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All Journal J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Djtechno: Jurnal Teknologi Informasi Jurnal Ilmiah Edutic : Pendidikan dan Informatika DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY J-ENSITEC (Journal of Engineering and Sustainable Technology)
Nurul Abdillah
Universitas Jambi
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Public Health

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Development of a Web-Based Application for Predicting Stroke Patient Emergency Levels Using the Naïve Bayes Algorithm Nurul Abdillah; Oki Dahwanu; Hamzah Alghifari; Niko Akbar
EDUTIC Vol 13, No 1: 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v13i1.34303

Abstract

Stroke is a serious disease that requires prompt and appropriate treatment; therefore, determining the level of patient Emenrgency Levels is critically important. This study aims to develop a web-based application for predicting the Emenrgency Levels level of stroke patients using the Naïve Bayes algorithm as a classification method. The research data were obtained from the medical records of stroke patients at RSUP Dr. M. Djamil Padang during March and April 2025, with a total of 222 data samples. The attributes used in this study include age, gender, address, length of stay, ward class, BPJS insurance membership status, and comorbidities, with Emenrgency Levels status as the class attribute classified into Emenrgency and non-Emenrgency. The application was developed as a web-based system to facilitate easy access for medical personnel in utilizing the prediction system. The experimental results indicate that the Naïve Bayes algorithm achieved an accuracy of 77.48% with an error rate of 22.52%. The findings of this study are expected to assist medical personnel in supporting faster and more objective decision-making regarding the Emenrgency Levels level of stroke patients.
Comparison of Machine Learning Algorithms (SVM, Random Forest, and Naïve Bayes) for Predicting Rice Production Oki Dahwanu; Nurul Abdillah; Niko Akbar; Hamzah Alghifari
J-ENSITEC (Journal of Engineering and Sustainable Technology) Vol. 12 No. 02 (2026): June 2026
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/j-ensitec.v12i02.18386

Abstract

Global rice production faces mounting pressure from population growth and climate change, yet traditional statistical models fail to capture the complex nonlinear dynamics between environmental factors and crop yields. To address this gap, this study systematically compares the accuracy of three machine learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) for predicting rice production fluctuations due to climate change using the latest local climate data from Indonesia. A dataset of 96 monthly observations (2018–2025) comprising climate features (temperature, humidity, wind speed, precipitation, cloud cover, sunshine duration) and rice production categories (Low, Medium, High) was analyzed. Algorithm performance was evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that Random Forest significantly outperforms the other methods, achieving an accuracy of 95%, precision of 0.9571, recall of 0.95, and F1-score of 0.95, compared to SVM (75% accuracy) and Naïve Bayes (70% accuracy). This study provides the first head-to-head comparison of these three algorithms for rice yield prediction in Indonesia using current climate data. The key benefit over pre-existing approaches is the empirical confirmation that ensemble learning, particularly Random Forest, offers superior predictive reliability for crop yield forecasting under high feature complexity, thereby enabling more accurate, data-driven agricultural policy and food security planning.
KLASIFIKASI DAN PREDIKSI KELUARGA BERISIKO STUNTING DI PROVINSI JAMBI MENGGUNAKAN METODE KNN DAN NAIVE BAYES Niko Akbar; Hamzah Alghifari; Nurul Abdillah; Oki Dahwanu
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 7, No 1: JUNI 2026
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v7i1.8522

Abstract

Di Indonesia Prevalensi stunting sebesar 37,2%, naik dari 35,6% pada tahun 2019 dan 36,8%, dengan mayoritas dipengaruhi oleh penduduk setempat. Kementerian Kesehatan Indonesia memperkirakan bahwa prevalensi stunting akan mencapai 38,9% pada tahun 2020. Permasalahannya Beberapa Data yang diambil dan dipakai berupa data sekunder yang di ada diwebsite opendata provinsi jambi yang berjudul Faktor Penapisan Keluarga Berisiko Stunting di Provinsi Jambi. Dengan menggunakan algoritma KNN dan Naïve Bayes, maka didapatkan hasilnya berupa kedua algoritma cocok untuk pengklasteran dan Scoring dari algoritma menunjukkan hasil yang berbeda. Karena keakurasiannya sesuai dengan perhitungan manual yang telah dijabarkan pada bagian pengolahan data. Beberapa hasil dari cross-validasi menyatakan bahwa nilai accuracy 85,29%, nilai precision berupa 83,33%, nilai recall 85,29%.
Pendekatan Machine Learning untuk Menganalisis Faktor-Faktor yang Berkontribusi Terhadap Kesalahan Pengkodean ICD-10 di Semen Padang Hospital Rury Moryanda; Nurul Abdillah; Denos Imam Fratama; Dicky Fatrias
J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Vol 7 No 3 (2026): June
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-remi.v7i3.6775

Abstract

ICD-10 coding errors remain a major challenge in healthcare facilities, affecting the validity of morbidity data, the accuracy of BPJS Kesehatan claims processing, and the quality of health information management. This study aimed to identify the main factors contributing to ICD-10 coding errors and evaluate the effectiveness of machine learning algorithms in detecting such errors. Electronic medical record data were analyzed through data cleaning, preprocessing, descriptive analysis, and machine learning modeling. Three algorithms were applied: Random Forest, Support Vector Machine (SVM), and Neural Network. Model performance was evaluated using accuracy, precision, and sensitivity metrics. The findings revealed an ICD-10 coding error rate of 33.8%, primarily caused by nonspecific diagnoses and insufficient clinical information. Among the tested models, the Neural Network achieved the highest accuracy (72%), followed by SVM (68%) and Random Forest (60%). These results suggest that machine learning techniques can effectively support the early detection of ICD-10 coding errors and enhance the quality of health data management. The adoption of machine learning–based predictive models may improve coding accuracy and facilitate evidence-based decision-making in health information management.
PENERAPAN ALGORITMA K-MEANS UNTUK KLASTERISASI POLA IKLIM STUDI KASUS: PROVINSI JAMBI PERIODE 2020-2024 Yandi Anzari; M. Yonggi Puriza; Niko Akbar; Nurul Abdillah; Oki Dahwanu; Elsi Alfionita Syawal
Djtechno: Jurnal Teknologi Informasi Vol 6, No 3 (2025): Desember
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i3.7994

Abstract

Penelitian ini menerapkan algoritma K‑Means untuk mengidentifikasi pola iklim dominan di Provinsi Jambi pada periode Januari 2020 sampai Desember 2024. Dataset bulanan berjumlah 60 observasi yang memuat enam variabel meteorologi: curah hujan, suhu rata‑rata, suhu maksimum, suhu minimum, kecepatan angin, dan kelembaban relatif. Data diagregasi dari data harian menjadi bulanan dan dipra‑proses dengan standardisasi Z‑Score untuk mengatasi heterogenitas skala antar fitur. Penentuan jumlah klaster optimal dilakukan secara kuantitatif menggunakan Elbow Method berdasarkan nilai inersia, yang menunjukkan titik belok pada k=3. Model K‑Means diinisialisasi secara acak dan dijalankan dengan beberapa pengulangan untuk menilai stabilitas hasil; keluaran divisualisasikan dalam proyeksi 2D dan 3D untuk memudahkan interpretasi spasial. Analisis centroid mengidentifikasi tiga rezim iklim: rezim kering/transisi (curah hujan rendah dan suhu relatif tinggi), rezim monsonal normal (curah hujan menengah‑tinggi dan kelembaban tinggi), serta rezim basah ekstrem (curah hujan sangat tinggi dan kelembaban tinggi). Hasil ini menyediakan tipologi iklim berbasis data yang relevan untuk perencanaan pertanian, mitigasi bencana hidrometeorologi, dan kebijakan adaptasi iklim di tingkat provinsi.
Co-Authors Alghifari, Hamzah Denos Imam Fratama Dicky Fatrias Elsi Alfionita Syawal M. Yonggi Puriza Niko Akbar Niko Akbar Oki Dahwanu Rury Moryanda Yandi Anzari