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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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Arrhythmia Classification Using Long Short-Term Memory with Adaptive Learning Rate Hilmy Assodiky; Iwan Syarif; Tessy Badriyah
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 Muhlis Tahir; Tessy Badriyah; Iwan Syarif
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; Tessy Badriyah; Iwan Syarif
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.
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%.
Sistem Rekomendasi Collaborative Filtering Berbasis User Algoritma Adjusted Cosine Similarity Tessy Badriyah; Ika Restuningtyas; Fitri Setyorini
Retii Prosiding Seminar Nasional ReTII ke-12 2017
Publisher : Institut Teknologi Nasional Yogyakarta

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

Abstract

Dengan perkembangan teknologi saat ini, menuntut perusahaan e-commerce untuk memiliki daya saing yang tinggi dengan tidak hanya hanya mengandalkan pada kekuatan produknya saja, tapi diperlukan fitur tambahan lainnya yang menambah daya saing semisal dengan memberikan usulan pembelian pada konsumen pada penggunaan sistem rekomendasi (recommender system). Banyaknya variasi produk yang ditawarkan pada website online shopping menyebabkan customer tidak memiliki cukup waktu untuk melihat keseluruhan barang yang ditawarkan dan juga kesulitan untuk memilih barang yang akan dibeli, biasanya customer hanya akan membeli barang yang pernah dia dengar sebelumnya. Sistem rekomendasi yang dapat memberikan nilai lebih kepada pelanggan mengenai produk yang dianggap sesuai atau sama dengan keinginan pelanggan adalah solusi tepat untuk mengatasi hal tersebut. Makalah ini menggunakan User based collaborative filtering yang menggunakan data rating antar pengguna untuk mendapatkan rekomendasi. Metode ini menghitung kesamaan diantara customer dilihat dari rating yang diberikan customer untuk suatu item. Ketika customer merating suatu item, maka nilai rating tersebut akan dibandingkan dengan nilai rating dari pengguna lainnya. Kemudian sistem akan membuat suatu rekomendasi berdasarkan kesamaan antar customer. Hasil pengujian menunjukkan bahwa metode user based collaborative filtering dengan algoritma adjusted cosine similarity dapat menampilkan rekomendasi yang sesuai dengan rating yang diberikan oleh customerKata Kunci: sistem rekomendasi, user based collaborative, rating
Detecting Alter Ego Accounts using Social Media Mining Deyana Kusuma Wardani; Iwan Syarif; Tessy Badriyah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4919

Abstract

Alter ego is a condition of someone who creates a new character with a conscious state. Original character role play is a game to create new imaginary characters that is used as research material for identification alter ego accounts. The negative effects of playing alter ego are stress, depression, and multiple personalities. Current research only focuses on the phenomenon and impacts of a role-playing game. We propose a new method to detect accounts of alter ego players in social media, especially Twitter. We develop an application to analyze the characteristics of alter ego accounts. Psychologists can use this application to discover the characteristics of alter ego accounts that are useful for analyzing personality so that the results can be used to appropriately handle alter ego players. Most user profiles, tweets, and platforms are used to detect account Twitter. This research proposes a new method using bio features as input data. We crawled and collected 565 bios from Twitter for one month. We observe the data to search for unique words and collect them into a classification dictionary. In this research, we use the cosine similarity method because this method is popular for detecting text and has a good performance in many cases. This research could identify alter ego accounts and other types of Twitter accounts. From the detection results of alter ego accounts, it is possible to analyze the characteristics of Twitter accounts. We use a sampling technique that takes 30% of the data as testing data. According to the results of the experiment cosine similarity obtained an accuracy of 0.95.
SEGMENTATION OF LUNG CANCER IMAGE BASED ON CYTOLOGIC EXAMINATION USING THRESHOLDING METHOD Rulisiana Widodo; Tessy Badriyah; Iwan Syarif; Willy Sandhika
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.277

Abstract

Lung cancer is the most dangerous cases which mostly attacks the man with the biggest causes of smoking. This cancer threatens the second largest death after heart attack, lung cancer cases increase significantly every year in various countries. Several methods have been established to detect lung cancer, including Computed Tomography of the thorax, sputum examination and cytology examination. The most decisive examination is through cytologic examination of the pleural fluid. However, the current state of biopsy performed by doctors does not always get a lot of specimens, making it difficult to determine the presence of cancer cells in the lungs. Cytological examination through the pleural fluid has difficulty in detecting cell images. The image of pleural fluid that has a high density between cells will produce an image with low detail, while an image with a low density will produce an image with high detail. Image segmentation is an important part in determining the cellular anatomy of pleural fluid to characterize images with cancer or normal categories. We propose the methodology of research by using group images to separate objects from other objects by highlighting important parts using image segmentation on pleural fluid of patients suspected of having lung cancer. Thresholding method used to see the comparison is Adaptive Thresholding, binary thresholding and Otsu Thresholding. The classification results of the three methods show a high accuracy of 99% on binary thresholding, then 97% accuracy on otsu thresholding and the lowest accuracy of 96% on adaptive thresholding, the three methods are considered to increase in proportion to the addition of the epoch parameter.
Optimalisasi Fitur Pencarian Pada E-Catalog Menggunakan Query Expansion Dan Algoritma TF-IDF Selvia Ferdiana Kusuma; Tessy Badriyah; Prasetyo Wibowo; Rosiyah Faradisa; Solichul Huda
Techno.Com Vol 22, No 3 (2023): Agustus 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i3.8698

Abstract

E-catalog adalah sebuah katalog elektronik dapat digunakan untuk mempublikasikan dan mempromosikan sebuah produk atau layanan secara online kepada berbagai pihak. Pengguna dapat memperoleh informasi terkait produk yang diinginkan melalui fitur pencarian pada e-catalog tersebut. Oleh sebab itu fitur pencarian memiliki peran yang signifikan untuk menunjang performa e-catalog. Proses pencarian produk pada sebuah e-catalog dilakukan berdasarkan kesamaan kata yang dimasukkan oleh pengguna dengan judul produk yang tersedia. Biasanya semua judul yang mengandung kata kunci yang dimasukkan pengguna akan ditampilkan tanpa adanya perankingan kedekatan hasil pencarian. Hal tersebut tentunya menyulitkan pengguna untuk menemukan produk yang sesuai dengan keinginan. Oleh sebab itu penelitian ini bertujuan untuk melakukan optimalisasi fitur pencarian produk yang ada pada e-catalog menggunakan query expansion dan algoritma TF-IDF. Berdasarkan hasil uji coba yang telah dilakukan, terbukti bahwa penambahan  query expansion dan algoritma TF-IDF dapat mengoptimalkan kinerja fitur pencarian pada e-catalog tersebut. Hasil pencarian produk dapat terangking berdasarkan kedekatan kata kunci yang dimasukkan oleh pengguna dengan judul produk yang diinginkan oleh pengguna
Co-Authors Achmad Basuki Achmad Basuki Adhi P., Basik Afifah, Izza Nur Agung Muliawan Ali Ridho Barakbah Alwan Fauzi Andri Permana Wicaksono APRIANDY, KEVIN ILHAM Arna Fariza Assidiqi, Moh Hasbi 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 Hilmy Assodiky Huda, Achmad Thorikul Idris Winarno Ika Restuningtyas Ira Prasetyaningrum iwan Syarif Kholifah, Binti Maulana, Hanan Ammar 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 Tri Harsono Wicaksono, Andri Permana Willy Sandhika Yufri Isnaini Rochmat Maulana