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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Kaputama (JIK) Media Jurnal Informatika
Irmawati Irmawati
Universitas Bina Sarana Informatika
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Implementasi artificial neural network dalam mendeteksi penyakit hati (liver) Irmawati Irmawati; Kudiantoro Widianto; Faruq Aziz; Achmad Rifai; Ami Rahmawati
Journal of Information System, Applied, Management, Accounting and Research Vol 6 No 1 (2022): JISAMAR: February 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v6i1.694

Abstract

Acute liver disease can affect liver function, but can identify the patient's clinical and physical symptoms. One of the problems faced by society today is the delay in treatment of liver disease patients, most patients do not carry out self-examination until an advanced stage is found. To overcome this problem, we need a system that can determine whether a person is a patient with liver disease, so that they can carry out routine checks as soon as possible and allow liver disease patients to get timely treatment. The system can generate classification with the help of data mining algorithms. In this paper, Liver Patients have been investigated using an Artificial Neural Network model to predict a Liver Patient or not and analysis using ANN with Python was used to determine the effect of input variables based on data in the literature and obtained an accuracy of 74%.
Penerapan Metode Association Rule Terhadap Pola Data Penyakit Pada RSUD Jakarta Menggunakan Algoritma Apriori Citra Permana Putra; Achmad Rifai; Irmawati Irmawati; Kudiantoro Widianto
JISAMAR (Journal of Information System, Applied, Management, Accounting and Research) Vol 6 No 4 (2022): JISAMAR : November 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v6i4.828

Abstract

The hospital is a place to treat patients who come from various regions with various diseases, because lifestyle and the environment play a role in the course of the disease. Some of the problems that arise in dealing with disease include lack of information about the spread of disease and the percentage level of disease spread in the community. One solution to this problem is the application of the association rule method that looks for patterns or relationships between items in a dataset determined by an a priori algorithm that can search historical data to identify data patterns based on previously identified properties. This research method is quantitative data with primary data taken from medical records of Johar Baru Hospital Jakarta. Which then can be obtained an information on what diseases most often appear together in hospitals so that prevention of these diseases can be done. This study aims to find patterns of disease that are developing in the community. The resulting information can then be used by the hospital to maximize the availability of drugs, complete medical equipment, and rooms in the hospital.
Deep learning untuk pendeteksian penyakit kanker payudara dengan optimasi Adam Irmawati Irmawati; Yuris Alkhalifi; Agung Fazriansyah; Mohammad Syamsul Azis; Kudiantoro Widianto
JISAMAR (Journal of Information System, Applied, Management, Accounting and Research) Vol 7 No 1 (2023): JISAMAR : February 2023
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v7i1.1015

Abstract

Breast cancer is the second leading cause of death in female patients in the world. Breast cancer has caused death in more than 100 countries. Early diagnosis of breast cancer patients is important to reduce the possibility of death. Researchers focus on accurate breast cancer detection, automated diagnostic methods and breast cancer diagnosis. This paper proposes Adam's optimization for Deep Learning Algorithm to classify breast cancer detection. This study aims to overcome the problem of data instability and overfitting, as well as update network weights on deep learning training data. In this study, the authors conducted experiments with a combination of three hidden layers and learning speed to improve classification accuracy. The experiment used the breast cancer data set obtained from the UCI Study: the WBCD data set (Original) while the experimental results showed that the proposed scheme achieved 96.3% accuracy for classifying breast cancer.
ANALISA POLA PEMBELIAN KONSUMEN MENGGUNAKAN ALGORITMA FP-GROWTH PADA NUSA RICEBOWL & BURGER Lutfiyatul Ulfa; Syaifur Rahmatullah; Irmawati Irmawati
JISAMAR (Journal of Information System, Applied, Management, Accounting and Research) Vol 7 No 2 (2023): JISAMAR : May 2023
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v7i2.1066

Abstract

Currently, competition in the trading business world is very tight, especially in the fast food restaurant industry, such as Nusa Ricebowl & Burger, to attract customer interest in the food products being sold. So a strategy is needed to beat the market, especially in product sales at Nusa Ricebowl & Burger. With the right strategy, it will sell products quickly so that sales can increase to achieve maximum profit so that maximum profit is achieved which is the company's goal. By using Nusa Ricebowl & Burger transaction data, you can find out what products consumers buy the most and can also determine consumer buying patterns. The amount of sales transaction data available, of course, makes it more difficult to analyze data manually, so it must be done with the help of a system so that sales patterns are easily obtained. One common method used to analyze consumer buying patterns is basket analysis or market basket analysis (MBA). The research applies the FP-Growth algorithm in carrying out the data mining process to find consumer purchasing patterns at Nusa Ricebowl & Burger Stores. To find the frequency between items with a support value of 20% and 30% confidence. This is done by looking for a single path that is combined with the Conditional FP-Tree that has been obtained. The overall results obtained from the sales sample data are 22 rules consisting of 2 association rules that meet 20% support and 2 rules that meet 30% confidence. Of the 110 association rule transaction data that met the requirements ≥ 0.30 were: P→H 0.40 (if the consumer buys Aqua, then he buys Nusa Dua) it can be concluded that the Nusa Dua and Aqua menus are the most frequently purchased by consumers together. The rules have generated new information about consumer buying patterns that are not yet known. This can be used to help shop owners maintain good quality for consumers.
PENERAPAN TEKNIK SMOTE DALAM MEMPREDIKSI KEGAGALAN MESIN MENGGUNAKAN SUPPORT VECTOR MACHINE DAN LOGISTIC REGRESSION irmawati irmawati
Jurnal Informatika Kaputama (JIK) Vol 7 No 2 (2023): Volume 7, Nomor 2, Juli 2023
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v7i2.86

Abstract

Kegagalan peralatan dan waktu henti yang singkat dari proses manufaktur sering menyebabkan kerugian ekonomi yang besar bagi perusahaan. Berdasarkan tingkat keparahan degradasi kinerja mesin dan penggalian pengetahuan dari beberapa input, model penurunan kinerja dari berbagai jenis peralatan yang sama, tetapi faktor manusia, mesin, dan lingkungan yang berbeda. Dengan evaluasi efektivitas perawatan pada kebijakan perawatan yang berbeda, biaya relevan, sumber daya, dan strategi perawatan yang optimal dapat ditentukan. Dengan memanfaatkan berbagai model machine learning, pengklasifikasi diharapkan mampu memprediksi kondisi mesin apakah gagal mesin atau tidak gagal mesin sehingga dapat menjadi input dalam mengestimasi biaya pemeliharaan serta peningkatan masa pakai mesin produksi. Pada penelitian ini memanfaatkan model machine learning dengan menggunakan dua algoritma yaitu Support Vector Machine dan Logistic Regression, kedua model tersebut menggunakan teknik Smote untuk mengatasi imbalance class dan menggunakan metode data preprocessing yang sama, kan tetapi mendapatkan hasil yang berbeda, dengan perbedaan persentase sebesar 9%. Sehingga dapat disimpulkan model dengan menggunakan algoritma SVM bisa lebih baik dalam memprediksi kegagalan mesin atau tidak dengan tingkat keakuratan sebesar 93% dibandingkan dengan model menggunakan algoritma Logistic Regression yang hanya menghasilkan keakuratan sebesar 84%.
Klasifikasi Status Gizi Anak Berdasarkan Indikator BB/TB Menggunakan Gradient Boosting Classifier Achmad Rifai; Irmawati Irmawati; Syarah Seimahuira; Eka Herdit Juningsih
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9393

Abstract

Abstrak - Status gizi anak merupakan indikator penting dalam menentukan kondisi kesehatan dan tumbuh kembang balita. Penelitian ini bertujuan untuk mengklasifikasikan status gizi anak berdasarkan data antropometri dan demografis menggunakan algoritma Gradient Boosting Classifier (GBM). Data yang digunakan berasal dari sumber primer dengan total 521 sampel valid setelah proses pembersihan. Tahapan penelitian meliputi prapemrosesan data, eksplorasi data, pelatihan model, serta evaluasi performa. Proses prapemrosesan mencakup transformasi usia menjadi numerik, standardisasi fitur numerik, one-hot encoding pada fitur kategorikal, dan label encoding pada target. Model dievaluasi menggunakan metrik akurasi, F1-score, dan ROC AUC. Hasil evaluasi menunjukkan bahwa model GBM mampu mencapai akurasi sebesar ±90%, F1-score tertimbang 0.89, dan ROC AUC macro sebesar 0.88. Namun, model menunjukkan performa yang jauh lebih baik dalam mengklasifikasikan kelas mayoritas (“Normal”) dibandingkan kelas minoritas seperti “Gizi Kurang” dan “Gizi Lebih”, yang mengindikasikan adanya masalah ketidakseimbangan kelas. Oleh karena itu, penelitian ini merekomendasikan penerapan teknik penyeimbangan data seperti oversampling (SMOTE), undersampling, serta eksplorasi model lain yang lebih adaptif terhadap distribusi kelas tidak seimbang untuk meningkatkan kinerja klasifikasi secara keseluruhan.Kata Kunci: Status Gizi; Gradient Boosting; Machine Learning; Class Imbalance; BB/TB; Abstract - Nutritional status of children is a crucial indicator in determining the health and developmental conditions of toddlers. This study aims to classify children's nutritional status based on anthropometric and demographic data using the Gradient Boosting Classifier (GBM) algorithm. The data were obtained from a primary source, comprising a total of 521 valid samples after the data cleaning process. The research stages include data preprocessing, exploratory data analysis, model training, and performance evaluation. The preprocessing involved transforming age into numerical format, standardizing numerical features, applying one-hot encoding for categorical features, and label encoding for the target variable. The model was evaluated using accuracy, F1-score, and ROC AUC metrics. The results showed that the GBM model achieved an accuracy of approximately 90%, a weighted F1-score of 0.89, and a macro-average ROC AUC score of 0.88. However, the model performed significantly better in classifying the majority class ("Normal") compared to minority classes such as "Undernutrition" and "Overnutrition," indicating a class imbalance issue. Therefore, this study recommends the application of data balancing techniques such as oversampling (SMOTE), undersampling, and exploring alternative models that are more robust to imbalanced class distributions to improve overall classification performance. Keywords: Nutritional Status; Gradient Boosting; Machine Learning; Class Imbalance; Weight-for-Height (WHZ);
Machine Learning-Based Classification of Family Planning Participant Status Using Random Forest and the CRISP-DM Framework Irmawati Irmawati; Syaifur Rahmatullah; Mohammad Syamsul Azis
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6498

Abstract

The Family Planning (FP) program requires accurate information to support evidence-based decision-making and improve the quality of reproductive health services. Classification of FP participant status can assist health authorities in identifying participant patterns and monitoring program implementation. Previous research using the Support Vector Machine (SVM) algorithm on the same dataset achieved an accuracy of 56.20%, indicating that improvements in classification performance are still required. This study proposes the Random Forest algorithm within the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to classify FP participant status. The dataset consists of 1,402 FP participant records obtained from SATPEL PPKB, Cilebar District, Karawang Regency. Data preprocessing included data transformation, One Hot Encoding for categorical predictor variables, Label Encoding for the target variable, and Hold-Out Validation with an 80:20 train-test split using stratified sampling. The predictor variables were registration month, wife's birth year, wife's age, and contraceptive method, while the target variable was FP participant status (New, Change Method, and Repeat). Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, classification report, and feature importance analysis. The Random Forest model achieved an accuracy of 59.43%, with weighted precision, recall, and F1-score of 59.00%. However, the macro-average precision, recall, and F1-score were 45.00%, 44.00%, and 44.00%, respectively, indicating performance differences across classes caused by class imbalance. The model achieved the highest F1-score for the New class (0.63), followed by the Repeat class (0.59), whereas the Change Method class obtained the lowest F1-score (0.11). Feature importance analysis identified wife's birth year and wife's age as the most influential predictor variables. Compared with the previous SVM-based model, Random Forest provided a modest improvement in accuracy and enhanced model interpretability through feature importance analysis. Nevertheless, the low macro-average performance indicates that further research should investigate class-balancing techniques and hyperparameter optimization to improve classification performance, particularly for minority classes.
Co-Authors Achmad Rifai Agung Fazriansyah Ami Rahmawati Citra Permana Putra Eka Herdit Juningsih Faruq Aziz Kudiantoro Widianto, Kudiantoro Lutfiyatul Ulfa Mohammad Syamsul Azis Mohammad Syamsul Azis Syaifur Rahmatullah Syaifur Rahmatullah Syarah Seimahuira Yuris Alkhalifi