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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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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)

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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).
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.
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 | 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.
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 | DOI: 10.24003/emitter.v6i1.258

Abstract

Diabetes is a very serious chronic. Diabetes can occurs when the pancreas doesnt 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, thats 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.
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.
Alat Bantu Klasifikasi dengan Pohon Keputusan untuk Sistem Pendukung Keputusan Tessy Badriyah; Ria Rahmawati
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2006
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Penelitian ini bertujuan membuat alat bantu klasifikasi yang menerapkan model pohon keputusan dengan menggunakan algoritma ID3.Tahapan yang dilakukan dalam pembuatan alat bantu atau tools klasifikasi data ini adalah membuat interface dengan melibatkan database, GUI (Graphical User Interface), dan program. Penggunaan antar muka dimulai dengan membuat tabel, menginputkan parameter masing-masing atribut, dan menginputkan data pada tabel. Kemudian digenerate ke dalam bentuk tree dan rule If-Then.Dari pembuatan alat bantu klasifikasi ini dapat dihasilkan tree dan rule If-Then yang membentuk klasifikasi data secara benarKata kunci: klasifikasi, algoritma ID3
DETECTION OF LUNG CANCER CELL BASED ON CYTOLOGICAL EXAMINATION USING CONVOLUTIONAL NEURAL NETWORK Rulisiana Widodo; Tessy Badriyah; Iwan Syarif
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 3 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i3.344

Abstract

Lung cancer is one of the most dangerous cases with the largest number of new cases in the world. The number of Lung Cancer in Indonesia is increasing rapidly every day until it is ranks 8th position in Southeast Asia, experiencing an increase in the last five years by 10.85 percent. This study aims to build a tool to detect lung cancer using the Deep Learning classification method with the Convolutional Neural Network (CNN) Algorithm. The tools that are made can be used for consideration in detecting from the results of cytological examinations, can be classified into normal (negative) and abnormal (positive) types of cancer. The experiment was carried out by performing hyperparameter optimization. The results show that the hyperparameter optimization has superior results compared to others, using the hyperparameter Gradient Boosted Regression Tree method. Experiments without hyperparameters give an accuracy value of 97%, while with the Gaussian Process it gives 98% accuracy and with a hyperparameter gradient boosted regression tree gives 99% accuracy, which is the best accuracy.Keywords : Lung Cancer, Cytological Examinations, Deep Learning, Convolutional Neural Network (CNN)terbanyak di dunia. Jumlah penderita Kanker Paru di Indonesia semakin hari semakin meningkat pesat hingga menduduki urutan ke-8 di Asia Tenggara, mengalami peningkatan dalam lima tahun terakhir sebanyak 10.85 persen. Penelitian ini bertujuan untuk membangun alat pendeteksi kanker paru menggunakan metode klasifikasi Deep Learning dengan Algoritma Convolutional Neural Network (CNN). Alat yang dibuat dapat digunakan sebagai pertimbangan dalam mendeteksi Kanker Paru dari hasil pemeriksaan sitologi, diklasifikasikan menjadi jenis normal (negatif) dan abnormal (positif) kanker. Percobaan dilakukan dengan melakukan optimasi hyperparameter. Hasil penelitian menunjukkan bahwa optimasi hyperparameter memiliki hasil yang lebih unggul yaitu dengan menggunakan metode hyperparameter Gradient Boosted Regression Tree. Percobaan tanpa hyperparameter memberikan nilai akurasi 97%, sedangkan dengan Gaussian Process memberikan akurasi 98% dan dengan hyperparameter Gradient Boosted Regression Tree memberikan akurasi terbaik yaitu 99%.Kata Kunci : Kanker Paru, Pemeriksaan Sitologi, Deep Learning, Convolutional Neural Network (CNN)
Improving stroke diagnosis accuracy using hyperparameter optimized deep learning Tessy Badriyah; Dimas Bagus Santoso; Iwan Syarif; Daisy Rahmania Syarif
International Journal of Advances in Intelligent Informatics Vol 5, No 3 (2019): November 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i3.427

Abstract

Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for determining the right hyperparameter. The CT scan images were processed by scaling, grayscale, smoothing, thresholding, and morphological operation. Then, the images feature was extracted by the Gray Level Co-occurrence Matrix (GLCM). This research was performed a feature selection to select relevant features for reducing computing expenses, while deep learning based on hyperparameter setting was used to the data classification process. The experiment results showed that the Random Search had the best accuracy, while Bayesian Optimization excelled in optimization time.
Mental Disorder Detection via Social Media Mining using Deep Learning Binti Kholifah; Iwan Syarif; Tessy Badriyah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 4, November 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v5i4.1120

Abstract

Due to the imperceptible nature of mental disorders, diagnosing a patient with a mental disorder is a challenging task. Therefore, detection in people with mental disorders can be done by looking at the symptoms they experience. One symptom in patients with mental disorders is solitude. Patients with mental disorders feel indifferent to their environment and mainly focus on their own thoughts and emotions. Therefore, the patient looks for a place that can accommodate his feelings. Twitter is one of the most widely used media in measuring one's personality through everyday statements. The symptoms as suggested by psychologists can be explored more broadly using Natural Languages Processing. The process involves taking a lexicon containing keywords that could indicate symptoms of depression. This study uses five criteria as a measure of mental health in a statement: sentiment, basic emotions, the use of personal pronouns, absolutist words, and negative words. The results show that the use of sentiments, emotions, and negative words in a statement is very influential in determining the level of depression. A depressed person more often uses negative words that indicate his self-despair, prolonged sadness, even suicidal thoughts (e.g. "sadly”, “scared”, “die”, “suicide”). In the classification process, LSTM Deep Learning generates an accuracy of 70.89%; precision of 50.24%; recall 70.89%.
Co-Authors Achmad Basuki Adhi P., Basik Afifah, Izza Nur Agung Muliawan Ali Ridho Barakbah Alwan Fauzi APRIANDY, KEVIN ILHAM Arna Fariza Assidiqi, Moh Hasbi Assodiky, Hilmy Assodiky, Hilmy Bima Sena Bayu Dewantara Binti Kholifah Dadet Pramadihanto Daisy Rahmania Syarif Deyana Kusuma Wardani Dimas Bagus Santoso Faradisa, Rosiyah Fitri Setyorini Hamida, Silfiana Nur Hardiyanti, Fitriani Rohmah Huda, Achmad Thorikul Idris Winarno Ika Restuningtyas Ira Prasetyaningrum iwan Syarif Kholifah, Binti Maulana, Hanan Ammar Maulana, Yufri Isnaini Rochmat Maulana, Yufri Isnaini Rochmat Muhlis Tahir Nur Sakinah Pahlevi, Muhammad Nur Riza Prasetyo Wibowo Rabiatul Adawiyah Rachmawati, Oktavia Citra Resmi Ria Rahmawati Rosiyah Faradisa Rudi Kurniawan Rulisiana Widodo Selvia Ferdiana Kusuma Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Solichul Huda Susanti, Puspasari Tahir, Muhlis Tri Harsono Wicaksono, Andri Permana Wicaksono, Andri Permana Willy Sandhika