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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Artificial Intelligence and Engineering Applications (JAIEA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Nurhakim, Bani
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Implementasi Algoritma Naïve Bayes untuk Prediksi Penerima Bantuan Sosial di Desa Cigayam Hoeriah, Dede; Nurhakim, Bani; Permana, Sandy Eka; Prihartono, Willy; Dwilestari, Gifthera
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 1 (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No1.pp52-58

Abstract

Social assistance is one of the government's programmes aimed at improving the lives of people especially for those who are economically disadvantaged. However, there are several reasons why some people are unable to access social assistance. In the case of this study, the authors used the Naïve Bayes algorithm with the KDD (Knowledge Discovery Database) method to predict the population in obtaining social assistance. The data was taken from the population data of Cigayam Village and the social welfare recipient data in the village ofCigayam with the results showing high accuracy in this study, for the true or false outcome of 1047 data and 53 data with the precision grade of 95.18%, 81.17%, for the real outcome, and 28.38% for the wrong outcome. So with the ROC curve shows the accuracy of the spinning visually, with an AUC of 0.868% for naïve bayes using the ROK curve of 0.90.1.
PENARAPAN ALGORITMA K-MEANS CLUSTERING DALAM MENGANALISIS RESIKO PENYAKIT STROKE Nurhakim, Bani; Septiani, Intan; Anam, Khaerul; Pratama, Denni
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 1 (2024): JATI Vol. 8 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i1.8452

Abstract

Di kawasan Asia Tenggara, stroke menempati peringkat ketiga sebagai penyebab kecacatan dan menjadi penyakit dengan risiko kematian tertinggi kedua. penyakit Stroke terjadi ketika pembuluh darah mengalir ke otak terhambat atau terjadi pecah, mengakibatkan beberapa sel jaringan otak tidak dapat menerima oksigen yang dibutuhkan dari aliran darah. Permasalahan mencakup mengidentifikasi faktor risiko penyakit stroke dengan akurat sehigga menjadi suatu tantangan dan Menentukan jumlah cluster (K) yang paling sesuai untuk mencerminkan kelompok resiko penyakit stroke dengan optimal. Dengan menerapkan pendekatan data mining K-means clustering, pasien dapat dikelompokkan ke dalam kategori resiko yang beragam. Beberapa kelompok pasien menunjukkan dominasi faktor risiko tertentu, sehingga memberikan pemahaman lebih rinci mengenai variasi resiko stroke dalam populasi. Temuan dari analisis K-means clustering telah diperkuat melalui validasi menggunakan data klinis tambahan, menegaskan kehandalan dan relevansi hasil. Proses validasi melibatkan uji model pada dataset yang berbeda untuk memastikan keberlakuan temuan secara umum. Penelitian ini melibatkan langkah-langkah teliti dalam penentuan jumlah cluster (K) untuk menjamin hasil clustering yang optimal. Pendekatan ini membantu mengurangi risiko overfitting atau underfitting, sehingga meningkatkan akurasi analisis. Visualisasi hasil clustering memberikan gambaran yang jelas tentang sebaran faktor risiko di antara kelompok pasien, dengan grafik dan diagram yang memudahkan interpretasi serta komunikasi temuan kepada pihak-pihak yang berkepentingan.
IMPLEMENTASI ALGORITMA NAIVE BAYES UNTUK MEMPREDIKSI CUACA MENGGUNAKAN RAPIDMINER Nur Kirana, Anita; Nurhakim, Bani; Eka Permana, Sandy; Prihartono, Willy; Dwilestari, Gifthera
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 2 (2024): JATI Vol. 8 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i2.8967

Abstract

Cuaca adalah kondisi udara di suatu tempat dalam waktu relatif singkat yang dinyatakan dengan nilai parameter oleh kecepatan angin, suhu, tekanan, curah hujan, dan faktor atmosfer lainnya. Keadaan cuaca dapat mempengaruhi berbagai aspek kegiatan manusia, termasuk transportasi, pertanian, perkebunan, pembangunan, dan bahkan aktivitas olahraga. Sehingga untuk memperoleh informasi cuaca yang dapat dimanfaatkan oleh masyarakat diperlukan pembuatan penentuan cuaca. Salah satu metode prediksi cuaca yang digunakan adalah dengan menggunakan algoritma Naive Bayes. Algoritma ini menghitung probabilitas dari setiap kelas untuk setiap atribut kelompok yang ada dan menentukan kelas yang paling optimal. Dengan kata lain, pengelompokan cuaca dapat dilakukan berdasarkan kategori yang diinputkan oleh pengguna pada aplikasi. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi cuaca dengan menerapkan metode data mining menggunakan algoritma Naive Bayes. Metode penelitian melibatkan pengklasifikasian data cuaca berdasarkan atribut kelompok seperti kecepatan angin, suhu, curah hujan, dan cuaca. Naive Bayes digunakan untuk menghitung probabilitas kelas cuaca berdasarkan input pengguna pada aplikasi. Hasil pengujian menunjukkan tingkat akurasi sebesar 84.26%, menunjukkan bahwa metode ini memiliki potensi untuk memberikan prediksi cuaca yang lebih akurat, yang dapat bermanfaat dalam berbagai aspek kegiatan manusia.
CLUSTERING JENIS SUMBER AIR DI PROVINSI JAWA BARAT MENGGUNAKAN ALORITMA K-MEDOIDS Apriliyani, Ela; Nurhakim, Bani; Eka Permana, Sandy; Dwilestari, Gifthera; Mulyawan, Mulyawan
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 2 (2024): JATI Vol. 8 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i2.8982

Abstract

Air sangat penting bagi seluruh makhluk hidup di Bumi karena merupakan kebutuhan dasar yang harus dipenuhi dan dikonsumsi. Sumber air merupakan fondasi ekosistem dan keanekaragaman hayati. Terbatasnya ketersediaan air menunjukkan pentingnya melindungi dan mengelola penggunaannya secara bijaksana. Perubahan iklim dapat mempengaruhi siklus air di bumi. Dengan data sumber air yang di keluarkan dalam periode 1 tahun sekali oleh Dinas Pemberdayaan Masyarakat Tujuan penelitian ini adalah mengelompokkan jenis sumber air dengan metode K-Medoids dan menganalisis hasilnya dengan implementasinya menggunakan RapidMiner. Penelitian ini menghasilkan sebanyak 3 cluster, pada masing-masing cluster memiliki anggota yang berbeda. Cluster 0 memiliki 200 item, cluster 1 memiliki 96 items, dan cluster 2 memiliki 160 items yang, dengan total dari ketiga cluster tersebut sebanyak 456 items.
ANALISIS TEKNIK BOOTSTRAP AGGREGATING DENGAN ALGORITMA DECISION TREE UNTUK MEMPREDIKSI DATA TRACER LULUSAN Nurhakim, Bani; Mulyawan, Mulyawan; Lukman Rohmat, Cep
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 4 (2024): JATI Vol. 8 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i4.10940

Abstract

Ketidaksesuaian antara bidang kerja lulusan dengan kompetensi mereka di bidang pekerjaan masing-masing, yang tidak sesuai dengan latar belakang pendidikan dan keahlian mereka, memiliki dampak signifikan terhadap fleksibilitas pekerjaan dan dapat mempengaruhi kinerja kerja. Langkah antisipatif yang dapat digunakan untuk pengambilan keputusan terkait penelitian yang dilakukan, dan menurut hasil wawancara dengan manajemen tracer study STMIK IKMI Cirebon, hingga saat ini, informasi mengenai prediksi kesesuaian pekerjaan lulusan belum diperoleh, meskipun prediksi tersebut diperlukan untuk mempersiapkan sumber daya manusia agar bekerja sesuai dengan kompetensi mereka. Penelitian ini merupakan penelitian eksperimental dan studi kasus menggunakan metode Knowledge Discovery in Database (KDD). Algoritma yang digunakan adalah pohon keputusan C4.5 dengan teknik bootstrap aggregating. Dengan menganalisis, mengimplementasikan, dan mengeksperimenkan model yang akan menghasilkan evaluasi akurasi model pohon keputusan C4.5 terbaik dengan teknik bootstrap aggregating. Kesesuaian bidang kerja lulusan dipengaruhi oleh atribut yang paling dominan, yaitu IPK lulusan (100%) dengan IPK tertinggi maksimal 3,71 untuk 12 orang, usia lulusan (97,4%) dengan 776 lulusan yang termuda berusia 21 tahun, dan durasi studi (53,85%). Sementara itu, atribut yang paling sedikit mempengaruhi kesesuaian bidang kerja lulusan adalah tahun kelulusan (10,4%). Model optimasi klasifikasi terbaik diperoleh melalui pengaturan parameter gain ratio, menghasilkan model prediksi yang sangat baik. Berdasarkan evaluasi akurasi model terbaik, skor akurasi adalah 95,27% dan skor AUC adalah 98,60%.
Implementasi Algoritma Naïve Bayes untuk Prediksi Penerima Bantuan Sosial di Desa Cigayam Hoeriah, Dede; Nurhakim, Bani; Permana, Sandy Eka; Prihartono, Willy; Dwilestari, Gifthera
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 1 (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No1.pp52-58

Abstract

Social assistance is one of the government's programmes aimed at improving the lives of people especially for those who are economically disadvantaged. However, there are several reasons why some people are unable to access social assistance. In the case of this study, the authors used the Naïve Bayes algorithm with the KDD (Knowledge Discovery Database) method to predict the population in obtaining social assistance. The data was taken from the population data of Cigayam Village and the social welfare recipient data in the village ofCigayam with the results showing high accuracy in this study, for the true or false outcome of 1047 data and 53 data with the precision grade of 95.18%, 81.17%, for the real outcome, and 28.38% for the wrong outcome. So with the ROC curve shows the accuracy of the spinning visually, with an AUC of 0.868% for naïve bayes using the ROK curve of 0.90.1.
Data Augmentation Techniques on the Accuracy of Fertile and Infertile Egg Classification Using Convolutional Neural Networks Nurhakim, Bani; Solihudin, Dodi; Amalia, Dina; Arelia, Irly
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5234

Abstract

The classification of fertile and infertile chicken eggs is crucial in the poultry industry to ensure optimal incubation efficiency and hatchability. However, the visual similarity between both egg types under candling conditions poses a significant challenge for manual inspection. This study aims to develop a convolutional neural network (CNN) model using the EfficientNetB4 architecture to automatically classify egg fertility based on image data. The dataset comprises candling images of chicken eggs, which underwent preprocessing steps such as resizing, normalization, and histogram stretching to enhance contrast. To improve model generalization, aggressive data augmentation techniques were applied, including rotation, flipping, zooming, and brightness adjustment. The model was trained in two phases—feature extraction and fine-tuning—using transfer learning and class balancing strategies. Evaluation results demonstrated high performance with an F1-score of 0.95 and balanced classification across both classes. The model's interpretability was further enhanced using Grad-CAM visualization, showing relevant activation regions. These findings indicate that the proposed method is effective in automating egg fertility classification and has potential for broader application in agricultural image diagnostics.
A Decision Tree Model with Grid Search Optimization for Scholarship Recipient Classification Suprapti, Tati; Nurhakim, Bani; Warni Ayu Hermina, Bintang; Syahputra Simbolon, Vrendi Amro
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5235

Abstract

This study aims to classify scholarship recipients using the Decision Tree algorithm implemented in RapidMiner. The dataset consists of 1.404 records with socioeconomic and academic attributes. Preprocessing was conducted using two Replace Missing Value operators, where categorical attributes such as No. BANTUAN, No. KKS, and Prestasi were filled with "Tidak Punya," while Kepemilikan Rumah was imputed using the average value. The model was built using a Decision Tree algorithm, optimized with the Optimize Parameters (Grid) operator to determine the best values for maximal depth and confidence. Evaluation was performed using 10-fold Cross Validation to ensure reliability. The results show that the optimized Decision Tree model achieved a high accuracy of 97.72%, with strong precision, recall, and F1-score values in both the "Eligible" and "Not Eligible" classes. These findings demonstrate that the Decision Tree algorithm, when properly optimized and validated, can effectively support decision-making processes in scholarship eligibility classification. The model provides an interpretable and robust tool for educational institutions to evaluate student applications based on critical socioeconomic features, This research contributes to educational data mining by offering a validated and interpretable model that enhances fairness, transparency, and efficiency in the scholarship selection process.
Optimization of Classification of Tea Leaf Disease Images Using LBP–HOG and MobileNetV2 Ezar Qotrunnada; Nurdiawan, Odi; Dikananda, Arif Rinaldi; Putra, Aris Pratama; Nurhakim, Bani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1861

Abstract

This study was motivated by the need for an accurate and efficient system for detecting tea leaf diseases, given that the current method Manual identification has limitations in terms of consistency, speed, and It also depends on expert labor. To address these challenges, the study It developed a classification model for detecting diseases in tea leaves using a combination of features Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) integrated with the MobileNetV2 architecture. The research method includes the following stages: importing the dataset, data partitioning, exploratory data analysis (EDA), preprocessing, features, and training four model scenarios: baseline MobileNetV2, LBP-based model, HOG-based model, and hybrid LBP–HOG model. Evaluation is done with the metrics of accuracy, precision, recall, and F1-score. The results show that the baseline model achieved 91.67% accuracy, the LBP model achieved 60.67%, the HOG model achieved 68.67% accuracy, and the hybrid model achieved 66.67% accuracy. These findings indicate that MobileNetV2 is still the most optimal model, but the integration of texture features and gradients provides a deeper understanding of the characteristics of disease patterns. This study emphasizes the importance of exploring classic features to enriching visual representation in lightweight CNN models, as well as providing a contribution to the development of plant disease diagnosis systems that are efficient.
Comparative Analysis of Durian Leaf Disease Classification Using Transfer Learning VGG16, InceptionV3, and U-Net Nafisa Maysa Salma; Kurniawan, Rudi; Nurhakim, Bani; Bahtiar, Agus; Narasati, Riri
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1864

Abstract

Image-based durian leaf disease detection presents challenges due to high visual similarity among symptoms and the limited, imbalanced dataset. This study compares three deep learning architectures VGG16, InceptionV3, and U-Net encoder-based—using transfer learning for classifying five durian leaf conditions. The dataset of 4,437 images underwent preprocessing, augmentation, and preliminary segmentation using U-Net to enhance focus on leaf regions. Fine-tuning was applied to the upper layers of each model to adapt feature representations to tropical leaf characteristics. The results indicate that InceptionV3 achieved the most stable and accurate performance with an accuracy of approximately 0.66, while VGG16 showed balanced results but was more prone to overfitting. U-Net proved effective for segmentation but less optimal as a classifier due to loss of small-scale lesion details. Overall, the findings demonstrate that combining U-Net segmentation with CNN-based transfer learning improves disease identification performance, particularly under limited data conditions.
Co-Authors Ahmad Faqih Apriliyani, Ela Arelia, Irly Bambang Irawan Dikananda, Arif Rinaldi Dina Amalia Eka Permana, Sandy Ezar Qotrunnada Gifthera Dwilestari Gita Antar Wulan Hery Widijanto Hoeriah, Dede Lukman Rohmat, Cep Mulyawan, Mulyawan Nafisa Maysa Salma Narasati, Riri Narasati Nur Kirana, Anita Odi Nurdiawan Permana, Sandy Eka Pratama, Denni Prihartono, Willy Putra, Aris Pratama Rudi Kurniawan Septiani, Intan Solihudin, Dodi Syahputra Simbolon, Vrendi Amro Tati Suprapti Warni Ayu Hermina, Bintang