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All Journal Publikasi Pendidikan JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Simantec Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sistem Informasi dan Bisnis Cerdas Format : Jurnal Imiah Teknik Informatika Sistemasi: Jurnal Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer InComTech: Jurnal Telekomunikasi dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics bit-Tech Journal of Robotics and Control (JRC) ILKOMNIKA: Journal of Computer Science and Applied Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Indonesian Journal of Data and Science Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Nusantara Science and Technology Proceedings SINTA Journal (Science, Technology, and Agricultural) Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Manajemen Informatika Jayakarta Jurnal Teknologi dan Manajemen International Journal Of Computer, Network Security and Information System (IJCONSIST) Algoritme Jurnal Mahasiswa Teknik Informatika Literasi Nusantara Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science ILTEK : Jurnal Teknologi Kohesi: Jurnal Sains dan Teknologi Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Router : Jurnal Teknik Informatika dan Terapan Modem : Jurnal Informatika dan Sains Teknologi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Router : Jurnal Teknik Informatika dan Terapan
Eva Yulia Puspaningrum
Department Of Informatics, Universitas Pembangunan Nasional Veteran Jawa Timur, Jl. Raya Rungkut Madya, Surabaya, Indonesia
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Understanding the Effectiveness of Query Expansion in IndoSBERT-Based Semantic Retrieval Dela Puspita Lasminingrum; Eva Yulia Puspaningrum; Budi Mukhammad Mulyo
Journal of INISTA Vol 8 No 2 (2026): May 2026
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v8i2.2116

Abstract

The advancement of information retrieval systems has shifted from keyword-based approaches to semantic retrieval using transformer-based models such BERT and its variants. Despite their ability to capture contextual meaning, the vocabulary mismatch problem between queries and documents remains a key challenge. Query expansion (QE) is commonly used to address this issue, but its effectiveness in semantic retrieval is not always consistent. This study aims to analyze the impact of query expansion on a semantic retrieval system based on a fine-tuned IndoSBERT model using a dataset of undergraduate thesis titles and abstracts at repository UPN “Veteran” Jawa Timur. A hybrid QE approach is proposed by combining pretrained FastText and domain-specific Word2Vec embeddings, with and without filtering mechanisms. The system performance is evaluated using Precision@15, Recall@15, Mean Average Precision (MAP), and nDCG@15. The results show that QE can improve retrieval performance when properly controlled. The best performance is achieved by the hybrid QE with filtering, where MAP increases from 0.389 (without QE) to 0.483 and nDCG reaches 0.911. In contrast, FastText-based QE without filtering results in performance degradation due to query drift. It can be concluded that the effectiveness of QE in semantic retrieval is highly dependent on the quality of expansion terms and the application of filtering strategies. QE is not inherently beneficial, but requires careful design to improve retrieval performance.
KLASIFIKASI PRIORITAS PENERIMA BANTUAN RTLH MENGGUNAKAN LIGHTGBM DENGAN BAYESIAN OPTIMIZATION DAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM DI KABUPATEN JOMBANG Ferdi Firdaus Ega Pratama; Eva Yulia Puspaningrum; Muhammad Muharrom Al Haromainy
ILTEK : Jurnal Teknologi Vol. 21 No. 01 (2026): ILTEK : Jurnal Teknologi
Publisher : Fakultas Teknik Universitas Islam Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47398/iltek.v21i01.367

Abstract

Penentuan prioritas penerima bantuan Rumah Tidak Layak Huni (RTLH) di Kabupaten Jombang masih dilakukan secara manual melalui survei lapangan dan verifikasi berjenjang, sehingga rentan terhadap subjektivitas dan ketidakkonsistenan dalam pengambilan keputusan. Penelitian ini mengusulkan penerapan algoritma Light Gradient Boosting Machine (LightGBM) yang dioptimasi menggunakan Bayesian Optimization, serta membandingkan performanya dengan Adaptive Neuro-Fuzzy Inference System (ANFIS) untuk mengklasifikasikan prioritas penerima bantuan RTLH secara otomatis dan objektif. Dataset yang digunakan berjumlah 9.173 data dari Dinas Perumahan dan Permukiman Kabupaten Jombang, mencakup 36 variabel yang meliputi karakteristik sosial-ekonomi keluarga dan kondisi fisik bangunan, dengan dua kelas target yaitu Prioritas dan Non-Prioritas. Tahap preprocessing meliputi data cleaning dengan penghapusan missing values, transformasi data kategorikal menggunakan label encoding, dan normalisasi Min-Max Scaler. Bayesian Optimization digunakan untuk menentukan konfigurasi hyperparameter optimal pada LightGBM. Hasil optimasi menghasilkan konfigurasi terbaik dengan n_estimators=446, learning_rate=0,3, num_leaves=75, max_depth=7, min_child_samples=26, dan reg_lambda=6,12. Model LightGBM dengan Bayesian Optimization berhasil mencapai akurasi 95%, presisi 96%, recall 96%, dan F1-Score 96% pada kelas Prioritas. Sementara itu, model ANFIS yang menggunakan 15 fitur terpilih menghasilkan akurasi 91%, presisi 93%, recall 92%, dan F1-Score 92%. Validasi pada data lapangan menunjukkan akurasi LightGBM sebesar 90,88%, membuktikan bahwa model layak diimplementasikan sebagai sistem pendukung keputusan.
PENERAPAN CONTENT BASED FILTERING DAN HDBSCAN UNTUK REKOMENDASI DEVELOPER Muhmmad Fairus Ramadhani; Eva Yulia Puspaningrum; Fetty Tri Anggraeny
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.7193

Abstract

Penentuan developer yang tepat berdasarkan log aktivitas berupa teks tidak terstruktur merupakan tantangan penting dalam manajemen penugasan proyek. Penelitian-penelitian sebelumnya umumnya menggunakan Content-Based Filtering atau algoritma clustering konvensional seperti K-Means dan DBSCAN, yang memiliki keterbatasan dalam menangani data aktivitas developer yang padat. Penelitian ini mengusulkan pendekatan sistem rekomendasi yang mengintegrasikan Content-Based Filtering (CBF) dengan clustering berbasis kepadatan menggunakan HDBSCAN, yang  belum pernah diterapkan secara langsung dalam konteks rekomendasi developer berbasis log aktivitas. Log aktivitas direpresentasikan menggunakan TF-IDF dan direduksi dimensinya dengan UMAP, kemudian dikelompokkan menggunakan HDBSCAN tanpa memerlukan penentuan jumlah klaster di awal, sehingga lebih efektif dalam mengelola data aktivitas yang padat. Rekomendasi dihasilkan berdasarkan kedekatan jarak dalam klaster yang terbentuk. Evaluasi pada dataset yang terdiri dari 4.505 log aktivitas dan 45 data uji menunjukkan bahwa konfigurasi parameter min_cluster_size = 2 dan min_samples = 2 menghasilkan performa terbaik dengan nilai Hit Ratio sebesar 88,8%, Recall sebesar 80%, dan Mean Reciprocal Rank (MRR) sebesar 56,3%. Dibandingkan dengan pendekatan Content-Based Filtering saja, metode yang diusulkan menunjukkan peningkatan pada Hit Ratio dan Recall, yang mengindikasikan peningkatan cakupan dan relevansi rekomendasi developer.
The Effect of Contrast Enhancement on Retinal Blood Vessel Segmentation Using CAS-UNet with Coordinate Attention Mardhatilla Al Haadiy, Hilya Zada; Anggraeny, Fetty Tri; Puspaningrum, Eva Yulia
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.26937

Abstract

Low contrast variation, uneven intensity distribution, and the presence of noise in retinal fundus images pose major challenges for blood vessel segmentation, particularly regarding thin and complex structures. These conditions make it difficult for models to accurately distinguish between blood vessels and the background. This study aims to analyze the impact of contrast enhancement techniques on retinal blood vessel segmentation performance using a CAS-UNet architecture modified with Coordinate Attention (CA). The methodology involves three preprocessing scenarios: Grayscale, Grayscale + CLAHE, and Grayscale + CLAHE + Gamma Correction. The model was trained using the DRIVE and CHASE_DB1 datasets with an 80:20 data split, an SGD optimizer, a learning rate of 0.01, and a combined BCE and Dice loss function over 50 epochs. Evaluation was conducted using a confusion matrix based on accuracy, sensitivity, specificity, F1-score, and IoU metrics. The results indicate that the Grayscale + CLAHE combination yielded the best performance—achieving a sensitivity of 81.46%, an F1-score of 81.63%, and an IoU of 69.01%—while also improving the detection of small blood vessels more consistently. These findings demonstrate that the appropriate application of contrast enhancement plays a crucial role in improving the quality of medical image segmentation.
Effect of CBAM Integration on InceptionV3 for Improved Foot and Mouth Disease Detection Accuracy Mochammad Rifky Andrianto; Eka Prakarsa Mandyartha; Eva Yulia Puspaningrum
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.v8i3.3756

Abstract

Foot and Mouth Disease (FMD) is a highly contagious livestock disease that causes significant economic losses. Timely detection is essential to prevent rapid transmission. While deep learning has shown promise in image-based disease identification, the impact of integrating lightweight attention mechanisms, such as the Convolutional Block Attention Module (CBAM), into robust multi-scale backbones, such as InceptionV3, for FMD detection on small, imbalanced primary field datasets remains underexplored. This study contributes by providing a systematic evaluation of CBAM integration under varying data-splitting scenarios, highlighting the interaction between attention mechanisms and data distribution. This study evaluates the integration of CBAM into InceptionV3 for the classification of cattle lesion images. It compares its performance with the baseline InceptionV3 model across three train-validation-test splits (70:20:10, 80:10:10, and 70:15:15). The dataset comprises 798 primary images (514 FMD-positive and 284 healthy), indicating a limited size with moderate class imbalance. Images were resized to 299 × 299 pixels and normalized to [-1, 1], with augmentation applied only to the training set. The InceptionV3-CBAM model achieved the best performance under the 70:15:15 split, with 96.69% accuracy, 96.25% precision, 98.72% recall, and 97.48% F1-score. These findings suggest that CBAM can enhance lesion-focused feature representation and detection sensitivity. However, performance gains were inconsistent across splits and appear influenced by both architectural changes and dataset characteristics. The model demonstrates potential for early FMD screening in resource-limited settings, but further validation on larger, more diverse datasets is essential to confirm robustness and generalizability
RANCANG BANGUN SISTEM INFORMASI AKADEMIK SEKOLAH BERBASIS KURIKULUM MERDEKA TINGKAT SMA Felliani Kurniawati; Fawwaz Ali Akbar; Eva Yulia Puspaningrum
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 2 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i2.8270

Abstract

Sistem informasi mulai banyak digunakan di segala sektor kehidupan, terutama di sektor pendidikan. Kurikulum Merdeka merupakan kurikulum baru yang diterapkan di sekolah, terutama tingkat SMA. Kebiijakan baru yang memberikan opsi kepada siswa untuk bisa memilih mata Pelajaran pilihan sendiri. Pendataan sekolah menjadi kompleks dan membutuhkan waktu lama untuk mengelola data jika dilakukan secara manual. Maka dari itu, penelitian ini bertujuan untuk merancang sistem informasi akademik sekolah dengan fitur yang bisa melakukan proses pendataan mata pelajaran siswa. Tahapan metode penelitian berupa pengumpulan data, analisis dan perancangan sistem, pembuatan sistem, dan pengujian sistem. Pengujian menggunakan metode blackbox testing untuk menguji fungsionalitas sistem. Hasil penelitian ini menunjukkan hasil bahwa sistem bisa berjalan sesuai dengan fungsionalitas sistem dan dapat digunakan untuk pendataan data-data.
An Explainable Artificial Intelligence Approach to Rice Leaf Disease Classification Using MobileNetV3-Large Susanto, Adyatma Imam; Anggraeny, Fetty Tri; Puspaningrum, Eva Yulia
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 4 (2026): MALCOM October 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i4.2658

Abstract

In Indonesia, rice is a vital component of food security, but persistent leaf diseases threaten productivity. Currently, field identification relies on labor-intensive, human-error-prone manual observation. To address this, we developed an accurate and interpretable classification model integrating the MobileNetV3-Large with Gradient-weighted Class Activation Mapping (Grad-CAM). The model obtained 98.82% validation accuracy after being trained via transfer learning on a public dataset with four classes: healthy, bacterial leaf blight, brown spot, and blast. The dataset was partitioned using a 90:10 data split (5,018 training and 612 validation samples). The model achieved a validation accuracy of 98.82% alongside robust multi-metric performance, obtaining a weighted average precision of 99.00%, a recall of 98.80%, Micro AUC and Macro AUC both reached 0.9977, and an F1-score of 98.90%. Furthermore, Grad-CAM analysis confirmed that the model's predictions are interpretable. The resulting heatmaps consistently concentrated on leaf regions exhibiting disease symptoms, demonstrating that the network learned visually meaningful features despite minor background activations. These findings validate that combining MobileNetV3-Large with Grad-CAM yields an effective, transparent classification system, offering strong potential for robust, user-verifiable rice disease diagnosis in agricultural deployments.
Co-Authors Abiyan Naufal Hilmi Achmad Junaidi Adelia Putri Adyani Adityawan, Firza Prima Afina Lina Nurlaili Afina Lina Nurlaili Afina Lina Nurlaili Agung Mujiono, Alfinas Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Ahmad Fahry Hamidy Ahmad Hilman Dani Akbar, Fawwaz Ali Al Danny Rian Wibisono Ali Muhhamad Saleh Baaboud Ama Maulidatul Khairah Ananda Azra Razali Andhika Ahnaf Daniswara Andini Fitriyah Salsabilah Andreas Nugroho Sihananto Anggraini Puspita Sari Ani Dijah Rahajoe Ani Dijah Rahajoe Annisaa Sri Indrawanti annisaa sri indrawanti annisaa sri indrawanti Anny Yuniarti Aqsa Prima Cahya Ariani, Dian Dwi Ariyono Setiawan Aryananda, Rangga Laksana Aswan Aswan Attaqwa, Syukur Iman Awang, Mohd Khalid Azizah, Nabila Wafiqotul Bagus Sutikno Putra Basuki Rahmat Basuki Rahmat Basuki Rahmat Basuki Rahmat Masdi Siduppa Bimantara, Candra Kusuma Muhammad Budi Mukhammad Mulyo Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Chafid, M Putih Daniswara, Sena Dela Puspita Lasminingrum Devan Cakra Mudra Wijaya Dewi, Deshinta Arrova Dhian Satria Yudha K. Dimas Saputra Diyasa, I Gede Susrama Mas Dwi Anggraeni, Shinta Dwiki Aditama Supangkat Eka Prakarsa Mandyartha Eka Prakarsa Mandyartha Eka Prakarsa Mandyartha, Eka Elzandy, Imeldha Etniko Siagian, Pangestu Sandya Fahmi Al Hafidz, Achmad Faishal Fernando Hutama Fara Disa Durry Faris Syaifulloh Farkhan, Farkhan Felliani Kurniawati Ferdi Firdaus Ega Pratama Ferry Trilaksana Putra Fetty Tri Anggraeny Firyal Wishal Nabili Firyal Wishal Nabili Firza Prima Aditiawan Firza Prima Adityawan Firza Prima Adityawan Fitri Rahmawati Hadi, Surjo Hapsari Wiji Utami Hasby Bik, Ahmad Henni Endah Wahanani Humairah, Sayyidah Humam Maulana Tsubasanofa Ramadhan I Gede Susrama Mas Diyasa I Nyoman Sujana I Wayan Alston Argodi Idhana, Ilham Ainur indrawanti, annisaa sri Irsyad Rafi Naufaldi Karim, Mohammad Daniel Sulthonul Kartini Kartini Lestari, Kusmiyati Lina Nurlaili, Afina M. Syahrul Munir, M. Syahrul Mada Lazuardi Nazilly Made Hanindia Prami Swari Maisie Yunita Malva Mandyartha, Eka Prakarsa Manggala, Herwantoro Arya Marchel Adias Pradana Mardhatilla Al Haadiy, Hilya Zada Maulana, Hendra Merdin Risalul Abrori Moch. Hatta Mochammad Rifky Andrianto Mohammad Idhom Muchamad Dicky Alifiansyah Muhammad Aldi Maulana Muhammad Asyraf Muhammad Fernanda Naufal Fathoni Muhammad Misbachuddin Muhammad Muharrom Al Haromainy Muhammad Syafril Hidayat Muhmmad Fairus Ramadhani Nabilah, Qonitah Jihan Nanik Suciati Noor Fitria Azzahra Nugroho, Budi Nugroho, Budi Nugroho, Budi Nurcahyo, Syai'in Bayu Nurul Taukid, Mochamad Pallawabonang, Mahabintang Pratama, Gede Ardi Prisheila Dharmawan, Diaz Putra, Chrystia Aji Putra, Riza Satria Putri, Desya Ristya Rafani Bardatus Salsabilah Retno Mumpuni Ridho Fajar Fahturohman Rizki, Agung Mustika Rizqi Mar'atus Sholiihah, Eka Royan Fajar Sultoni Rozi, Atiqur S J Saputra, Wahyu Safira, Dwi Putri Samuel Krispama Lumbantoruan Saputra, Raka Aji Saputra, Wahyu S J Saputra, Wahyu S J Saputra, Wahyu S. J. Saputra, Wahyu S.J. Satria Yudha Kartika , Dhian Shawn Hafizh Adefrid Pietersz Shofiya Syidada Sukendah, Sukendah Sunarko, Victor Immanuel Surjohadi, Surjohadi Susanto, Adyatma Imam Susrama Mas Diyasa, I Gede Syahrul Hidayat Syaifullah JS, Wahyu Taruna Ardianto Tataq Distasianto Utami, Hapsari Wiji Vita Via, Yisti Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wahyu S.J. Saputra Wahyu Syaifullah Jauharis Saputra Wan Awang, Wan Suryani Wan Suryani Wan Awang Widiastuty, Riana Retno Wiji Utami, Hapsari Yisti Vita Via Yisti Vita Via yisti vita via Yogie Wilvren Saragih Yudha K., Dhian Satria Yudhistira Nanda Kumala YUSMI NUR AINI Zacky Yaser Malik Gumiwang Zalfa Ibtisamah Arishandy ZAMAZANI, ZAIN MUZADID Zuhriyah, Sitti