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All Journal 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
Asrul Abdullah
Universitas Muhammadiyah Pontianak
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Penerapan Metode Regresi Linier Berganda Untuk Memprediksi Panen Kelapa Sawit Hermansyah, Hermansyah; Abdullah, Asrul; Utami, Putri Yuli
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.v20i1.1816

Abstract

Perkebunan Nusantara XIII Kebun Rimba Belian requires effective planning and strategies to increase production yields. This research aims to apply the multiple linear regression method to predict palm oil production results in these plantations. The research uses a multiple linear regression method by observing patterns of increase or decrease in production results and predicting production results in the next few months. The results of the research show that the multiple linear regression method is effectively used to predict oil palm harvest at Perkebunan Nusantara XIII Kebun Rimba Belian. Analysis shows that the model has a high level of accuracy, with a Root Mean Squared Error (RMSE) value of 0.0698 and an R-squared (R2) Score of 0.9306. This indicates that the model has good abilities in predicting target values and explaining data variations well. As a result, this model can be a useful tool in planning plant care and pest control activities to increase oil palm production yields.Keywords: Data mining; Palm oil; Production prediction; Multiple linear regression. AbstrakPerkebunan Nusantara XIII Kebun Rimba Belian memerlukan perencanaan dan strategi yang efektif untuk meningkatan hasil produksi. Penelitian ini bertujuan untuk menerapkan metode regresi linier berganda untuk memprediksi hasil produksi kelapa sawit di perkebunan tersebut. Penelitian menggunakan metode regresi linier berganda dengan mengamati pola peningkatan atau penurunan hasil produksi dan memprediksi hasil produksi beberapa bulan ke depan. Hasil penelitian menunjukkan bahwa metode regresi linier berganda efektif digunakan untuk memprediksi panen kelapa sawit di Perkebunan Nusantara XIII Kebun Rimba Belian. Analisis menunjukkan bahwa model memiliki tingkat akurasi yang tinggi, dengan nilai Root Mean Squared Error (RMSE) sebesar 0.0698 dan R-squared (R2) Score sebesar 0.9306. Hal ini menandakan bahwa model memiliki kemampuan baik dalam memprediksi nilai target dan menjelaskan variasi data dengan baik. Sebagai hasilnya, model ini dapat menjadi alat yang berguna dalam merencanakan kegiatan perawatan tanaman dan pengendalian hama untuk meningkatkan hasil produksi kelapa sawit.Kata Kunci: Data mining; Kelapa sawit; Prediksi produksi; regresi linier berganda.
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.
Perbandingan Random Forest Regressor Dan Decision Tree Regressor Untuk Prediksi Hasil Panen Rizki Faizal; Abdullah, Asrul; Pangestika, Menur Wahyu
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.v6i2.9966

Abstract

Uncertainty in crop yields due to environmental factors remains a major challenge in Indonesia's agricultural sector. This study aims to compare the performance of the Random Forest Regressor and Decision Tree Regressor algorithms in predicting cultivated crop yields. The dataset used was sourced from Kaggle, consisting of 300,000 rows with features such as crop type, soil type, rainfall, fertilizer use, irrigation, and weather conditions. The system was developed using Python and Streamlit. The methodology includes data preprocessing, model training, and evaluation using the Mean Absolute Error (MAE) metric. The test results show that the Decision Tree Regressor achieved a lower MAE (0.43) compared to the Random Forest Regressor (0.48), resulting in more accurate predictions on this dataset. Feature analysis indicates that rainfall and crop type are the most influential factors. Although Random Forest is generally known for its stability, this study demonstrates that Decision Tree can outperform it within the context of the dataset used. The developed system is expected to assist farmers and policymakers in planning agricultural production more efficiently and in a data-driven manner.
Klasifikasi Penyakit Daun Tanaman Timun Berbasis Convolutional Neural Network (CNN) Yanto, Maryogi; Siregar, Alda Cendekia; Abdullah, Asrul
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.v6i2.9982

Abstract

Penyakit daun pada tanaman mentimun merupakan salah satu tantangan utama dalam meningkatkan hasil panen, terutama di Kalimantan Barat. Identifikasi penyakit secara manual seringkali tidak akurat dan memakan waktu. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis untuk penyakit daun mentimun berbasis Convolutional Neural Network (CNN) menggunakan arsitektur VGG-16. Dataset terdiri dari 2.000 citra daun mentimun yang dikategorikan ke dalam lima kelas: Bercak Daun Bakteri, Penyakit Bulai Berbulu, Daun Sehat, Penyakit Mosaik, dan Penyakit Bulai Tepung. Metode yang diterapkan meliputi praproses (pengubahan ukuran, augmentasi, normalisasi), pelatihan model, pengujian, dan evaluasi menggunakan metrik akurasi, presisi, recall, dan skor F1. Model mencapai akurasi 88% pada data pelatihan, 84% pada data validasi, dan 81,50% pada data pengujian. Model yang telah dilatih kemudian diintegrasikan ke dalam aplikasi berbasis web menggunakan Streamlit untuk memfasilitasi klasifikasi interaktif. Hasilnya menunjukkan bahwa Jaringan Saraf Konvolusional (CNN) efektif dalam mengklasifikasikan penyakit daun mentimun secara otomatis dan dapat diterapkan sebagai solusi teknologi di bidang pertanian.
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.
Co-Authors Abrar, Ihya' Nashirudin Adi, Pranowo Aditya Wicaksono Alda Cendekia Siregar Aldi Mulia Rismanto Alkhairi, Muhammad Ghozy Anita Apriliasari, Betty Arni - Yanti Arochman Barry Ceasar Octariadi Dedy Susanto Doddy Irawan Dwika, Arya Sukma Putra Egy Andryan Eka Indah Raharjo Ema Utami Ervayana Sari Ewa Oktaviaghi Prasetya Fadillah Bergas 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, Lidia Linda Suwarni Marlenywati Marlenywati Maryogi Menur Wahyu Pangestika, Menur Wahyu Mifthahul Fitrah Muhamad Reynaldi Rendi Muhammad Agus Muljanto Muhammad Fikri Bagus Pratama Muhammad Iwan Nur Rali Rahma Wati Octariadi, Barry Ceasar Pedi Irawan Putri Utami, Putri Putri Yuli Utami Rachmat Wahid Saleh Insani Rachmat Wahid Saleh Insani Ramadhan, Dwi Rohmat Rizki Faizal Rizki Faizal Rizky Wahyu Prasetyo Roni, Roni Sahid Agustian Hudjimartsu Salim, Vilvilia Selviana Selviana Setyawan, Rizki Fajar Siregar, Alda Cendekia SITI AMINAH Siti Aminah Sri Wahyuni Sucipto Sucipto Sucipto Sucipto Sucipto Syafaat Agung Prakoso Syarifah Putri Agustini Alkadri Taruk, Medi Try Kardina Unitama Utami, Nur Sri Utami, Putri Yuli Vidyastuti, Vidyastuti Viona, Tiara Aurilia Virgilius Dalta Suprias Nandigna Wawan Setiawan Yanto, Maryogi Zulfan Ahmadi