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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pendidikan Teknologi dan Kejuruan Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal EECCIS Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal Komputasi Jurnal Sains dan Informatika JURNAL PENGABDI Jurnal Teknoinfo ILKOM Jurnal Ilmiah Jurnal Ilmiah Media Sisfo Jurnal Tekno Kompak JUTIS : Jurnal Teknik Informatika EKONOMI BISNIS Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Abadimas Gorontalo Journal of Electrical Engineering and Computer (JEECOM) JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Infrastruktur Teknologi Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Telematics and Information Technology (TELEFORTECH) Journal of Engineering and Information Technology for Community Service Dharma: Jurnal Pengabdian Masyarakat Electrician : Jurnal Rekayasa dan Teknologi Elektro Jurnal Media Borneo Jurnal Informatika Polinema (JIP) Jurnal Kecerdasan Buatan dan Teknologi Informasi BACA: Jurnal Dokumentasi dan Informasi Jurnal Rekayasa Perangkat Lunak Green Engineering: Journal of Engineering and Applied Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Mitra Jurnal Pengabdian Masyarakat Multidisiplin (MJPMM) Global Science: Journal of Information Technology and Computer Science Jurnal Komputasi Jurnal Elektronika dan Telekomunikasi
Agus Wantoro
Universitas Aisyah Pringsewu
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Integrating Semantic Computing and Predictive Analytics to Enhance Reliability and Scalability of Global Information Systems Agus Wantoro; Adhie Thyo Priandika; Tiwuk Widiastuti; Yulaikha Mar’atullatifah; Krisna Widi Nugraha; Dwi Utari Iswavigra
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.v1i4.196

Abstract

Global information systems (GIS) are essential for managing large scale data across industries such as healthcare, finance, and urban planning. As the volume and complexity of data continue to grow, there is an increasing need for systems that can handle these demands while maintaining reliability and scalability. This research explores the integration of semantic computing and predictive analytics as a solution to improve the performance of GIS. Semantic computing, through the use of ontologies and standardized data models, enhances data interoperability, allowing systems to interpret and exchange data meaningfully across diverse platforms. On the other hand, predictive analytics uses statistical methods and machine learning models to forecast system behavior and optimize resource allocation, ensuring systems remain adaptive under varying loads. By integrating these two methodologies, this study demonstrates how they can address key challenges in global information systems, such as fault tolerance, system adaptability, and real time decision making. The results show significant improvements in system reliability and scalability, as well as better performance under high data volumes and diverse user interactions. The integrated approach was tested in several use cases, including urban planning, healthcare, and supply chain management, with results indicating that systems utilizing both semantic computing and predictive analytics are more resilient, accurate, and efficient. This paper discusses the practical implications of this integration for global scale applications and suggests future research directions, including the incorporation of emerging technologies like blockchain and artificial intelligence to further enhance the capabilities of GIS.
Pelatihan dan Pendampingan: Anak Muda Melek Social Media Marketing Naufal Sinatria; Fauzan Fuadi; Muhammad Lathief Syaifussalam; Nur Aminudin; Dwi Feriyanto; Agus Wantoro; Andi Mulyono
Dharma: Jurnal Pengabdian Masyarakat Vol. 7 No. 1 (2026): Mei
Publisher : Universitas Pembangunan Nasional "Veteran" Yogyakarta

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Abstract

The aim of this marketing content creation training and mentoring is to increase students' understanding, creativity, and confidence in creating simple content, as a generation growing up in the digital era. The methods used include material delivery, interactive discussions, and direct practice. The results showed high student enthusiasm throughout the process, indicating that the learning approach aligned with their characteristics and needs. In addition to acquiring basic content creation skills, students also demonstrated positive developments in their expressive and collaborative abilities. These results demonstrate that hands-on learning is highly effective in developing students' digital competencies.
P, Peningkatan PENINGKATAN LITERASI DIGITAL ORANG TUA DAN GURU MELALUI PELATIHAN APLIKASI PENCEGAHAN DINI KEKERASAN SEKSUAL PADA ANAK Agus Wantoro; Nur Aminudin; Nuafal Sinatria
Dharma: Jurnal Pengabdian Masyarakat Vol. 7 No. 1 (2026): Mei
Publisher : Universitas Pembangunan Nasional "Veteran" Yogyakarta

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Abstract

Sexual violence against children is a serious problem that requires early prevention efforts through the active involvement of parents and teachers. The development of digital technology presents opportunities to utilize educational applications as a medium to support the prevention of sexual violence against children. However, low digital literacy becomes an obstacle in optimizing the use of these applications. This community service activity aims to improve the digital literacy of parents and teachers through training in the use of applications for the early prevention of sexual violence against children. The methods used include participatory and educational approaches through the delivery of materials, demonstrations, hands-on practice, and interactive discussions. The results of the activity showed an increase in participants' understanding of the importance of early prevention of sexual violence as well as an improvement in skills in utilizing digital applications as a medium for education and child protection. This training contributes to strengthening the role of parents and teachers in creating a safe and child-friendly environment. This activity is expected to serve as a model of sustainable technology-based service in supporting child protection efforts
Optimization of Machine Learning Algorithms in Breast Cancer Classification: A Performance Based Analysis Agus Wantoro; Arie Setya Putra; Ochi Marshella Febriani
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.17160

Abstract

Timely identification of breast cancer recurrence is closely associated with patient survival and the effectiveness of treatment. Inaccurate detection can contribute to greater disease severity, higher treatment costs, longer recovery, and reduced quality of care. For Machine Learning (ML)-based decision-support systems, two important challenges are the unequal distribution of medical-data classes and the large number of features, both of which may affect model accuracy and computational efficiency. This study evaluates an approach that combines feature selection with class-imbalance handling to improve breast cancer detection performance. Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Relief-F are used to rank features according to their weights, while the Synthetic Minority Over-Sampling Technique (SMOTE) is applied to improve representation of the underrepresented class. Seven ML classifiers, namely k-Nearest Neighbor (k-NN), Tree, Support Vector Machine (SVM), Naive Bayes, AdaBoost, Random Forest (RF), and Neural Network (NN), are tested and assessed using confusion-matrix-based accuracy, precision, recall, and computational time. The experimental results indicate that incorporating class-imbalance handling improves the predictive performance of the ML algorithms. Among the evaluated combinations, Information Gain with Random Forest (IG+RF) provides the optimal result in this case. These findings highlight the value of integrating class-balancing and feature-selection procedures when developing machine-learning systems for breast cancer detection.
Evaluasi Teknik Seleksi Fitur terhadap Performa Model Machine Learning pada Dataset Penyakit Sirosis Agus Wantoro; Ferly Ardhy; Dwi Yana Ayu Andini; Aviv Fitria Yulia; Dwi Feriyanto
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.3102

Abstract

Cirrhosis is a chronic disease of the liver with high levels of morbidity and mortality, so accurate and efficient early detection efforts are needed. The use of Machine Learning (ML) in the health sector is one solution to help the process of classifying and diagnosing diseases based on patient clinical data. However, using all the features in a dataset without selection can cause a decrease in model performance due to the presence of irrelevant or redundant features. Therefore, this study aims to evaluate the effect of feature selection techniques on the performance of ML models such as SVM, Tree, kNN and Random Forest on the cirrhosis dataset. The stages used in this research include the application of several ML models as classification models, as well as the application of various feature selection techniques such as Information Gain (IG), Gain Ratio (GR), and Gini Decrease (GD) and performance evaluation. Model evaluation is carried out by comparing classification results before and after feature selection using accuracy, precision and recall metrics. The research results show that the application of feature selection can significantly improve the performance of the cirrhosis classification model, while reducing the complexity of the model. In addition, the effectiveness of feature selection techniques varies depending on the ML algorithm used, so there is no one feature selection method that is always superior for all models. This research proves that feature selection is an important stage in ML-based medical data processing and can increase the accuracy and efficiency of cirrhosis diagnosis
Evaluation of Google Teachable Machine for Tuberculosis Screening Using Chest X-Ray Images Vira Meldimira; Agus Wantoro; Kraugusteeliana Kraugusteeliana; Zulkifli Zulkifli
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.17318

Abstract

Tuberculosis (TB) remains a leading cause of death from infectious diseases worldwide, with Indonesia ranking second. Conventional TB diagnosis, which relies on the manual interpretation of chest X-rays, faces challenges due to a shortage of radiologists, particularly in resource-limited healthcare settings. This study aimed to develop a TB image classification model using Google Teachable Machine a no-code machine learning platform to create an easily deployable screening tool that requires no programming expertise. The model was developed using a transfer learning approach based on the Mobile Net architecture, trained on a dataset of chest X-rays labeled as either TB-positive or normal, and evaluated for accuracy across multiple training epochs. The results showed that both training and testing accuracies converged toward 1.0 after the 10th epoch; the curves moved in parallel without significant performance discrepancies, indicating good model generalization capabilities. Optimal results were achieved with an epoch count of 60, a batch size of 64, and a learning rate of 0.003. These findings demonstrate that the ease of implementation offered by no-code platforms does not necessarily compromise classification performance, making this a potentially practical solution to support early TB detection. Further research is recommended to conduct external validation and assess sensitivity and specificity to confirm the model's suitability as a clinical pre-screening instrument.
Co-Authors ., Rusliyawati Adam Japal Ade Surahman Adi Sucipto, Adi Adit Nurmansyah Admi Syarif Agum Anantama Ahmad Jurnaidi Wahidin Andini, Dwi Yana Ayu Apri Candra Widyawati Apri Candra Widyawati Ari Sulistiawati Ari Sulistyawati Aria Dadi Wibisono Arie Setya Putra Arie Setya Putra Arry Verdian Arry Verdian Arry Verdian Aryani, Venty Aviv Fitria Yulia Ayu Andini, Dwi Yana Ayu Sangging, Putu Ristyaning Bintoro, Panji Catur Ariwibowo Damayanti Daniel Prasetyo Tarigan Dedi Darwis Deny Prasetyo Devi Utari Diasari, Itce Dikpride Despa Dikpride Despa Dimas Aminudin Saputra Dimas Farian Savero Dita Septasari Dwi Feriyanto Dwi Utari Iswavigra Dwi Yana Ayu Andini Ega Budiman Elin Mayoana Fitri Erliyan Redi Susanto Erliyan Redy Susanto Erliyan Susanto Fadly, Muhtad Fahlul Rizki Fahri Damarjati Ferly Ardhy Fernando, Yusra Galuh Eka Saputra Hadibrata, Exsa Hafizhah Harjiati Rahmandini Hari Soetanto Heni Sulistiyani Hironimus Edit Kristanto Ikna Awaliyani Imam Ahmad Imam Alkarim Jafar Fakhrurozi Jayawarsa, A.A. Ketut Jhonnry Frengky Bire Logo Keith Francis Ratumbuisang Khairun Nisa Kisworo Kisworo Kraugusteeliana Kraugusteeliana Krisna Widi Nugraha Kurnia Muludi Lutfy, Azza’zunda Choibar Lyla Putri Deviana Mardha Ariyani Masdiana Masdiana Mehta, Abhishek R Merriam Listiany Modeong Monica Efniasari Mufid Aden Muhamad Fitratullah Muhammad Lathief Syaifussalam Muhammad Zihad Prasetyo Mulyono, Andi Mutiara Bulan Maharani Nanda Putra Wicaksono Naufal Sinatria Nisa Berawi, Khairun Nuafal Sinatria Nur Aminudin Ochi Marshella Febriani Parjito Parjito Pasha, Donaya Permata Permata, Permata Permatan Priandika, Adhie Thyo Purnama, Citra Andini Putra Syahwal Alam Rachmat Setiabudi Redi Ari Saputra Redy Susanto, Erliyan Rohmah, Nurbaiti Rusliyawati Ryan Randy Suryono Sampurna Dadi Rizkiono Sanriomi Sintaro Saputra, Dani setiawan, very dwi Setiawansyah Setiawansyah Siska Narulita Sri Ratna Sulistiyanti suaidah suaidah Susanto, Erliyan Redy Susanto, Lilik Joko Sutyarso Sutyarso Sutyarso Sutyarso Suyahman Suyahman Syazili Mustofa Tahta Herdian Andika Tien Yulianti Tiwuk Widiastuti Trisnawan, Ahmad Budi Verdian, Arry Very Hendra Saputra Vira Meldimira Wahyu Caesarendra Wahyu Caesarendra Wamiliana Wamiliana Waqas Arshad, Muhammad Warsito . Widiastuti, Rosalina Yani Wildani Hakim Yana Ayu, Dwi Yodhi Yuniarthe YOHANA TRI UTAMI, YOHANA TRI Yudistira Yudistira Yulaikha Mar’atullatifah Yuri Rahmanto Zulkifli