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All Journal International Journal of Advances in Applied Sciences Jurnal Buana Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan InComTech: Jurnal Telekomunikasi dan Komputer JATI (Jurnal Mahasiswa Teknik Informatika) JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Yumary: Jurnal Pengabdian kepada Masyarakat Journal of Applied Computer Science and Technology (JACOST) Jurnal Pendidikan dan Teknologi Indonesia Engineering, Mathematics and Computer Science Journal (EMACS) Nusantara Hasana Journal Jurnal Informatika Terpadu Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Informatika Polinema (JIP) Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Journal of Enhanced Studies in Informatics and Computer Applications Journal of Informatics and Advanced Computing International Journal of Computer Science and Information Technology Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of World Future Medicine, Health and Nursing
Risqy Siwi Pradini
Institut Teknologi, Sains, Dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Decision Tree Regression Untuk Prediksi Prevalensi Stunting di Provinsi Nusa Tenggara Timur Putri, Irnanda Septian Ika; Pradini, Risqy Siwi; Anshori, Mochammad
Jurnal Teknologi Informatika dan Komputer Vol. 10 No. 2 (2024): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v10i2.2179

Abstract

Stunting adalah kondisi terhambatnya pertumbuhan linier anak-anak karena kekurangan gizi dan perawatan yang tidak memadai sejak dalam kandungan hingga usia dua tahun. Stunting disebabkan oleh berbagai faktor, termasuk kurangnya asupan gizi yang memadai, infeksi kronis atau berulang, praktik pemberian makanan yang tidak sesuai, sanitasi yang buruk, serta akses terbatas terhadap layanan kesehatan dan pendidikan gizi. Di Indonesia, provinsi yang memiliki prevalensi stunting paling tinggi berada di Nusa Tenggara Timur (NTT). Penelitian ini bertujuan untuk membuat model prediksi menggunakan Decision Tree Regression untuk memprediksi prevalensi stunting di NTT. Dengan demikian, hasil penelitian ini selain menghasilkan model prediksi juga dapat memberikan pemahaman yang lebih komperhensif mengenai faktor-faktor yang mempengaruhi tingkat stunting di NTT dan mendukung upaya untuk menurunkan angka prevalensinya di provinsi tersebut. Untuk menguji model prediksi yang dihasilkan, penelitian ini menggunakan metrik RMSE. Hasil pengujian dengan metrik RMSE menunjukkan nilai 0,093. Nilai ini membuktikan bahwa model Decision Tree Regression yang digunakan memiliki tingkat kesalahan prediksi yang relatif rendah, sehingga cukup efektif dalam memprediksi prevalensi stunting berdasarkan data yang digunakan.
Perancangan Prototype Sistem Monitoring Ternak Ruminansia dengan Metode Human Centered Design Putriana, Rena; Pradini, Risqy Siwi; Haris, M. Syauqi
Jurnal Informatika Terpadu Vol 11 No 2 (2025): September, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v11i2.2553

Abstract

The ruminant livestock sector, such as sheep and cattle, makes a significant contribution to food security and the national economy. However, livestock data management, which is still carried out manually, remains a major challenge in improving operational efficiency, as seen in the Sarwa Adem Mulya (SAM) Cooperative. This study aims to design a prototype of a mobile-based livestock monitoring system called Ruminant Watch, using the Human-Centered Design (HCD) approach to align with the needs and limitations of field users. The research was conducted through five main stages: literature review, specification of the usage context, identification of user needs, design solution development using Figma, and usability evaluation through the System Usability Scale (SUS) questionnaire. The testing results showed an average SUS score of 87, which falls into the “Excellent” category. This indicates that the developed prototype system is not only easy to use but also relevant and effective in supporting livestock monitoring activities. This design is expected to serve as an initial step toward the digitalization of ruminant farming that is more efficient and adaptive to users’ capabilities.
Pemetaan Disparitas Stunting di Jawa Timur dengan Spatial Autoregressive Model (SAR) dan Spatial Error Model (SEM) Haris, M Syauqi; Risqy Siwi Pradini; Ahsanun Naseh Khudori
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 1 (2025): September 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i1.8877

Abstract

Stunting masih menjadi masalah kesehatan masyarakat yang signifikan di Indonesia, khususnya di Provinsi Jawa Timur, di mana terdapat disparitas yang mencolok dalam prevalensi stunting. Penelitian ini bertujuan untuk mengevaluasi distribusi spasial stunting dan mengidentifikasi faktor-faktor yang mempengaruhinya, dengan mempertimbangkan perbedaan geografis antarwilayah. Untuk mencapai tujuan tersebut, penelitian menggunakan pendekatan analisis spasial dengan menggunakan data sekunder dari 38 kabupaten dan kota di Jawa Timur. Analisis ini melibatkan beberapa tahap, termasuk eksplorasi pola geografis melalui indeks autokorelasi global Moran's I dan analisis LISA, diikuti dengan pemodelan regresi spasial menggunakan Spatial Autoregressive Model (SAR) dan Spatial Error Model (SEM) berdasarkan matriks bobot tetangga terdekat. Klaster hotspot diidentifikasi di wilayah Tapal Kuda, sementara klaster outlier ditemukan di Sampang dan Tulungagung. Selain itu, model regresi spasial menunjukkan kinerja yang lebih baik dibandingkan dengan model Ordinary Least Squares (OLS), dengan nilai pseudo R² SAR sebesar 0,7203 dan penurunan Akaike Information Criterion (AIC) menjadi 259,05. Hasil analisis menunjukkan bahwa inisiasi menyusui dini, cakupan ibu hamil, dan pemberian tablet tambah darah merupakan faktor signifikan yang mempengaruhi prevalensi stunting (p <0,05). Secara keseluruhan, model spasial memberikan representasi yang lebih akurat tentang pengaruh spasial di seluruh wilayah dibandingkan dengan regresi linier biasa, sehingga dapat menjelaskan variasi geografis stunting dengan lebih baik. Temuan ini menyoroti kebutuhan mendesak untuk mengembangkan kebijakan berbasis wilayah yang disesuaikan dengan karakteristik spasial yang unik di setiap wilayah. Penelitian ini berkontribusi pada bidang studi spasial dalam epidemiologi gizi dan menawarkan dasar ilmiah untuk mengimplementasikan intervensi kesehatan masyarakat yang lebih tepat sasaran.
Convolutional neural network model for fingerprint-based gender classification using original and degraded images Pradini, Risqy Siwi; Kusuma, Wahyu Teja; Budi, Agung Setia
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1350-1358

Abstract

Fingerprint-based gender classification is a crucial component of soft biometrics, providing valuable additional information to narrow the search space in forensic investigations and large-scale identification systems. Although deep learning models, particularly convolutional neural networks (CNNs), have demonstrated significant potential, performance validation is typically performed on high-quality fingerprint images. This creates a gap between laboratory results and real-world applications, where fingerprint evidence is often found in a degraded state, such as smudged, distorted, or partially damaged. This study attempts to bridge this gap by proposing a more realistic training approach. We design a lightweight and computationally efficient CNN and train it on a comprehensive combined dataset. The main contribution of this study lies in the data training strategy, which explicitly combines real and synthetically modified fingerprint images from the Sokoto coventry fingerprint (SOCOFing) dataset into a single, unified training set. Experimental results show that the proposed model achieves very high classification accuracy (97.39%) on a test set that also includes a combination of original and degraded images. This finding not only confirms the effectiveness of diverse data-based training to produce more robust models but also establishes a new benchmark for fingerprint based gender classification research under conditions more representative of practical scenarios.
Website Development of Sarwa Adem Mulya Cooperative as a Digital Platform for Promotion and Education of Ruminant Livestock Farmers: Pengembangan Website Koperasi Sarwa Adem Mulya sebagai Sarana Promosi dan Edukasi Digital Peternak Ruminansia M Syauqi Haris; Risqy Siwi Pradini; Mochammad Anshori
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 9 No. 3 (2025): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

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

Abstract

Koperasi Multi Pihak Sarwa Adem Mulya is a ruminant livestock-based cooperative located in Malang, East Java. The cooperative has great potential for business development but faces challenges in utilizing information technology, particularly in digital promotion and education. This community service activity aims to develop a cooperative website using the WordPress-based Content Management System (CMS) to strengthen its position in the digital era. The implementation method includes needs analysis, website design and development, training for cooperative administrators, as well as monitoring and evaluation of website usage effectiveness. The developed website features key components such as a cooperative profile, livestock product catalog, educational modules, and basic e-commerce integration. The results show that the website has been successfully implemented through the cooperative's official domain, and administrators are able to independently manage the content. Furthermore, an increase in digital literacy among administrators was observed based on pre-test and post-test evaluations. This website is expected to serve as a sustainable medium for promotion and education, and as a replicable model for the digitalization of other livestock cooperatives.
Development of a Mobile-Based Ruminant Livestock Monitoring System at Sarwa Adem Mulya Multi-Party Cooperative M Syauqi Haris; Risqy Siwi Pradini; Achmad Jaelani Rusdi
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 9 No. 4 (2025): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

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

Abstract

The Sarwa Adem Mulya Multi-Party Cooperative, located in Dusun Petung Wulung, Toyomarto Village, Singosari District, Malang Regency, oversees more than 80 ruminant livestock farmers with 13 key livestock management activities. Until now, record-keeping has been conducted semi-manually using Google Forms, which presents several challenges: slow processing, low accuracy, and limited accessibility for farmers with low digital literacy. This community service program aims to develop a mobile application based on a Progressive Web App (PWA) that facilitates real-time livestock recording, integrates with the cooperative’s dashboard, and can be used offline. The implementation methodology includes socialization, training, technology deployment, mentoring, and evaluation. As a result, over 70% of cooperative members participated in the training, and 57 farmers actively used the application, recording more than 1,200 activity entries within the first three months. Evaluation indicates a 25% improvement in data recording accuracy, a significant reduction in data duplication, and the availability of an analytical dashboard for the cooperative. This program supports SDG (Sustainable Development Goals) 2 (Zero Hunger), SDG 3 (Good Health and Well-Being), SDG 8 (Decent Work and Economic Growth), as well as SDG 13 and 15 (Climate Action and Life on Land).
Peningkatan Akurasi Rekomendasi Dokter pada Kondisi Data Sparsity Menggunakan Algoritma Content-Based Filtering Alwan Prasetya; Ahsanun Naseh Khudori; Risqy Siwi Pradini
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v16i01.10836

Abstract

Perkembangan aplikasi layanan kesehatan seperti Halodoc, Alodokter, dan Klikdokter telah menyediakan sistem rekomendasi yang memudahkan pasien untuk menentukan dokter yang relevan. Namun, rekomendasi dokter yang relevan masih menjadi tantangan. Salah satu permasalahannya adalah data sparsity, yaitu kelangkaan atribut data yang menyebabkan akurasi sistem rekomendasi bekerja kurang akurat. Penelitian ini mengembangkan sistem rekomendasi dokter menggunakan pendekatan Content-Based Filtering (CBF) untuk melakukan rekomendasi dokter sesuai dengan preferensi pasien dengan mempertimbangkan lima atribut utama: spesialisasi, rating, biaya konsultasi, lama praktik, dan jenis kelamin. Aturan imputasi data dan pembobotan atribut telah diimplementasikan untuk meningkatkan akurasi sistem rekomendasi. Hasil dari analisis data menunjukan teknik tersebut telah menurunkan Mean Absolute Error (MAE) dari 0,142 menjadi 0,102 dan Root Mean Squared Error (RMSE) dari 0,205 menjadi 0,150, sehingga teknik yang diimplementasikan meningkatkan sistem rekomendasi dokter dengan kondisi data sparsity.
Analysis of Image Preprocessing on EfficientNet-B5 Performance in Acne Severity Classification Dita Kurnia Rachmasari; Risqy Siwi Pradini; Ahsanun Naseh Khudori
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9818

Abstract

Deep learning based acne severity classification requires consistent color distribution and image illumination for stable feature extraction. Color imbalance, noise, and lighting variations can affect the accuracy and generalization ability of the model, making image preprocessing optimization a crucial aspect in dermatological classification. This study analyzes the impact of image preprocessing on the performance of EfficientNet-B5 in classifying three levels of acne severity using the Kaggle Acne Grading dataset (999 images; 80% training, 20% testing). The experiment compares the default preprocessing (resize, normalization) with the proposed preprocessing: gray-world white balance for color stabilization, bilateral filtering for edge preservation, and adaptive gamma correction for adaptive illumination. The evaluation uses accuracy and loss curves, confusion matrices, and classification reports, focusing on the macro F1-score to assess the balance between precision and recall. The results show a slight increase in accuracy from 77% to 78%, a macro F1 score of 75%, and more controlled overfitting with smaller differences in accuracy and loss between training and validation. Improving image quality before feature extraction contributes to feature representation and balance in multi-class classification.
Comparative Analysis of Texture Feature Extraction-Based Machine Learning Algorithms for Road Surface Condition Classification Naufal Alif Vivaldi; Novelia Puspita; Hilda Hilda Mujaddidah; Riski Lestari; Risqy Siwi Pradini
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 3 No. 2 (2026): JESICA Vol. 3 No. 2 2026
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v3i2.46

Abstract

Road surface conditions play a crucial role in ensuring transportation comfort and safety. Conventional road inspection methods that rely on manual observation are often time-consuming, expensive, and prone to subjectivity. This study proposes an automated approach to classify road surface conditions using texture-based feature extraction and machine learning algorithms. A total of 802 road images were independently collected, representing three classes: good, fair, and damaged. The images were preprocessed through resizing, grayscale conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), and pixel normalization to improve image quality. Texture features were then extracted using Gray Level Co-occurrence Matrix (GLCM), including contrast, homogeneity, energy, and correlation. The extracted features were used as input to four classification algorithms: Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Random Forest, and Naive Bayes. Experimental results show that KNN achieved the best performance with 96.27% accuracy, followed by SVM and Random Forest with comparable results. Naive Bayes performed the lowest due to its detrimental assumption of feature independence. These findings demonstrate that texture-based features combined with appropriate machine learning algorithms can effectively classify road surface conditions. This approach has strong potential for implementation in automated, real-time road monitoring systems, especially on devices with limited computing resources, contributing to more efficient and objective infrastructure management.
Prediction Model for Diagnosing Heart Disease Using Classification Algorithm Risqy Siwi Pradini; Mochammad Anshori; M. Syauqi Haris; Busatto Marilia; Tostes Geraldo
Journal of World Future Medicine, Health and Nursing Vol. 1 No. 2 (2023)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/health.v1i2.347

Abstract

Heart disease often causes death if not treated quickly and appropriately. Early diagnosis can prevent more serious complications and treat heart disease patients best. The existence of a disease prediction model can help health workers to diagnose diseases more quickly and accurately. The heart disease prediction model using a classification algorithm is a system built using machine learning techniques. The classification algorithm chosen is NN, Naive Bayes, Random Forest, and SVM because it is the best algorithm for predicting heart disease. This study makes a comparison of the four algorithms using a dataset of 918 instances with 11 features. The result is that the Random Forest algorithm produces the highest accuracy, with 86.8%, and has the best ability to distinguish classes based on the ROC curve.
Co-Authors ACHMAD JAELANI RUSDI Agung Setia Budi, Agung Setia Agus Widodo Ahsanun Naseh Khudori Ali Hasan, Hildan Adisqi Alwan Prasetya Apriyani Puji Hastuti Bintang Pramudya Busatto Marilia Cantika Nur Previana Danis Rifa Nurqotimah Dinda Chesar Putri Ramadhani Dio Wahyu Saputra Dita Kurnia Rachmasari Fery Fajar, Muhammad Geraldo, Tostes Henokh Kristiawan Hilda Hilda Mujaddidah Hilda Nuzulul Mujaddidah Indra Dharma Wijaya Irnanda Septian Ika Putri Jenike Gracelya Noke Kurniawan, Ardhiles Wahyu M. Syauqi Haris Makhrus, Moh Ali Makhrus, Moh. Ali Marilia, Busatto Mawardi, Ade Bagus Mayang Anglingsari Putri, Mayang Anglingsari Mochammad Anshori Naufal Alif Vivaldi Nindynar Rikatsih Ningrum, Afifah Vera Ferencia Fitria Novelia Puspita Nugroho Teguh Yuono Prasetya, Alwan Putri Salsabila, Dhea Putri, Irnanda Septian Ika Putriana, Rena Rahmania Ambarika Rika, Faurika Riski Lestari Roesardhyati, Ratna Salsabila, Dhea Putri Saputra, Dio Wahyu Saputra, Zainal Setia Budi, Agung Shaktyanti, Frenchyani Anggi Tostes Geraldo Trihapningsari , Denisha Trihapningsari Wahyu Tejakusuma, Wahyu Wisam Syahputra, Kelvin