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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Technomedia Journal Applied Technology and Computing Science Journal GUYUB: Journal of Community Engagement Proceeding ISETH (International Summit on Science, Technology, and Humanity) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Jurnal Informatika: Jurnal Pengembangan IT
Tessy Badriyah, Tessy
Politeknik Elektronika Negeri Surabaya
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Implementasi dan Optimasi Hyperparameter pada Model Machine learning untuk Prediksi Diabetes dengan Integrasi Aplikasi Telemedicine Pahlevi, Muhammad Nur Riza; Badriyah, Tessy
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 2 (2025): Volume 11 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i2.94600

Abstract

Deteksi dini diabetes merupakan langkah krusial dalam mencegah komplikasi jangka panjang yang dapat membahayakan kesehatan pasien. Melalui penelitian ini, dirancang dan diuji sebuah sistem prediksi risiko diabetes yang memanfaatkan algoritma machine learning untuk membantu deteksi dini serta mengurangi risiko kesehatan jangka panjang yang terintegrasi dalam aplikasi telemedicine. Dalam konteks ini, machine learning dimanfaatkan untuk melakukan klasifikasi terhadap status diabetes pasien berdasarkan parameter klinis. Empat algoritma digunakan dalam studi ini, yaitu K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression, dan Decision Tree. Keempat algoritma ini dipilih karena mewakili pendekatan klasifikasi yang berbeda, memiliki rekam jejak yang baik dalam penelitian medis, serta dapat dioptimasi untuk meningkatkan akurasi prediksi. Proses optimasi hyperparameter mencakup n_neighbors dan weights pada KNN; C, kernel, dan gamma pada SVM; C dan penalty pada Logistic Regression; serta max_depth dan min_samples_leaf pada Decision Tree untuk memaksimalkan kinerja masing-masing model. Dataset yang digunakan berasal dari RSD Balung Jember dengan total 1.450 data pasien dan 12 fitur medis. Hasil evaluasi menunjukkan bahwa model SVM dan Decision Tree memberikan performa terbaik, dengan akurasi masing-masing mencapai 98,97% dan 99,66%, serta nilai F1-score hingga 1,00. Sementara itu, metrik precision dan recall juga menunjukkan hasil yang tinggi, menandakan keandalan model dalam mengklasifikasikan kondisi diabetes secara tepat. Sistem prediksi yang dibangun kemudian diintegrasikan ke dalam platform telemedicine berbasis web menggunakan framework Laravel. Evaluasi integrasi menunjukkan bahwa model mampu memberikan hasil prediksi secara real-time dengan waktu respons rata-rata 0,86 detik dan akurasi tetap konsisten di atas 98% setelah diimplementasikan dalam aplikasi. Hal ini menunjukkan bahwa pendekatan berbasis machine learning dengan dukungan optimasi parameter berhasil menghasilkan sistem deteksi dini diabetes yang akurat, efisien, dan dapat diterapkan secara praktis dalam skenario layanan kesehatan digital di Indonesia.
Development of a Java Library with Bacterial Foraging Optimization for Feature Selection of High-Dimensional Data Badriyah, Tessy; Syarif, Iwan; Hardiyanti, Fitriani Rohmah
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2149

Abstract

High-dimensional data allows researchers to conduct comprehensive analyses. However, such data often exhibits characteristics like small sample sizes, class imbalance, and high complexity, posing challenges for classification. One approach employed to tackle high-dimensional data is feature selection. This study uses the Bacterial Foraging Optimization (BFO) algorithm for feature selection. A dedicated BFO Java library is developed to extend the capabilities of WEKA for feature selection purposes. Experimental results confirm the successful integration of BFO. The outcomes of BFO's feature selection are then compared against those of other evolutionary algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO).  Comparison of algorithms conducted using the same datasets.  The experimental results indicate that BFO effectively reduces features while maintaining consistent accuracy. In 4 out of 9 datasets, BFO outperforms other algorithms, showcasing superior processing time performance in 6 datasets. BFO is a favorable choice for selecting features in high-dimensional datasets, providing consistent accuracy and effective processing. The optimal fraction of features in the Ovarian Cancer dataset signifies that the dataset retains a minimal number of selected attributes. Consequently, the learning process gains speed due to the reduced feature set. Remarkably, accuracy substantially increased, rising from 0.868 before feature selection to 0.886 after feature selection. The classification processing time has also been significantly shortened, completing the task in just 0.3 seconds, marking a remarkable improvement from the previous 56.8 seconds.
Personalized Tourism in Surabaya: A Bayesian Network Approach Faradisa, Rosiyah; Badriyah, Tessy; Maulana, Hanan Ammar; Assidiqi, Moh Hasbi
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3376

Abstract

This study investigates the application of Bayesian Networks in developing a personalized tourist destination recommendation system focused on Surabaya, Indonesia. The research incorporates push and pulls factors alongside tourist activities as key input variables to model decision-making processes. Two distinct Directed Acyclic Graph (DAG) structures are evaluated: one proposed based on existing theoretical frameworks and another generated from empirical respondent data. The dataset comprises responses from 1,350 tourists visiting twenty-five popular attractions in Surabaya. The analysis reveals that Bayesian Networks effectively identify correlations between various influencing factors. From the tests carried out, the accuracy obtained from the two DAG structures did not significantly differ. The proposed DAG achieved 35% accuracy for the top-ranked destination recommendations, while the data-driven DAG was 25%. Both achieved 75% accuracy in the top five recommendations. The accuracy increased as the number of output states was reduced. Meanwhile, in the test with binary output, BN was able to accurately classify tourist destinations with an average accuracy of 95% for both DAGs. These findings highlight the potential of Bayesian Networks to enhance tourism decision support systems by providing nuanced insights into tourists' preferences and motivations. For further research, hybridization or feature engineering can be employed to improve model accuracy. In addition, determining more appropriate push factors and tourist activities based on the tourism case studies also needs to be done to obtain better tourist preferences. This research highlights the promising role of Bayesian Networks in improving the personalization and effectiveness of tourist recommendations.
Hybrid Modeling KMeans – Genetic Algorithms in the Health Care Data Badriyah, Tessy
EMITTER International Journal of Engineering Technology Vol 2 No 1 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v2i1.18

Abstract

K-Means is one of the major algorithms widely used in clustering due to its good computational performance. However, K-Means is very sensitive to the initially selected points which randomly selected, and therefore it does not always generate optimum solutions. Genetic algorithm approach can be applied to solve this problem. In this research we examine the potential of applying hybrid GA- KMeans with focus on the area of health care data. We proposed a new technique using hybrid method combining KMeans Clustering and Genetic Algorithms, called the “Hybrid K-Means Genetic Algorithms” (HKGA). HKGA combines the power of Genetic Algorithms and the efficiency of K-Means Clustering. We compare our results with other conventional algorithms and also with other published research as well. Our results demonstrate that the HKGA achieves very good results and in some cases superior to other methods.Keywords: Machine Learning, K-Means, Genetic Algorithms, Hybrid KMeans Genetic Algorithm (HGKA).
Comparison of The Data-Mining Methods in Predicting The Risk Level of Diabetes Wicaksono, Andri Permana; Badriyah, Tessy; Basuki, Achmad
EMITTER International Journal of Engineering Technology Vol 4 No 1 (2016)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (813.592 KB) | DOI: 10.24003/emitter.v4i1.119

Abstract

Mellitus Diabetes is an illness that happened in consequence of the too high glucose level in blood because the body could not release or use insulin normally. The purpose of this research is to compare the two methods in The data-mining, those are a Regression Logistic method and a Bayesian method, to predict the risk level of diabetes by web-based application and nine attributes of patients data. The data which is used in this research are 1450 patients that are taken from RSD BALUNG JEMBER, by collecting data from 26 September 2014 until 30 April 2015. This research uses performance measuring from two methods by using discrimination score with ROC curve (Receiver Operating Characteristic).  On the experiment result, it showed that two methods, Regression Logistic method and Bayesian method, have different performance excess score and are good at both. From the highest accuracy measurement and ROC using the same dataset, where the excess of Bayesian has the highest accuracy with 0,91 in the score while Regression Logistic method has the highest ROC score with 0.988, meanwhile on Bayesian, the ROC is 0.964. In this research, the plus of using Bayesian is not only can use categorical but also numerical.
Influence of Logistic Regression Models For Prediction and Analysis of Diabetes Risk Factors Maulana, Yufri Isnaini Rochmat; Badriyah, Tessy; Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (419.424 KB) | DOI: 10.24003/emitter.v6i1.258

Abstract

Diabetes is a very serious chronic. Diabetes can occurs when the pancreas doesn't produce enough insulin (a hormone used to regulate blood sugar), cause glucose in the blood to be high. The purpose of this study is to provide a different approach in dealing with cases of diabetes, that's with data mining techniques mengguanakan logistic regression algorithm to predict and analyze the risk of diabetes that is implemented in the mobile framework. The dataset used for data modeling using logistic regression algorithm was taken from Soewandhie Hospital on August 1 until September 30, 2017. Attributes obtained from the Hospital Laboratory have 11 attribute, with remove 1 attribute that is the medical record number so it becomes 10 attributes. In the data preparation dataset done preprocessing process using replace missing value, normalization, and feature extraction to produce a good accuracy. The result of this research is performance measure with ROC Curve, and also the attribute analysis that influence to diabetes using p-value. From these results it is known that by using modeling logistic regression algorithm and validation test using leave one out obtained accuracy of 94.77%. And for attributes that affect diabetes is 9 attributes, age, hemoglobin, sex, blood sugar pressure, creatin serum, white cell count, urea, total cholesterol, and bmi. And for attributes triglycerides have no effect on diabetes.
Arrhythmia Classification Using Long Short-Term Memory with Adaptive Learning Rate Assodiky, Hilmy; Syarif, Iwan; Badriyah, Tessy
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (847.757 KB) | DOI: 10.24003/emitter.v6i1.265

Abstract

Arrhythmia is a heartbeat abnormality that can be harmless or harmful. It depends on what kind of arrhythmia that the patient suffers. People with arrhythmia usually feel the same physical symptoms but every arrhythmia requires different treatments. For arrhythmia detection, the cardiologist uses electrocardiogram that represents the cardiac electrical activity. And it is a kind of sequential data with high complexity. So the high performance classification method to help the arrhythmia detection is needed. In this paper, Long Short-Term Memory (LSTM) method was used to classify the arrhythmia. The performance was boosted by using AdaDelta as the adaptive learning rate method. As a comparison, it was compared to LSTM without adaptive learning rate. And the best result that showed high accuracy was obtained by using LSTM with AdaDelta. The correct classification rate was 98% for train data and 97% for test data.
Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization Tahir, Muhlis; Badriyah, Tessy; Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (565.13 KB) | DOI: 10.24003/emitter.v6i2.287

Abstract

Preeclampsia is a pregnancy abnormality that develops after 20 weeks of pregnancy characterized by hypertension and proteinuria.  The purpose of this research was to predict the risk of preeclampsia level in pregnant women during pregnancy process using Neural Network and Deep Learning algorithm, and compare the result of both algorithm. There are 17 parameters that taken from 1077 patient data in Haji General Hospital Surabaya and two hospitals in Makassar start on December 12th 2017 until February 12th 2018. We use particle swarm optimization (PSO) as the feature selection algorithm. This experiment shows that PSO can reduce the number of attributes from 17 to 7 attributes. Using LOO validation on the original data show that the result of Deep Learning has the accuracy of 95.12% and it give faster execution time by using the reduced dataset (eight-speed quicker than the original data performance). Beside that the accuracy of Deep Learning increased 0.56% become 95.68%. Generally, PSO gave the excellent result in the significantly lowering sum attribute as long as keep and improve method and precision although lowering computational period. Deep Learning enables end-to-end framework, and only need input and output without require for tweaking the attributes or features and does not require a long time and complex systems and understanding of the deep data on computing.
Hospital Length of Stay Prediction based on Patient Examination Using General features Rabiatul Adawiyah; Badriyah, Tessy; Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 9 No 1 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i1.609

Abstract

As of the year 2020, Indonesia has the fourth most populous country in the world. With Indonesia’s population expected to continuously grow, the increase in provision of healthcare needs to match its steady population growth. Hospitals are central in providing healthcare to the general masses, especially for patients requiring medical attention for an extended period of time. Length of Stay (LOS), or inpatient treatment, covers various treatments that are offered by hospitals, such as medical examination, diagnosis, treatment, and rehabilitation. Generally, hospitals determine the LOS by calculating the difference between the number of admissions and the number of discharges. However, this procedure is shown to be unproductive for some hospitals. A cost-effective way to improve the productivity of hospital is to utilize Information Technology (IT). In this paper, we create a system for predicting LOS using Neural Network (NN) using a sample of 3055 subjects, consisting of 30 input attributes and 1 output attribute. The NN default parameter experiment and parameter optimization with grid search as well as random search were carried out. Our results show that parameter optimization using the grid search technique give the highest performance results with an accuracy of 94.7403% on parameters with a value of Epoch 50, hidden unit 52, batch size 4000, Adam optimizer, and linear activation. Our designated system can be utilised by hospitals in improving their effectiveness and efficiency, owing to better prediction of LOS and better visualization of LOS done by web visualization.
Water Quality Control System Based on Web Application for Monitoring Shrimp Cultivation in Sidoarjo, East Java Arna Fariza; Setiawardhana Setiawardhana; Bima Sena Bayu Dewantara; Aliridho Barakbah; Dadet Pramadihanto; Idris Winarno; Tessy Badriyah; Tri Harsono; Iwan Syarif; Wahjoe Tjatur Sesulihatien; Puspasari Susanti; Achmad Thorikul Huda; Oktavia Citra Resmi Rachmawati; Izza Nur Afifah; Rudi Kurniawan; Silfiana Nur Hamida
GUYUB: Journal of Community Engagement Vol 4, No 3 (2023)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/guyub.v4i3.7245

Abstract

Shrimp farming plays a crucial role to the Indonesian economy, but it is facing challenges from shifting weather patterns and global warming. This research focuses on the development and implementation of a web-based water quality monitoring system for shrimp farming to address these concerns. The research, conducted in collaboration with shrimp farmers in Sidoarjo, East Java, introduces PENS Aquaculture program, which is designed to efficiently monitor pH, salinity, and temperature. The system employs Internet ofThings (IoT) technology, which allows farmers to register several ponds, analyze water parameters, and receive real-time data through tables and graphs. The research takes a mixed-methods approach, integrating quantitative data from IoT devices with qualitative insights gathered through surveys and interviews with shrimp farmers. The study aims to evaluate the influence of IoT technology on shrimp pond quality and its contribution to the production. The findings show that PENS Aquaculture application is helpful in increasing shrimp farming efficiency, providing significant insights for the fisheries and cultural sectors.
Co-Authors Achmad Basuki Achmad Thorikul Huda Agung Muliawan Ali Ridho Barakbah Alwan Fauzi APRIANDY, KEVIN ILHAM Arna Fariza Assidiqi, Moh Hasbi Assodiky, Hilmy Assodiky, Hilmy Basik Adhi P. Bima Sena Bayu Dewantara Binti Kholifah Dadet Pramadihanto Daisy Rahmania Syarif Deyana Kusuma Wardani Dimas Bagus Santoso Faradisa, Rosiyah Fitri Setyorini Hardiyanti, Fitriani Rohmah Idris Winarno Ika Restuningtyas Ira Prasetyaningrum iwan Syarif Izza Nur Afifah Kholifah, Binti Maulana, Hanan Ammar Maulana, Yufri Isnaini Rochmat Maulana, Yufri Isnaini Rochmat Muhlis Tahir Nur Sakinah Oktavia Citra Resmi Rachmawati Pahlevi, Muhammad Nur Riza Prasetyo Wibowo Puspasari Susanti Rabiatul Adawiyah Ria Rahmawati Rosiyah Faradisa Rudi Kurniawan Rudi Kurniawan Rulisiana Widodo Selvia Ferdiana Kusuma Setiawardhana Setiawardhana Silfiana Nur Hamida Solichul Huda Tahir, Muhlis Tri Harsono Wahjoe Tjatur Sesulihatien Wicaksono, Andri Permana Wicaksono, Andri Permana Willy Sandhika