022): August: Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v1i2.137

Abstract

Peer-to-peer systems nowadays are widely used because of the scalability and high reliability. File replication and consistency maintenance are widely used techniques to achieve high system performance. These techniques are connected to each other. The connection of these techniques is consistency maintenance is needed in file replication to keep the consistency between a file and the replicas. Traditional file replication and consistency maintenance methods need a high cost. The usage of IRM (Integrated file Replication and Consistency Maintenance inP2P systems) which will achieve high efficiency at a significantly lower cost can be used to solve this problem. IRM reduces redundant file replicas, consistency maintenance overhead, and unnecessary file updates.
Optimizing AI Performance in Industry: A Hybrid Computing Architecture Approach Based on Big Data Dewi, Maya Utami; Santoso, Lukman; Santoso, Agustinus Budi
Journal of Technology Informatics and Engineering Vol. 3 No. 3 (2024): December (Special Issue: Big Data Analytics) | JTIE: Journal of Technology Info
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i3.201

Abstract

In the era of Industry 4.0, integrating artificial intelligence (AI) and big data analytics in the industrial sector demands high-performance computing infrastructure to handle increasingly complex and voluminous datasets. This study investigates the optimization of AI performance by implementing a hybrid computing architecture, integrating CPUs, GPUs, FPGAs, and edge-cloud computing. The research aims to enhance processing speed, model accuracy, and energy efficiency, addressing the limitations of standalone computing systems. A quantitative methodology was employed, using over 1 TB of industrial data from IoT sensors and production logs. A hybrid architecture was implemented with dynamic workload scheduling to distribute tasks efficiently across computational components. Performance metrics included processing time, model accuracy, energy consumption, and cost analysis. Results demonstrated that hybrid architectures significantly improved performance: the CPU-GPU combination reduced processing times to 650 ms, increased model accuracy to 88.3%, and achieved an energy consumption of 2.1 kWh. Meanwhile, the CPU-FPGA configuration, while slightly less accurate (87.5%), proved more energy-efficient at 1.3 kWh. AI models developed using hybrid systems exhibited superior predictive accuracy, with Mean Squared Error (MSE) as low as 0.0248 and R² of 0.91. The study concludes that hybrid computing architecture is a transformative approach for optimizing AI systems in industrial applications, balancing speed, accuracy, and energy efficiency. These findings provide actionable insights for industries aiming to leverage advanced computing technologies for improved operational efficiency and sustainability. Future research should focus on advanced workload scheduling and cost-effectiveness strategies to maximize the potential of hybrid systems.
Comparative Study of Feature Engineering Techniques for Predictive Data Analytics Santoso, Lukman; Priyadi
Journal of Technology Informatics and Engineering Vol. 3 No. 2 (2024): Agustus : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i2.225

Abstract

In the rapidly evolving era of big data, predictive analytics has become a crucial approach in supporting data-driven decision-making across various sectors such as finance, healthcare, and marketing. However, the effectiveness of predictive models is highly dependent on the quality of features utilized in model training. This study aims to evaluate and compare various feature engineering techniques to enhance the accuracy of predictive models based on Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms. The research employs a quantitative experimental approach by applying different feature engineering techniques, including SHAP-based feature importance, Principal Component Analysis (PCA), and categorical variable encoding. The evaluation results indicate that the implementation of SHAP-based feature importance yields the best outcomes, with a Mean Squared Error (MSE) of 0.150 and a Root Mean Squared Error (RMSE) of 0.387 in the XGBoost model. These values outperform those without feature engineering, which recorded an MSE of 0.230 and an RMSE of 0.479. The combination of PCA and encoding techniques also shows a significant performance improvement with an MSE of 0.160 and an RMSE of 0.400. The XGBoost algorithm consistently demonstrates superior performance compared to RF across various testing scenarios. The contribution of this study lies in its recommendation of appropriate feature engineering techniques to improve the predictive quality of Machine Learning (ML)  models. This research provides insights for researchers and practitioners in developing more effective feature engineering strategies and opens opportunities for exploring advanced techniques in more complex data domains.
Co-Authors Abdul Munim Abdul Rachman Ade Iriani Agus Priyadi Agustinus Budi Santoso Ahmad Ashifudin Aqham Ahsan, Fuad Ahsan, Ikhwan Fuad Ainiah, Zumrotul Ainiah, Zumrotul Aisyah Aisyah Al Haqiqi, Muhamad Jihad Amaliyah, Arij Aminuddin, Lutfi Hadi Arifin, Muh Fauzi Arifin, Muhammad Fauzi Arliana Agustin Faridhatul Shima Ati Suryana, Dinda Auliahaq, Dafa Agta Auliasari, Auliasari Bustanul Ariin Bustanul Arifin Cahyani, Yutisa Damayanti, Elok Danny Manongga Dardiri, Masyhudan Dawam Abror Fathoni, Muhammad Anwar Fathuri, Hani Zain Haris, Rahmat Hasan, Ikhwan Fuad Hidayati, Niswatul Ikhwan Fuad Ahsan Indra Djodikusumo Izati, Hanim Kurnia Juni Amanullah Kanifah, Anisa Nur Kotimah, Erwin Kusnul Kuncoro, Wreda Agung Kurniawan, Yusuf Fendi Kusnul Ciptanila Yuni K Laili, Rika Nur Layyin Mahfiana Lestari, Devi Indah LIA NOVIANA Ma'mun, Sukron Marcus Gunadi Wibawa Masrukin Masrukin Maya Utami Dewi, Maya Utami Miatun, Sumbu Latim Miftahul Huda Miftaqurrohman, Miftaqurrohman Moh Muthohir Muhamad Syarifudin Muhammad Fauzi Arifin Mun'im, Abdul Musfiroh, Anita Nur Efendi, Mohamad Nurwijayanti, Maya Paulina Kinanti Eka Praningtyas Priyadi , Agus Priyadi Priyadi Priyadi Priyadi Raden Mohamad Herdian Bhakti Rejeki , Rara Sri Artati Reni Veliyanti Risma Nurhapsari Rizani, Rahmadini Mutiara Rofi'ah, Khusniati Rohmah Maulidia Roi’fah, Nisfathur Sa'adah, Fiki Nur Sagita, Insharie Amarylis Salim, Annisa Jannatus Sari, Yeny Nurita Sary, Desy Puspita Soleh Hasan Wahid, Soleh Hasan Sukamto, Ika Sumiyarsi Suryani Suryani Titi Christiana Tobing, Wesly Tumbur ML Tri Cahyani, Yutisa Tri Wahyu Surya Lestari Tri Wahyu Surya Lestari, Tri Wahyu Surya Wesly Tumbur ML Tobing Wibi Ardi Alvianto Yudi Ahmad Faisal Yuliantika, Nias Yutisa Tri Cahyani Zahro, Diana