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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Surya Masyarakat JSiI (Jurnal Sistem Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) ScientiCO : Computer Science and Informatics Journal Journal of Information Systems and Informatics Aiti: Jurnal Teknologi Informasi Jurnal Surya Masyarakat Djtechno: Jurnal Teknologi Informasi Jurnal Akuntansi Dan Bisnis Indonesia (JABISI) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Riptek SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal INFOTEL Tematik Jurnal Informatika: Jurnal Pengembangan IT KETIK : Jurnal Informatika Jurnal Ilmiah Informatika dan Komputer Signal and Image Processing Letters International Journal of Artificial Intelligence and Science Jurnal DIMASTIK
Aria Hendrawan, Aria
Universitas Semarang
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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A Hybrid Image Processing Approach for Real-Time Face Recognition in Attendance Monitoring Agho, Davit Cany; Hendrawan, Aria
International Journal of Artificial Intelligence and Science Vol. 2 No. 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2.i1.17

Abstract

In the era of digital transformation, institutions are increasingly adopting automation to enhance administrative efficiency, particularly in human resource management. At Tanggirejo Village Hall, a critically low employee attendance rate of 46.45% in January 2024 exposed the limitations of manual attendance systems, which are prone to errors and manipulation. This study proposes a face recognition-based attendance system utilizing OpenCV’s Haar Cascade Classifier for face detection and the Local Binary Pattern Histogram (LBPH) for face recognition. A total of 500 grayscale facial images from 10 employees were collected and processed to train and test the system. Evaluation using a Confusion Matrix revealed an accuracy of 72%, precision of 93%, and recall of 75%. While a 27% error rate was observed—primarily due to lighting inconsistencies and limited training data—the system performed reliably in real-time scenarios. The integration of these lightweight algorithms allows for fast and accurate identification, suitable for resource-constrained environments. This solution not only addresses the local attendance challenges but also presents a scalable, automated model that can be adopted by similar institutions seeking to improve productivity and operational transparency through real-time employee monitoring.
A Systematic Review of Deep Learning for Intelligent Transportation Systems with Analysis and Perspectives Hendrawan, Aria; Gernowo, Rahmat; Nurhayati, Oky Dwi; Dewi, Christine
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1085

Abstract

This study presents a systematic review of deep learning for intelligent transportation systems. Statistics are used to find the most cited articles, and the number of articles and quotes are used to find the most productive and influential authors, institutions, and countries or regions. Key topics and patterns of change are discovered using the authors’ keywords, and the most common issues and themes are revealed using flow maps and showing the corresponding trends. A co-occurrence keyword network is also developed to present the research landscape and hotspots in the field. The results explain how publications have changed over the past seven years. Researchers can use this study to have a deeper understanding of the current state and future trends in the role of deep learning in intelligent transportation systems.
Comparative Sentiment Analysis on Mobile JKN Application Using Logistic Regression with SMOTE Based Statistical Feature Selection Awaliyah, Rafika Farkhul; Hendrawan, Aria
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10520

Abstract

This study investigates public sentiment on the Mobile JKN application using Logistic Regression enhanced with SMOTE-based statistical feature selection. Unlike prior works that relied solely on conventional feature combinations such as TF-IDF or Word2Vec, this research performs a comparative evaluation of three statistical feature selection techniques: Recursive Feature Elimination (RFE), Chi-Square, and Mutual Information, under both TF-IDF and Word2Vec representations in a low-resource Indonesian language setting. The dataset consists of 2,382 user reviews from the Google Play Store, balanced using SMOTE to mitigate class imbalance. The best configuration, TF-IDF combined with Mutual Information, achieved an accuracy of 73.38% and an F1-score of 50%, indicating a moderate yet consistent performance. A confusion matrix-based error analysis revealed that most misclassifications occurred between neutral and negative classes due to semantic overlap. The relatively low F1-score highlights challenges in sentiment separability, while the superior performance of Mutual Information demonstrates its ability to capture discriminative linguistic features. The superior performance of Mutual Information is attributed to its ability to capture non-linear dependencies between features and sentiment labels, yielding richer discriminative information compared to Chi-Square or RFE. This research establishes a comparative methodological framework that integrates feature selection and data balancing techniques, providing interpretable sentiment classification insights for under-resourced language settings.
Speech-Driven Visitor Notification System Using Telegram Bot and Voice Activity Detection for Real-Time Retail Applications Setiaji, Aria; Hendrawan, Aria; Christioko, Bernadus Very; Huizen, Lenny Margaretta
International Journal of Artificial Intelligence and Science Vol. 2 No. 2 (2025): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2i2.44

Abstract

In the retail industry, fast and responsive service is essential for maintaining customer satisfaction and loyalty. A key challenge faced by store owners is delayed responses to customer arrivals, leading to dissatisfaction and potential lost sales. This project develops an automatic notification system using Speech-to-Text technology and a Telegram bot to detect voice keywords and send real-time notifications to store owners. The system was developed using the prototype methodology, allowing for iterative testing and refinement to ensure it met user needs and functional requirements. It integrates the Deepgram API for accurate speech transcription, the Telegram Bot API for notifications, and a web interface for managing keywords and monitoring system status. To enhance efficiency, a Voice Activity Detection (VAD) module was added, ensuring that only human speech is processed, thereby reducing unnecessary processing. Experimental results showed that the system achieved 100% accuracy in quiet environments and 80% in noisy conditions. The system's response time was also impressive, with an average time of 3.72 seconds in quiet conditions and 3.8 seconds in noisy environments. Word Error Rate (WER) and Character Error Rate (CER) evaluations indicated perfect accuracy in quiet conditions (WER 0%, CER 0%) and slight errors in noisy conditions (WER 13.33%, CER 12.5%). Overall, the system effectively improved service speed and responsiveness, offering store owners a valuable tool for enhancing customer experience in retail environments.
The Comparative Analysis Of Multi-Criteria Decision-Making Methods (MCDM) In Priorities Of Industrial Location Development Agusta Praba Ristadi Pinem; Aria Hendrawan; Nur Wakhidah
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1099

Abstract

The process of prioritizing the development of an industrial area's site is a matter that necessitates a mature approach. The establishment of an industrial region has significant social implications for the surrounding locality. However, it is also necessary to take into account the availability of variables that facilitate the functioning of such an industrial zone. The goal of the study "A Comparative Analysis of Multi-Criteria Decision Making Methods (MCDM) for Determining the Priority of Industrial Area Location Development" is to compare and contrast different MCDM methods in the context of deciding which industrial area locations should be developed first. A case study was undertaken, examining various possible industrial sites for future development. Multiple approaches, namely MOORA, WASPAS, ARAS, COPRAS, and AHP, are employed to ascertain the prioritization of industrial area development locations. This study presents a comparative analysis of each approach by using the Spearman Rank correlation and utilizing the factual data obtained from the Department of Capital Plantation and Integrated One Door Services (DPMPTSP). The external research is anticipated to involve a comprehensive review of the literature on the efficacy of Multiple Criteria Decision Making (MCDM) methods. This research has the potential to assist both governmental bodies and private entities in establishing priorities for the development of industrial areas, taking into account prevailing circumstances and conditions while also considering various significant factors and criteria.
Analysis of the Best Social Media Platforms for Promotion Using Machine Learning and RFE Feature Selection: A Comparative Study of Gradient Boosting, XGBoost, CNN, and SVR Putri, Maulina; Hendrawan, Aria
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12049

Abstract

This study aims to identify the most effective social media platforms for digital marketing. The use of social media for promotion continues to grow, yet many businesses still struggle to determine which platforms have the greatest impact. Therefore, this study compares the performance of various machine learning platforms to predict the best platform. The algorithms used are Gradient Boosting Regressor, XGBoost Regressor, Convolutional Neural Network (CNN), and Support Vector Regression (SVR) to estimate digital conversion potential based on user reviews, ad reach, and content trend patterns. A Knowledge Discovery in Databases (KDD) workflow is used to identify the most important key factors. This process includes data preprocessing, TF-IDF feature extraction, sentiment analysis, feature engineering, and feature elimination (RFE). The results showed that the CNN algorithm excelled in prediction, with the highest R² score of 0.74 and the lowest RMSE of 14.78. CNN predictions showed YouTube topping the list in terms of conversion potential, followed by Facebook and TikTok. These results highlight the higher promotional effectiveness of video-based platforms and the importance of machine learning in digital marketing decision-making. However, this study is limited by its reliance on static user review and ad reach data, which may not fully capture the dynamic changes of social media platforms.
Data-Driven Traffic for Infrastructure Planning: An LSTM Approach Using Indonesian Road-Vehicle Trends Aria Hendrawan; Nabilah Putri
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1516

Abstract

The rapid growth of motorized vehicles in Indonesia, unmatched by proportional expansion in road infrastructure, has intensified pressure on the national transportation system. This study examines the application of a Long Short-Term Memory (LSTM) model to analyze and forecast the national traffic load ratio, defined as the ratio of total motorized vehicles to total road length. Annual aggregate data from the Indonesian Central Bureau of Statistics (BPS) for the period 2016–2023 were used in the analysis. The results indicate that the model achieved a strong fit on the training data, with RMSE = 0.3652 and MAE = 0.3617, but performed substantially worse on the test data, with RMSE = 1.7585 and MAE = 1.7585. This discrepancy suggests overfitting, largely attributable to the extremely limited sample size. As such, the findings should be interpreted as exploratory rather than as evidence of reliable forecasting performance. Despite these limitations, the model projects a continued upward trend in national infrastructure pressure over the next five years. These findings provide an initial data-driven indication that transportation infrastructure demand in Indonesia is likely to intensify, while also underscoring the need for future research using larger datasets and baseline model comparisons before policy-level application can be justified.
Penerapan Teknologi Robotic Ship Fishery untuk Pemberdayaan Nelayan Desa Surodadi Kabupaten Demak Supari Supari; Aria Hendrawan; Purwanto Purwanto
Jurnal Surya Masyarakat Vol 8, No 2 (2026): Mei 2026
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.8.2.2026.251-258

Abstract

Traditional fishermen in Surodadi Village face productivity constraints due to their reliance on conventional fishing gear and the lack of post-harvest facilities. This community service program offers a solution through the planned implementation of Internet of Things (IoT)-based Robotic Ship Fishery (RSF) technology and the construction of cold storage infrastructure. The implementation method uses the Participatory Rural Appraisal (PRA) approach, encompassing socialization, technical and managerial training, and hardware engineering, and involves 20 fishermen as target partners. The results indicate that the RSF prototypes are currently undergoing functional laboratory testing, while the construction of the 100 kg capacity cold storage is in the physical development phase. This intervention has significantly improved human resource cognitive capacity, with posttest evaluations showing that 100% of partners (20 people) understand the function of navigation technology for fuel efficiency and 93.3% (19 people) understand cold chain management. It is concluded that the program has successfully established a foundation for human resource readiness and a paradigm shift toward modern, independent fishery businesses. However, the real economic impacts will require monitoring following full implementation of the technology.
Pemanfaatan Instagram untuk Media Promosi dalam Meningkatkan Ketahanan UMKM bagi Forum Gerakan Terintegrasi Masyarakat Koperasi dan Usaha Mikro (Gerai Kopimi) Lamper Lor Semarang Selatan Aria Hendrawan; Khoirudin Khoirudin; Vensy Vydia
Jurnal Surya Masyarakat Vol 6, No 1 (2023): November 2023
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.6.1.2023.26-30

Abstract

This dedication was motivated by a request from one of the GERAI KOPIMI FORUM (Gerakan Terintegrasi Masyarakat Koperasi Dan Usaha Mikro) located in Lamper Lor, South Semarang, so that Lamper Lor, UMKM South Semarang could reach a wider market.  With the COVID-19 pandemic, product marketing must be transformed using internet media, due to the limited space for product marketing. USM community service team through the Community Service program will provide assistance to the UMKM Forum Gerai Kopimi Semarang Selatan using social media to promote their products more broadly. Armed with information technology knowledge, especially in the field of marketing using social media and product shooting techniques owned by the service team, it is hoped that they will be able to expand the reach of marketing their products. This activity will be carried out in several stages, First, conducting field surveys related to partner problems, Second, designing the most appropriate methods for utilizing social media for product marketing and designing the most suitable packaging designs for products so that they can increase the value of partner products. Third, conduct training for partners to optimize social media in marketing products. Fourth, evaluate the results of the training. The positive response was given by almost all participants who wanted to use the Instagram platform as a medium for promoting products of UMKM and felt satisfied with the training provided, so they wanted more training.
Penerapan Teknologi Robotic Ship Fishery untuk Pemberdayaan Nelayan Desa Surodadi Kabupaten Demak Supari Supari; Aria Hendrawan; Purwanto Purwanto
Jurnal Surya Masyarakat Vol 8, No 2 (2026): Mei 2026
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.8.2.2026.251-258

Abstract

Traditional fishermen in Surodadi Village face productivity constraints due to their reliance on conventional fishing gear and the lack of post-harvest facilities. This community service program offers a solution through the planned implementation of Internet of Things (IoT)-based Robotic Ship Fishery (RSF) technology and the construction of cold storage infrastructure. The implementation method uses the Participatory Rural Appraisal (PRA) approach, encompassing socialization, technical and managerial training, and hardware engineering, and involves 20 fishermen as target partners. The results indicate that the RSF prototypes are currently undergoing functional laboratory testing, while the construction of the 100 kg capacity cold storage is in the physical development phase. This intervention has significantly improved human resource cognitive capacity, with posttest evaluations showing that 100% of partners (20 people) understand the function of navigation technology for fuel efficiency and 93.3% (19 people) understand cold chain management. It is concluded that the program has successfully established a foundation for human resource readiness and a paradigm shift toward modern, independent fishery businesses. However, the real economic impacts will require monitoring following full implementation of the technology.
Co-Authors Abhinaya Putra Adiuntoro, Alwan Agho, Davit Cany Agusta Praba Ristadi Pinem Agusta Praba Ristadi Pinem Agusta Praba Ristadi Pinem Aprih Santoso April Firman Daru Ardiani Ika Sulistyawati Aria Setiaji Aristo Kevin Ardyaneira Pratama Asmiatun, Siti Astrid Novita Putri, Astrid Atmoko Nugroho Awaliyah, Rafika Farkhul Basworo Ardi Pramono Charis Maulana Cholil, Saifur Rohman Christine Dewi Christioko, B.Very Davit Cany Agho Dimas Dwi Budiarjo Febrian Wahyu Christanto Hasna Rizqia Khairunnisa Henny Indriyawati Jihan Kusumawardhani Jovita Wayan Ramadhani Khoiriyah, Rofiatul Khoirudin Khoirudin Lenny Margaretta Huizen Luqman Hakim Maulida, Innayah Maulifa Sekar Arum Nabilah Putri Nur Wakhidah Oky Dwi Nurhayati Pradana, Muhamamd Surya Jati Prind Triajeng Pungkasanti, Prind Triajeng Pulung Nurtantio Andono Purwanto Purwanto Putri, Maulina Rahmat Gernowo Rizky Haryanto Putro Saputri, Anita Nur Widdia Satria Dava Riansa Sekar Sari, Mega Setiaji, Aria Soiful Hadi Supari Supari, Supari Susanto, Susanto Toti Kresna Wardana Vensy Vydia Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit Winarti, Titin