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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Informatika Ekonomi Bisnis JUKI : Jurnal Komputer dan Informatika MEANS (Media Informasi Analisa dan Sistem) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknologi Informasi dan Komunikasi Vivabio : Jurnal Pengabdian Multidisiplin Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Komtekinfo Gotong Royong : Jurnal Pengabdian, Pemberdayaan Dan Penyuluhan Kepada Masyarakat (J-P3KM) Community: Jurnal Pengabdian Pada Masyarakat Jurnal Manajemen Dan Akuntansi Medan Jurnal Informatika Ekonomi Bisnis Jurnal Riset Sistem Informasi Global Science: Journal of Information Technology and Computer Science
Lawrence Adi Supriyono
Universitas Jakarta Internasional
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Sosialisasi Kegiatan Gerakan Masyarakat Hidup Sehat di Kota Tangerang Selatan Novita, Wanda; Adi Supriyono , Lawrence; Hartanto, Prasetyo; Ardolof Toar, Yandri; Putri Andini, Siwi; Damas Ario Wicaksono, Dading; Juniarto, Antonius; Ramitha Janira Cindi
Community : Jurnal Pengabdian Pada Masyarakat Vol. 3 No. 3 (2023): November : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/community.v3i3.411

Abstract

The Healthy living community movement as known as GERMAS is a systematic and planned program that symergized with several ministries and institution. This program aims to increase people’s willingness and awareness to adopt a clean and healthy lifestyle and behavior, such as : physical activity, consuming fruits, and vegetables, and also regularly check up. This program (community service) was carried out in kota tangerang selatan, attended by 200 participants from various elements of the surrounding community, and reached an agreement to commit to healthy living, one of which was signing a healhy living commitment.
PERANCANGAN OTOMASI ALAT INFUS BERBASIS FUZZY LOGIC Lawrence Adi Supriyono; Arief Marwanto; Suryani Alifah
Elkom: Jurnal Elektronika dan Komputer Vol. 15 No. 1 (2022): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v15i1.785

Abstract

Starting from the development of medical technology that is increasingly sophisticated and rapidly growing,researchers conduct medical research, namely about patient infusion handling services. In handling patient infusion, currently it is still manual which is carried out by nurses / medical personnel. Infusion handling services for patients still have shortcomings, namely the process of monitoring and replacing infusion fluids which are often late. If the problem is not treated quickly, it can lead to problems, namely the presence of air embolism in the blood vessels (the entry of foreign objects into the blood vessels, for example air). From that problem, the researchers made a new innovation in medical technology in handling infusions automatically and based on IoT. In this study, the smart online infusion device that has been made has good features and is very effective in handling infusions. This device has 3 main functions, namely: it can monitor the remaining infusion, it can change the infusion fluid automatically and it can indicate a blocked patient's infusion. This device already has a method for processing data with fuzzy logic. Media monitoring has been supported by a website that can be controlled remotely and in real time. Tests have been carried out and the effectiveness of the system is found to have an error rate of 0.2% - 0.7% and has an accuracy of 98%. Thus this tool can be used in terms of handling patient infusion automatically.
The Role of Key Opinion Leaders and Customer Experience on Purchase Decisions: The Mediating Effect of Brand Image Hartanto, Prasetyo; Supriyono, Lawrence Adi; Juniarto, Antonius
Jurnal Manajemen Dan Akuntansi Medan Vol. 8 No. 1 (2026): Jurnal Manajemen Dan Akuntansi Medan Januari 2026
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jumansi.v8i1.7859

Abstract

Background: Indonesia has become one of the countries with the highest number of mobile game downloads, with a total of 3.65 billion downloads in 2023, emerging as one of the largest mobile game markets in the world. This study examines the role of Key Opinion Leaders and Customer Experience in purchasing decisions for hero skins in the Mobile Legends game, with Brand image as a mediating variable. This study aims to understand the relationship between variables and their impact on consumer behavior. Research method: The type of research used in this study is quantitative exploratory. Exploratory research is a type of research that examines the causal relationship between the variables used in this study. The population used in this study are consumers who have purchased Mobile Legends skins within the past year. The sample size in this study is 253 respondents. Research results: The descriptive analysis results for each indicator in the Key Opinion Leader, Customer Experience, Brand Image, and Purchase Decision variables are valid and reliable. The hypothesis in this study is accepted and has a high enough influence and significance. Conclusion: Based on the results of this study, it was found that Key Opinion Leaders and Customer Experience have a positive influence, both directly and indirectly, on Purchase Decision through Brand Image. These findings indicate that an approach that combines digital strategies through KOL and improved Customer Experience can strengthen Brand Image and encourage consumer purchasing decisions. This reinforces the important role of digital marketing communication and customer experience in the context of the creative and digital industries.
Explainable End-to-End Autonomous Driving Using Vision-Based Deep Learning in Safety-Critical Scenarios Dani Sasmoko; Lawrence Adi Supriyono; Toni Wijanarko Adi Putra
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.185

Abstract

End-to-end autonomous driving has emerged as a promising paradigm in which deep neural networks directly map raw visual inputs to continuous control actions. Despite its effectiveness, this approach suffers from limited transparency, posing significant challenges for deployment in safety-critical driving scenarios. This study addresses the lack of interpretability in vision-based end-to-end autonomous driving systems and aims to analyze model decision-making behavior under critical conditions such as sharp steering maneuvers and abrupt control transitions. To this end, an explainable end-to-end autonomous driving framework is proposed, combining a convolutional neural network trained via imitation learning with gradient-based visual attribution techniques, including Grad-CAM. The model predicts continuous steering, throttle, and braking commands directly from front-facing camera images, while explainability mechanisms are applied to reveal input regions influencing each control decision. Model performance is evaluated using both prediction accuracy and safety-oriented behavioral metrics. Experimental results show that the proposed explainable model achieves lower control prediction errors compared to a baseline end-to-end CNN, reducing steering mean squared error from 0.034 to 0.031, throttle error from 0.021 to 0.019, and brake error from 0.018 to 0.016. Moreover, safety-oriented analysis indicates improved driving stability, with steering variance reduced from 0.087 to 0.072 and abrupt control changes decreased from 14.6 to 10.3 events. Visual explanations consistently highlight road surfaces and lane-related structures during complex maneuvers, indicating reliance on semantically meaningful cues. In conclusion, the results demonstrate that integrating explainability into end-to-end autonomous driving not only preserves predictive performance but also correlates with smoother and more stable driving behavior. This framework contributes to the development of transparent and trustworthy autonomous driving systems suitable for safety-critical applications
Regression-Based Prediction of Benzene Concentration Using PT08.S1 and PT08.S2 Gas Sensors Setyo Hartono; Ida Ernawati; Lawrence Supriyono
JUKI : Jurnal Komputer dan Informatika Vol. 8 No. 1 (2026): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v8i1.2414

Abstract

Air pollution, particularly benzene (C6H6), is a serious urban environmental issue with significant public health impacts. Benzene is a carcinogenic compound originating from motor vehicle emissions and industrial processes. This study aims to develop a prediction model for benzene concentration using PT08.S1 (CO) and PT08.S2 (NMHC) gas sensor data along with meteorological factors (temperature, relative humidity, absolute humidity). Data was obtained from the UCI Machine Learning Repository, totaling 9,357 samples collected from five metal oxide sensors in an urban area. Preprocessing was performed by removing -200 values representing missing data, resulting in 8,779 valid samples. The methods employed are Multiple Linear Regression and Random Forest Regressor. Evaluation results show that Random Forest outperforms with MAE of 0.0155, RMSE of 0.1311, and R² of 0.9997, while Linear Regression yields MAE of 0.9966, RMSE of 1.3864, and R² of 0.9666. Feature importance analysis reveals that absolute humidity (AH) is the most dominant predictor with a weight of 0.9049, followed by PT08.S2(NMHC) with 0.0276. This study demonstrates that gas sensor data can be reliably used for benzene estimation and Random Forest is more accurate than linear regression due to its ability to capture non-linear relationships among variables.
Arduino-Based Plasma Filtration System with Real-Time Monitoring for Smoke Removal in Enclosed Rooms Lawrence Adi Supriyono; Safira Fegi Nisrina; Mohammad Alfian Mudzakir; Dwi Setiawan; Jarot Dian Susatyono; Kartiko Eko Putranto
JUKI : Jurnal Komputer dan Informatika Vol. 8 No. 1 (2026): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v8i1.2417

Abstract

Indoor air quality degradation due to cigarette smoke exposure reduces oxygen levels and increases carbon monoxide (CO) concentration beyond the safe threshold of 9 ppm. This study develops an Arduino-based plasma filtration system with real-time monitoring for smoke removal in enclosed rooms. The system employs an MQ-7 sensor (CO detection), an MQ-135 sensor (smoke/CO₂ detection), an Arduino Uno R3 as the controller, an exhaust fan (suction capacity of 150 m³/h), and an ignition coil (15 kV output) to generate corona discharge (plasma). Testing was conducted in a 2 m × 3 m × 3 m room (volume 18 m³) using one cigarette as the smoke source. Data were recorded every 30 seconds over 10 minutes. Results show that the proposed system reduces CO concentration from an initial peak of 25 ppm to 8 ppm within 4 minutes, stabilizing at 3 ppm after 10 minutes, achieving an 88% reduction rate. In contrast, the conventional system (20 W electric fan) increased CO concentration to 47 ppm due to smoke dispersion through ventilation openings. The system's response time from smoke detection to filtration activation averages 2.5 seconds. Energy efficiency was recorded at 45 watts during active filtration. The system's success rate in maintaining CO levels below 9 ppm reached 100% after the first 4 minutes. This system proves to be effective, automatic, energy-efficient, and feasible for implementation in various public and private enclosed spaces.
Co-Authors A Andhika Agus Wibowo Agus Wibowo Amalia Shifa Aldila Andhika Andhika, A Andi Saryoko Andini, Siwi Putri Andy Febrian Wibowo Andy Febrian Wibowo Ardolof Toar, Yandri Arief Marwanto Arief Marwanto Aziz, Faruq Candra Supriadi Damas Ario Wicaksono, Dading Dani Sasmoko Daryati, Elisabeth Isti Dwi Setiawan Dwi Setiawan Dwi Setiawan Dwiyogo, Santos Winarso Erna, Maria Agustina Fegi Nisrina, Safira Gunawan, Stella Putri Habibie, Dedi Rahman Ida Ayu Putu Sri Widnyani Ida Ernawati Instianti Elyana, Instianti Jarot Dian Susatyono Juniarto, Antonius Kartiko Eko Putranto Kumala Sari, Cempaka Marcus Gunadi Miranti Andhita Scantya Mohammad Alfian Mudzakir Mohammad Alfian Mudzakir Nisrina, Safira Fegi Novita, Wanda Okafor, Ngozi Lilian Prasetyo Hartanto Previana, Cantika Nur Putranto, Kartiko Eko Putri Andini, Siwi Ramadhan, Aditya Rian Ramitha Janira Cindi Saeda, Ida Safira Fegi Nisrina Safira Fegi Nisrina Sarah Khairunnisa Budiyanto Setyo Hartono Sujatmiko, Devlin Wen Suryani Alifah Susanto, Chandra Toar, Yandri Ardolof B.L. Toni Wijanarko Adi Putra Wadu, Novita M Kana Yuli Fitrianto Zhafirah, Rafdah