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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Informatika dan Teknik Elektro Terapan InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JIKO (Jurnal Informatika dan Komputer) Information System for Educators and Professionals : Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Teknik Informatika UNIKA Santo Thomas JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Jurnal ICT : Information Communication & Technology Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Informatika dan Rekayasa Perangkat Lunak JATI (Jurnal Mahasiswa Teknik Informatika) MEANS (Media Informasi Analisa dan Sistem) Instal : Jurnal Komputer Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Informasi interaktif : jurnal informatika dan teknologi informasi
Ade Rizki Rinaldi
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Other

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Peningkatan Kompetensi Digital Guru Madrasah Melalui Pelatihan Aplikasi Perkantoran Berbasis Microsoft Office Ade Irma Purnama Sari; Ade Rizki Rinaldi; Ahmad Syaeful Ma’arief; Alfi Rizqi Falih
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 03 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

This community service program aims to enhance the digital competence of teachers at Madrasah Tsanawiyah (MTs) in Cirebon City through office application training. Preliminary surveys revealed that the majority of educators lacked optimal skills in using office software such as Microsoft Word, Excel, PowerPoint, and Google Workspace, which negatively impacted administrative efficiency and the quality of digital-based learning. This issue prompted the implementation of an intensive training program using a learning by doing approach, featuring hands-on practice, case studies, and simulations based on real educational needs. The training was conducted in several structured modules, covering basic application use, academic data management, and the development of digital teaching materials. Participants completed assignments and were evaluated through pre-tests and post-tests. Results showed a significant improvement in digital skills, with 80% of participants demonstrating technical progress and 92% expressing satisfaction and finding the training highly beneficial. Furthermore, several madrasahs began adopting digital-based administrative systems, indicating a tangible impact on digital transformation within educational institutions. Program outcomes include digital training modules, ready-to-use administrative document templates, and improved teacher capacity in data management and the development of engaging learning content. This initiative also fostered greater awareness of the importance of digital literacy among educators and opened up opportunities for sustained professional development through follow-up mentoring and advanced training sessions. Overall, the program successfully contributed to improving educational quality and teacher efficiency in the digital era.
Peningkatan Literasi Digital UMKM melalui Pelatihan Pemasaran Digital dan E-Commerce di Kota Cirebon Ade Rizki Rinaldi; Agus Bahtiar; Aziz Chairul Imam; Bima Anurgraha Djajnegara
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 03 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

This community service program aims to improve digital literacy among Micro, Small, and Medium Enterprises (MSMEs) through training in online marketing and e-commerce. The background of this program stems from the low level of understanding and skills among MSME actors in utilizing digital technology to support business marketing and management online. The problems faced by MSME partners include limited use of social media, suboptimal management of online marketplaces, and a lack of digital branding strategies to expand market reach. To address these issues, the program offers a solution in the form of digital literacy training, which includes online marketing strategies, social media optimization, marketplace account management, and digital-based transaction management. The implementation method of this program involves several stages: identifying partner needs, preparing training materials and modules, conducting workshops, providing intensive mentoring, and evaluating the development of the partners. The results of this program show that participants experienced a significant increase in their understanding and skills in online marketing. Most participants were able to create and manage social media and marketplace accounts for their businesses and apply digital marketing strategies to promote their products. In addition, this program produced several outputs, including training modules, digital guide templates, activity documentation, and the formation of a digital-based MSME community. With this program, it is expected that MSMEs can become more adaptive to technological developments, expand their market reach, increase their revenue, and strengthen their competitiveness in an ever-evolving digital era. The program also recommends ongoing mentoring so that MSMEs can continuously develop their digital skills in a sustainable manner.
OPTIMIZING VGG-16 CONVOLUTIONAL NEURAL NETWORK FOR PAP SMEAR IMAGE CLASSIFICATION IN CERVICAL CANCER DETECTION Odi Nurdiawan; Heliyanti Susana; Ade Rizki Rinaldi; Ahmad Asyraful Hijrah; Indah Diniarti
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.v11i3.7131

Abstract

Early detection of cervical cancer through Pap smear image analysis plays a crucial role in reducing mortality rates associated with this disease. This study aims to optimize the VGG16 architecture to improve the classification accuracy of Pap smear images. The proposed method employs transfer learning with pre-trained ImageNet weights, customization of fully connected layers, and data augmentation techniques to enhance the diversity of training images. Experimental results demonstrate a significant improvement in training accuracy, reaching 98.50%, while validation accuracy remained stable at 88.24%, indicating potential overfitting. Performance testing on unseen data yielded an accuracy of 80%, with high precision for the negative class but low recall for the positive class, suggesting a bias toward the majority class. These findings highlight the need for additional strategies, such as data balancing and hybrid method integration, to improve sensitivity to positive cases. This research contributes to the development of adaptive deep learning-based classification models that support clinical decision-making in cervical cancer screening and opens opportunities for further research on model optimization and dataset expansion.
SOLAR-POWERED IOT-BASED BEHAVIORAL VALIDATION SYSTEM FOR SUSTAINABLE RAT PEST CONTROL IN RURAL RICEFIELDS Willy Prihartono; Ade Rizki Rinaldi; Cep Lukman Rohmat; Odi Nurdiawan
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.v11i4.7247

Abstract

Rice-field rats (Rattus argentiventer) continue to cause substantial rice yield losses in Indonesia, reaching up to 30% per season. This study presents a solar-powered IoT-based ultrasonic deterrent system designed for autonomous operation in off-grid rural environments. The system integrates PIR motion detection, PWM-controlled ultrasonic emission (16–20 kHz; 85–95 dB), and a solar-battery energy subsystem to ensure continuous nocturnal functionality. Field validation involving 21 rats demonstrated measurable short-term behavioral disruption, with 42.9% avoidance and 33.3% panic responses. Electrical testing confirmed stable night-time performance, with an average power output of 26.8 W during peak rodent activity. Statistical analysis showed χ²(2, N = 21) = 2.38, p = 0.30. While statistical significance was not achieved, the observed effect size (Cramer’s V = 0.24) indicates a moderate behavioral association, supporting practical deterrent potential under field conditions. Unlike prior studies that evaluate sensing or energy components separately, this research integrates renewable energy autonomy, real-field behavioral validation, and IoT-based automation within a single operational framework. The findings establish a foundation for adaptive, machine-learning-driven pest control systems to enhance sustainable rice-field management
Optimalisasi Strategi Pemasaran melalui Segmentasi Pelanggan dengan Analisis RFM dan Algoritma K-Means untuk Bisnis Ritel Aliya Anisa Rahma; Ahmad Faqih; Ade Rizki Rinaldi
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 2 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i2.1737

Abstract

Industri ritel yang kompetitif memerlukan pemahaman mendalam tentang kebutuhan pelanggan untuk menyusun strategi pemasaran yang relevan dan efektif. Penelitian ini bertujuan untuk melakukan segmentasi pelanggan di Toko Mitra 10 Cirebon menggunakan analisis Recency, Frequency, dan Monetary (RFM) yang dikombinasikan dengan algoritma K-Means. Segmentasi ini bertujuan mendukung strategi pemasaran yang lebih terarah dan meningkatkan loyalitas pelanggan. Data yang digunakan berasal dari catatan transaksi pelanggan dalam periode tertentu. Nilai RFM dihitung untuk setiap pelanggan berdasarkan Recency (waktu sejak transaksi terakhir), Frequency (jumlah transaksi), dan Monetary (total nilai transaksi). Metode K-Means digunakan untuk mengelompokkan pelanggan menjadi beberapa segmen, dengan jumlah kluster optimal ditentukan melalui metode elbow. Analisis menghasilkan tiga segmen utama: Lost Customers, dengan Recency tinggi, Frequency rendah, dan Monetary rendah; Potential Loyalists, dengan Frequency sedang dan Monetary bervariasi; serta Loyal Customers, dengan Frequency tinggi dan kontribusi Monetary signifikan. Hasil segmentasi ini mendukung penyusunan strategi pemasaran yang berbeda untuk setiap kluster: kampanye reaktivasi untuk Lost Customers, program loyalitas untuk Potential Loyalists, dan layanan eksklusif untuk Loyal Customers. Pendekatan berbasis data ini meningkatkan efektivitas pemasaran, loyalitas pelanggan, serta kontribusi pendapatan, sekaligus menegaskan pentingnya analisis data dalam pengambilan keputusan pemasaran yang relevan dan personal.
House Price Prediction Analysis Using a Comparison of Machine Learning Algorithms in the Jabodetabek Area Indah Ratna Ningsih; Ahmad Faqih; Ade Rizki Rinaldi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.733

Abstract

Jabodetabek, as the largest metropolitan area in Indonesia, has complex property price dynamics, making it difficult for developers and buyers to determine house prices. This study aims to analyze and compare the performance of the Multiple Linear Regression and Random Forest Regression algorithms in predicting house prices in the region. The data was obtained through scraping techniques from the rumah123.com website in October 2024, covering 999 data points with variables such as price, location, building area, land area, number of bedrooms, bathrooms, and garages. A comparative approach with cross-validation was applied to evaluate the performance of both algorithms using the metrics MAE, MSE, RMSE, MAPE, and R². The research results show that Random Forest Regression using GridsearchCV has better predictive performance, with an MAE value of Rp.645,764,815, MAPE of 28.12%, and R² of 0.864. The main factors influencing house prices in Jabodetabek include building size, land size, number of bedrooms, bathrooms, garages, and location. This finding emphasizes the superiority of Random Forest Regression in capturing complex data patterns and the significant role of these variables in determining house prices.
Co-Authors Ade Irma Purnama Sari Ade Irma Purnamasari Agung Saeful agus bahtiar Ahmad Asyraful Hijrah Ahmad Faqih Ahmad Faqih Ahmad Najib Yahya Ahmad Syaeful Ma’arief Ainun Nisa Sari Alfi Rizqi Falih Aliya Anisa Rahma Amelia, Mita Amida, Anggi Fitria Arga Esa Putra Aria Pratama Arif Rinaldi Dikananda Arunika Wijaya Asep Budiyanto Aziz Chairul Imam Bani Nurhakim Basysyar, Fadhil Muhammad Bima Anurgraha Djajnegara Cep Lukman Rohmat Cep Lukman Rohmat Dadang Sudrajat Dea Miftahul Huda Dian Ade Kurnia Dikananda, Arif Rinaldi Dodi Solihudin Edi Tohidi Fathrurrahman Fathurrohman Fathurrohman Fidya Arie Pratama Gifthera Dwilestari Hayati, Umi Heliyanti Susana Hery Widijanto Iin Iin Solihin Iin, Iin Indah Diniarti Indah Ratna Ningsih Irfan Ali Irma Agustina Jayawarsa, A.A. Ketut Kevin Salsabil Arlandy Kurnia, Dian Ade Lana Surlanto Luthfi Adianto Muhamad Abdurohman Muhamad Aditya Sunaryo Mulyawan Mulyawan Mulyawan, Mulyawan Nana Suarna Narasati, Riri Narasati Nining Rahaningsih Noviati Odi Nurdiawan Odi Nurdiawan Permana, Sandy Eka Pratama, Denni Pratama, Fidya Arie Raditya Danar Dana Ryan Hamonangan Sandy Eka Permana Septianto, Muhamad Arif Syarif Maulana Yaasin Tati Suprapti Tineka Handayani Tohidi, Edi Tuti Hartati Umi Hayati Wahyu Saputra Willy Prihartono