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All Journal Syntax Jurnal Informatika Seminar Nasional Informatika (SEMNASIF) International Journal of Artificial Intelligence Research Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Faktor Exacta JOURNAL OF APPLIED INFORMATICS AND COMPUTING Conference on Innovation and Application of Science and Technology (CIASTECH) Petir Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Building of Informatics, Technology and Science Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Scientific Student Journal for Information, Technology and Science Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Algoritma Jurnal Informatika: Jurnal Pengembangan IT Jurnal Komtika (Komputasi dan Informatika)
Hanny Hikmayanti Handayani
Teknik Informatika, Universitas Buana Perjuangan, Karawang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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PENGARUH SMOTE TERHADAP PERFORMA ALGORITMA RANDOM FOREST DAN ALGORITMA GRADIENT BOOSTING DALAM MEMPREDIKSI PENYAKIT STROKE Fadmadika, Fadilla; Handayani, Hanny Hikmayanti; Mudzakir, Tohirin Al; Indra, Jamaludin
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1575

Abstract

Stroke is a disease that can occur suddenly, causing progressive brain damage due to non-traumatic blood flow disruption in the brain. Common symptoms of stroke include numbness in the limbs and impaired communication. Stroke is the second leading cause of death in the world and the third leading cause of mental retardation globally. Predictive machine learning-based technology can help identify early symptoms of stroke for prevention and early intervention. This study aims to compare the performance of the Random Forest and Gradient Boosting algorithms in predicting stroke. By applying the SMOTE method to address class accuracy in the dataset, this study shows that the Random Forest model is superior, with an accuracy of 95.5%, a precision of 78.8%, a recall of 93.1%, and an f1-score of 84.2%. In conclusion, the Random Forest algorithm performs better than Gradient Boosting in predicting stroke, showing significant potential in assisting early detection and medical decision making.
Implementasi YOLOv5 pada Deteksi Defect Hasil Pengelasan Robot Welding Arc Pajar Arifin; Hanny Hikmayanti Handayani; Dwi Sulistya Kusumaningrum
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Penelitian ini dilatarbelakangi oleh masih sering terjadinya defect pada welding robot yang lolos quality inspection dalam proses produksi. Untuk mengatasi permasalahan tersebut, penelitian ini dilakukan dengan tujuan: (1) membangun sebuah prototipe sistem deteksi defect pada robot welding arc, dan (2) mencapai nilai akurasi atau mean average precision (mAP) lebih dari 0,9 pada deteksi defect pengelasan. Dalam penelitian ini, model atau prototipe yang dibangun menggunakan algoritma Convolutional Neural Network (CNN) dengan arsitektur YOLOv5. Objek yang digunakan dalam penelitian ini adalah citra defect hasil pengelasan robot welding arc, yaitu hole dan porosity, yang kemudian dijadikan dataset. Untuk meningkatkan jumlah data dan mendukung pencapaian akurasi yang diinginkan, dilakukan teknik augmentasi data. Citra hasil augmentasi kemudian dilabeli menggunakan situs web Makesense.ai untuk membangun bounding box dan class dari defect tersebut. Setelah itu, dataset diinput ke dalam Google Colab untuk proses training data. Training dilakukan dengan menjalankan kode train.py,di mana peneliti mengubah parameter custom_data, serta menggunakan variasi epoch dan batch size yang berbeda. Pada konfigurasi batch size 16 dan epoch 100, diperoleh model dengan akurasi sebesar 0,95 mAP.
Penerapan Algoritma Convolutional Neural Network (CNN) untuk Deteksi Telur Bebek Fertil dan Infertil Ricky Steven Chandra; Hanny Hikmayanti Handayani; Yana Cahyana
Scientific Student Journal for Information, Technology and Science Vol. 6 No. 1 (2025): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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

Abstract

Dalam penetasan telur bebek, telur yang infertil perlu disortir dari mesin tetas agar tidak membusuk di dalamnya. Proses penyortiran umumnya dilakukan dengan meneropong telur menggunakan senter atau lampu yang diletakkan di balik telur. Tujuan dari penelitian ini adalah untuk mengembangkan sistem deteksi telur bebek fertil dan infertil menggunakan algoritma Convolutional Neural Network (CNN) guna menggantikan peneropongan secara manual, karena tingkat kelelahan manusia dapat menyebabkan kesalahan dalam penyortiran telur bebek fertil dan infertil. Model yang digunakan dalam penelitian ini adalah You Only Look Once (YOLO), yang merupakan salah satu model deep learning yang efektif untuk pengenalan objek. Penelitian ini terdiri dari beberapa tahap, yaitu pengumpulan data, pra-proses data, konfigurasi jaringan YOLO, pelatihan model YOLO, dan pengujian. Jumlah data citra yang digunakan dalam penelitian ini sebanyak 800, yang terdiri dari dua jenis telur bebek, yaitu fertil dan infertil. Hasil pengujian yang dilakukan sebanyak 40 kali menunjukkan bahwa dengan menggunakan algoritma Convolutional Neural Network (CNN), akurasi yang dicapai dalam membedakan telur bebek fertil dan infertil mencapai sekitar 95%. Hasil ini menunjukkan bahwa CNN memiliki potensi yang signifikan dalam membedakan telur bebek fertil dari infertil, serta memberikan solusi yang cepat dan efisien bagi peternak bebek.
Model Klasifikasi Nominal Mata Uang Kertas Republik Indonesia Menggunakan Convolutional Neural Network Saputra, Arbi Niandi; Handayani, Hanny Hikmayanti; Sukmawati, Cici Emilia; Siregar, Amril Mutoi
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5927

Abstract

Uang kertas adalah alat pembayaran umum di seluruh dunia saat ini karena digunakan dalam transaksi jual beli barang dan jasa. Nilai uang kertas Rupiah di Indonesia memiliki variasi yang mencakup ukuran, warna, dan pola yang berbeda. Identifikasi manual dapat menyebabkan kesalahan, sehingga diperlukan sistem pengenalan uang kertas yang efisien dan akurat. Permasalahan dalam mata uang terbaru menekankan pentingnya sistem pendeteksi yang selalu memperbarui data referensinya agar tetap akurat. Mata uang baru dengan desain atau fitur keamanan yang berbeda dapat menantang kemampuan sistem dalam mengenali keasliannya. Sistem harus mampu dengan cepat mengidentifikasi elemen baru dan memperbarui database referensi untuk menghindari risiko kesalahan atau penipuan. Oleh karena itu, penelitian perlu difokuskan pada pengembangan mekanisme pembaruan data secara real-time untuk menjaga responsivitas sistem terhadap perubahan mata uang. Maka dari itu, dilakukan klasifikasi nominal mata uang kertas Republik Indonesia Tahun Emisi 2022 menggunakan Convolutional Neural Network. Tahapan yang dilakukan yaitu proses akuisisi citra, preprocessing, pelatihan model, dan evaluasi. Dengan teknik pengenalan berdasarkan pola bunga yang terdapat pada uang kertas Republik Indonesia. Hasil yang peroleh yaitu akurasi sebesar 99% dengan 694 data berhasil diklasifikasi dari 700 data pengujian.
Klasifikasi Penyakit Hipertensi Menggunakan Support Vector Machine dan Naive Bayes Jannah, Malika Dakhola; Fauzi, Ahmad; Emilia, Cici; Hikmayanti, Hanny
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2316

Abstract

Hipertensi merupakan penyakit tidak menular yang dapat menimbulkan komplikasi serius seperti stroke dan penyakit jantung. Penelitian ini bertujuan untuk mengklasifikasikan status hipertensi menggunakan algoritma Support Vector Machine dan Naive Bayes. Dataset berjumlah 1.898 data pasien dari Puskesmas, yang telah melalui tahap pembersihan, normalisasi, serta pembagian menjadi data latih dan data uji. Akurasi, presisi, recall, dan skor f1 digunakan untuk mengevaluasi kinerja model. Hasil menunjukkan bahwa SVM memperoleh akurasi sebesar 95,71%, sedangkan Naive Bayes mencapai 93,37%. Temuan ini menunjukkan bahwa SVM lebih unggul dalam mengklasifikasikan status hipertensi. Meski demikian, Naive Bayes tetap layak digunakan karena kesederhanaan implementasinya. Algoritma ini menjadi pilihan alternatif yang efisien untuk klasifikasi medis yang cepat dan ringan. Temuan ini dapat digunakan sebagai basis pengembangan sistem deteksi dini untuk mendukung layanan kesehatan masyarakat.
ANALISIS PERBANDINGAN ALGORITMA MACHINE LEARNING PADA PREDIKSI PRODUKSI BUAH DI ACEH Rafikoh, Zahra Anggun; Faisal, Sutan; Baihaqi, Kiki Ahmad; H, Hanny Hikmayanti
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5765

Abstract

Prediksi produksi buah merupakan aspek penting dalam mendukung perencanaan dan pengelolaan sektor pertanian. Penelitian ini membandingkan performa dua algoritma machine learning, yaitu Random Forest dan Support Vector Machine (SVM), dalam memprediksi produksi buah di Provinsi Aceh menggunakan data produksi dari Badan Pusat Statistik (BPS) periode 2018–2023. Data telah melalui proses preprocessing termasuk penanganan missing values, outliers, serta transformasi variabel kategori. Evaluasi model menggunakan metrik Mean Squared Error (MSE), Mean Absolute Error (MAE), dan R² Score menunjukkan bahwa Random Forest memberikan hasil prediksi yang lebih akurat dibandingkan SVM. Penambahan fitur eksternal seperti curah hujan, jenis pupuk, dan metode budidaya yang disimulasikan tidak memberikan peningkatan signifikan pada performa model. Penelitian ini merekomendasikan penggunaan Random Forest untuk prediksi produksi buah di Aceh dan menyarankan pengumpulan data eksternal yang lebih lengkap untuk meningkatkan akurasi prediksi di masa depan.
Sentiment Analysis on the Relocation of the National Capital (IKN) on Social Media X Using Naive Bayes and K-Nearest Neighbor (KNN) Methods Wulandari, Nova; Cahyana, Yana; Rahmat, Rahmat; Hikmayanti, Hanny
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9552

Abstract

This study investigates public sentiment toward the relocation of Indonesia’s capital from Jakarta to East Kalimantan, focusing on reactions from social media platforms such as X (formerly Twitter). Understanding these sentiments is crucial for the government to gauge support for this significant policy shift. The study compares the performance of two classification algorithms, Naïve Bayes and K-Nearest Neighbor (K-NN), in sentiment analysis. A total of 1.277 comments were collected using the tweet-harvest library through a crawling process. The data underwent preprocessing, including cleaning, case folding, normalization, stopword removal, tokenization, and stemming. Sentiment labels were assigned through both manual and automated methods, while feature extraction was performed using the TF-IDF technique. The algorithms' performance was assessed using accuracy, precision, recall, and F1-score metrics. The results revealed that Naïve Bayes outperformed K-NN, with an accuracy of 70%, precision of 72%, recall of 70%, and an F1-score of 69%. In contrast, K-NN achieved an accuracy of 60%, precision of 62%, recall of 60%, and an F1-score of 59%. These results suggest that Naïve Bayes is more effective in classifying sentiment related to the capital relocation. The findings offer valuable insights for policymakers and highlight the potential of automated sentiment analysis as a tool for monitoring public opinion on major governmental policies.
Improvement of FPS and Efficiency of Parameters Mask R-CNN with MobileNetV3 Small for Cardboard Detection Tri Vicika, Vikha; Indra, Jamaludin; Faisal, Sutan; Hikmayanti, Hanny
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 16 No. 1 (2025): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v16i1.26349

Abstract

Inventory management in warehouses often experiences discrepancies in recording the number of cardboard boxes due to errors during the manual recording process. To overcome this problem, a cardboard detection method was developed using the Default Mask R-CNN model and a modified model using MobileNetV3 Small. The training data was obtained from a collection of cardboard photos which then went through an annotation stage. In the cReonfiguration stage, various anchor scales were applied to determine the bounding box parameters, while the training process used Stochastic Gradient Descent (SGD). The default model is trained with the initial Mask R-CNN settings, while the custom model modifies the backbone and Feature Pyramid Network (FPN) adjustments. The test results show that the custom model has higher efficiency with a parameter count of 20,857,704 and an average FPS of 10.92. However, the accuracy level of the custom model is lower than that of the default model
Optimasi AdaBoost dan XGBoost untuk Klasifikasi Obesitas Menggunakan SMOTE Sukmawati, Cici Emilia; Pratama, Adi Rizky; Hikmayanti, Hanny; Juwita, Ayu Ratna
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8536

Abstract

Obesity is a condition in which a person's weight exceeds the normal limit due to excessive accumulation of fat tissue. Thus, obesity is considered a global public health challenge. This is evidenced by the latest data from the World Health Organization (WHO) in 2022, namely that 2.5 billion adults aged 18 years and over are overweight and 890 million of them are obese. Therefore, it is very important to accurately identify these risk factors in order to implement effective interventions in the prevention and management of obesity. However, in previous studies there has been no application of SMOTE with the AdaBoost and XGBoost algorithms, so this study aims to compare the performance of the AdaBoost and XGBoost algorithms with SMOTE. The stages of this research begin with problem identification, data collection, preprocessing and model evaluation and model comparison. This study also applies the SMOTE technique to balance unbalanced data. Based on the results of the research that has been carried out, it shows that the accuracy and recall values of the XGBoost algorithm with SMOTE are 0.945 and precision 0.947. Meanwhile, the accuracy and recall values on AdaBoost with SMOTE are 0.388. Then, the precision is 0.371. Thus, it is expected that the results of the XGBoost model with SMOTE can be a source for other research and can help in efforts to prevent and manage obesity.
Optimasi Metode Support Vector Machine Menggunakan Seleksi Fitur Recursive Feature Elimination dan Forward Selection untuk Klasifikasi Kanker Payudara Septiany, Eva Senia; Handayani, Hanny Hikmayanti; Mudzakir, Tohirin Al; Masruriyah, Anis Fitri Nur
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5324

Abstract

Cancer, the leading cause of global death, results from abnormal cell proliferation that spreads beyond the boundaries of normal tissue. Breast cancer is one of the most common types of cancer, with approximately 2.26 million cases reported in 2020. This research aims to develop a more effective Support Vector Machine (SVM) algorithm for breast cancer classification through efficient feature selection techniques. Previous research has used various algorithms such as K-Nearest Neighbor and Logistic Regression for breast cancer identification. This research focuses on improving accuracy by using alternative feature selection methods such as Recursive Feature Elimination (RFE) and Forward Selection. The dataset used consists of 569 instances with 32 features sourced from the UCI Machine Learning Repository, and classified into benign and malignant categories. Data pre-processing methods, including data cleaning, coding, and feature selection, were applied to the dataset. RFE and Forward Selection techniques were used to identify the most important features for model training. Evaluation of the improved SVM model shows a training accuracy of nearly 100% and a Cross Validation accuracy of 97%, demonstrating the effectiveness of the proposed approach in the context of breast cancer. In addition, the Learning Curve and testing showed the stability of the SVM model with no signs of overfitting or underfitting. Thus, this study developed an SVM algorithm with a feature selection method that produces better accuracy results in breast cancer classification.
Co-Authors Adi Rizky Pratama Ahmad Fauzi Ahmad Fauzi Alif Kirana Amril Mutoi Siregar Andri Safari Anis Fitri Nur Masruriyah Anis Fitri Nurmasruriyah Anton Romadoni Junior April Lia Hananto Arfian Pua Meno Aries Suharso Arif Nurman Arphilia Nur Rani Ayu Ratna Juwita Azhar, Wafiqah Yasmin Bagus Setiawan Baihaqi, Kiki Ahmad Bilawa, Firdho Akbar Carudin . Chorirotun Cholifah Cici Sukmawati Cucu Sri Cahyanti Deden Wahiddin Deden Wahiddin Dedih, Dedih Devi Fitrianah Dina Wulan Nurjanah Dinar Sugiri Dinda Resna Amalia Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Dwiki Jatikusumo Eldawati, Eldawati Elsa Elvira Awal Emilia, Cici Enoh Suhada Fadmadika, Fadilla Faisal, Sutan Fauzi Ahmad Muda Firdaus, Thoriq Janati Fitri Nur Masruriyah, Anis Garno . Genta Kusuma Atmaja Heri Hermawan Hilda Fitriana Dewi Hilda Yulia Novita Hilman Abdurrohim Azis Indra, Jamaludin Irfan Jannah, Malika Dakhola Jasmin Putri Jayidan, Zirji Juwita, Ayu Ratna Lestari, Santi Arum Puspita Lila Setiyani Mudzakir, Tohirin Al Muhamad Enoh Suhada Nai Siti Jenab Nazori AZ Nita Fitriyani Nofita Sari Nova Wulandari Nugraha, Najmi Cahaya Nunung Nurjanah Nurlaelasari, Euis Pajar Arifin Prasetyo Ajie Pratama, Adi Rizky Rafikoh, Zahra Anggun Rahmat Rahmat Rahmat, R Ramadhan, Naufal Cahya Ricky Steven Chandra Ridwan, Ridwan Rishma Maudyna Rohana, Tatang Sandi Susanto Saputra, Arbi Niandi Saruni Dwiasnati Septiany, Eva Senia Setyawan Widyarto Siregar, Amril Mutoi suhliyyah Sukmawati, Cici Emilia Sutan Faisal Sutan Faisal Tri Vicika, Vikha Vikha Tri Vicika Wati, Devi Fajar Yana Cahyana Yogi Firman Alfiansyah Youbel Jansen