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All Journal Prosiding SNATIKA Vol 01 (2011) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Creative Information Technology Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Information Technology and Computer Science (JOINTECS) Network Engineering Research Operation [NERO] Multitek Indonesia : Jurnal Ilmiah JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Generation Journal Jurnal ABDINUS : Jurnal Pengabdian Nusantara JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknika Innovation in Research of Informatics (INNOVATICS) JAMI: Jurnal Ahli Muda Indonesia Joutica : Journal of Informatic Unisla Nusantara of Engineering (NOE) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal Pengabdian Masyarakat Nusantara Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
Danar Putra Pamungkas, Danar Putra
Universitas Nusantara PGRI Kediri
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Analisis Sentimen Ulasan Produk Ponsel Pada E-commerce Menggunakan Algoritma Naive Bayes Gusti, Fadzilah Prayoganing; Pamungkas, Danar Putra; Kasih, Patmi
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 9 No. 1 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/140nc134

Abstract

Pertumbuhan e-commerce di Indonesia telah mendorong peningkatan pembelian produk secara daring, termasuk ponsel. Namun, kurangnya transparansi kualitas produk menjadi tantangan bagi konsumen. Untuk mengatasi hal ini, penelitian ini menerapkan analisis sentimen terhadap ulasan pelanggan menggunakan algoritma Naïve Bayes. Data ulasan diambil dari platform Shopee dan melalui beberapa tahapan preprocessing seperti case folding. cleansing, tokenisasi, stopword removal, dan stemming. Sentimen ditentukan menggunakan TextBlob, lalu data dilatih dan diuji menggunakan Naïve Bayes. Dari 365 ulasan yang dianalisis, model menghasilkan akurasi sebesar 81%. Hasil ini menunjukkan bahwa algoritma Naïve Bayes mampu mengklasifikasikan sentimen ulasan secara efektif, memberikan manfaat bagi konsumen dalam pengambilan keputusan serta bagi penjual dalam meningkatkan layanan.
Perbandingan Akurasi Arsitektur MobileNet dan EfficientNet dalam Mendeteksi Produk Kemasan Baehaqie, Lu'ay; Swanjaya, Daniel; Pamungkas, Danar Putra
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 9 No. 2 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/g6txkh58

Abstract

 Penelitian ini membahas perbandingan performa dua arsitektur deep learning, yaitu MobileNet dan EfficientNet, dalam mendeteksi produk kemasan berbasis citra. Dataset yang digunakan terdiri dari 150 gambar tiga jenis produk kemasan, yaitu Le Minerale, Isoplus, dan Sprite, yang telah melalui proses anotasi dan dibagi menjadi data latih, validasi, dan uji. Proses pelatihan dilakukan selama 10 epoch dengan ukuran citra 448x448 piksel. Evaluasi dilakukan menggunakan metrik accuracy, precision, recall, dan f1-score. Hasil penelitian menunjukkan bahwa MobileNet memiliki performa sangat baik dengan akurasi 100% pada ketiga kelas produk, serta nilai precision, recall, dan f1-score sebesar 1.00 pada semua kategori. Sementara itu, EfficientNet menunjukkan hasil yang kurang optimal dengan akurasi hanya 33%, disertai bias prediksi terhadap satu kelas saja. Berdasarkan hasil tersebut, MobileNet direkomendasikan sebagai arsitektur yang lebih efisien dan andal dalam kasus deteksi produk kemasan berdasarkan dari hasil dan penelitian yang dilakukan.
OPTIMISASI HYBRID YOLOV9C-VGG16 UNTUK KLASIFIKASI JERUK LOKAL PADA SISTEM SORTASI OTOMATIS PADA INDUSTRI PERTANIAN Inna Fatahna; Danar Putra Pamungkas; Danang Wahyu Widodo
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6024

Abstract

Oranges have provided the benefits of vitamin C to the human body, so it is necessary to cultivate local orange fruit varieties using the implementation of computer vision technology. With the optimization of accuracy results using the CNN method, one of which is a combination of YoloV9c and VGG-16 can be realized on local oranges to overcome the problem of inaccuracy in the inefficiency of the classification process influenced by human visual subjectivity so as not to produce inconsistency in local orange fruit defect detection. Optimization was carried out to obtain the best accuracy results of 97% in this study, compared to the accuracy results using the YoloV9c method alone of 74% or the VGG-16 method alone of 59%. The accuracy optimization used a frame rate dataset of 2,221 images which were divided into 1,555 training data, 444 testing data, and 222 validation data images with a percentage of sorting of 70% for training data, 20% for testing data, and 10% for validation data. With this research, it provides new insights and knowledge to combine the YoloV9c method with VGG-16, where VGG-16 is used in the data pre-processing stage using feature extraction and fine-tuning with a batch size of 32 while YoloV9c is used to classify the results of local orange fruit quality detection with 100 epochs. With the combination of the CNN algorithm, it can increase the accuracy value of the detection results.
PENCARIAN DAN EVALUASI RELEVANSI DONGENG BAHASA INDONESIA BERBASIS SEMANTIK MENGGUNAKAN LATENT SEMANTIC INDEXING (LSI) Laurenza Aprilya Melati; Daniel Swanjaya; Danar Putra Pamungkas
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6093

Abstract

Information retrieval in Indonesian folktales faces challenges in identifying semantically relevant stories, particularly due to language variation and synonymy. Keyword-based approaches such as TF-IDF often fail to capture semantic relationships, resulting in suboptimal search results. This study applies Latent Semantic Indexing (LSI) using Singular Value Decomposition (SVD) to enhance the relevance of story retrieval based on latent textual meaning. Evaluation was conducted through subjective assessments by 20 respondents, who rated search results generated from four types of queries: single-word, two-word, three-word, and full-sentence queries. The Friedman test indicated significant differences among query types for both LSI (χ²(3, N=20) = 29.27, p < 0.00001) and TF-IDF (χ²(3, N=20) = 35.45, p < 0.00001). LSI consistently yielded higher relevance scores, especially for more complex queries, demonstrating more effective semantic processing than TF-IDF. These findings suggest that semantic-based approaches are better suited for narrative texts such as folktales. Future research is recommended to explore query expansion and integration with deep learning-based semantic representations.
SISTEM PENCARIAN DONGENG BERBASIS TOPIK MENGGUNAKAN LATENT DIRICHLET ALLOCATION (LDA) PADA APLIKASI NUSANTARA PANJI KEDIRI Yeshinta Mira Yolanda; Daniel Swanjaya; Danar Putra Pamungkas
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6106

Abstract

In the digital era, folktales as part of Indonesia's cultural heritage are increasingly being digitized and widely disseminated through various media platforms. This study aims to apply the Latent Dirichlet Allocation (LDA) method to analyze 300 digitized Indonesian folktales. After the data collection and preprocessing stages, the dataset was divided into two parts: 70% for training and 30% for testing. The training process produced 9 topics that represent various themes found in the folktales. Evaluation was carried out using four main metrics: Coherence Score, Precision, Recall, and F1-Score. The Coherence Score measures the quality of the semantic relationship between words in each topic, while Precision, Recall, and F1-Score are used to assess the system’s accuracy, completeness, and balance in presenting relevant folktales based on the topic entered by the user. The results show that the LDA model achieved the highest coherence score of 0.6250 with 9 topics. Meanwhile, evaluation showed a precision of 76%, recall of 71.2%, and an F1-score of 73.5%, indicating that the system can effectively present relevant folktales. This study contributes to the development of a topic-based search system implemented in the Nusantara Panji Kediri application, enabling users to easily find stories according to their preferred topics. In addition, the study fills a research gap by applying LDA to a corpus of Indonesian folktales, which remains underexplored in the context of thematic search systems. The system also demonstrates great potential in the development of information technology based on local cultural content. However, the limited amount of data used—only 300 folktales—presents a challenge and opens opportunities for future improvements with a broader dataset, as well as testing the model on more diverse types of texts to enhance the system’s generalizability.
Application Of The C4.5 Method Based On K-Means Segmentation For Loan Risk Classification At Savings And Loan Cooperative Jihan Martha; Danar Putra Pamungkas; Made Ayu Dusea Widyadara
MULTITEK INDONESIA Vol 20 No 1 (2026): July
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i1.13856

Abstract

The Sri Hadi Dharma Savings and Loan Cooperative faces challenges in conducting loan risk analysis, which is still done manually as a result, the decisions made tend to be subjective and inconsistent. This situation has the potential to lead to errors in risk assessment, which could increase the likelihood of non-performing loans. Additionally, the available payment history data is not labeled with risk categories and therefore cannot be directly used in the classification process. This study aims to build a more objective loan risk prediction model by integrating the K-Means and C4.5 algorithms. The data used in this study consists of 7,155 loan payment history records from the 2020-2025 period. The research stages included data preprocessing through cleaning, reduction, creation of a maximum delinquency feature, feature selection, and Min-Max normalization, resulting in 471 unique data points. Next, K-Means was used to form risk groups based on financial characteristics, the clustering results were labeled as low, medium, and high risk categories, then the labeled data were classified using C4.5 with an 80:20 split of training and test data to generate a prediction model. Model evaluation was performed using a confusion matrix with accuracy, precision, recall, and F1-score parameters. The results show that the resulting model is capable of providing predictions with an accuracy rate of 95.79% and generates decision rules that are easy to understand and interpret. Furthermore, the integration of these two methods transforms payment history data into valuable information for identifying members’ risk patterns. This model is effective in supporting more objective, transparent, and measurable decision-making in loan risk management at savings and loan cooperatives
ANALISIS HASIL KLASIFIKASI PENYAKIT DAUN BAWANG MERAH MENGGUNAKAN CNN ARSITEKTUR EXCEPTION Danar Putra Pamungkas; M. Farij Amrulloh
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5875

Abstract

Dalam beberapa dekade terakhir, industri pertanian telah mengalami transformasi signifikan dengan penerapan teknologi canggih seperti kecerdasan buatan (AI) dan pembelajaran mesin (ML). Tantangan utama dalam sektor ini adalah identifikasi dan klasifikasi penyakit tanaman secara akurat dan efisien. Salah satu solusi yang menjanjikan adalah penerapan Convolutional Neural Networks (CNN), khususnya arsitektur Xception yang terkenal efektif dalam tugas klasifikasi gambar. Penelitian ini mengeksplorasi implementasi Xception dalam klasifikasi penyakit daun bawang merah (Allium ascalonicum), yang merupakan tanaman penting namun rentan terhadap berbagai penyakit seperti bercak daun (Alternaria porri), layu bakteri (Erwinia carotovora), dan ulat Grayak (Spodoptera exigua). Dataset gambar daun bawang merah digunakan untuk menguji kinerja model Xception dalam mengidentifikasi berbagai jenis penyakit. Hasil penelitian menunjukkan bahwa model terbaik yang menggunakan batch size 16 dan epoch 100 mencapai akurasi 99.71% dan validasi 97.37%. Pengujian menggunakan confusion matrix terhadap 96 data uji menghasilkan 89 klasifikasi benar dan 7 klasifikasi salah, menunjukkan tingkat akurasi 92%. Penelitian ini berkontribusi dalam peningkatan efisiensi dan akurasi deteksi penyakit tanaman, mendukung pertanian presisi dan pengembangan sistem deteksi penyakit tanaman yang lebih maju dan terotomatisasi.
Identifikasi Dini Penyakit Kulit Sapi Melalui Pendekatan Pengolahan Citra Digital Muhammad Farros; Ratih Kumalasari Niswatin; Danar Putra Pamungkas
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 10 No. 3 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/tgsrkc30

Abstract

Abstrak—Penelitian ini dimaksudkan untuk pengembangan model deteksi penyakit kulit pada sapi yang berbasis citra digital menggunakan arsitektur MobileNetV2. Dataset yang digunakan terdiri dari 1.319 citra dengan tiga kelas, yaitu LSD, Ringworms, dan Sapi Sehat. Data tersebut kemudian dibagi menjadi data latih, data validasi, dan data uji dengan rasio 70:20:10. Tahapan penelitian meliputi pra-pemrosesan citra, augmentasi data, pelatihan model, fine-tuning, dan evaluasi menggunakan accuracy, precision, recall, F1-score, serta confusion matrix. Hasil pengujian menunjukkan bahwa model memperoleh akurasi sebesar 75,56 persen dengan nilai test loss sebesar 0,6646. Kelas Sapi Sehat memperoleh performa terbaik dengan F1-score sebesar 0,86. Hasil penelitian menunjukkan bahwa MobileNetV2 dapat digunakan sebagai dasar sistem deteksi awal penyakit kulit sapi, meskipun peningkatan dataset dan kualitas citra masih diperlukan. Kata Kunci— citra digital, MobileNetV2, penyakit kulit sapi Abstract— This study is intended for the development of a cattle skin disease detection model based on digital images using the MobileNetV2 architecture. The dataset used consists of 1,319 images with three classes, namely LSD, Ringworms, and Healthy Cattle. The data were then divided into training data, validation data, and test data with a ratio of 70:20:10. The research stages include image preprocessing, data augmentation, model training, fine-tuning, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix. The test results showed that the model achieved an accuracy of 75.56 percent with a test loss of 0.6646. The Healthy Cattle class achieved the best performance with an F1-score of 0.86. The results indicate that MobileNetV2 can be used as the basis for an early detection system for cattle skin diseases, although improvements in the dataset and image quality are still needed. Keywords— cattle skin disease, digital image, MobileNetV2
Pengaruh Teknik Pre-Processing Citra X-Ray terhadap Performa ResNet18 dalam Klasifikasi Penyakit Paru-Paru Alvino Graha Nusantara; Ratih Kumalasari Niswatin; Danar Putra Pamungkas
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 10 No. 3 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/rh544w60

Abstract

Kanker paru-paru merupakan penyakit dengan tingkat kematian tertinggi yang membutuhkan deteksi dini untuk meningkatkan peluang kesembuhan pasien. Penggunaan citra X-ray dada sering terkendala masalah kontras rendah dan noise. Penelitian ini bertujuan menganalisis pengaruh penerapan teknik pre-processing citra terhadap performa model ResNet18 dalam mengklasifikasikan kelainan paruparu menggunakan 3.475 data dari Kaggle. Tahapan pre-processing yang diterapkan meliputi grayscale conversion, resizing, CLAHE, normalization, dan Gaussian Filtering. Hasil penelitian menunjukkan bahwa penerapan pre-processing berhasil meningkatkan performa model secara konsisten, dengan nilai akurasi meningkat dari 85,0% menjadi 87,5%, presisi menjadi 90,2%, recall menjadi 85,3%, dan F1-score menjadi 87,6%. Model juga menunjukkan tingkat kepercayaan prediksi mencapai 99,01% dengan waktu pemrosesan efisien sebesar 1,391 detik. Kesimpulannya, pengondisian data masukan terbukti secara signifikan mengoptimalkan ekstraksi fitur visual model sehingga sangat potensial diimplementasikan sebagai alat bantu triase medis yang cepat dan akurat.
Perbandingan Model EfficientNetV2B0 dan ResNet50 untuk Klasifikasi Citra Penyakit Mulut dan Kuku pada Sapi Farhan Kumara Abdiel; Ratih Kumalasari Niswatin; Danar Putra Pamungkas
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 10 No. 3 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/817cfc93

Abstract

Abstrak—Penyakit Mulut dan Kuku (PMK) merupakan penyakit menular pada hewan ternak yang berdampak besar terhadap perekonomian peternak. Deteksi dini berbasis citra digital menggunakan deep learning menjadi pendekatan yang menjanjikan untuk mendukung identifikasi PMK secara cepat dan efisien. Penelitian ini membandingkan dua arsitektur Convolutional Neural Network (CNN), yaitu EfficientNetV2B0 dan ResNet50, dalam mengklasifikasikan citra sapi ke dalam empat kelas: Kuku_PMK, Kuku_Sehat, Mulut_PMK, dan Mulut_Sehat. Dataset berjumlah 406 citra asli yang kemudian diperbesar menjadi 2.400 citra melalui augmentasi offline, dengan pembagian 80:20 untuk data latih dan validasi. Kedua model dilatih menggunakan transfer learning dua fase dengan optimizer Adam dan teknik fine-tuning. Hasil evaluasi menunjukkan bahwa ResNet50 mencapai akurasi 92,50% dan F1-score makro 0,93, sedangkan EfficientNetV2B0 menghasilkan akurasi 91,25% dengan F1-score makro 0,92. Meskipun ResNet50 unggul secara akurasi, EfficientNetV2B0 lebih efisien dari sisi jumlah parameter, menjadikannya kandidat potensial untuk implementasi pada perangkat dengan sumber daya terbatas.
Co-Authors Abdul Azis Achmad Fachrudi, Rafi Ahmad Bagus Setiawan Alfiantama, Ilham Alghozali, Muhammad Attiqi Alvino Graha Nusantara Amrulloh, M. Farij Andriawan, Riko Anggi Nur Fadzila Aprilia, Tri Krisna Wati Arafat, Filach Akbar Ardhi Mardiyanto Indra Purnomo, Ardhi Mardiyanto Indra Ardi Sanjaya Ardiansyah, Abdul Riqza Arfani, A. Rifqi Yarzuq Armadyah Amborowati Audianingrum, Arike Septi Aziz, Ahmad Minanul Baehaqie, Lu'ay Bagus Nugraha, Bagus Bayu Wijayanto Budi Darmawan Cahyono, Eko Nur Candra, Gea Vista Yulia Danang Wahyu Widodo Daniel Swanjaya Deni Wahyu Trisdianto Dewi Kurnia Sari Ema Utami Fajar Rohman Hariri Fajar, Indra Aditya Farhan Kumara Abdiel Fatahna, Inna Fauziyah, Laili Rahma Febrianto, Yahya Eko Firdaus, Afrizal Ahmad Firmansyah Mukti Wijaya Fitriana, Dwi Fitriyana, Wahyu Tia Gusti, Fadzilah Prayoganing Haika, Dwi Fikri hamzah, saiful Heffi Awang Cahya Imam Wicaksono Inna Fatahna Jihan Martha Joko Purwanto Kresnawan, Michael Ilham Kristantio, Triyo Kumalasari , Ratih Kurniawan, Taufik Rizki Laurenza Aprilya Melati M. Farij Amrulloh Machfudin, Imam Made Ayu Dusea Widyadara - Universitas Nusantara Kediri, Made Ayu Dusea Widyadara Mahardika, Tanggon Maulana Mahdiyah, Umi Ma’arif, A’an Tamim MUCHAMMAD YOHAN EKA ANDREANE Mudjiono, Stifen Zuro Muhammad Farros Murhatiningtyas, Yulia Mustofa, Hasan Bisri Muwafiq, Atho’ul Nugroho, Wahyu Rahman Listiyanto Nuryanto Nuryanto Pamungkas, Danar Puta Pangestu, Mohamad Inung Patmi Kasih Prahesta, Hadi Rizky Dwi Via Prahesta Prakosa, Ade Novit Dedey Prastya, Damar Zanuar Eka Pratama, Nando Adi Tya Prayoga, Ryan Sea Prayogo, M. Renhat Ade Rabiatul Adawiyah Ratih Kumalasari Niswatin Restuning Pamuji, M Anas Resty Wulanningrum Risa Helilintar Risky Aswi R, Risky Rizky Prasetyo, Aprisa RIZQI VIERI, MUHAMMAD ARIEL Rochana, Siti Rohmah, Anis Nur Rohman, Agus Nur Salis Nilam Amartama Saputra, Avif Bayu Saputra, Muh. Aris Shodiq, Muchamad Fajar Sholih, Faris Ashofi Sholih, Faris Ashofi Sidqika, Trinanda Majid Cipta suara, Andy Subekti, Lutfi SUCININGRUM, DYA AYU Sukardi, Bayu Adjirahman Syahrudin, Erwin Toni Gunawan Tri Setiawan, Didik Triprasetyo, Anggi Wahyu Wulaningrum, Resty Yeshinta Mira Yolanda Yulingga Nanda Hanief Yuningsih, Yayuk Yusuf, Ibram Farhani Zainuri, Mohamad Zuhal, Nadya Khalisah