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All Journal ELKHA : Jurnal Teknik Elektro Techno.Com: Jurnal Teknologi Informasi Jurnal Pseudocode Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUSIFO : Jurnal Sistem Informasi Jurnas Nasional Teknologi dan Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal Teknik Komputer AMIK BSI JURNAL MEDIA INFORMATIKA BUDIDARMA Machine : Jurnal Teknik Mesin JURNAL REKAYASA TEKNOLOGI INFORMASI Justek : Jurnal Sains Dan Teknologi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) NUSANTARA : Jurnal Ilmu Pengetahuan Sosial CYBERNETICS BULETIN AL-RIBAATH Global Health Management Journal Jurnal Tekno Insentif JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer bit-Tech Jurnal Teknika Jurnal Sains Komputer dan Teknologi Informasi Jurnal Computer Science and Information Technology (CoSciTech) JUTECH : Journal Education and Technology Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Comprehensive Science J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) International Journal of Health, Engineering and Technology The Indonesian Journal of Computer Science Advance Sustainable Science, Engineering and Technology (ASSET) Journal of Digital Business and Data Science JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Jurnal Publikasi Teknik Informatika TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika Jurnal Teknologi Dan Sistem Informasi Bisnis
Asrul Abdullah
Universitas Muhammadiyah Pontianak
Agriculture, Biological Sciences & Forestry 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 Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Penetration Testing pada Kerentanan Keamanan Sistem PELAKAT Menggunakan SQL Injection Khairul; Asrul Abdullah; Sucipto Sucipto
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 1 (2025): April 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i1.2025.78-86

Abstract

Penetration Testing bertujuan untuk mengidentifikasi kerentanan sistem dengan cara mensimulasikan serangan dengan teknik tertentu seperti SQL Injection. Sistem Pelayanan Administrasi Kependudukan yang Mendekatkan Masyarakat (PELAKAT) adalah sebuah aplikasi berbasis website yang dibuat oleh Dinas Kependudukan dan Pencatatan Sipil (Disdukcapil) Kabupaten Sambas untuk memudahkan proses pengelolaan beberapa dokumen administrasi kependudukan (Adminduk). Pengujian keamanan sistem PELAKAT menggunakan teknik SQL Injection diperlukan untuk mengidentifikasi kerentanannya serta memberikan rekomendasi mitigasi. Tahapan metode penetration testing yang dilakukan yaitu reconnaissance, scanning, vulnerability assessment, exploitation, dan reporting. Tools yang digunakan yaitu Burp Suite untuk menganalisis HTTP request dan SQLMap untuk eksploitasi kerentanan. Berdasarkan hasil pengujian, salah satu parameter pada form login sistem PELAKAT diketahui rentan terhadap SQL Injection. Eksploitasi berhasil mengakses sembilan database, lima tabel pada salah satu database, dan 13 kolom pada salah satu tabel. Kerentanan ini disebabkan karena sistem dikembangkan tanpa fitur keamanan yang memadai. Tingkat kerentanan sistem dinilai tinggi karena sistem PELAKAT dinyatakan rentan terhadap SQL Injection sehingga diperlukan tindakan mitigasi. Rekomendasi mitigasi meliputi penerapan WAF (Web Application Firewall), validasi input pada form input, penggunaan prepared statements, implementasi framework seperti Laravel, dan migrasi database ke penyimpanan berbasis cloud. Dengan penerapan mitigasi ini, diharapkan dapat meningkatkan keamanan sistem dan meminimalisir kerentanan sistem.
Implementasi Naïve Bayes dan Decision Tree Untuk Klasifikasi Jenis Tanaman Roni, Roni; Abdullah, Asrul; Insani, Rachmat Wahid Saleh
Jurnal Tekno Insentif Vol 19 No 2 (2025): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v19i2.2072

Abstract

Abstrak Sektor pertanian berkontribusi penting bagi perekonomian Indonesia, namun pemilihan tanaman masih mengandalkan cara tradisional yang kurang efisien. Penelitian ini mengembangkan sistem klasifikasi tanaman berbasis parameter tanah dan iklim dengan algoritma Naïve Bayes serta Decision Tree. Proses penelitian mengikuti enam tahap CRISP-DM. Data diambil dari Kaggle dengan variabel nitrogen, fosfor, kalium, suhu, kelembapan, pH, dan curah hujan. Evaluasi memakai Confusion Matrix dan Cross-Validation dengan akurasi, presisi, recall, dan F1-score. Hasilnya, Decision Tree akurat pada data latih (97,95%) namun turun di data uji (91,57%), sedangkan Naïve Bayes lebih stabil (95,25%–95,32%) sehingga direkomendasikan karena hasil yang konsisten dan lebih dapat diandalkan. Perbedaan ini terjadi karena kompleksitas struktur Decision Tree membuatnya lebih rentan terhadap overfitting, sedangkan Naïve Bayes yang bersifat probabilistik lebih stabil terhadap variasi data. Kata kunci: Pertanian, Klasifikasi Tanaman, Naïve Bayes, Decision Tree, CRISP-DM Abstract The agricultural sector plays an important role in Indonesia’s economy, yet crop selection still relies on traditional practices that are often inefficient. This study develops a crop classification system based on soil and climate parameters using the Naïve Bayes and Decision Tree algorithms. The research process follows the six stages of CRISP-DM. The dataset, obtained from Kaggle, includes nitrogen, phosphorus, potassium, temperature, humidity, soil pH, and rainfall. Evaluation was conducted with a Confusion Matrix and Cross-Validation using accuracy, precision, recall, and F1-score. Results indicate that Decision Tree achieved 97.95% accuracy on training data but decreased to 91.57% on testing data, while Naïve Bayes remained more stable (95.25%–95.32%), thus recommended for its consistent and more reliable performance. This difference occurs because the complexity of the Decision Tree structure makes it more prone to overfitting, while the probabilistic Naïve Bayes is more stable against data variations. Keywords: Agriculture, Crop Classification, Naïve Bayes, Decision Tree, CRISP-DM.
COMPARISON OF RANDOM FOREST AND XGBOOST ALGORITHMS IN CREDIT CARD FRAUD CLASSIFICATION Abdullah, Asrul; Khairah, Della Udya; Pangestika, Menur Wahyu
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.v6i3.10470

Abstract

Credit card fraud is a serious issue that can cause significant losses for both consumers and financial service providers. Therefore, a reliable and accurate fraud detection system is essential. The research adopts the CRISP-DM methodology, which includes six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The dataset used was obtained from the Kaggle platform, consisting of 1,048,574 rows and 23 Features, including transaction amount, merchant category, location, and customer attributes. Model evaluation was conducted using a Confusion Matrix with accuracy, precision, recall , and F1-score as performance metrics. The evaluation results indicate that Xgboost outperforms Random Forest, achieving an accuracy of 99.19%, precision of 98.73%, recall of 99.66%, and F1-score of 99.19%. In comparison, Random Forest achieved an accuracy of 97.68%, precision of 97.38%, recall of 98.01%, and F1-score of 97.69%. These results demonstrate that Xgboost is more effective in consistently identifying fraud ulent transactions. Furthermore, this study successfully developed a web-based application using the Streamlit framework, integrating both models interactively to allow users to input data and obtain classification results in real time. Thus, this study has successfully achieved three main objectives: identifying the most suitable algorithm for fraud classification, thoroughly evaluating model performance, and developing an application as a decision support system for credit card fraud detection.
Comparison of Naïve Bayes and SVM Methods in Detecting Hoax News Pedi Irawan; Asrul Abdullah; Istikoma Istikoma
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3122

Abstract

This study aims to detect hoax news in Indonesian-language media by comparing two popular text classification methods: Naïve Bayes and Support Vector Machine (SVM). Unlike most prior studies that focus on English-language datasets, this research addresses a significant gap by analyzing hoax detection in the Indonesian context. The growing spread of misinformation online has made it increasingly difficult for the public to distinguish between factual and false information, often leading to anxiety, confusion, and social unrest. To tackle this issue, a dataset of 2,010 news headlines comprising 1,005 hoax and 1,005 factual titles was collected through web scraping from verified news portals and fact-checking websites. After undergoing text preprocessing and feature engineering using TF-IDF and N-Gram models, the data was classified using Naïve Bayes and SVM. Performance was evaluated in terms of accuracy, precision, recall, and computation time. The SVM model achieved 93% accuracy, 94% precision, and 93% recall, whereas the Naïve Bayes model yielded 93% across all three metrics. Notably, Naïve Bayes required only 5.2 seconds for classification, significantly faster than SVM's 15.7 seconds, highlighting a trade-off between speed and precision. A web application was developed using Streamlit to make the models publicly accessible, enabling users to test news headlines directly. This practical tool can assist journalists, fact-checkers, and policymakers in verifying information more efficiently. The findings confirm that both models are effective, with distinct advantages depending on the context of use.
Klasifikasi Citra Penyakit Tanaman pada Daun Paprika dengan Metode Transfer Learning Menggunakan DenseNet-201 Salim, Vilvilia; Abdullah, Asrul; Utami, Putri Yuli
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3746

Abstract

Penyakit bercak daun yang disebabkan oleh bakteri Xanthomonas campestris pv. vesicatoria merupakan salah satu penyakit penting pada tanaman paprika di Indonesia. Penyakit ini dapat menurunkan kualitas dan kuantitas hasil panen paprika. Metode yang digunakan yaitu transfer learning dengan menggunakan model DenseNet-201. Penelitian ini menggunakan data gambar daun paprika yang terinfeksi dan tidak terinfeksi sebanyak 4.876 gambar. Data tersebut dibagi menjadi data latih, data validasi, dan data uji. Hasil penelitian menunjukkan bahwa model transfer learning mampu mendeteksi penyakit bercak daun pada paprika dengan akurasi keseluruhan sekitar 99.5%. Evaluasi model terhadap kelas “Bacterial Spot” dan “Healthy” menghasilkan precision, recall, dan F1-score rata-rata sekitar 99.5%. Penelitian ini menunjukkan bahwa metode transfer learning dapat digunakan sebagai sistem deteksi penyakit tanaman yang efektif dan efisien.
Pengembangan Sistem Informasi Kesesuaian Lahan Tanaman Pangan Berdasarkan Faktor Cuaca Berbasis Website Utami, Putri; Abdullah, Asrul; Hudjimartsu, Sahid Agustian; Wicaksono, Aditya; Viona, Tiara Aurilia
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3758

Abstract

Evaluasi lahan dapat dilakukan untuk meningkatkan kualitas dan kuantitas komoditas pertanian. Salah satunya dengan persyaratan penggunaan lahan dengan mempertimbangkan karakteristik lahan. Namun, Dinas Pertanian selaku koordinator sulit mendapatkan informasi terkait karakteristik lahan yang sesuai dengan jenis tanaman berdasarkan faktor cuaca. Anomali cuaca menyebabkan turunnya produktitivitas tanaman. Tujuan penelitian ini adalah mengembangkan sistem informasi kesesuaian lahan untuk menentukan jenis tanaman pangan beradasarkan karakteristik lahan serta evaluasi kesesuaian lahan tanaman. Metode dalam penelitian ini adalah Framework for the Application of System Thinking (FAST). Tahapan FAST yaitu scope definition, problem analysis, requirement analysis, decision analysis, design, contruction and testing, dan instalation and delivery. Berdasarkan hasil uji kelayakan aplikasi menghasilkan nilai 87% dengan kriteria baik. Hasil ini menunjukkan bahwa sistem informasi kesesuaian lahan tanaman pangan dapat digunakan dengan baik.
PERBANDINGAN FUNGSI OPTIMIZER PADA IDENTIFIKASI DIABETES MENGGUNAKAN METODE FEED FORWARD Setyawan, Rizki Fajar; Abdullah, Asrul; Octariadi, Barry Ceasar
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7757

Abstract

Identifikasi dini diabetes merupakan kebutuhan mendesak untuk mencegah komplikasi serius dan mengurangi beban kesehatan global. Penelitian ini bertujuan untuk membandingkan kinerja fungsi optimizer pada model Feed Forward Neural Network (FFNN) dalam mengklasifi-kasikan data diabetes. Optimizer yang diuji meliputi RMSprop, Adam, Adagrad, dan Stochastic Gradient Descent (SGD). Dataset diabetes dari platform Kaggle, yang terdiri dari 768 sampel dengan 9 fitur, dibagi men-jadi data latih dan uji dengan rasio 80:20. Evaluasi model dilakukan menggunakan metrik Accuracy, Precision, Recall, dan F1-Score ber-dasarkan Confusion Matrix. Hasil penelitian menunjukkan bahwa RMSprop memberikan performa terbaik dengan akurasi sebesar 0,759740, Precision 0,660377, Recall 0,648148, dan F1-Score 0,654206, diikuti oleh Adam dengan akurasi 0,746753. RMSprop menunjukkan generalisasi yang lebih baik pada data uji berkat mekanisme pembaruan bobot adaptifnya. Penelitian ini merekomendasikan RMSprop sebagai optimizer optimal untuk model FFNN dalam identifikasi diabetes, memberikan kontribusi bagi pengembangan alat diagnosis yang lebih akurat dan efisien.
Optimasi Rute Pengambilan Bantuan Sosial Lazismu Menggunakan Algoritma Genetika Travelling Salesman Problem Iskandar Hadiatma; Rachmat Wahid Saleh Insani; Asrul Abdullah
Techno.Com Vol. 25 No. 2 (2026): May 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i2.16016

Abstract

Lembaga Amil Zakat, Infaq, dan Sadaqah Muhammadiyah (Lazismu) di Pontianak Tenggara menghadapi kendala operasional dalam pengambilan donasi dari kotak infaq yang tersebar di berbagai lokasi. Proses penentuan rute yang belum optimal menyebabkan inefisiensi dari segi waktu dan biaya bahan bakar. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan menerapkan Algoritma Genetika untuk menyelesaikan Travelling Salesman Problem (TSP), guna menemukan rute terpendek untuk mengunjungi seluruh titik donasi. Sistem optimasi ini dibangun dalam bentuk aplikasi berbasis website menggunakan kerangka kerja Laravel untuk proses backend dan pustaka LeafletJS untuk visualisasi peta interaktif. Metode pengembangan sistem yang digunakan adalah model Waterfall, yang mencakup tahapan analisis kebutuhan, perancangan, implementasi, dan pengujian. Pengujian sistem dilakukan dengan metode Black Box Testing dan User Acceptance Testing (UAT). Hasil penelitian menunjukkan bahwa Algoritma Genetika berhasil mengoptimalkan rute pengambilan donasi. Pada studi kasus dengan 18 titik lokasi, rute yang dihasilkan sistem adalah 19.79 km, lebih efisien 7.57 km dibandingkan rute manual sebelumnya (27.36 km). Hasil pengujian UAT oleh staf Lazismu mencapai persentase penerimaan 94%, yang menunjukkan bahwa aplikasi yang dikembangkan sangat bermanfaat, mudah digunakan, dan sesuai dengan kebutuhan operasional.   Kata kunci – Algoritma Genetika, Lazismu, Optimasi Rute, Travelling Salesman Problem.
Implementation of a Web-Based Decision Support System for New Employee Recruitment Using the VIKOR Method Arochman; Sucipto; Asrul Abdullah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2298

Abstract

An effective and objective employee selection process is essential to obtain high-quality human resources. This study aims to develop a web-based decision support system to assist in the recruitment of new employees using the VIKOR method. The VIKOR method is chosen because it can rank alternatives based on their closeness to the ideal solution while considering compromise among criteria. The criteria used in the system include education, work experience, skills, interview results, and work personality. This research adopts the waterfall approach for system development and implements PHP programming language with a MySQL database. The testing results indicate that the system is capable of providing accurate and consistent rankings of job candidates, as well as facilitating the HR team in conducting evaluations more efficiently.
Recommendation System for Selecting Maternity Hospitals in Pontianak using Weighted Product Method Ervayana Sari; Asrul Abdullah; Istikoma
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2309

Abstract

In this research, a decision support system for recommending the selection of maternity hospitals in Pontianak was developed using the Weighted Product (WP) method, with the constructed system in the form of a web-based application. The aim of this study is to facilitate pregnant women in choosing maternity hospitals in Pontianak based on criteria obtained from a survey of pregnant women, including distance, facilities, cost, and reputation. The WP method was applied through three main stages: weight normalization, vector S calculation, and vector V computation for final ranking. Testing in this research involves five alternative maternity hospitals, and each criterion is assessed on indicators ranging from 1 to 5. The results obtained indicate that Anugerah Bunda Khatulistiwa Maternity Hospital achieved the highest final ranking score among all evaluated alternatives. This system is expected to assist expectant mothers in making more informed decisions when selecting a maternity hospital that best suits their needs.
Co-Authors Abrar, Ihya' Nashirudin Adi, Pranowo Aditya Wicaksono Alda Cendekia Siregar Aldi Mulia Rismanto Alkhairi, Muhammad Ghozy Andri Santoso Anita Arni - Yanti Arochman Barry Ceasar Octariadi Betty Apriliasari Dedy Susanto Doddy Irawan Dwika, Arya Sukma Putra Egy Andryan Eka Indah Raharjo Ema Utami Ervayana Sari Ewa Oktaviaghi Prasetya Fadillah Bergas Faizal, Rizki Fakhruzi, Izhan Fenni Supriadi G. Gunawan Gunarto Gunarto Gusti Ardhasna Fauzan Hermansyah, Hermansyah Indah Budiastutik Iskandar Hadiatma Isra Pebrianti Istikoma Istikoma Istikoma Istikoma Istikoma Istiqoma Iwan, Muhammad Juliana Panemaan, Anita Karisma, Nova Khairah, Della Udya Khairul Khorlis Jainudin Khusnul Karomah Lea Candra Lidia lidia Linda Suwarni Marlenywati Marlenywati Maryogi Maryogi, Maryogi Medi Taruk Menur Wahyu Pangestika Mifthahul Fitrah Muhamad Reynaldi Rendi Muhammad Agus Muljanto Muhammad Fikri Bagus Pratama Muhammad Iwan Nur Rali Rahma Wati Nur Sri Utami Octariadi, Barry Ceasar Pedi Irawan Putri Utami, Putri Putri Yuli Utami Rachmat Wahid Saleh Insani Ramadhan, Dwi Rohmat Rizki Faizal Rizki Faizal Rizky Wahyu Prasetyo Roni, Roni Sahid Agustian Hudjimartsu Salim, Vilvilia Selviana Selviana Siti Aminah SITI AMINAH Sri Wahyuni Sucipto Sucipto Sucipto Sucipto Sucipto Syafaat Agung Prakoso Syarifah Putri Agustini Alkadri Try Kardina Unitama Utami, Putri Yuli Vidyastuti, Vidyastuti Viona, Tiara Aurilia Virgilius Dalta Suprias Nandigna Wawan Setiawan Yanto, Maryogi Zulfan Ahmadi