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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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AKSI UNIASIAH (Blood Transfusion Unit) Based on Android With Waterfall Apriliasari, Betty; Utami, Nur Sri; lidia, Lidia; Abdullah, Asrul
Jurnal Transformatika Vol. 17 No. 1 (2019): July 2019
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v17i1.1410

Abstract

Teknologi informasi merupakan salah satu teknologi yang sedang berkembang dengan pesat pada saat ini, sehingga informasi yang tersedia dapat berlangsung dengan cepat, efisien serta akurat. Pada Unit Transfusi Darah PMI ini sangat minim pelayanan informasi mengenai ketersediaan darah melalui mobile device sehingga banyak masyarakat yang kurang informasi mengenai persedian darah. Maka perlu ditemukan cara memecahkan masalah yang ada dengan menyediakan sebuah aplikasi berbasis android yang dapat diakses oleh masyarakat. Metode yang digunakan untuk membangun sistem ini menggunakan metode waterfall dan divisualisasikan dengan use case diagram. Hasil dari penelitian ini adalah aplikasi donor darah yang mampu menyediakan data stok darah, jadwal kegiatan donor dan lokasi, serta informasi donor darah.
Sistem Pendukung Keputusan Untuk Rekomendasi Topik Skripsi Dengan Metode Fuzzy AHP Abdullah, Asrul; Sucipto, Sucipto
Jurnal Transformatika Vol. 18 No. 2 (2021): January, 2021
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v18i2.2708

Abstract

The ability to produce a scientific work both journals, scientific articles and thesis is needed to support the competence of undergraduate students in a university. The existence of a thesis course in a number of universities is one of the efforts to train students in producing a scientific work. To arrive at the stage of making a thesis, a student must first study various kinds of courses in each study program. Choosing a thesis topic is one of the most difficult stages in the preparation of a thesis. This is because there are still many students who do not understand and know their interest and ability in one of the courses which will later be used as a topic in thesis preparation. The focus of this research is a decision support system that is able to answer student problems in determining thesis topics according to their abilities. The method used is a waterfall within the SDLC (system development life cycle) framework starting from system requirement, data collection, design, implementation, testing, and maintenance. The result of this research is the ability of the decision support system to provide recommendations for thesis topics according to the abilities of students. The greatest weight is for A5 students for Computer Networking interest of 1,410 while the smallest weight is for A4 students for Multimedia interest of 0.080.
Klasifikasi Penyakit Kanker Paru-Paru Menggunakan Metode Decision Tree C4.5 Jainudin, Khorlis; Abdullah, Asrul; Sucipto, Sucipto
Justek : Jurnal Sains dan Teknologi Vol 8, No 3 (2025): September
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v8i3.31981

Abstract

The incidence of lung cancer in Indonesia has shown a significant increase, positioning the country as the eighth highest in Southeast Asia, with a growth rate of 10.85% over the past five years. A considerable number of lung cancer cases remain undiagnosed at earlier stages due to difficulties in detection, which contributes to the high mortality rate associated with this disease. Consequently, there is a need for a relatively efficient and straightforward technique to uncover knowledge, patterns, and interrelationships among data. The objective of this study is to develop a classification model for lung cancer using the C4.5 decision tree method and to evaluate its predictive performance. The methodology comprises several stages, including data preprocessing, exploratory data analysis (EDA), handling of missing values, identification of duplicate records, assessment of feature correlations, separation of features and target variables, partitioning of data into training and testing sets, model implementation, and performance evaluation through a confusion matrix. The experimental results demonstrate that the proposed model achieves a recall of 90%, a precision of 86%, an F1-score of 88%, and an overall accuracy of 89%. These findings indicate that the C4.5 decision tree method is effective in classifying lung cancer cases and holds potential as a reliable approach in medical data analysis for early detection and diagnosis.
Liver Disease Classification Using the Elbow Method to Determine Optimal K in the K-Nearest Neighbor (K-NN) Algorithm Abrar, Ihya' Nashirudin; Abdullah, Asrul; Sucipto, Sucipto
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1643

Abstract

Diagnosing liver disease in the field of healthcare is not an easy task. However, by utilizing medical records as datasets and applying data mining methods such as K-Nearest Neighbor (K-NN), we can analyze and extract knowledge automatically. The K-NN method has proven to be more effective compared to other methods as it clusters new information by selecting the nearest neighbors based on the value of k. In this study, we employed the Elbow method to determine the optimal value of k by measuring the error rate. The test results revealed that the optimal value of k was found to be 4, with the lowest error rate. In the third test, we achieved a training accuracy of 80.5% and a testing accuracy of 78.9%. After fine-tuning the training data, we were able to improve the accuracy to 82.2% for training and 77.1% for testing. However, in the fourth test, we encountered overfitting issues due to data imbalance caused by inappropriate resampling, resulting in a model that was overly complex and prone to excessive noise.
Penerapan MOORA dan SAW Dalam Sistem Pendukung Keputusan Pemilihan Petani Penerima Bantuan Try Kardina Unitama; Abdullah, Asrul; Istiqoma
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 10 No. 1 (2025): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v10i1.2480

Abstract

Kabupaten Bengkayang merupakan kabupaten agraris dengan mata pencarian utama dari pertanian agro industri yang mencakup industri pengolahan hasil komoditas tanaman pangan pertanian seperti jagung, padi, sorgum. Dinas Pangan Pertanian dan Perkebunan Kabupaten Bengkayang berupaya meningkatkan sumber daya pertanian dengan cara pemanfaatan lahan dan melakukan penyaluran bantuan pembibitan komoditi tanaman pangan diantaranya bibit jagung. Tujuan penelitian ini yaitu dengan menerapkan metode Multi-Objective Optimization The Basis Of Ration Analysis (Moora) dan Simple Additive Weigthing (SAW) pada sistem pendukung keputusan dengan kriteria yang telah ditentukan berdasarkan kuisioner yang dibagikan kepada 25 petani, kriteria yang digunakan yaitu Luas Lahan, Penghasilan, Hasil Panen, Lama Usaha Tani, Jumlah Anggota Keluarga. Berdasarkan aplikasi sistem pendukung keputusan yang telah dibuat, sistem mampu memberikan rekomendasi pilihan calon penerima bantuan sesuai dengan kriteria. Berdasarkan hasil dari kombinasi MOORA dan SAW didapatkan hasil yang sama, yakni setiap alternatif mendapatkan urutan dan ranking yang sama dengan nilai yang berbeda.
Risk factors for severe stunted among Children aged 2-5 years with stunting in Pontianak City, Indonesia Suwarni, Linda; Selviana, Selviana; Vidyastuti, Vidyastuti; Abdullah, Asrul; Adi, Pranowo
GHMJ (Global Health Management Journal) Vol. 6 No. 2 (2023)
Publisher : Yayasan Aliansi Cendekiawan Indonesia Thailand (Indonesian Scholars' Alliance)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35898/ghmj-62965

Abstract

Background: Stunting is still a major public health in developing countries, including Indonesia. There are many predictors that might contribute to stunting, including child factors, mother factors, household factors, and community. This study focuses on children and mother level. Aims: This study aimed to examine the factors associated with severe stunted among children aged 2 to 5 years old. Methods: This study uses primary data in Pontianak City, Indonesia the data has been collected from January to February 2023. Respondents were selected by total sampling method. Univariate, bivariate, and multivariate have been done using STATA 17. Results: The analysis data revealed that 75.98% of children were stunted and 24.20 were severe stunted. The factors including low birth weight and birth interval were found significantly associated with severe stunted, other independent variables did not have a correlation for being severe stunted.  Conclusion: According to children's factors and maternal factors, the variables of low birth weight and birth interval were found to correlate with being severe stunted.
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.
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