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All Journal Elkom: Jurnal Elektronika dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Informatika Ekonomi Bisnis 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 Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) 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 Control & Systems Engineering 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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Predictive Modeling of Covid-19 Spread with Machine Learning: A Focus on Decision Tree Accuracy Aldila, Amalia Shifa; Supriyono, Lawrence Adi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 2 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Virus Sars CoV-2 merupakan penyebab utama wabah Covid-19 yang pertama kali terdeteksi di Wuhan, Tiongkok, pada Desember 2019 dan dengan cepat menyebar ke seluruh dunia. Penelitian ini bertujuan untuk memprediksi jumlah kasus terkonfirmasi dan tingkat keparahan wabah dalam rentang 23 Januari hingga 10 Juni 2020. Data yang digunakan adalah dataset terbuka dari Kaggle berjudul "Global Forecasting Covid-19 Week 5”. Untuk menghasilkan prediksi yang optimal, penelitian ini menguji berbagai algoritma pembelajaran mesin dan pembelajaran mendalam, yaitu Random Forest, XGBoost, Polynomial Regression, Decision Tree, ANN, dan LSTM. Kinerja model dinilai melalui skor dan Root Mean Square Error (RMSE). Hasil terbaik dicapai oleh model Decision Tree dengan skor sebesar 0,97 dan RMSE 52,57, menunjukkan akurasi tinggi dalam prediksi kasus Covid-19. Penelitian ini mengindikasikan bahwa model Decision Tree unggul dalam prediksi Covid-19 dibandingkan algoritma lain dan menawarkan potensi signifikan untuk pengembangan strategi mitigasi yang lebih efektif di masa mendatang.
PkM Penerapan Panel Surya Untuk Penghematan Daya Operasional Agar Masyarakat Mendapatkan Harga Lebih Terjangkau Di Bandarjo, Ungaran Barat Fegi Nisrina, Safira; Kumala Sari, Cempaka; Adi Supriyono, Lawrence; Hartanto, Prasetyo
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 5 No. 2 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v5i2.3263

Abstract

Kelompok Pelaksanaan kegiatan Pengabdian kepada Masyarakat Universitas Widya Husada mengembangkan pemanfaatan panel surya untuk penghematan daya operasional terhadap peternakan ikan di Bandarjo, Ungaran Barat, di Bandarjo, Ungaran Barat masih mengandalkan suplai energi listrik dari PLN untuk pengairan di kolam. Olehitu, karena kelompok Pengabdian Kepada Masyarakat Universitas Widya Husada telah membuat dan memasang sistem panel surya untuk menggerakkan pompa sirkulasi air, yang mana panel surya ini sebagai energi alternatif pengganti listrik dari PLN. Pada kegiatan pengabdian ini menggunakan Panel surya berkapasits 200WP dengan baterai kapasitas 12,6Volt, 24000maH yang digunakan untuk mensuplai pompa air 12 Volt 22 Watt. Selain itu produk yang dihasilkan juga dilengkapi dengan sistem pengatur penyalaan relay berbasis Arduino Uno. SIstem Arduino Uno ini digunakan untuk menangkap sensor suhu. Hasil rata-rata dari pengukuran daya panel surya setiap 30 menit sekali yaitu 24,48Watt per hari, kondisi tersebut saat dilakukan pengujian saat cuaca tidak cerah. Namun dalam hal tersebut masih dapat berubah-ubah untuk mendapatkan daya yang maksimal bergantung pada kondisi cuacanya terutama saat matahari terik. Pada kegatan pengabdia ini telah menghasilkan kesimpulan menghemat biaya listrik jika dibandingkan dengan penggunaan energi listrik yang berasal dari PLN, hal ini sangat menguntungkan untuk petani ikan diantara keuntungannya adalah mengurangi tagihan listrik secara signifikan, sehingga harga jual ikan dapat lebih terjangkau, meningkatkan produksi ikan, dapat meningkatkan taraf hidup kesejahteraan para petani ikan dan memberikan inspirasi, ilmu dan contoh nyata bagi wilayah-wilayah disekitarnya dalam memanfaatkan teknologi energi terbarukan untuk mendukung kegiatan perekonomian.
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 Sasmoko, Dani; Adi Supriyono, Lawrence; Wijanarko Adi Putra, Toni
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 Shifa Aldila, Amalia; Supriyono, Lawrence
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.
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 Dwiyogo, Santos Winarso Erna, Maria Agustina Fegi Nisrina, Safira Gunawan, Stella Putri Habibie, Dedi Rahman Ida Ayu Putu Sri Widnyani Instianti Elyana, Instianti Juniarto, Antonius Kumala Sari, Cempaka Marcus Gunadi Miranti Andhita Scantya 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 Sarah Khairunnisa Budiyanto Shifa Aldila, Amalia Sujatmiko, Devlin Wen Suryani Alifah Susanto, Chandra Toar, Yandri Ardolof B.L. Toni Wijanarko Adi Putra Wadu, Novita M Kana Yuli Fitrianto Zhafirah, Rafdah