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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 Khatulistiwa Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Al-Khidmah JURNAL EDUCATION AND DEVELOPMENT NUSANTARA : Jurnal Ilmu Pengetahuan Sosial 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) Journal of Innovation Information Technology and Application (JINITA) 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 Jurnal Pengabdian Masyarakat Nusantara Jurnal Media Informatika 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 Jurnal Ragam Pengabdian JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Syarifah Putri Agustini Alkadri
Universitas Muhammadiyah Pontianak, Indonesia
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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Klasifikasi Tingkat Kepuasan Pelayanan Pembuatan Paspor Menggunakan Algoritma Naïve Bayes Dini Oktaviani; Syarifah Putri Agustini Alkadri; Sucipto Sucipto
JURAL RISET RUMPUN ILMU TEKNIK Vol. 4 No. 1 (2025): April : Jurnal Riset Rumpun Ilmu Teknik
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurritek.v4i1.4572

Abstract

This research is motivated by the importance of improving the quality of passport making services at the Pontianak City Immigration Office which still faces obstacles such as complicated procedures, limited quotas, lack of officer direction, and mismatches in passport collection schedules that cause public dissatisfaction. This research aims to classify the level of satisfaction of passport making services using the Naïve Bayes algorithm, measure classification accuracy, and develop a website-based system that helps evaluate and improve service quality effectively and efficiently. The method used is a quantitative approach with data collection through questionnaires, interviews, and direct observation of 205 respondents, then the data is processed using the Naïve Bayes algorithm which assumes independence between variables to classify satisfaction levels based on variables such as officer friendliness, officer ability, ease of procedure, and timeliness of service. The main findings show that the Naïve Bayes algorithm is able to classify satisfaction levels with 73% accuracy, 76% precision, 70% recall, and 73% F1-score, signaling the effectiveness of this method in identifying community satisfaction patterns. However, the results also indicate the need for improvement in user interface aspects and system responsiveness so that the system can be widely accepted and provide optimal benefits. The implication of this research is that the application of Naïve Bayes-based data mining methods can be an effective tool in evaluation and decision-making to improve the quality of public services, especially in the field of passport making, and encourage the development of interactive and empirical data-based public service information systems.
Penerapan Metode Certainty Factor Dalam Sistem Pakar Deteksi Kerusakan Truk Berbasis Android Hasim, Wahyudi; Syarifah Putri Agustini Alkadri
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 8 No. 2 (2023): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v8i2.771

Abstract

Perkembangan dunia otomotif hingga saat ini meningkat dengan pesat, salah satu jenis kendaraan yaitu truk Mitsubishi Canter. Masalah yang terjadi dalam menangani kerusakan pada kendaraan truk, seperti tidak adanya mekanik ditempat, jumlah bengkel dan mekanik yang terbatas, serta mekanik yang belum memiliki banyak pengalaman sering kali mekanik kesulitan dalam mentukan kerusakan yang terjadi pada kendaraan truk. Masalah ini menjadi perhatian karena dapat mempengaruhi kinerja truk dan merugikan pemilik truk. Dengan menerapkan metode certainty factor untuk perhitungan kemungkinan kerusakan berdasarkan gejala yang dipilih maka akan menerima hasil berupa kemungkinan terbesar kerusakan yang terjadi sehingga bisa mengetahui keruskan apa yang dialami pada kendaraanya. Hasil perhitungan ditampilkan berupa persentase kerusakan yang dihitung berdasarkan nilai MB dan MD yang telah ditetapkan oleh sistem.Aplikasi sistem Pakar ini dapat memberikan informasi kerusakan mendeteksi dan mengetahui kerusakan pada mesin truk Mitsubishi canter yang sedang menalami kerusakan, maka sistem pakar akan menampilkan hasil akhir berupa keterangan kerusakan, solusi perbaikan, sehingga mekanik atau pengguna bisa melakukan perbaikan sesuai masalah yang dialami.
Development of a Web-Based Thesis Repository for Information Technology Education Students Permana, Ryan; Alkadri, Syarifah Putri Agustini
Journal of Multidiscipline and Collaboration Research Vol. 2 No. 2 (2025)
Publisher : Yayasan Pendidikan dan Pengembangan Harapan Ananda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58740/jmcr.v2i2.625

Abstract

This study aims to develop a Web-Based Thesis Repository System for the Information Technology Education Study Program at Universitas PGRI Pontianak. The system was designed to address challenges in manual thesis archiving and limited accessibility for students seeking academic references. The research employed the Research and Development (R&D) approach using the ADDIE model, which includes five systematic stages: Analysis, Design, Development, Implementation, and Evaluation. Data were collected through expert validation and user feedback. The system provides key features such as user registration, secure login, thesis upload, metadata management, and advanced search functionality. Validation results from two product experts and one content expert indicated that the system achieved an average product score of 3.34 and a content score of 3.38, both categorized as “Good.” The findings demonstrate that the developed repository is feasible, efficient, and effective in managing digital thesis data while enhancing accessibility and transparency in academic information retrieval. This repository supports digital transformation in higher education by promoting sustainable knowledge management and improving research visibility. The system is recommended for broader implementation and future development through integration with institutional databases and cloud-based storage for enhanced scalability and security.
Klasifikasi Penyakit Hipertensi Menggunakan Metode Random Forest Novianti, Novianti; Alkadri, Syarifah Putri Agustini; Fakhruzi, Izhan
Progresif: Jurnal Ilmiah Komputer Vol 20, No 1: Februari 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i1.1663

Abstract

This study discusses the classification of hypertension using the Random Forest method with a focus on age as the main factor. Given the serious impact of hypertension on health, research aims to simplify understanding of the problem, identify treatment gaps, and propose algorithm-based solutions. Using the PPG-BP Database, research methods involve problem identification, data collection, preprocessing, Random Forest modeling, hyperparameter tuning, and model evaluation. The findings show a high level of accuracy, 98% on training data and 95% on testing data, with the model being able to predict hypertension classification based on the variables age, blood pressure, heart rate and body mass index. Despite data imbalance, the preprocessing steps proved to be effective. The research conclusions contribute to the understanding of disease classification, especially hypertension, as well as practical guidance in efforts to prevent and treat it.Keywords: Classification; Data Mining; Hypertension; Random Forest AbstrakPenelitian ini membahas klasifikasi penyakit hipertensi menggunakan metode Random Forest dengan fokus pada usia sebagai faktor utama. Dengan dampak serius hipertensi terhadap kesehatan, penelitian bertujuan untuk menyederhanakan pemahaman masalah, mengidentifikasi celah penanganan, dan mengusulkan solusi berbasis algoritma. Menggunakan PPG-BP Database, metode penelitian melibatkan identifikasi masalah, pengumpulan data, preprocessing, permodelan Random Forest, tuning hyperparameter, dan evaluasi model. Hasil temuan menunjukkan tingkat akurasi tinggi, 98% pada data training dan 95% pada data testing, dengan model mampu memprediksi klasifikasi hipertensi berdasarkan variabel usia, tekanan darah, detak jantung, dan indeks massa tubuh. Meskipun ada ketidakseimbangan data, langkah-langkah preprocessing terbukti efektif. Simpulan penelitian memberikan kontribusi pada pemahaman klasifikasi penyakit, khususnya hipertensi, serta panduan praktis dalam upaya pencegahan dan penanganannya.Kata kunci: Klasifikasi; Data Mining; Hypertension; Random Forest
IDENTIFIKASI GERAKAN TANGAN PADA SANDI SEMAPHORE PRAMUKA SECARA REALTIME MENGGUNAKAN DECISION TREE Dwika, Arya Sukma Putra; Abdullah, Asrul; Alkadri, Syarifah Putri Agustini
JUTECH : Journal Education and Technology Vol 5, No 2 (2024): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v5i2.4163

Abstract

Identifying hand gestures in semaphore code accurately and in real time is a challenge. Especially for Scouts who are just learning this skill to minimize errors that can result in inappropriate information received and can affect the safety and effectiveness of communication. The use of Decision Tree in identifying hand gestures can make a significant contribution for Scouts to communicate more effectively. Based on the test results, this model can recognize letter classes in semaphore ciphers with normal lighting as evidenced by a higher accuracy rate. The average accuracy in normal light is 94%. In low-light conditions, it showed lower performance. In the first test, the model achieved 74% accuracy by recognizing 20 classes, while in the second test, the accuracy dropped to 66% by recognizing 18 classes. Confusion matrix testing is used to evaluate the Accuracy, Recall, and Precision levels in model training using Decision Tree.
Sistem Pendukung Keputusan Rekomendasi Penerima Bantuan Iuran BPJS Kesehatan Menggunakan Metode ROC dan SMART Masroni; Syarifah Putri Agustini Alkadri; Rachmat Wahid Saleh Insani
JURNAL FASILKOM Vol. 13 No. 3 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i3.6271

Abstract

Bantuan Sosial Penerima Bantuan Iuran (PBI) yang merupakan program dari pemerintah untuk pelayanan kesehatan dalam pemberian bantuan yang berupa jaminan kesehatan kepada masyarakat indonesia, dalam proses rekomendasi untuk Penerima Bantuan Iuran (PBI) BPJS Kesehatan di Dinas Sosial Kabupaten Sambas selama ini masih berdasarkan hasil penilaian musyawarah desa (MUSDES) baru kemudian diserahkan ke Dinas Sosial, penilaian dan pendataan yang dilakukan masih secara manual yaitu pendataan dengan mengisi form kertas verifikasi dan validasi data yang belum terkomputerisasi dengan baik, sehingga memakan waktu paling cepat seminggu atau 21 hari paling lama. Sistem yang dibangun menggunakan metode ROC dan SMART bertujuan agar dapat membantu pihak pendata untuk mengurangi waktu dalam pendataan dan efektif dalam merekomendasikan penerima bantuan sosial PBI sesuai kriteria yang digunakan. Hasil penelitian ini hanya 13 alternatif yang akan direkomendasikan untuk mendapatkan bantuan sosial yaitu alternatif bahar dengan nilai 0,07381 sampai alternatif tambrin dengan nilai 0,6993 dengan hasil perhitungan yang cukup akurat diketahui melalui pengujian MAPE (Mean Absolute Perentage Error) menunjukkan hasil 25,31%.
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.
Implementasi Data Mining untuk Klasifikasi Penyakit Stroke Menggunakan Algoritma K-Nearest Neighbor Enkan Feny Nopitasari; Syarifah Putri Agustini Alkadri; Rachmat Wahid Saleh Insani
KREATIF: Jurnal Pengabdian Masyarakat Nusantara Vol. 5 No. 3 (2025): Jurnal Pengabdian Masyarakat Nusantara
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/kreatif.v5i3.8215

Abstract

Stroke remains a major global health challenge, with diagnoses often delayed, particularly in primary care facilities with limited infrastructure. This study aimed to develop a stroke risk classification system using the K-Nearest Neighbor (KNN) algorithm, optimized through comprehensive data preprocessing. A secondary dataset of 5,110 patient records was processed using mean imputation for missing BMI values, winsorization to manage outliers, label encoding for categorical variables, and Min-Max normalization for feature scaling. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied prior to stratified data splitting into 70% training and 30% testing sets. The KNN model with K=5 demonstrated strong performance, achieving 96% accuracy, 96% precision, 99% recall, and a 97% F1-score on the test data. Multivariate correlation analysis identified age, hypertension, and blood glucose levels as the primary predictors of stroke risk, consistent with established clinical pathophysiology. These findings highlight the critical role of cardiometabolic risk factors in early detection. The system was implemented as a web application using Streamlit, enabling rapid and interactive screening in primary healthcare centers with minimal infrastructure. This practical application has the potential to assist healthcare providers in early stroke detection, accelerating clinical intervention and reducing the likelihood of long-term complications. Nevertheless, several limitations exist. The reliance on secondary data introduces the possibility of regional bias, and the use of SMOTE generates synthetic data that may affect model generalizability. Future research is recommended to validate the model across multi-source datasets, apply advanced hyperparameter tuning, and explore ensemble learning techniques to further enhance predictive reliability. In conclusion, the KNN-based classification system demonstrates promising potential as a practical decision-support tool for early stroke risk assessment in resource-limited healthcare settings.
Co-Authors Acep Supriyanto Ade Zaldi Eureka Zendar Ade Aditya Saputra, Rangga Agustian Agustian Ajmi, Nur Dzakiyyah Alda Cendekia Siregar Alda Cendekia Siregar Alifiansyah, Zuhrie Aliya Supandih, Fathia Alkhairi, Muhammad Ghozy Andalas Rivaldi Permana Anita Anugerah, Ade Arninda, Arninda Asrul Abdullah Asyari, Bisma Barry Caesar Octariadi Barry Ceasar Octariadi Ceasar Octariadi, Barry Chatarina Umbul Wahyuni Darniva, Fara Diky Pratama Dila Adellia Dini Oktaviani Dwika, Arya Sukma Putra Eko Julianto Enkan Feny Nopitasari Fakhruzi, Izhan Hafi Risandika Hasim, Wahyudi Hazilina, Hazilina Heni Kriswanti Herlangga, Herlangga Isra Pebrianti Istikoma Istikoma Kalsum, Dayang Nur Kristin Damay Asmara Marwan, Yusuf Masroni Maysa, Ade Medi, Medi Menur Wahyu Pangestika, Menur Wahyu Mochamad Wahyudi Muhammad Dwi Ramadhianto Novianti, Novianti Nurmelidia Larasati Octariadi, Barry Ceasar Otafyani, Mega Pirman Pirman Putri Yuli Utami Rachmat Wahid Saleh Insani Raihan Anugrah Fakhri Ramadhianto, Muhammad Dwi Ria Sapitri Rizka Amalia Rizki Akbar Pratama Rizky Wahyu Prasetio Roni, Roni Ruhama, Ufi Ryan Permana Ryani Yulian Setiaji, Wanda Primadita Sinta Rama Dani Siti Nur Fadillah, Utin Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sufi Vanitra Sumirah Sumirah Sumirah Sumirah Tiara Tri Anita vera vibiola Vika Ummu Hani Wahyu Nugroho Wiguna, Seftyan Yulrio Brianorman