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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri Jurnal Bina Praja Bappenas Working Papers Jurnal Teknik Informatika (JUTIF) Buletin Poltanesa Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Lokomotif Abdimas: Jurnal Pengabdian kepada Masyarakat BACA: Jurnal Dokumentasi dan Informasi
Rianto, Yan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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WASTEWISE: AI-POWERED WASTE EDUCATION FOR ELEMENTARY STUDENTS USING YOLOV8 AND ESP32-CAM Aldi, Kenny; Rianto, Yan
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6414

Abstract

The growing volume of global waste poses a significant challenge for effective waste management, particularly in developing countries where awareness and practices around waste sorting remain limited. This study aims to enhance elementary school students' understanding and efficiency in sorting organic and inorganic waste using an interactive, AI-powered educational tool. The proposed system, WasteWise, integrates YOLOv8 for real-time object detection and ESP32-CAM for capturing waste images. A pre-test and post-test experimental design was conducted to assess students’ performance before and after using the system. The results showed a notable improvement in sorting accuracy, increasing from 60% with manual sorting to 90% using the WasteWise system, alongside reduced sorting time. These findings highlight the system's potential not only as an automated waste classification tool but also as a cost-effective and engaging platform for promoting environmental awareness and digital literacy among young learners.
Quality Analysis of Low-Code No-Code Application Development Using ISO/IEC 25010 Standard: A Systematic Literature Review Hanggoro, Dimas Banu Dwi; Kurniawati, Laela; Rianto, Yan
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1633

Abstract

The acceleration of digital transformation has increased the demand for fast and efficient software development. This condition has led to the emergence of Low-Code/No-Code (LC/NC) approaches, which allow users with limited technical expertise to build applications through visual interfaces and pre-built components. This study aims to analyze the quality of Low-Code No-Code (LCNC) application development based on the ISO/IEC 25010 standard through a Systematic Literature Review (SLR) method, supported by bibliometric visualization using VOSviewer software. The reviewed articles were published between 2020 and 2024. The visualization results indicate that keywords such as context, development, and system are the primary focus in the study of LCNC application quality. Furthermore, the findings of Meira et al. (2020) demonstrate that the Analytical Hierarchy Process (AHP) method, when combined with the ISO/IEC 25010 standard, is effective in evaluating and comparing software system quality within an industrial context. This study concludes that a standards-based evaluation approach, complemented by bibliometric visualization, can provide valuable insights for decision-making processes related to the development and implementation of LCNC applications.
The Role of Deep Learning in Cancer Detection: A Systematic Review of Architectures, Datasets, and Clinical Applicability Abdurrahman, Muhammad Farhan; Rianto, Yan; Hamzah, Nasir; Firmansyah, Muhammad; Prawira, Nurul Adi; Nugraha, Thomas Fajar
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4748

Abstract

Early cancer detection continues to be a significant challenge in clinical practice due to limitation of conventional diagnostic technique that often takes time and error prone. This systematic review evaluates the efficacy of deep learning (DL) architecture and datasets to improve cancer detection and diagnosis. We performed a structural analysis on 40 high-impact research paper published in Q1 journals between 2014 and 2025, considering DL model performance, datasets, and clinical relevance. Results indicate that fundamental architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) consistently report high diagnostic accuracy (>90%) on radiology- and histopathology-based imaging datasets. Conversely, DL performance on non-imaging clinical data, including electronic medical records (EMDs), is more varied. Evaluation metrics such as AUC and DICE shows the trade-off between classification precision and segmentation accuracy. Despite their potential, DL models have significant limitations in terms of generalization, interpretability, and integration within real-world clinical workflows. This review highlights the need for standardized evaluation, implementation of ethical models, and multi-modal data fusion to facilitate wider and more equitable clinical uptake of DL in cancer diagnostics.
Ensembled Voting Techniques for Advanced Breast Cancer Prediction Septiani, Riska Kurnia; Rianto, Yan
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13004

Abstract

Breast cancer is the most common type of cancer affecting women worldwide, with a significant increase in incidence rates each year. Information and Communication Technology (ICT) has made substantial contributions to the medical field, particularly through the use of Big Data and machine learning algorithms to enhance diagnostic accuracy and healthcare efficiency. This research aims to assess the performance of five breast cancer classification algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), k-Nearest Neighbors (k-NN), Logistic Regression, and Ensembled Voting, using the Breast Cancer Wisconsin (Diagnostic) dataset. The study findings indicate that all models achieved high levels of accuracy, precision, recall, and F1-Score, with Ensembled Voting reaching the highest accuracy of 98.57%. This study confirms that machine learning algorithms, particularly Ensembled Voting, can be relied upon to improve breast cancer diagnosis accuracy, thereby significantly contributing to better healthcare outcomes.
Performance Evaluation of Multiple Deep Learning Models for Wine Quality Prediction Fabiyanto, Dedik; Rianto, Yan
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13007

Abstract

Research utilizing a dataset from the UCI repository evaluated the predictive accuracy of nine machine learning models for wine quality. The models employed include Logistic Regression, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM), Random Forest, XGBoost, LightGBM, CatBoost, and Gradient Boosting. The dataset comprises 1,599 samples with 12 chemical parameters. Data preprocessing, including oversampling, normalization, standardization, and seeding, was performed to enhance model performance.The study's findings indicate that the models with the highest accuracy values were LightGBM (87.80%), CatBoost (86.60%), and Random Forest (85.70%). A voting classifier combining these three models achieved an accuracy of 87.29%. Further analysis using a confusion matrix demonstrated that this combined model effectively predicts the "Good" and "Not Good" classes.In conclusion, the combination of LightGBM, CatBoost, and Random Forest models proves to be an effective approach for predicting wine quality based on chemical parameters, with an accuracy value of 87.29%.
Implementing Sukuk Financing in Indonesia: Policy Actions to Improve and Support Development Research and Development Infrastructure (RDI) Putera, Prakoso Bhairawa; Widianingsih, Ida; Rianto, Yan; Ningrum, Sinta; Suryanto, Suryanto
Jurnal Bina Praja Vol 14 No 3 (2022)
Publisher : Research and Development Agency Ministry of Home Affairs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21787/jbp.14.2022.555-570

Abstract

The Indonesian government realizes that developing alternative instruments for financing the state budget needs to continue—one of them with financing instruments based on sharia principles. This study examines the response of the Indonesian government in policy actions to seek financing for research and development infrastructure through Sukuk financing. This study uses a qualitative method with a narrative approach. This study uses three data collection techniques: regulatory survey, secondary data research, and Expert Elicitation with Experts' Judgments. Meanwhile, the data analysis technique uses content analysis, stakeholder analysis matrix, 're-analysis,' and digital discourse analysis. The results of this research reveal that the policy actions taken by the Indonesian government are through 1) regulatory in the form of policies related to the Sukuk financing policy in Indonesia, by issuing one regulation in the form of a law, one rule in the form of a Government Regulation of the Republic of Indonesia, and four rules at the level of ministerial regulations. On the other hand, the results of the mapping of stakeholders show that there are four clusters of actors, namely decision makers, project initiators, support partners, and civil society - media. 2) applicative action in the form of budget allocation related to infrastructure financing Sukuk in the research and development sector, during 2018-2022, financed 41 projects from 10 ministries/agencies. In the end, this research provides recommendations; namely, it is necessary to diversify the types of infrastructure that can be financed from Sukuk financing so that they follow the needs of Ministries/Agencies and Local Governments.
Pelatihan Transformasi Digital Organisasi Melalui Implementasi AI Tanpa Coding Pada Remaja Islam Al Hikmah (RISMAH) Pratama Putra, Zico; Parningotan Manik , Lindung; Gata , Windu; Rianto, Yan; Kurniawati, Laela
LOKOMOTIF ABDIMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 2 (2024)
Publisher : LPPM UIN Sulthan Thaha Saifuddin Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30631/lokomotifabdimas.v3i2.2744

Abstract

Pada era modern saat ini teknologi berkembang dengan sangat pesat khususnya dalam bidang komputer yang telah didukung dengan teknologi artificial intelligence. Artificial intelligence sebuah studi tentang bagaimana membuat komputer dapat melakukan hal-hal yang pada saat ini dapat dilakukan lebih baik dari apa yang telah dilakukan oleh manusia. Dalam pembuatan aplikasi AI, saat ini pengguna atau developer tidak perlu lagi menuliskan kode. Dalam rangka melaksanakan kegiatan Tri Dharma Perguruan Tinggi yaitu Pengabdian kepada Masyarakat, Fakultas Teknologi Informasi Universitas Nusa Mandiri akan menyelenggarakan pelatihan dengan tema “Pelatihan Transformasi Digital Organisasi melalui Implementasi AI Tanpa Coding Pada Remaja Islam Al Hikmah (RISMAH)”. Kegiatan ini bertujuan untuk meningkatkan wawasan dan kemahiran peserta untuk membuat aplikasi artificial intelligence dengan mudah tanpa menggunakan coding, pelaksanaan kegiatan ini dilakukan dengan 3 tahapan yaitu persiapan, pelaksanaan yang terakhir evaluasi dan monitorin dengan hasil yang dicapai dari kegiatan ini adalah adalah peserta memiliki keterampilan untuk membuat aplikasi artificial intelligence dengan mudah tanpa menggunakan coding.
Segmentasi Lemak Visceral dan Kanker Paru-paru Menggunakan Deep Learning Abdullah, Ifnu Abdul Aziz; Sadikin, Rifki; Rianto, Yan; Priansyah, Dedi
Poltanesa Vol 23 No 2 (2022): Desember 2022
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v23i2.1354

Abstract

Lemak Visceral memberikan dampak yang sangat besar terhadap beberapa penyakit dan salah satunya Kanker paru-paru penyebab utama kematian, Kondisi ini memberikan pengaruh besar dalam dunia kesehatan terhadap permasalahan Lemak visceral dan kanker paru-paru Computer vision menghembalangkan penelitian dari permasalahan yang besar ini computer vision ingin melihat keakuratan penelitian, dengan dataset yang telah di tentukan dengan ini peneliti menggunakan metode deep learning dan di dukung dengan metode Segmentasi-CNN, Segmentation & VFI Calculation. Metode tersebut mampu menampilkan gambar yang tersegmentasi dengan detail dari mengubah warna satu dimensi dan dua dimensi sehingga akan memberikan keakuratan segmentasi, lemak yang telah tersegmentasi daerah gambar lemak dan garis tepinya pada Lemak visceral dan kanker paru-paru memisahkan lemak dan kanker dengan jumlah pixel putih lemak lemah dengan ration area akurat dan Memiliki pixel yang tinggi dan tepat dalam pemrosesan pixel.
Co-Authors Abdullah, Ifnu Abdul Aziz Abdurrahman, Muhammad Farhan Aldi, Kenny Antonius Yadi Kuntoro Cipta Endyana Dedi Priansyah Emma Rochima Fabiyanto, Dedik Fahlapi, Riza Gata , Windu Hanggoro, Dimas Banu Dwi Ida Widianingsih Laela Kurniawati, Laela Lindung Parningotan Manik Marcelly, Frizca Fellicita Muhammad Firmansyah, Muhammad Nasir Hamzah Nova, Lusindah Nugraha, Thomas Fajar Parningotan Manik , Lindung Prakoso Bhairawa Putera Pratama Putra, Zico Prawira, Nurul Adi Rika Wulandari Sadikin, Rifki Septiani, Riska Kurnia Sinta Ningrum Sunendar, Nendi Supriyadi, Muhammad Suryanto Suryanto