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All Journal 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) CYBERNETICS BULETIN AL-RIBAATH Global Health Management Journal Jurnal Tekno Insentif J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Teknika Jurnal Sains Komputer dan Teknologi Informasi Teknosains : Jurnal Sains,Teknologi dan Informatika Jurnal Computer Science and Information Technology (CoSciTech) JUTECH : Journal Education and Technology Journal of Artificial Intelligence and Engineering Applications (JAIEA) J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) The Indonesian Journal of Computer Science Advance Sustainable Science, Engineering and Technology (ASSET) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions 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 Other

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Development of an IoT-based Soil Nutrient Monitoring and GIS Mapping System for Precision Agriculture Asrul Abdullah; Eka Indah Raharjo; Muhammad Iwan; Rizki Faizal; Maryogi
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2191

Abstract

Agriculture is a field that contributes to Indonesia's economic development.  Unpredictable weather, temperature fluctuations, and the difficulty in assessing soil quality hinder farmers in enhancing crop productivity. The IoT in signifies a beneficial progression that will assist farmers in their endeavors. Precision agriculture is an innovative approach that employs information technology for sustainable agricultural management. This research aims to assess soil nutrients and provide mapping data based on the evaluated agrarian sites. The testing sites are situated in three sub-districts within Kubu Raya Regency: Sungai Kakap, Ambawang, and Rasau Jaya. The soil study indicated a temperature range of 29.40 °C to 36.80 °C. Soil moisture varied from 4 % to 89.10 %. The soil pH varied between 6.90-8.07 PH. The soil salinity was rather modest. Nutrient levels, particularly nitrogen, were slightly lower than those of phosphate and potassium, necessitating fertilizer use to enhance plant vegetative development. Incorporating the Internet of Things onto agricultural land delivers data as real-time monitoring, which will be essential for improving agricultural output. This scalable method mitigates contemporary agricultural difficulties by diminishing environmental impact and enhancing crop resilience. This study facilitates sustainable, intelligent agricultural techniques to address the escalating needs of a swiftly expanding global population. 
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.
Co-Authors Abrar, Ihya' Nashirudin Adi, Pranowo Aditya Wicaksono Alda Cendekia Siregar Alkadri, Syarifah Putri Agustini Alkhairi, Muhammad Ghozy Anita Apriliasari, Betty Arni - Yanti Barry Ceasar Octariadi Chatarina Umbul Wahyuni Dedy Susanto Doddy Irawan Dwika, Arya Sukma Putra Egy Andryan Eka Indah Raharjo Ema Utami Fadillah Bergas Fakhruzi, Izhan Fenni Supriadi Fitrah, Mifthahul Gunarto Gunarto Gusti Ardhasna Fauzan Hermansyah, Hermansyah Indah Budiastutik Isra Pebrianti Istikoma Istikoma Istiqoma Iwan, Muhammad Jainudin, Khorlis Juliana Panemaan, Anita Karisma, Nova Khairah, Della Udya Khairul Lea Candra Lidia Lidia, Lidia Linda Suwarni Marlenywati Marlenywati Maryogi Menur Wahyu Pangestika, Menur Wahyu Muhammad Agus Muljanto Muhammad Iwan Putri Utami, Putri Putri Yuli Utami Rachmat Wahid Saleh Insani Ramadhan, Dwi Rohmat Rizki Faizal Rizky Wahyu Prasetyo Roni, Roni Sahid Agustian Hudjimartsu Salim, Vilvilia Selviana Selviana Siregar, Alda Cendekia Siti Aminah SITI AMINAH Sri Wahyuni Sucipto Sucipto Sucipto Sucipto Sucipto Syafaat Agung Prakoso Taruk, Medi Try Kardina Unitama Utami, Nur Sri Vidyastuti, Vidyastuti Viona, Tiara Aurilia Wawan Setiawan Yanto, Maryogi Zulfan Ahmadi