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All Journal Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Informatika dan Teknik Elektro Terapan International conference on Information Technology and Business (ICITB) JURNAL MEDIA INFORMATIKA BUDIDARMA JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Artificial Intelligence and Data Mining SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Antivirus : Jurnal Ilmiah Teknik Informatika JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Journal of Information Systems and Informatics bit-Tech Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jifosi TIN: TERAPAN INFORMATIKA NUSANTARA Nusantara Science and Technology Proceedings Jurnal Ilmiah Teknologi Informasi dan Robotika Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Sewagati
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Universitas Pembangunan Nasional Veteran Jawa Timur
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Implementasi Modified K-Nearest Neighbor (MKNN) untuk Deteksi Penyakit Anemia Putra Dwi Wira Gardha Yuniahans; Anggraini Puspita Sari; Yisti Vita Via
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 1 (2025): Juni 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i1.13425

Abstract

Anemia is a condition where the hemoglobin level in the human body drops below the normal threshold. It can cause several negative effects, such as delayed psychomotor development, a higher risk of infectious diseases, and in women, the possibility of premature birth. Therefore, early detection of anemia is essential to speed up treatment and recovery. One method that can support the diagnostic process is machine learning, particularly the Modified K-Nearest Neighbor (MKNN) algorithm. MKNN is an improved of standard KNN, incorporating additional steps such as validity calculation and weighted voting, which are not present in the original version. In this study, MKNN was applied to detect anemia and achieved an accuracy of 84% using a 75:25 train-test data split and k=5. The dataset was collected from Jemursari Hospital in Surabaya, consisting of 100 patient records. These records were used to evaluate the performance of the MKNN algorithm in anemia detection.
Peningkatan Kinerja Algoritma FP-Growth Untuk Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Optimasi Tabu Search Nurrahman, Sintya Fadillah; Via, Yisti Vita; Al Haromainy, Muhammad Muharrom
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9227

Abstract

Along with the rapid development of technology, the volume and scale of data stored by business entities continue to increase, particularly sales transaction data that contain valuable information to support decision-making and business development. Therefore, this study aims to analyze customer purchasing patterns by combining the Tabu Search and FP-Growth algorithms. Tabu Search is applied as a preprocessing stage to filter and sort transaction data before further analysis using the FP-Growth algorithm as an association analysis method. The results of applying these algorithms are association rules that represent relationships among items and can be used as a basis for business decision-making. The evaluation is conducted using support, confidence, and lift metrics to assess the strength of the generated rules, as well as execution time and the number of itemsets to compare the performance of FP-Growth with and without Tabu Search. The experimental results show that Tabu Search is able to effectively filter itemsets, where at a minimum support value of 0.01 the number of itemsets is reduced from 1,390 to 237, and at a minimum support value of 0.1 from 64 to 34. Although the combination of Tabu Search and FP-Growth requires a longer execution time due to the iterative process of Tabu Search, the resulting patterns are more focused, demonstrating the effectiveness of Tabu Search in improving the efficiency and quality of customer purchasing pattern analysis.
Pemanfaatan teknologi digital untuk pemberdayaan UMKM melalui pemanfaatan website di BUMDes Langgeng Jaya Nganjuk Maulana, Hendra; Kartika, Dhian Satri Yudha; Via, Yisti Vita; Atasa, Dita; Fitri, Anivea Fachmi Nur
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 10, No 1 (2026): February
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v10i1.35917

Abstract

Abstrak Kegiatan pengabdian masyarakat ini dilaksanakan untuk mendukung transformasi digital bagi pelaku UMKM di bawah naungan BUMDes Langgeng Jaya, Desa Gempol, Kabupaten Nganjuk. Fokus utama kegiatan ini adalah pengembangan dan pelatihan pengelolaan website sebagai sarana promosi dan pemasaran produk lokal, seperti kerajinan tangan, hasil pertanian, dan makanan olahan. Program ini dilaksanakan melalui empat tahapan, yaitu identifikasi kebutuhan, perancangan website, pelatihan literasi digital, serta evaluasi hasil kegiatan. Website yang dikembangkan berfungsi menampilkan katalog produk, profil pengrajin, informasi harga, dan kontak pemesanan agar mampu menjangkau pasar yang lebih luas. Pelatihan diikuti oleh 30 peserta yang terdiri atas pengurus BUMDes, perwakilan kelurahan, dan pelaku UMKM. Hasil evaluasi yang menggunakan pengukuran pemahaman sebelum dan sesudah pemaparan menunjukkan peningkatan signifikan dalam pemahaman digital, dengan nilai rata-rata peserta meningkat dari 5,6 pada pre-test menjadi 9,2 pada post-test. Capaian ini menegaskan bahwa pelatihan berjalan efektif dalam meningkatkan kemampuan peserta dalam pengelolaan website dan strategi pemasaran digital. Melalui kegiatan ini, diharapkan pelaku UMKM Desa Gempol mampu memanfaatkan teknologi secara mandiri untuk memperkuat daya saing, memperluas jangkauan pasar, serta mendorong keberlanjutan ekonomi desa di era digital. Kata kunci: digital marketing; digital transformation; community empowerment; msmes; website development. Abstract This community service activity is carried out to support digital transformation for MSME actors under the management of BUMDes Langgeng Jaya, Gempol Village, Nganjuk Regency. The main focus of this activity is the development and training of website management as a means to promote and market local products, such as handicrafts, agricultural products, and processed foods. The program is implemented through four stages: needs identification, website design, digital literacy training, and activity evaluation. The developed website functions to display product catalogs, artisan profiles, pricing information, and order contact details, enabling it to reach a wider market. The training was attended by 30 participants, including BUMDes administrators, village representatives, and MSME actors. Evaluation using pre-test and post-test measurements shows a significant increase in digital understanding, with the participants’ average score rising from 5.6 in the pre-test to 9.2 in the post-test. These results confirm that the training effectively improves participants’ skills in website management and digital marketing strategies. Through this activity, it is expected that MSME actors in Gempol Village can independently utilize technology to strengthen competitiveness, expand market reach, and promote the sustainability of the village economy in the digital era. Keywords: community empowerment; digital marketing; digital transformation; msmes; website development.
Implementation of MobileNetV3-Large in Rhizome Classification Nurdiansyah N.A, M. Ryan; Via, Yisti Vita; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3365

Abstract

Rhizomes are widely used in Indonesia as cooking spices and herbal ingredients, yet their visual similarity often causes misidentification when recognition relies on manual inspection, leading to inconsistent product quality and economic losses. This study presents an automatic rhizome image classification system based on the MobileNetV3-Large architecture to distinguish eight Indonesian rhizome types (bangle, ginger, kencur, kunci, turmeric, galangal, temu ireng, and temulawak) from RGB images. The dataset is organised by class and processed with a pipeline that includes resizing to 224×224 pixels, image flipping and rotation, brightness adjustment, zoom, and normalisation, before being split into training, validation, and testing subsets with an 80:10:10 ratio. MobileNetV3-Large pretrained on ImageNet is used as a feature extractor with a three layer dense classification head and dropout regularisation, trained using the Adam optimiser with a learning rate of 0.0001 and a checkpoint mechanism to store the best validation model. The proposed model achieves 97.50% accuracy, 97.65% precision, 97.50% recall, and 97.51% f1-score on the test set, indicating stable and balanced performance across all rhizome classes despite their similarity. Compared with earlier rhizome classification approaches based on handcrafted features, which typically report lower accuracies on fewer classes, and with heavier VGG or ResNet backbones, this work provides, to the best of the authors’ knowledge, the first evaluation of MobileNetV3-Large for multi class rhizome classification and shows that it offers a practical and computationally efficient baseline for image based rhizome identification on resource constrained mobile or embedded devices.
Optimization of Tea Leaf Disease Detection Based on YOLOv8 Using CBAM and BFP Armijantoro, Gilang Rahmadhan; Nugroho, Budi; Via, Yisti Vita
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3394

Abstract

Early identification of tea leaf diseases is essential for sustaining crop productivity and preventing significant yield losses, making accurate automated detection a critical requirement in modern agricultural management. This study aims to improve the robustness of YOLOv8 for disease detection by integrating two complementary optimization modules chosen for their suitability in addressing common challenges in plant imagery: the Convolutional Block Attention Module (CBAM), which enhances discriminative feature focus under complex visual noise, and the Bidirectional Feature Pyramid Network (BiFPN), which strengthens multi-scale feature fusion to capture small or low-contrast lesions. The target diseases include Algal Leaf Spot, Brown Blight, and Grey Blight, using a combined dataset of primary field images and secondary data from Kaggle. Four models were developed—YOLOv8n (baseline), YOLOv8-CBAM, YOLOv8-BiFPN, and YOLOv8-CBAM-BiFPN. Experimental results demonstrate consistent performance gains across all enhanced variants. The baseline model obtained a precision of 0.760, recall of 0.735, and mAP50 of 0.793. Incorporating CBAM increased precision to 0.824 and recall to 0.780, while BiFPN yielded the highest recall of 0.820 with superior multi-scale generalization. The combined CBAM-BiFPN model achieved the strongest overall results, with a precision of 0.879, recall of 0.814, mAP50 of 0.886, and mAP50–90 of 0.739. These findings indicate that integrating CBAM and BiFPN substantially enhances YOLOv8’s capability in complex leaf-disease scenarios and offers practical potential for deployment in real agricultural settings to support faster decision-making and more effective disease management.
Co-Authors Affandi, Masfi Ulil Akbar, Fawwaz Ali Alfath Daryl Alhajir Andreas Nugroho Sihananto Anggraini Puspita Sari Armijantoro, Gilang Rahmadhan Budi Nugroho Dhian Satria Yudha Kartika Dita Atasa Diyasa, I Gede Susrama Mas Eka Prakarsa Mandyartha Eva Yulia Puspaningrum Fetty Tri Anggraeny Fitri, Anivea Fachmi Nur I Gede Susrama Mas Diyasa Intan Yuniar Purbasari, Intan Yuniar Isnaini, Frisda Dita Kartika, Dhian Satri Yudha Matondang, Natalia Maulana, Hendra Maulana, Raihan Thobie Nabil Miftahul Nuril Silviyah Muhammad Muharrom Al Haromainy Natalia Matondang Nurdiansyah N.A, M. Ryan Nurlaili, Afina Lina Nurrahman, Sintya Fadillah Pande Putu Sunaryang Yogam Prasanta Prayoga, Aji Putra Dwi Wira Gardha Yuniahans Retno Mumpuni Sadewa, Rahma Danu SALAMUN ROHMAN NUDIN Saputra, Dewa Raka Krisna Sari, Nabella Permata Shodiq, Mochammad Lukman Sufi Miftakhoneki SUGIARTO - Sujayanti, Forentina Kerti Pratiwi Syahrul Hidayat Tataq Distasianto Teguh Yulianto, Teguh Utami, Hapsari Wiji Wahyu Saifullah Jauharis Saputra Wahyu Syaifullah Jauharis Saputra Wijaya, Pandu Ali Yana, Baktiar Yudha Yudha K., Dhian Satria ZAMAZANI, ZAIN MUZADID