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Pelatihan PMBA bagi Kader Kampung Keluarga Berkualitas Desa Membangun dalam Rangka Pencegahan Stunting Indah Budiastutik; Marlenywati; Abdullah, Asrul; Karisma, Nova; Juliana Panemaan, Anita
J-Dinamika : Jurnal Pengabdian Masyarakat Vol 9 No 3 (2024): Desember
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stunting is a disorder that attacks the growth and development of children and babies caused by chronic malnutrition which is characterized by a body length or height that is below standard. Many factors underlie the high incidence of stunting, one of which is improper Infant and Child Feeding. The growth and development of toddlers is closely related to food intake which can have a major influence on optimizing the development and growth of infants and toddlers in 1000 HPK. The Kampung KB Membangun Desa is located in the Rasau Jaya Umum Village, Kubu Raya Regency, which is the focus of accelerating stunting reduction. The stunting rate in the district reached 27.8% in 2022. The stages of community service activities begin with coordination of activities with the coordinator, then continued with counseling and training. Community service is carried out using lecture, demonstration and question and answer methods supported by PMBA module media and equipment. Evaluation of counseling activities is carried out by providing a questionnaire sheet containing 10 questions. From the results of the SPSS analysis test, the respondents knowledge during the pre-test was 66.67%. After being educated, there was an increase in respondents knowledge during the post-test of 83.3%.
Perancangan Presensi Menggunakan Radio Frequency Identification (RFID) Dan Fingerprint Ramadhan, Dwi Rohmat; Abdullah, Asrul; Fakhruzi, Izhan
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 3: Desember 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i3.2443

Abstract

Attendance is the process of recording attendance as a form of employee discipline and professionalism. Manual attendance is very time-consuming and errors often occur in making attendance recaps because you have to count attendance one by one and attendance data can still be manipulated. Therefore, designing attendance using RFID and fingerprints can help to improve employee discipline and make the attendance process easier. Apart from that, attendance using RFID and fingerprints has advantages compared to manual attendance, such as making the attendance process easier, recording and storing attendance data in a database. Fingerprints also have quite high security because human fingers have unique fingerprints. By using the NodeMCU ESP8266 as a microcontroller that can connect to the internet, the attendance process is only done by scanning the attendance tool, and then processing the data using the website, which will produce more accurate attendance information compared to manual attendance.Keywords: Attendance; RFID; Fingerprint; NodeMCU ESP8266 microcontroller.    AbstrakPresensi merupakan proses pencatatan kehadiran sebagai bentuk kedisiplinan dan profesionalisme karyawan. Presensi manual sangat menyita waktu dan sering terjadinya kesalahan dalam melakukan pembuatan rekap presensi dikarenakan harus menghitung presensi satu per satu dan data presensi masih dapat dimanipulasi. Oleh karena itu, perancangan presensi menggunakan RFID dan fingerprint ini dapat membantu untuk meningkatkan kedisiplinan karyawan dan mempermudah dalam proses presensi, selain itu presensi menggunakan RFID dan fingerprint memiliki kelebihan dibandingkan presensi manual seperti dapat mempermudah proses presensi, perekapan serta data presensi tersimpan didalam database. Fingerprint atau sidik jari juga memiliki keamanan yang cukup tinggi dikarenakan jari manusia memiliki sidik jari yang unik. Dengan menggunakan NodeMCU ESP8266 sebagai mikrokontroler yang dapat terhubung ke internet proses presensi hanya dengan scanning pada alat presensi, kemudian dilakukan pengolahan data dengan website maka akan menghasilkan informasi kehadiran yang lebih akurat dibandingkan dengan presensi manual. 
Identifikasi Unsur Hara pada Lahan Pertanian Padi menggunakan Soil Integrated Sensor dan Sistem Informasi Geografis Abdullah, Asrul; Iwan, Muhammad; Alkhairi, Muhammad Ghozy
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 10, No 3 (2024): Volume 10 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v10i3.83329

Abstract

Pemenuhan SDG"™s di Indonesia salah satunya ditunjang oleh sektor pertanian. Produk pertanian dengan yang menjadi perhatian adalah padi. Pertumbuhan padi di Indonesia dapat tumbuh dengan subur karena di dalam tanah memiliki kandungan unsur hara. Kandungan unsur hara penting yang dibutuhkan oleh padi agar tumbuh dengan cepat adalah nitrogen, phosporus dan kalium / potassium (NPK). Kandungan unsur NPK yang terkandung di pupuk atau tanah melalui analisis laboratorium dengan biaya yang relatif mahal dan waktu yang lama. Untuk itu perlu cara lain yakni menggunakan soil integrated sensor. Penelitian ini memiliki tujuan untuk mendapatkan informasi terkait kandungan unsur hara pada lahan pertanian padi menggunakan soil integrated sensor serta lokasi menggunakan sistem informasi geografis. Parameter yang digunakan antara lain suhu tanah, kelembapan tanah, electrical conductivity, pH tanah, nitrogen, phosporus, potassium dan salinitas tanah. Parameter lain yang di dapatkan dari GPS adalah longitude, latitude, velocity, altitude serta tanggal pengukuran. Tahapan yang ada di dalam penelitian antara lain studi pustaka, pengumpulan data, analisis kebutuhan, rancangan, implementasi dan pengujian prototipe. Hasil dari penelitian ini adalah prototipe berhasil mengidentifikasi kandungan unsur hara tanah, membaca semua parameter dan mengirimkan hasilnya ke Node-RED menggunakan MQTT dan menyimpan data ke MySQL. Parameter lain yang juga ikut disimpan adalah titik lokasi dari pengambilan data menggunakan GPS. Kesimpulan dari penelitian ini protipe berhasil diuji coba pada lahan pertanian padi dan mengidentifikasi kandungan unsur hara. Titik-titik lokasi yang dikirimkan ke database berhasil dipetakan menggunakan Leaflet JS.    
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.