Article Per Year (5 Year)
p-Index From 2021 - 2026
5.754
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Jurnal Buana Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan ELINVO (Electronics, Informatics, and Vocational Education) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang JurTI (JURNAL TEKNOLOGI INFORMASI) MIND (Multimedia Artificial Intelligent Networking Database) Journal Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal ICT : Information Communication & Technology E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi IJAIT (International Journal of Applied Information Technology) Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Innovation in Research of Informatics (INNOVATICS) Informatics and Digital Expert (INDEX) Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Jurnal Prajaiswara Digital Transformation Technology (Digitech) Smart Techno (Smart Technology, Informatic and Technopreneurship) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Jurnal Ilmiah Informatika dan Komputer Jurnal Sistem Informasi dan Teknologi Informasi JPPkM: Jurnal Pengabdian dan Pemberdayaan kepada Masyarakat
Ruuhwan
Teknik Informatika, Universitas Perjuangan Tasikmalaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

Published : 55 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 55 Documents
Search

 2   3   4   5   6 
IMPACT OF ROI SEGMENTATION METHODS ON ANTHRACNOSE DETECTION IN PAPAYA LEAVES USING RESNET-50 Shinta Siti Sundari; Ruuhwan; Evi Dewi Sri Mulyani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7445

Abstract

Early detection of anthracnose disease on papaya leaves is important for mitigating crop yield losses, but manual methods are inefficient and prone to subjectivity; this study evaluates the effect of region of interest (ROI) extraction strategies on deep learning-based classification performance. The objective of this study is to compare four classification pipelines: ResNet-50 without segmentation, (M1) ExG+Otsu + ResNet-50, (M2) U-Net + ResNet-50, and (M3) RCNN + ResNet-50, in detecting anthracnose on papaya leaf images. The methods included the use of the public BDPapayaLeaf dataset, pre-processing and augmentation, and evaluation using stratified K-fold cross-validation with evaluation metrics including precision, recall, and F1. The results show that the semantic segmentation-based pipeline, U-Net + ResNet-50 (M2), provides the best performance with an F1-score (macro/weighted) ≈ 0.961 and precision–recall balance in both classes; M0 showed the highest recall for the anthracnose class (≈0.99) while M1 based on color thresholding provided the lowest performance due to sensitivity to lighting variations; M3 (RCNN) was in the middle. The findings recommend the use of segmentation and classification pipelines for field applications, noting the need for dataset expansion and inference optimization for deployment.
Implementation of the Apriori Algorithm on Outdoor Equipment Rental Transaction Data Based on Clustering Using the K-Means Algorithm Randi Rizal; Ruuhwan Ruuhwan; Muhammad Al Husaini; Dede Rizal Nursamsi; Meto Rizki M
IJAIT (International Journal of Applied Information Technology) Vol 08 No 02 (November 2024)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v8i2.6367

Abstract

Outdoor equipment rental services play a critical role in meeting climbers’ needs prior to expeditions. Sustaining business continuity in this sector requires effective marketing strategies, particularly given the increasing market competition. This study employs data mining techniques to analyze rental transaction data and identify patterns that support strategic decision-making. Specifically, clustering is performed using the K-Means algorithm to group transactions with similar attributes, followed by association rule mining using the Apriori algorithm within each cluster. A dataset comprising 1,276 valid transactions was processed, resulting in three clusters containing 324, 264, and 688 records, respectively, with an accuracy of 0.998. Apriori analysis generated 13 association rules in Cluster 0 and 2 rules in Cluster 1, while no rules met the minimum support and confidence thresholds in Cluster 2 or the overall dataset. These findings demonstrate that clustering prior to association rule mining can uncover meaningful patterns that are not evident in aggregated data. Such insights can inform targeted marketing strategies, including recommendations for item combinations frequently rented together. Future research may integrate alternative algorithms such as ECLAT or FP-Growth and explore framework-based systems to enhance scalability and precision in data-driven decision-making.
Implementation of the Naive Bayes Algorithm for Public Sentiment Analysis Toward Power Plant Development Rasyid, Vithan; Ruuhwan
Smart Techno (Smart Technology, Informatics and Technopreneurship) Article in Press
Publisher : Primakara University

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

Abstract

This study aims to analyze public sentiment toward power plant development using data collected from the social media platform Twitter. The dataset consisted of 2,493 tweets obtained through a crawling process using keywords related to power plant development, such as “PLTS” and “PLTS Cirata.” The preprocessing stage included cleaning, case folding, stopword removal, tokenization, and stemming using the Sastrawi library to produce more structured textual data. The dataset was then divided into a training set comprising 85% of the data (2,120 tweets) and a testing set comprising 15% (373 tweets). The classification process was performed using the Multinomial Naive Bayes algorithm, as this method is well suited for text data represented by word-frequency features extracted through CountVectorizer. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score as performance metrics. The results showed that the model achieved an accuracy of 75%, indicating that the Multinomial Naive Bayes method is reasonably effective for text-based sentiment classification. Furthermore, the findings revealed that public opinion regarding power plant development is influenced by perceptions of renewable energy benefits, environmental impacts, and government policies.
Sentiment Analysis of Cooking Oil Prices in Indonesia Using the Long Short-Term Memory Method Evi Dewi Sri Mulyani; Cepi Rahmat Hidayat; Teuku Mufizar; Shinta Siti Sundari; Dede Syahrul Anwar; Ruuhwan; Jamal Ma'ruf; M. Akbar Kasyfurrahman
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 1 (2025): May 2025
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i1.73972

Abstract

The policy that was intensively discussed from early January to March 2022 was related to the setting and lifting of the Highest Retail Price for cooking oil, which was proven by the busy news on television, print media and social media. Many responses or speculations arise as a result of this policy. The importance of research on public speculation or sentiment analysis is to create a system model and find out how the public responds to government policy after the Highest Retail Price for cooking oil is determined and revoked, which ranges from February 4 to March 31 2022, as a benchmark and material. government considerations in making policy. The data that was collected in the period after the Maximum Retail Price (MRP) was set amounted to 904 datasets, and after the repeal of the MRP, it amounted to 874. Research can function as a basis for completing important information to support public policy decisions. The data is trained to obtain an optimal model and can predict sentiment with the Long Short-Term Memory (LSTM) model. To get the best model, random parameter testing was carried out using 80% of the training data and 20% of the validation dataset. The test results in fairly good accuracy with the softmax activation function, with an accuracy of 82.34%.
Pemanfaatan Aplikasi Kecerdasan Artifisial Untuk Kegiatan Pembelajaran Di Pesantren Ma’rifa Cigalontang Kabupaten Tasikmalaya Evi Dewi Sri Mulyani; Ariel Ivan Al-Hakim; Fauzan M. Iqbal; Teguh Ikhlas Ramadhan; Agus Supriatman; Dede Syahrul Anwar; Rudi Hartono; Missi Hikmatyar; Cepi Rahmat Hidayat; Aso Sudiarjo; Ruuhwan; Shinta Siti Sundari; Teuku Mufizar; Widia Nurindriyanti
JPPkM: Jurnal Pengabdian dan Pemberdayaan kepada Masyarakat Vol. 2 No. 2 (2026): JPPkM:Juli
Publisher : Yayasan Pemimpin Inovasi Science

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

Abstract

The rapid advancement of Large Language Model (LLM)-based Artificial Intelligence (AI) has created new opportunities to improve the quality and efficiency of teaching and learning processes. However, the adoption of this technology remains constrained by limited AI literacy and prompt engineering skills among educators, particularly in Islamic boarding schools (pesantren). This community service program aimed to enhance teachers' knowledge and skills in utilizing LLM applications through training on prompt engineering techniques based on the C.R.E.A.T.E. and S.T.A.R. frameworks. The program was conducted at Ma'rifa Islamic Boarding School in Cigalontang, Tasikmalaya Regency, involving 32 participants. The implementation methods included lectures, AI application demonstrations, hands-on prompt development practice, and evaluations through pre-tests, post-tests, practical assessments, and participant satisfaction questionnaires. The evaluation results showed that the participants' average score increased from 58.4 in the pre-test to 85.7 in the post-test, representing an improvement of 46.7%. In the practical assessment, 82% of participants successfully developed prompts using the C.R.E.A.T.E. and S.T.A.R. techniques with good to excellent performance. Furthermore, the participant satisfaction survey yielded an average score of 4.58 out of 5, indicating that the training materials were considered relevant, easy to understand, and beneficial for supporting teaching and learning activities. Overall, the program successfully improved participants' AI literacy, understanding of LLM technology, and prompt engineering competencies. Therefore, similar training programs should be implemented continuously to support digital transformation and foster innovation in teaching and learning within Islamic boarding school environments.
Co-Authors Adherda, Diva Tifanny Agung Nugraha Bahri, Rangga Agus Supriatman Alam Rahmatulloh Andriani, Aan Anwar, Muhammad Saepul Ariel Ivan Al-Hakim Aysicom Paraguay, Muhammad Azzahra, Salma Natasya Cepi Rahmat Hidayat Cepi Rahmat Hidayat Chandra, Deni Dadan Subagja Dede Rizal Nursamsi Dede Syahrul Anwar Dinda M. Khatami Evi Dewi Sri Mulyani Evi Dewi Sri Mulyani Fadilah, Nizar fahmizher, muhammad Fahmy Fauzi Alvianda Fauzan M. Iqbal Gea Aristi, Gea Gumelar, Lasmi Lasmini Hartono, Rudi Herdi Muhammad Syaban Hikmatyar, Missi Imam Riadi Imam Riadi Indra Karyana Indrawan, Telly Ismu Arief, Mizar Jamal Ma'ruf Kania Aulia Khatami, Dinda M. Khoirurrizal, Moch Fikri linggar nursinggah M. Akbar Kasyfurrahman Maulana Hidayat, Cecep Meto Rizki M Moch Fikri Khoirurrizal Moh Salimi Muhammad Al Husaini muhammad fahmizher Mustofa, Prawira Zamal Nurani, Riga Zahara nursinggah, linggar Randi Rizal Rasyid, Vithan Ridwan, Ahmad Fauzi Rifki Maulana Insan Risma Julistiana Rizal Kurniawan Rizal, Randi rudi hartono Septian Chandra Shinta Siti Sundari Shinta Siti Sundari Sudiarjo, Aso Suherman, Nurisya Rahma Sukma, Rexy Khoerul Syaban, Herdi Muhammad Teguh Ikhlas Ramadhan Teuku Mufizar Tiara Komala Sutra, Melanda Widia Nurindriyanti Yudi prayudi Yusuf Sumaryana Zainnuri, Hasan Zery Hendra