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All Journal Jurnal UNITEK Jurnal Material dan Energi Indonesia HUMANITIS : Jurnal Humaniora, Sosial dan Bisnis Sinergi International Journal of Law Green Engineering: Journal of Engineering and Applied Science
TUTI SUSILAWATI
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Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Public Health Social Sciences

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KARAKTERISTIK NANOPARTIKEL ZnO: STUDI EFEK PELARUT PADA PROSES HIDROTHERMAL TOGAR SARAGI; YONATAN R PURBA; SATRIA AUFFA D U; MARIA OKTAVIANI; TUTI SUSILAWATI; RISDIANA RISDIANA; AYI BAHTIAR
Jurnal Material dan Energi Indonesia Vol 6, No 01 (2016)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.403 KB) | DOI: 10.24198/jmei.v6i01.9366

Abstract

Telah berhasil disintesis nanopartikel ZnO (ZnO-NP) pada pelarut yang berbeda dengan metode hidrotermal. Bahan dasar yang digunakan adalah zinc acetate dihydrate (Zn(CH3COO)2.2H2O, Merck, 99 %), sodium hydroxide (NaOH, Merck), dengan pelarut 2-propanol (Sigma Aldrich, 99%) dan ethanol. Karakterisasi optik, morfologi dan struktur kristal nanopartikel ZnO masing-masing dilakukan melalui pengukuran UV-Vis, TEM dan XRD. Dari hasil pengukuran UV-Vis diperoleh bahwa band gap ZnO-NP pada pelarut 2-propanol memiliki energi band gap yang lebih besar dibandingkan dengan sampel pada pelarut ethanol. Dari hasil pengukuran TEM diperoleh bahwa morfologi nanopartikel ZnO pada pelarut 2-propanol memiliki bentuk nano-rod (20 nm ´ 9 nm), sedangkan nanopartikel ZnO pada pelarut etanol lebih cenderung oval (26 nm ´ 15 nm). Karakteristik kristal nanopartikel ZnO pada kedua pelarut memiliki memiliki struktur kristal hexagonal wurtzite.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PEMILIHAN MODA TRANSPORTASI UDARA PADA MASYARAKAT KAB. KAPUAS HULU KALIMANTAN BARAT Tuti Susilawati
HUMANITIS: Jurnal Homaniora, Sosial dan Bisnis Vol. 2 No. 9 (2024): September
Publisher : ADISAM PUBLISHER

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Abstract

In a region, transportation is one of the keys to the economic activities of a community and the development of a region. In Kapuas Hulu Regency itself there are already various modes of transportation available with their respective advantages and disadvantages, one of which is air transportation. the use of air transportation in the Kapuas Hulu Regency area is very minimal because people prefer to use land transportation. This research uses a mix method approach and analysis using the AHP (Analytical Hierarchy Process) method. Based on the calculation results obtained CR value of 0.00769, it is considered consistent and means that the above calculations have been declared correct. From the four demographics, namely gender, age, occupation and income, it is concluded that men aged 19-24 years and working as students with an income of Rp. 1,000,000-Rp.3,000,000 are the dominant passengers at Pangsuma Putussibau Airport. The most influential factors are cost, second wake travel, third travel destination, fourth security and last comfort.
Restorative Justice in Domestic Violence Cases: Law Implementation and Challenges in Indonesia Tuti Susilawati; Setiadi, Edi; Darusman, Yoyon
Sinergi International Journal of Law Vol. 3 No. 3 (2025): August 2025
Publisher : Yayasan Sinergi Kawula Muda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61194/law.v3i3.797

Abstract

Despite Law Number 23 of 2004, the problem of domestic violence (KDRT) remains unsolved. The majority of victims of domestic violence are women, and the retributive method of punishment is believed to be less effective in protecting them. The purpose of this research is to examine how well the restorative justice policy in Indonesia complies with current legislation and how it helps victims of domestic violence regain their rights. This study explores the possibility of adopting restorative justice through the use of normative legal research methodologies that take a legislative approach and conduct a literature review. By facilitating healing for victims and offenders and facilitating reconciliation, the study found that restorative justice could be a mnore compassionate alternative. But there are a lot of problems with putting it into practice, including the fact that police officers don't comprehend it and that mediators need training. Thus, in order to guarantee that restorative justice is effectively implemented, training is necessary for mediators as well as community and law enforcement outreach.
Pemanfaatan Machine Learning untuk Peningkatan Akurasi Sistem Pendukung Keputusan Prediktif Ahmad Budi Trisnawan; Tuti Susilawati
JURNAL UNITEK Vol. 18 No. 2 (2025): Juli-Desember 2025
Publisher : Sekolah Tinggi Teknologi Dumai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52072/unitek.v18i2.1702

Abstract

The rapid development of information technology and the increasing availability of large-scale data have driven the need for decision-making systems that are more intelligent, faster, and more accurate. Conventional Decision Support Systems (DSS) generally rely on rule-based approaches or simple statistical analyses, which have limitations in recognizing complex patterns and are less adaptive to changes in data. Therefore, the integration of machine learning technology represents a strategic solution to enhance the predictive capability and decision quality produced by DSS. This study aims to analyze the utilization of machine learning algorithms in improving the accuracy of predictive decision support systems. The method employed is a comparative experimental approach involving three algorithms, namely Decision Tree, Random Forest, and Support Vector Machine. The dataset used consists of historical decision outcomes along with their determining variables derived from a case study. The research stages include data cleaning, normalization, training–testing set splitting, and evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of machine learning significantly improves DSS accuracy compared to conventional methods. Random Forest achieved the best performance with an accuracy of 91%, followed by Support Vector Machine at 87% and Decision Tree at 84%. In addition to improving accuracy, the integration of machine learning also enhances data processing efficiency and decision-making speed. These findings demonstrate that artificial intelligence–based DSS has great potential for application across various domains, such as business, healthcare, education, and government.
Development of a Digital Twin Based Smart Green Building Energy Management Model Integrating IoT Sensors and Predictive Sustainability Analytics Asro Asro; Solihin Solihin; John Chaidir; Febri Adi Prasetya; Tuti Susilawati; Muhamad Furqon; Bentar Priyopradono
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.287

Abstract

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.
Co-Authors Ahmad Budi Trisnawan Asro Asro Ayi Bahtiar Bentar Priyopradono Darusman, Yoyon Edi Setiadi Febri Adi Prasetya John Chaidir MARIA OKTAVIANI Muhamad Furqon RISDIANA RISDIANA SATRIA AUFFA D U Solihin Solihin Togar Saragi, Togar YONATAN R PURBA