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All Journal Jurnal Teknologi Informatika dan Komputer DEDIKASI : Jurnal Pengabdian Kepada Masyarakat Jurnal Multidisiplin Borobudur Green Engineering: Journal of Engineering and Applied Science Journal of Computer Science Advancements
soleman soleman
Scopus ID [57212621666] Universitas Borobudur
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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Pelatihan Desain Grafis Pada Kompetensi Keahlian Multimedia SMK Bintang Nusantara soleman soleman; Wahyu Widji Pamungkas; Ratih Widayanti Kosaman
Dedikasi : Jurnal Pengabdian Kepada Masyarakat Vol. 2 No. 1 (2023): Dedikasi : Jurnal Pengabdian Kepada Masyarakat
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah III DKI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53276/dedikasi.v2i1.56

Abstract

Multimedia merupakan salah satu teknologi dan informasi yang sangat cepat berkembang, dimasa pandemi SMK Bintang Nusantara kompetensi multimedia pembelajaran dilakukan secara online, kemampuan praktek siswa kelas XII tidak bisa diterima secara maksimal, sehingga perlunya pengembangan pengetahuan multimedia secara praktek langsung untuk menunjang bekal ilmu setelah lulus sekolah kejuruan. Metode dalam pelatihan ini dilakukan praktek langsung oleh dosen ke siswa.  Mengingat banyak bermunculan konten-konten kreatif di sosial media yang membutuhkan sentuhan pengembangan teknologi informasi dan multimedia yaitu membuat, mengolah, layout dan teknik secara manual maupun otomatis editing grafis dengan menggunakan Adobe Photosop CS6, Adobe Illustrator CS6, Adobe Acrobat 9.0 Pro, Pitstop dan Imposser Pro. Berdasarkan hasil pelatihan kemampuan peserta meningkat 5% - 23%, dan keberhasilan kegiatan berdasarkan kuesioner sebanyak 32 peserta dengan hasil : Baik=28,1% dan Sangat Baik=71,9%. Setelah pelaksanaan pelatihan ini dapat meningkatkan pengetahuan dan pengembangan diri secara kreatif, kompeten serta menjadikan siswa-siswi siap kerja dan wirausaha.
PENGENDALIAN BIAYA UNTUK PENGHEMATAN BIAYA PRODUKSI PUDDING DENGAN METODE VARIABEL COSTING DI PT NIRAMAS UTAMA (INACO) Soleman; Vivi Lusia; Mawan Arifin; Meilan Agustin
Jurnal Multidisiplin Borobudur Vol. 1 No. 1 (2023)
Publisher : Universitas Borobudur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37721/jmb.v1i1.1212

Abstract

The production costs at PT Niramas Utama (Inaco) are focused on the production costs of pudding, which include raw material costs, direct labor costs, and factory overhead costs. The increasing difference between the planned production costs and the actual costs indicates poor control over pudding production costs. To determine the magnitude of the difference and suitable methods for controlling production costs, this research is needed. The calculation of the Cost of Goods Manufactured for Pudding in 2017 is as follows: mango pudding: H2 - H1 = Rp. 5,000 - 2,512 = Rp. 2,488, passion fruit pudding: Rp. 5,000 - 2,422 = Rp. 2,578, and melon pudding: Rp. 4,800 - 2,849 = Rp. 1,951. The calculation of the Cost of Goods Manufactured for Pudding in 2019 is as follows: mango pudding: E H2 - H1 = Rp. 5,000 - 2,488 = Rp. 2,512, passion fruit pudding: Rp. 5,000 - 2,429 = Rp. 2,571. The company can achieve cost savings in the production of mango pudding amounting to Rp. 2,488, passion fruit pudding amounting to Rp. 2,578, and melon pudding amounting to Rp. 2,720. The results obtained from this research indicate that the variable costing method is capable of reducing costs for the cost of goods manufactured for pudding.
ARTIFICIAL INTELLIGENCE MODELS FOR PREDICTIVE ANALYTICS USING BIG DATA MINING TECHNIQUES Soleman Soleman; Ahmed Al Harthy; Mirza Ilhami
Journal of Computer Science Advancements Vol. 4 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i3.4104

Abstract

Rapid digital transformation has generated unprecedented volumes of heterogeneous data, creating significant opportunities for predictive analytics while simultaneously increasing challenges related to data quality, scalability, computational complexity, and decision reliability. Conventional predictive models frequently experience performance degradation when processing high-dimensional and continuously evolving Big Data environments. This study aimed to develop and evaluate an integrated Artificial Intelligence framework that combines advanced Big Data mining techniques with hybrid machine learning models to improve predictive accuracy, computational efficiency, and analytical robustness. Quantitative computational research was conducted using large-scale structured and semi-structured datasets processed through data preprocessing, feature engineering, dimensionality reduction, ensemble learning, deep learning, distributed computing, and hyperparameter optimization. Model performance was assessed using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, computational time, memory utilization, and scalability. Experimental results demonstrated that the proposed hybrid framework achieved 98.63% prediction accuracy, an AUC-ROC of 0.995, substantially reduced computational time, lower memory consumption, and superior scalability compared with conventional machine learning and deep learning approaches. Statistical analyses confirmed significant performance improvements across all principal evaluation metrics. Findings indicate that integrating intelligent data mining with Artificial Intelligence enhances predictive capability by optimizing the complete analytical pipeline rather than individual algorithms alone, providing a scalable, efficient, and reliable framework for predictive analytics across diverse Big Data application domains.
Evaluating the Impact of Distributed Solar-Battery Systems on Urban Electricity Resilience and Community Carbon Emissions Reduction Idi Jang Acik; Soleman; Syeda Azwa Asif
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 1 (2025): January: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i1.272

Abstract

This study evaluates the impact of distributed solar-battery systems on urban electricity resilience and community carbon emissions reduction. As urban areas continue to grow, the demand for electricity has placed considerable strain on traditional centralized grids, resulting in increased vulnerabilities. The integration of decentralized energy resources (DERs), particularly solar photovoltaic (PV) systems paired with battery energy storage systems (BESS), has emerged as a promising solution to enhance grid resilience, reduce carbon emissions, and support the transition to more sustainable energy systems. This research uses a simulation-based approach to model the integration of solar-battery systems into residential blocks, assessing their impact on grid reliability, downtime reduction, and the frequency of power outages. Additionally, the study estimates the reduction in carbon dioxide (CO₂) emissions achieved by shifting from fossil-fuel-based energy generation to renewable sources such as solar PV. The results demonstrate that solar-battery systems significantly improve electricity reliability by providing backup power during outages, while also reducing CO₂ emissions by decreasing reliance on conventional grids. The study also discusses the technical and financial challenges associated with the integration of these systems, such as energy storage capacity, system efficiency, and upfront installation costs. Policy recommendations emphasize the importance of government incentives, grid modernization, and long-term financial benefits to encourage the adoption of decentralized energy solutions. Finally, the study highlights areas for future research, including advanced storage technologies and the integration of electric vehicles with solar-battery systems to further enhance energy resilience and sustainability.
Evaluating the Impact of AI-Driven Decision Support Systems on Organizational Performance in the Digital Economy Soleman
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3240

Abstract

This study aims to evaluate the impact of AI-driven Decision Support Systems (AI-DSS) on organizational performance within the context of the digital economy. Specifically, the research examines how the adoption of AI-based decision-support technologies influences decision quality, operational efficiency, strategic agility, and overall organizational effectiveness. Employing a quantitative research design, the study collected primary data through a structured questionnaire distributed to managers and decision-makers across various industries that have implemented AI-DSS. A total of valid responses were analyzed using Structural Equation Modeling (SEM) with Partial Least Squares (PLS) to assess both direct and indirect relationships among variables. The data analysis focused on measuring the reliability and validity of constructs, hypothesis testing, and the explanatory power of AI-DSS in predicting organizational performance outcomes. The findings indicate that AI-driven decision support systems have a significant and positive effect on organizational performance, primarily through improvements in decision accuracy, speed, and data-driven strategic alignment. Moreover, the results reveal that organizational digital capability acts as a partial mediator, strengthening the relationship between AI-DSS adoption and performance outcomes. This study contributes to the growing body of literature on digital transformation by providing empirical evidence on the strategic value of AI-enabled decision support systems and offers practical implications for organizations seeking to enhance competitiveness in the digital economy.
Co-Authors Ahmed Al Harthy Idi Jang Acik Lusia, Vivi Mawan Arifin Meilan Agustin Mirza Ilhami Ratih Widayanti Kosaman Syeda Azwa Asif Wahyu Widji Pamungkas