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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Sistem dan Informatika International Journal of Law Reconstruction Jurnal Pendidikan Informatika dan Sains JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Al-Khidmah CYBERNETICS BULETIN AL-RIBAATH JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) GERVASI: Jurnal Pengabdian kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Jurnal Teknika Jurnal Abdi Insani JIKA (Jurnal Informatika) Innovation in Research of Informatics (INNOVATICS) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) JUTECH : Journal Education and Technology JUSTIN (Jurnal Sistem dan Teknologi Informasi) Joutica : Journal of Informatic Unisla Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) Jurnal Ilmiah Teknik Informatika dan Komunikasi Kohesi: Jurnal Sains dan Teknologi SmartComp Jurnal Informatika Polinema (JIP) Journal of Multidiscipline and Collaboration Research JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Alkadri, Syarifah Putri Agustini
Universitas Muhammadiyah Pontianak
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Penerapan Learning Vector Quantization Dalam Pengolahan Citra Digital Untuk Deteksi Penyakit Kulit Rizki Akbar Pratama; Barry Ceasar Octariadi; Syarifah Putri Agustini Alkadri
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9270

Abstract

Skin, as the largest human organ, covers more than two square meters and accounts for about 15% of body mass. Consisting of three main layers of epidermis, dermis, and subcutaneous tissue, the skin serves as a physical shield and barrier against infection, injury, and UV radiation. Skin diseases such as chickenpox, monkey pox, measles and herpes are medical challenges that require quick and accurate diagnosis. This study used 520 digital images (130 per category) from Mendeley Data and online sources. The Learning Vector Quantization (LVQ) algorithm was applied for image classification based on the extracted features. Results showed an overall accuracy of 90.74%, with respective accuracies: 97% (chickenpox), 98% (monkey pox), 91% (measles), and 100% (herpes). Evaluation using confusion matrix resulted in accuracy, precision, recall, and F1-score values of 0.91, indicating strong model performance. These findings demonstrate the potential of LVQ as a digital image-based skin disease diagnosis tool.
The Development of Android Based on Legal Protection System for Women and Children Hazilina, Hazilina; Alkadri, Syarifah Putri Agustini
International Journal of Law Reconstruction Vol 8, No 1 (2024): International Journal of Law Reconstruction
Publisher : UNISSULA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26532/ijlr.v8i1.36235

Abstract

The issue of protecting women and children is becoming a concern in many parts of the world, including Indonesia. Applications can help women and children face dangerous conditions, increase public awareness, and empower them in handling cases of sexual harassment, requiring alternative technology-based solutions. The research method is juridical-empirical using a positivism paradigm, with a population of Pontianak city. Data was collected through literature study, documentation, and questionnaires. The legal protection for women and children in Pontianak City in terms of overcoming violence was found to be good, The community believed that the government was not adequately addressing incidences of abuse against women and children. Supporting factors suggest that the public can be helped by reporting acts of violence online.
Penerapan Jaringan Syaraf Tiruan Backpropagation dalam Pengenalan Huruf Hijaiyah Sufi Vanitra; Barry Ceasar Octariadi; Syarifah Putri Agustini Alkadri
Jurnal Sistem dan Informatika (JSI) Vol 19 No 2 (2025): Jurnal Sistem dan Informatika (JSI)
Publisher : Direktorat Penelitian,Pengabdian Masyarakat dan HKI - Institut Teknologi dan Bisnis (ITB) STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/jsi.v19i2.736

Abstract

Pemanfaatan teknologi dalam pembelajaran bahasa Arab dan Al-Qur'an masih kurang dan terkendala oleh minimnya sistem yang mampu mengenali huruf hijaiyah tulisan tangan secara akurat. Penelitian ini bertujuan mengembangkan sistem klasifikasi huruf hijaiyah tulisan tangan menggunakan Jaringan Syaraf Tiruan (JST) algoritma backpropagation yang digabungkan dengan teknik ekstraksi ciri bentuk dan tekstur (GLCM). Dataset terdiri dari 1200 data latih dan 450 data uji dengan citra huruf hijaiyah tulisan tangan. Tahapan penelitian meliputi preprocessing citra (resize, grayscale, Gaussian filter, binarisasi Otsu), ekstraksi 24 fitur (8 fitur bentuk dan 16 fitur GLCM), normalisasi, serta pelatihan dan pengujian model. Hasil pelatihan model mencapai akurasi sempurna 100%, sedangkan hasil pengujian pada data tulisan tangan menggunakan data Kaggle sebesar 93,77%. Sedangkan pengujian menggunakan tulisan tangan secara langsung sebesar 93%. Namun, ketika diuji dengan data huruf font digital yang belum pernah dilihat sebelumnya, akurasi sistem menurun drastis menjadi 20%. Hasil ini menyimpulkan bahwa model backpropagation yang dibangun sangat efektif untuk mengenali pola spesifik dari dataset tulisan tangan yang dilatih, namun memiliki kemampuan generalisasi yang terbatas terhadap variasi bentuk huruf yang baru.
Prediksi Harga Mobil Bekas Menggunakan Algoritma Support Vector Regression Herlangga, Herlangga; Pangestika, Menur Wahyu; Alkadri, Syarifah Putri Agustini
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10545

Abstract

The growth of the automotive industry in Indonesia has contributed to high demand for used cars as a more economical alternative to new cars. However, determining the price of used cars is often a challenge for showrooms and prospective buyers because it involves many factors and is subjective. This study aims to develop a used car price prediction model using the Support Vector Regression (SVR) algorithm with a Radial Basis Function (RBF) kernel approach. A total of 1,000 entries were obtained through web scraping from the cintamobil.com website. The research methodology refers to the CRISP-DM framework, starting from business understanding to model deployment through a web application using Streamlit. The preprocessing process involves handling missing values, outliers, data duplication, and numerical and categorical feature transformations. The SVR model was evaluated using RMSE, MAPE, and MAE metrics to assess prediction accuracy. The results show that SVR is capable of providing fairly accurate price predictions, with parameters C=1, gamma=0.1, and epsilon=0.1 producing the best performance, namely an MAE value of IDR 6,472,572, an RMSE of IDR 8,958,555, and a MAPE of 3.41%. Referring to the prediction accuracy category based on the MAPE value, where a MAPE value ≤ 10% is categorized as high accuracy, it can be concluded that this model has high prediction accuracy. This shows that the SVR model used is capable of estimating used car prices with a low error rate and good accuracy.
Comparison Analysis of Equivalence Class Partitioning and Boundary Value Analysis Techniques in Software Quality Testing of ReservasiPolnep Application Alifiansyah, Zuhrie; Alkadri, Syarifah Putri Agustini; Insani, Rachmat Wahid Saleh
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16789

Abstract

Software testing is a crucial phase before the official launch of an application to ensure its functionality and quality. This study compares two black box testing techniques—Equivalence Class Partitioning (ECP) and Boundary Value Analysis (BVA)—in identifying functional defects in the ReservasiPolnep application. The study involved testing key application features using both techniques, and results were measured using standard software testing metrics: test case coverage, success rates, test time, and cost per defect. The results showed that ECP is more time and cost-efficient, requiring only 26 test cases and 15 minutes 27 seconds per test, with a cost of Rp30 per defect and an 84.6% success rate. In contrast, BVA covers more test scenarios with 36 test cases, taking 27 minutes 5 seconds and costing Rp40 per defect, with a slightly higher success rate of 86.1%. The study concludes that each technique has advantages depending on the context, and highlights the need for input validation improvements in the application.
Prediksi Jumlah Kasus Penyakit Demam Berdarah Dengue Menggunakan Metode Long Short-Term Memory (LSTM) Nurmelidia Larasati; Sucipto Sucipto; Syarifah Putri Agustini Alkadri
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 6 No. 1 (2026): Maret : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v6i1.2100

Abstract

Dengue Hemorrhagic Fever (DHF) an infectious disease with fluctuating case numbers that can suddenly increase, posing significant public health challenges. In Pontianak City, 106 cases with 1 death were recorded in 2019, decreasing from 195 cases with 3 deaths in 2018. In 2020, the number dropped further to 27 cases with no fatalities. This condition indicates the need for a prediction system capable of accurately estimating the number of cases to support decision-making processes. This study aims to develop a model for predicting daily DHF cases in Pontianak City using the Long Short-Term Memory (LSTM) method. The data used includes daily DHF cases, average temperature, average humidity, and rainfall from 2020 to 2025. The research stages included data cleaning, normalization using Min-Max Scaling, historical data formation, model training, and evaluation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The best model employed a single LSTM layer with 64 neurons, 50 epochs, and a batch size of 32, yielding an RMSE of 0.87 and MAE of 0.63. These results indicate that the LSTM method is capable of generating predictions close to actual values and is reliable for estimating daily DHF cases in Pontianak City. The developed Streamlit-based application provides interactive visualization and accurate predictions, making it a valuable tool for health authorities in DHF prevention and control efforts.
Co-Authors Acep Supriyanto Agustian Agustian Ajmi, Nur Dzakiyyah Alda Cendekia Siregar Alifiansyah, Zuhrie Alkhairi, Muhammad Ghozy Andalas Rivaldi Permana Anita Asrul Abdullah Barry Ceasar Octariadi Chatarina Umbul Wahyuni Dila Adellia Dini Oktaviani Dwika, Arya Sukma Putra Eko Julianto Fakhruzi, Izhan Hasim, Wahyudi Hazilina, Hazilina Heni Kriswanti Herlangga, Herlangga Isra Pebrianti Istikoma Istikoma Kalsum, Dayang Nur Kristin Damay Asmara Masroni Maysa, Ade Medi, Medi Menur Wahyu Pangestika, Menur Wahyu Muhammad Dwi Ramadhianto Novianti, Novianti Nurmelidia Larasati Otafyani, Mega Putri Yuli Utami Rachmat Wahid Saleh Insani Ria Sapitri Rizka Amalia Rizki Akbar Pratama Rizky Wahyu Prasetio Ruhama, Ufi Ryan Permana Ryani Yulian Setiaji, Wanda Primadita Sucipto Sucipto Sucipto Sucipto Sucipto Sufi Vanitra Sumirah Sumirah Sumirah Sumirah vera vibiola Vika Ummu Hani Wahyu Nugroho Wiguna, Seftyan Yulrio Brianorman