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All Journal Elkom: Jurnal Elektronika dan Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) JITK (Jurnal Ilmu Pengetahuan 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 Informatika Ekonomi Bisnis Indonesian Journal of Innovation Science and Knowledge Jurnal Riset Sistem Informasi Jurnal Informatika Dan Tekonologi Komputer Global Science: Journal of Information Technology and Computer Science Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Lawrence Adi Supriyono
Universitas Jakarta Internasional, Indonesia
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice 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.
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
EVALUATING LOGISTIC REGRESSION, SVM, KNN, AND ENSEMBLE MODELS FOR ACCURATE HEART DISEASE RISK PREDICTION Amalia Shifa Aldila; Lawrence Supriyono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.6738

Abstract

Cardiovascular disease remains the most significant contributor to global mortality, highlighting the importance of early and precise risk assessment within preventive healthcare frameworks. Alongside the rapid growth of clinical data availability, machine learning approaches have increasingly been adopted to assist medical decision-making, particularly for interpreting complex and high-dimensional health information. This research investigates the predictive capability of six supervised machine learning models in determining the likelihood of cardiovascular disease incidence: Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosting. The Cleveland Heart Disease dataset from the UCI Machine Learning Repository served as the study's foundation. It includes 303 patient samples with a total of 76 recorded attributes. From this dataset, 14 clinically significant variables frequently reported in previous studies were selected for analysis. Considering the relatively small dataset size and the possibility of redundant or low-impact features, a feature selection approach was implemented to improve model robustness, minimize overfitting, and enhance interpretability. The data preparation process involved cleaning, normalization, feature selection, and division into datasets for testing and training. Metrics like accuracy, precision, recall, and F1-score were used to evaluate the model. The results of the experiment show that Random Forest and Logistic Regression models produced the highest predictive performance, followed by k-Nearest Neighbours and Support Vector Machine. These results indicate that supervised machine learning techniques, when supported by appropriate feature selection methods, are effective as decision-support tools for the early detection of cardiovascular disease.
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.
Integrating artificial intelligence into accounting systems: a qualitative study on user experiences and challenges Andhika Andhika; Lawrence Adi Supriyono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26409

Abstract

This research explores the integration of artificial intelligence (AI) in accounting systems, focusing on user experiences and challenges faced by accountants and financial professionals. Using qualitative methods, in-depth interviews with diverse accounting professionals reveal key themes: optimism mixed with skepticism about AI’s potential, concerns over algorithm transparency, and trust issues due to the “black box” nature of AI systems. Participants highlight inadequate training programs, which hinder effective AI use and fuel resistance to adoption. The study also discusses the impact of AI on job roles, emphasizing a shift towards strategic thinking and advisory functions while routine tasks are automated. Implementation challenges include system compatibility, data integration issues, and significant resource investments, compounded by organizational resistance and lack of executive support. The findings stress the need for transparent AI algorithms, comprehensive training programs, and managed job role transitions to maximize AI benefits. This research provides insights into real-world user experiences, offering a roadmap for organizations to support effective AI integration in accounting, leading to improved performance, job satisfaction, and acceptance of AI technologies.
Sistem Monitoring Suhu, Kelembaban dan Kandungan Nutrisi Budidaya Tanaman Sawi Caisim Hidroponik Berbasis IoT Lawrence Adi Supriyono; Andy Febrian Wibowo
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 3 No. 1 (2023): Maret: Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v3i1.2035

Abstract

Seiring dengan perkembangan zaman khususnya di Indonesia mulai kehilangan sedikit demi sedikit lahan sebagai media tanam. Salah satu cara untuk mengatasi masalah tersebut adalah dengan bercocok tanam dengan media hidroponik. Salah satu sayuran yang menggunakan media tanam hidroponik adalah sayuran sawi caisim. Permasalahan yang dihadapi petani hidroponik sawi caisim adalah penyediaan nutrisi yang cukup bagi tanaman. Tingkat kepekatan nutrisi yang dibutuhkan tanaman sawi caisim adalah 840-1400 PPM, apabila tanaman kekurangan unsur hara maka menyebabkan tanaman berkurang dalam melakukan penyerapan air dan ion nutrisi pada akar tanaman. Selanjutnya sawi dapat tumbuh dengan baik pada suhu rata-rata 15-30ᵒC dan apabila suhu terlalu tinggi dapat menyebabkan tanaman mudah layu atau bahkan mati. Berdasarkan permasalahan yang telah diuraikan di atas, maka penulis membuat “Sistem Monitoring Suhu, Kelembaban dan Kandungan Nutrisi Budidaya Tanaman Sawi Caisim Hidroponik Berbasis IoT". Penelitian ini bertujuan untuk mengembangkan sistem yang dapat memantau suhu dan kelembaban dengan menggunakan sensor DHT11, kandungan nutrisi menggunakan sensor TDS Meter dan mikrokontroller Wemos D1 R1. Data pemantauan sensor akan ditampilkan menggunakan android dengan memanfaatkan teknologi Internet of Things.
The Impact of Supercomputer Frontier for Scientific Research Shandy Afrian Mashuri; Salwa Afrina Fadly; Seeman Sohail Butt; Daniel Joseph Nyanda; Miranti Andhita Scantya; Amalia Shifa Aldila; Lawrence Adi Supriyono
Indonesian Journal of Innovation Science and Knowledge Vol. 3 No. 3 (2026): IJISK 2026
Publisher : Fakultas Pendidikan Ilmu Keguruan, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijisk.v3i3.316

Abstract

This research paper will examine the powerful machine created by the Oak Ridge National Laboratory (ORNL), the Frontier supercomputer, also known as the world’s first exascale supercomputer. Frontier is a key tool for studying climate change in the US, as it’s the first supercomputer capable of performing over a quintillion calculations per second. This breakthrough enables more detailed and accurate study and prediction of climate patterns, helping scientists develop detailed models of weather patterns and climate systems. These models show how climate change is affecting the country, including stronger storms, rising sea levels, and other extreme weather events. Through its powerful computing capabilities, the Frontier supercomputer enables advanced climate modelling. It supports the SCREAM programme, which aims to improve the accuracy of climate predictions by simulating global weather patterns at unprecedented scales. This paper will explore how Frontier works, what makes it so powerful, and how it helps the United States better understand and fight climate change. It will also discuss the advanced technology behind Frontier and how it solves problems that were once too complex to handle.
Sistem Cerdas Pemantauan Kualitas Air dan Pembersihan Otomatis Berbasis IoT dengan Turbidity Sensor Menggunakan GPRS SIM900A Berbasis Android. Mohammad Alfian Mudzakir; Lawrence Adi Supriyono; Agus Subandono
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 2 (2025): Juli : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i2.6061

Abstract

Water quality is a crucial factor in supporting life, especially in water storage systems used for domestic and industrial purposes. A common issue that arises is the degradation of water quality due to increased turbidity, which can pose health risks and reduce water utilization efficiency. This study aims to design and develop an intelligent Internet of Things (IoT)-based system capable of real-time water quality monitoring and automatic cleaning of water storage tanks when turbidity exceeds a predefined threshold. The system utilizes a turbidity sensor to detect water clarity levels, a SIM900A GPRS module for data communication, and an Android application for real-time data visualization to the user. Monitoring and cleaning processes are controlled by an Arduino microcontroller integrated with an SMS-based alert system and a mobile user interface. Test results show that the system accurately detects turbidity levels, transmits data in real-time to the Android app, and automatically activates the cleaning mechanism when necessary. Additionally, the system can send early warning notifications via text messages. Therefore, this system offers an effective and efficient solution for maintaining water quality in a sustainable manner and contributes to the application of IoT technologies in smart water resource management.
Co-Authors A Andhika Agus Subandono Agus Wibowo Agus Wibowo Amalia Shifa Aldila Amalia Shifa Aldila Andhika Andhika Andhika Andhika, A Andi Saryoko Andini, Siwi Putri Andy Febrian Wibowo Andy Febrian Wibowo Ardolof Toar, Yandri Arief Marwanto Arief Marwanto Aziz, Faruq Basuki Rahmat Candra Supriadi Damas Ario Wicaksono, Dading Dani Sasmoko Daniel Joseph Nyanda 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 Ida Ernawati Instianti Elyana, Instianti Jarot Dian Susatyono Juniarto, Antonius Kartiko Eko Putranto Kumala Sari, Cempaka Marcus Gunadi Miranti Andhita Scantya Miranti Andhita Scantya Mohammad Alfian Mudzakir 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 Safira Fegi Nisrina Salwa Afrina Fadly Sarah Khairunnisa Budiyanto Seeman Sohail Butt Setyo Hartono Setyo Hartono Shandy Afrian Mashuri Sujatmiko, Devlin Wen Suryani Alifah Susanto, Chandra Toar, Yandri Ardolof B.L. Toni Wijanarko Adi Putra Trisno Sanyoto Wadu, Novita M Kana Yuli Fitrianto Zhafirah, Rafdah